JP2024150627A - Attitude estimation device, control method, program, and storage medium - Google Patents

Abstract

An attitude estimation device is provided that can suitably estimate the mounting attitude of a measuring device attached to a moving body relative to the moving body.
[Solution] An on-board device 1 acquires a target measurement point cloud Ptag obtained by measuring road signs having a surface perpendicular to the traveling direction of the vehicle's roadway or a surface perpendicular to the width direction of the roadway by a lidar 2 attached to the vehicle. Then, the on-board device 1 estimates the mounting attitude of the lidar 2 on the vehicle based on the inclination of the longitudinal direction of the road sign calculated from the target measurement point cloud Ptag with respect to a coordinate system based on the installation information of the lidar 2.
[Selected figure] Figure 10

Description

The present invention relates to a technology for estimating the attitude of a measurement device.

Technologies for estimating a vehicle's own position based on measurement data from measurement devices such as radar and cameras have been known for some time. For example, Patent Document 1 discloses a technology for estimating a vehicle's own position by matching the output of a measurement sensor with position information of features registered in advance on a map. Patent Document 2 discloses a vehicle's own position estimation technology that uses a Kalman filter.

JP 2013-257742 A JP 2017-72422 A

The data obtained from the measuring device is a value in a coordinate system based on the measuring device, and is dependent on the attitude of the measuring device relative to the vehicle, so it needs to be converted to a value in a coordinate system based on the vehicle. Therefore, if a deviation occurs in the attitude of the measuring device, it is necessary to accurately detect the deviation and reflect it in the data of the measuring device, or to correct the attitude of the measuring device.

The present invention has been made to solve the above-mentioned problems, and has as its main objective the provision of an attitude estimation device that can suitably estimate the mounting attitude of a measuring device attached to a moving body relative to the moving body.

The invention described in the claims is an attitude estimation device having an acquisition means for acquiring a measurement point cloud obtained by measuring a feature having a plane perpendicular to the direction of travel of the travel path of the moving body or a plane perpendicular to the width direction of the travel path by a measurement device attached to the moving body, and an estimation means for estimating the mounting attitude of the measurement device to the moving body based on the inclination of the vertical plane calculated from the measurement point cloud with respect to a coordinate system based on the installation information of the measurement device.

The invention described in the claims is a control method executed by an attitude estimation device, and includes an acquisition step of acquiring a measurement point cloud measured by a measurement device attached to the moving body of a feature having a plane perpendicular to the direction of travel of the moving body's travel path or a plane perpendicular to the width direction of the travel path, and an estimation step of estimating the mounting attitude of the measurement device to the moving body based on the inclination of the vertical plane calculated from the measurement point cloud with reference to a coordinate system based on the installation information of the measurement device.

The invention described in the claims is a program executed by a computer, which causes the computer to function as an acquisition means for acquiring a cloud of measurement points measured by a measurement device attached to a moving body of a feature having a plane perpendicular to the direction of travel of the moving body's path or a plane perpendicular to the width direction of the path, and an estimation means for estimating the mounting attitude of the measurement device to the moving body based on the inclination of the vertical plane calculated from the cloud of measurement points with respect to a coordinate system based on the installation information of the measurement device.

FIG. 1 is a schematic configuration diagram of a vehicle system. FIG. 2 is a block diagram showing a functional configuration of an in-vehicle device. FIG. 2 is a diagram showing the relationship between a vehicle coordinate system and a rider coordinate system represented by two-dimensional coordinates. FIG. 2 is a diagram showing the relationship between a vehicle coordinate system and a rider coordinate system represented by three-dimensional coordinates. 1 shows an overhead view of the surroundings of a vehicle when the vehicle is traveling near a road sign that is a detection target feature when estimating the roll angle of the lidar. This shows the positional relationship between the road sign and the target measurement point cloud when the measured surface of the road sign is viewed from the front. 1 shows the target measurement point cloud and the extracted outer edge point cloud in the lidar coordinate system. The approximate straight lines for each side of the quadrangle formed by the boundary points are shown. 13 is a flowchart showing a procedure for roll angle estimation processing. 1 shows an overhead view of the surroundings of a vehicle when the vehicle is traveling near a road sign that is a detection target feature when estimating the pitch angle of the lidar. 10 is a flowchart showing a procedure for a pitch angle estimation process. 1 shows an overhead view of the surroundings of a vehicle when the vehicle is traveling along a lane marking that is a detection target feature when estimating the yaw angle of the lidar. FIG. 11 is a diagram showing the center point of a detection window by a circle. The angle between the center line of the lane marking and the yaw direction reference angle calculated for each detection window is shown. 10 is a flowchart showing a procedure for yaw angle estimation processing. 10 is a flowchart showing a specific example of a process based on estimated values of a roll angle, a pitch angle, and a yaw angle.

According to a preferred embodiment of the present invention, the attitude estimation device has an acquisition means for acquiring a cloud of measurement points obtained by measuring features whose measurement surface is parallel to the road surface on which the mobile body travels and formed along the road surface using a measurement device attached to the mobile body, a calculation means for calculating a line on the measurement surface that runs along the traveling direction of the mobile body based on the measurement point cloud, and an estimation means for estimating the mounting attitude of the measurement device to the mobile body based on the direction of the line relative to a direction that the measurement device uses as a reference. With this aspect, the attitude estimation device can suitably estimate the mounting attitude of the measurement device to the mobile body based on a feature whose measurement surface is parallel to the road surface on which the mobile body travels and formed along the road surface.

In one aspect of the posture estimation device, the acquisition means recognizes the position of the feature based on feature information about the feature, and acquires the measurement point cloud measured by the measurement device at that position as the measurement point cloud of the feature. With this aspect, the posture estimation device can grasp the position of a feature having a vertical plane along the travel path of the moving body or a vertical plane along the width direction of the travel path, and suitably acquire the measurement point cloud of the feature.

In another aspect of the above-mentioned attitude estimation device, the estimation means estimates the attitude of the measurement device in the yaw direction based on the angle between the line and a reference line in the yaw direction of the measurement device. With this aspect, the attitude estimation device can preferably estimate the attitude of the measurement device in the yaw direction.

In another aspect of the above-mentioned attitude estimation device, the estimation means estimates the attitude of the measurement device in the yaw direction by averaging the angles between the reference line and the multiple lines calculated by the calculation means based on the different measurement point groups. With this aspect, the attitude estimation device can more accurately estimate the attitude of the measurement device in the yaw direction.

In another aspect of the above-mentioned attitude estimation device, the calculation means calculates a plurality of points on the measured surface along the traveling direction of the moving body based on the measurement point cloud, and calculates the line from the plurality of points, and the estimation means estimates the attitude of the measurement device in the yaw direction by averaging the angles between the plurality of lines calculated by the calculation means based on the different measurement point clouds and the reference line based on the number of points used in calculating each of the lines. In this aspect, the attitude estimation device can more accurately estimate the attitude of the measurement device in the yaw direction by weighting that appropriately takes into account the accuracy of each of the calculated lines.

In a preferred example, the feature may be a dividing line or a curbstone provided on the roadway. In another preferred example, the measurement device may be an optical scanning device that scans light.

In another aspect of the above-mentioned posture estimation device, the reference direction is a direction that is regarded as the traveling direction of the moving body in the coordinate system of the measurement device when there is no deviation in the mounting posture, and when there is a deviation in the mounting posture, the reference direction indicates a direction different from the traveling direction of the moving body according to the deviation. With this aspect, the posture estimation device can suitably estimate the mounting posture of the measurement device to the moving body based on the calculated deviation of the reference direction with respect to a line on the measurement surface.

According to another preferred embodiment of the present invention, a control method executed by an attitude estimation device includes an acquisition step of acquiring a measurement point cloud obtained by measuring features whose measurement surface is parallel to a road surface on which a moving body travels and that are formed along the road surface by a measuring device attached to the moving body, a calculation step of calculating a line on the measurement surface that runs along the traveling direction of the moving body based on the measurement point cloud, and an estimation step of estimating the mounting attitude of the measuring device to the moving body based on the direction of the line relative to a direction that the measuring device uses as a reference. By executing this control method, the attitude estimation device can suitably estimate the mounting attitude of the measuring device to the moving body.

According to another preferred embodiment of the present invention, a program executed by a computer causes the computer to function as an acquisition means for acquiring a cloud of measurement points obtained by measuring features formed along a road surface along which a moving body travels, the surface to be measured being parallel to the road surface on which the moving body travels, using a measurement device attached to the moving body; a calculation means for calculating a line on the surface to be measured along the traveling direction of the moving body based on the cloud of measurement points; and an estimation means for estimating the mounting orientation of the measurement device to the moving body based on the direction of the line relative to a direction that the measurement device uses as a reference. By executing this program, the computer can preferably estimate the mounting orientation of the measurement device to the moving body. Preferably, the program is stored in a storage medium.

Below, preferred embodiments of the present invention will be described with reference to the drawings.

[General Configuration]
Fig. 1 is a schematic diagram of a vehicle system according to the present embodiment. The vehicle system shown in Fig. 1 includes an on-board device 1 that controls driving assistance for the vehicle, and a group of sensors including a Lidar (Light Detection and Ranging, or Laser Illuminated Detection and Ranging) 2, a gyro sensor 3, an acceleration sensor 4, and a GPS receiver 5.

The vehicle-mounted device 1 is electrically connected to a group of sensors, such as the lidar 2, gyro sensor 3, acceleration sensor 4, and GPS receiver 5, and acquires output data from these sensors. It also stores a map database (DB: DataBase) 10 that stores road data and feature information on features installed near roads. The vehicle-mounted device 1 estimates the position of the vehicle based on the above-mentioned output data and the map DB 10, and performs control related to vehicle driving assistance such as automatic driving control based on the position estimation result. The vehicle-mounted device 1 also estimates the attitude of the lidar 2 based on the output of the lidar 2, etc. The vehicle-mounted device 1 then performs processing such as correcting each measurement value of the point cloud data output by the lidar 2 based on the estimation result. The vehicle-mounted device 1 is an example of an "attitude estimation device" in the present invention.

The lidar 2 emits a pulsed laser in a predetermined angular range in the horizontal and vertical directions to discretely measure the distance to an object in the external world and generate three-dimensional point cloud information indicating the position of the object. In this case, the lidar 2 has an irradiation unit that irradiates laser light while changing the irradiation direction, a light receiving unit that receives reflected light (scattered light) of the irradiated laser light, and an output unit that outputs scan data based on the light receiving signal output by the light receiving unit. The scan data is generated based on the irradiation direction corresponding to the laser light received by the light receiving unit and the distance to the object in the irradiation direction of the laser light identified based on the above-mentioned light receiving signal, and is supplied to the vehicle-mounted device 1. The lidar 2 is an example of a "measurement device" in the present invention.

Figure 2 is a block diagram showing the functional configuration of the vehicle-mounted device 1. The vehicle-mounted device 1 mainly has an interface 11, a memory unit 12, an input unit 14, a control unit 15, and an information output unit 16. These elements are interconnected via a bus line.

The interface 11 acquires output data from sensors such as the lidar 2, gyro sensor 3, acceleration sensor 4, and GPS receiver 5, and supplies the data to the control unit 15. The interface 11 also supplies signals related to vehicle driving control generated by the control unit 15 to the vehicle's electronic control unit (ECU: Electronic Control Unit).

The storage unit 12 stores a program executed by the control unit 15 and information required for the control unit 15 to execute a predetermined process. In this embodiment, the storage unit 12 has a map DB 10 and lidar installation information IL. The map DB 10 is a database including, for example, road data, facility data, and feature data around the road. The road data includes lane network data for route search, road shape data, traffic law data, and the like. The feature data includes information (for example, position information and type information) on signs such as road signs, road markings such as stop lines, road dividing lines such as white lines, and structures along the road. The feature data may also include highly accurate point cloud information of features to be used for vehicle position estimation. In addition, the map DB may store various data required for position estimation.

The rider installation information IL is information about the attitude and position of the rider 2 relative to the vehicle at a certain reference time (for example, when there is no attitude deviation, such as immediately after the alignment adjustment of the rider 2). In this embodiment, the attitude of the rider 2, etc. is represented by the roll angle, pitch angle, and yaw angle (i.e., Euler angles). The rider installation information IL may be updated based on the estimation result when the estimation process of the attitude of the rider 2, which will be described later, is executed.

The input unit 14 is a button, touch panel, remote controller, voice input device, etc. that the user can operate, and accepts inputs specifying a destination for route search, inputs specifying whether autonomous driving is on or off, etc. The information output unit 16 is, for example, a display, speaker, etc. that outputs information based on the control of the control unit 15.

The control unit 15 includes a CPU that executes a program and controls the entire vehicle-mounted device 1. The control unit 15 estimates the vehicle position based on the output signals of each sensor supplied from the interface 11 and the map DB 10, and performs control related to vehicle driving assistance including automatic driving control based on the estimation result of the vehicle position. At this time, when the control unit 15 uses the output data of the rider 2, it converts the measurement data output by the rider 2 from a coordinate system based on the rider 2 to a coordinate system based on the vehicle (hereinafter referred to as the "reference coordinate system") based on the attitude and position included in the installation information of the rider 2 recorded in the rider installation information IL. Furthermore, in this embodiment, the control unit 15 estimates the current (i.e., processing reference time) attitude of the rider 2 relative to the vehicle, calculates the amount of change with respect to the attitude recorded in the rider installation information IL, and corrects the measurement data output by the rider 2 based on the amount of change. As a result, even if a deviation occurs in the attitude of the rider 2, the control unit 15 corrects the measurement data output by the rider 2 so that it is not affected by the deviation. The control unit 15 is an example of a "computer" that executes the program in this invention.

[Coordinate system conversion]
The three-dimensional coordinates indicated by each measurement point of the three-dimensional point cloud data acquired by the Rider 2 are expressed in a coordinate system based on the position and attitude of the Rider 2 (also called the "Rider coordinate system"), and need to be converted into a coordinate system based on the position and attitude of the vehicle (also called the "Vehicle coordinate system"). Here, the conversion between the Rider coordinate system and the vehicle coordinate system will be described.

3 is a diagram showing the relationship between the vehicle coordinate system and the rider coordinate system expressed by two-dimensional coordinates. Here, the vehicle coordinate system has the center of the vehicle as the origin, and has a coordinate axis "x _b " along the vehicle's traveling direction and a coordinate axis "y _b " along the vehicle's lateral direction. The rider coordinate system has a coordinate axis "x _L " along the front direction of the rider 2 (see arrow A2) and a coordinate axis "y _L " along the lateral direction of the rider 2.

Here, if the yaw angle of the rider 2 relative to the vehicle coordinate system is "L _ψ " and the position of the rider 2 is [L _x , L _y ] ^T , the measurement point [x _b (k), y _b (k)] ^T at time " _k " as viewed from the vehicle coordinate system is converted to coordinates [x _L (k), y _L (k)] ^T in the rider coordinate system by the following equation (1) using the rotation matrix "C ψ ".

On the other hand, the conversion from the lidar coordinate system to the vehicle coordinate system can be performed by using the inverse matrix (transpose matrix) of the rotation matrix, so that the measurement point [ _xL (k), _yL (k)] ^T at time k acquired in the lidar coordinate system can be converted to the coordinates [ _xb (k), _yb (k)] ^T in the vehicle coordinate system by the following equation (2).

4 is a diagram showing the relationship between the vehicle coordinate system and the rider coordinate system expressed by three-dimensional coordinates. Here, the coordinate axis perpendicular to the coordinate axes _xb and _yb is defined as " _zb ", and the coordinate axis perpendicular to the coordinate axes _xL and _yL is defined as " _zL ".

If the roll angle of the rider 2 relative to the vehicle coordinate system is " _Lφ ", the pitch angle is " _Lθ ", the yaw angle is " _Lψ ", the position of the rider 2 on the coordinate axis _xb is " _Lx ", the position on the coordinate axis _yb is " _Ly ", and the position on the coordinate axis _zb is " _Lz ", then the measurement point [ _xb (k), _yb (k), _zb (k)]T at time "k" as viewed from the vehicle coordinate system is converted into coordinates [xL( _k ), yL( _k ), _zL ( _k )] ^T in the rider coordinate system by the following equation (3) using a direction cosine matrix "C" represented by rotation matrices " _Cφ ", " _Cθ ", and "Cψ" corresponding to roll, pitch, and ^yaw, respectively.

On the other hand, the conversion from the lidar coordinate system to the vehicle coordinate system can be performed by using the inverse matrix (transpose matrix) of the direction cosine matrix, so that the measurement point [ _xL (k), _yL (k), _zL (k)] ^T at time k acquired in the lidar coordinate system can be converted to coordinates [ _xb (k), _yb (k), _zb (k)] ^T in the vehicle coordinate system by the following equation (4).

In the following, a method for estimating the roll angle L _φ , the pitch angle L _θ , and the yaw angle L _ψ used in equation (4) for transforming from the rider coordinate system to the vehicle coordinate system will be described in order.

[Roll Angle Estimation]
First, a method for estimating the roll angle _Lφ of the LIDAR 2 will be described. The vehicle-mounted device 1 regards a square (rectangular) road sign having a perpendicular surface to the traveling direction of the road (i.e., the front side of the road) as a target feature to be detected by the LIDAR 2 (also referred to as a "detection target feature Ftag"), and estimates the roll angle _Lφ of the LIDAR 2 by calculating the inclination of the road sign based on the measurement point cloud measured by the LIDAR 2.

Figure 5 shows an overhead view of the area around the vehicle when the vehicle is traveling near a road sign 22 that is the target feature Ftag when estimating the roll angle of the lidar 2. In Figure 5, a dashed circle 20 indicates the range in which the lidar 2 can detect features (also called the "lidar detection range"), and a dashed frame 21 indicates a detection window for detecting the road sign 22 that is the target feature Ftag.

In this case, the vehicle-mounted device 1 recognizes that a rectangular road sign 22 having a surface perpendicular to the traveling direction of the road is present within the lidar detection range by referring to the map DB 10, and sets a detection window shown in the dashed frame 21 based on the feature data related to the road sign 22. In this case, the vehicle-mounted device 1 pre-stores, for example, type information of the road sign that is the detection target feature Ftag, and determines whether or not a road sign of the type indicated by the type information is present within the lidar detection range by referring to the feature data in the map DB 10. Note that if the feature data in the map DB 10 includes information on the size and/or shape of the feature, the vehicle-mounted device 1 may determine the size and/or shape of the detection window based on the information on the size and/or shape of the road sign 22 that is the detection target feature Ftag. Then, the vehicle-mounted device 1 extracts the measurement point group (also called the "target measurement point group Ptag") that is present within the set detection window from the measurement point group measured by the lidar 2 as the measurement point group of the road sign 22.

6A to 6C show the positional relationship between the target measurement point cloud Ptag and the scanning line 23 of the laser light by the LIDAR 2 (i.e., a line connecting the irradiated points of the laser light in a time series) when the measurement surface of the road sign 22 is viewed from the front. For ease of explanation, a case where the initial attitude angles _Lφ , _Lθ , and _LΨ of the LIDAR 2 are 0 degrees is taken as an example. Here, FIG. 6A shows the relationship between the target measurement point cloud Ptag and the scanning line 23 of the LIDAR 2 when no deviation in the roll direction occurs in the LIDAR 2, and FIGS. 6B and 6C show the relationship between the target measurement point cloud Ptag and the scanning line 23 of the LIDAR 2 when a deviation _ΔLφ occurs in the roll direction. 6(B) shows the relationship between the target measurement point group Ptag and the scanning line 23 of the LIDAR 2 in the yb _{- zb} plane of the vehicle coordinate system, and FIG. 6(C) shows the relationship between the target measurement point group Ptag and the scanning line 23 of the LIDAR 2 in the _yb' - _zb ' plane of the reference coordinate system. Note that, for ease of explanation, it is assumed here that when there is no attitude deviation of the LIDAR 2, the longitudinal direction of the road sign 22 is parallel to the _yb axis, and that the vehicle is on a flat road.

As shown in FIG. 6(A), when there is no deviation in the roll angle of the lidar 2, the lidar 2 scans parallel to the lateral direction (i.e., the horizontal or longitudinal direction) of the road sign 22. In this case, the number of points in the longitudinal direction of the target measurement point cloud Ptag is the same regardless of the position in the lateral direction (six in FIG. 6(A)).

On the other hand, when there is a deviation in the roll angle of the lidar 2, the scanning line is inclined obliquely with respect to the road sign 22. Here, since the longitudinal direction of the road sign 22 is not inclined with respect to the vehicle, and the scanning line of the lidar 2 is inclined with respect to the vehicle, when observed in the vehicle coordinate system, the _yb axis and the longitudinal direction of the road sign 22 are parallel as shown in Fig. 6(B), and the scanning line of the lidar 2 is inclined with respect to the _yb axis. On the other hand, when observed in the reference coordinate system, the yb _' axis and the scanning line of the lidar 2 are parallel as shown in Fig. 6(C), and the longitudinal direction of the road sign 22 is inclined with respect to the _yb ' axis.

Next, the processing after extraction of the target measurement point group Ptag will be described. First, the vehicle-mounted device 1 judges the received light intensity for each measurement point of the target measurement point group Ptag using a predetermined threshold, and excludes measurement points corresponding to received light intensities below the predetermined threshold from the target measurement point group Ptag. Then, the vehicle-mounted device 1 extracts a point group constituting the outer edge (also called the "outer edge point group Pout") from the target measurement point group Ptag after the exclusion process based on the received light intensity. For example, the vehicle-mounted device 1 extracts measurement points of the target measurement point group Ptag that have no adjacent measurement points in at least one direction of up, down, left, or right as the outer edge point group Pout.

Figure 7(A) shows the target measurement point cloud Ptag in the lidar coordinate system, and Figure 7(B) shows the target measurement point cloud Ptag after excluding measurement points corresponding to light receiving intensities below a predetermined threshold. Also, Figure 7(C) shows the outer edge point cloud Pout extracted from the target measurement point cloud Ptag shown in Figure 7(B). As shown in Figures 7(A) and (B), among the measurement points shown in Figure 7(A), for measurement points that are missing a portion due to only a portion of the laser light being reflected, the received light intensity is below a predetermined threshold and is excluded from the target measurement point cloud Ptag. Also, as shown in Figures 7(B) and (C), measurement points that do not have adjacent measurement points in at least one direction, up, down, left, or right, are extracted as the outer edge point cloud Pout.

Next, the vehicle-mounted device 1 calculates the inclination of the longitudinal direction of the road sign 22 from the outer edge point cloud Pout. Here, since the outer edge point cloud Pout forms a quadrangle, the outer edge point cloud Pout is classified for each side of this quadrangle, and the inclination of the approximate straight line of each side is calculated from the outer edge point cloud Pout classified for each side by a regression analysis method such as the least squares method.

Figure 8(A) shows a diagram in which the outer edge point group Pout that forms the top side of a quadrangle is extracted, and a straight line is drawn using the least squares method. Figure 8(B) shows a diagram in which the outer edge point group Pout that forms the bottom side of a quadrangle is extracted, and a straight line is drawn using the least squares method. Figure 8(C) shows a diagram in which the outer edge point group Pout that forms the left side of a quadrangle is extracted, and a straight line is drawn using the least squares method. Figure 8(D) shows a diagram in which the outer edge point group Pout that forms the right side of a quadrangle is extracted, and a straight line is drawn using the least squares method.

8A to 8D, and calculates the inclinations "φ ₁ " to "φ ₄ " of each line relative to the y _b ' axis. Then, the vehicle-mounted device 1 calculates the average of these inclinations "φ ₁ " to "φ ₄ " as the inclination "φ" of the road sign 22, as shown in the following equation (5).

Here, since the left and right sides of the rectangle are shifted by approximately 90° from the top and bottom sides, in equation (5), the slopes _φ3 and _φ4 of the straight lines obtained from the outer edge point group Pout that constitutes the left and right sides of the rectangle are corrected by “π/2”.

Preferably, when determining the inclination φ, the vehicle-mounted device 1 may weight each of the inclinations φ ₁ to φ ₄ based on the number of the outer edge point group Pout constituting each side of the quadrangle. In this case, assuming that the number of the outer edge point group Pout constituting the top side of the quadrangle is "n ₁ ", the number of the outer edge point group Pout constituting the bottom side of the quadrangle is "n ₂ ", the number of the outer edge point group Pout constituting the left side of the quadrangle is "n ₃ ", and the number of the outer edge point group Pout constituting the right side of the quadrangle is "n ₄ ", the vehicle-mounted device 1 calculates the inclination φ by performing a weighted average based on the following formula (6).

In another preferred example, when determining the inclination φ, the vehicle-mounted device 1 may weight each of the inclinations φ 1 to φ ₄ based on the interval "I _H " of the target measurement point group Ptag in the horizontal direction of the lidar 2 and the interval "I _V " of the target measurement point group Ptag in the vertical direction. Here, the horizontal interval I _H and the vertical interval I _V are the intervals shown in FIG. _7B . In this case, the vehicle-mounted device 1 assumes that the longer the vertical interval I _V , the lower the accuracy of the inclinations φ ₁ and φ ₂ of the top and bottom sides of the rectangle, and that the longer the horizontal interval I _H , the lower the accuracy of the inclinations φ ₃ and φ ₄ of the left and right sides of the rectangle. As shown in the following formula (7), the vehicle-mounted device 1 calculates the inclination φ by performing a weighted average using the reciprocals of the horizontal interval I _H and the vertical interval I _V.

Furthermore, the vehicle-mounted device 1 may calculate the inclination φ by a weighted average that takes into account the numbers _n1 to _n4 of points constituting each side of the quadrangle and both the vertical interval _IV and the horizontal interval _IH . In this case, the vehicle-mounted device 1 calculates the inclination φ by taking a weighted average based on the following formula (8).

The vehicle-mounted device 1 also calculates the roll angle "φ ₀ " of the vehicle body based on the outputs of the gyro sensor 3 and the acceleration sensor 4, and calculates the difference between this and the tilt φ calculated by any one of the equations (5) to (8) to calculate the roll angle L _φ of the rider 2. In this case, the vehicle-mounted device 1 calculates the roll angle L _φ of the rider 2 based on the following equation (9).

This allows the vehicle-mounted device 1 to suitably calculate the roll angle _Lφ of the rider 2 while eliminating the influence of the inclination of the vehicle.

Preferably, taking into consideration the possibility that the road sign 22, which is the detection target feature Ftag, is inclined with respect to the horizontal direction, measurement errors, etc., the vehicle-mounted device 1 performs the above-mentioned calculation of the roll angle _Lφ of the LIDAR 2 for many road signs, which are the detection target feature Ftag, and averages them. This allows the vehicle-mounted device 1 to preferably calculate the likely inclination _Lφ of the LIDAR 2 in the roll direction.

In addition, since the roll angle φ ₀ of the vehicle body can be regarded as 0° on average, by lengthening the averaging time described above, it becomes unnecessary to acquire the roll angle φ ₀ of the vehicle body. For example, when N sufficiently large tilt angles φ are acquired, the vehicle-mounted device 1 may determine the roll angle L _φ of the rider 2 by the following formula (10).

The above-mentioned N is the number of samples for which the average of the vehicle body roll angle φ ₀ can be regarded as approximately 0°, and is determined in advance based on, for example, an experiment.

Figure 9 is a flowchart showing the steps of the roll angle estimation process.

First, the vehicle-mounted device 1 refers to the map DB 10 and sets a detection window for the detection target feature Ftag around the vehicle (step S101). In this case, the vehicle-mounted device 1 selects a rectangular road sign having a perpendicular surface to the traveling direction of the road as the detection target feature Ftag, and identifies the above-mentioned road sign that exists within a predetermined distance from the current position from the map DB 10. Then, the vehicle-mounted device 1 sets the detection window by acquiring position information, etc. related to the identified road sign from the map DB 10.

Next, the vehicle-mounted device 1 acquires the measurement point cloud of the lidar 2 within the detection window set in step S101 as a point cloud of the detection target feature Ftag (i.e., the target measurement point cloud Ptag) (step S102).Then, the vehicle-mounted device 1 extracts the outer edge point cloud Pout that forms the outer edge of these point clouds from the target measurement point cloud Ptag acquired in step S102 (step S103).

Then, the vehicle-mounted device 1 calculates the lateral gradient φ of the outer edge point group Pout extracted in step S103 (step S104). In this case, as described with reference to Figures 8(A) to 8(D), the vehicle-mounted device 1 obtains gradients _φ1 to _φ4 of straight lines corresponding to each side from the points corresponding to each side of the quadrangle formed by the outer edge point group Pout, and calculates the gradient φ by employing any one of the formulas (5) to (8).

Thereafter, the vehicle-mounted device 1 calculates the roll angle _Lφ of the LIDAR 2 by averaging the tilt φ and/or by subtracting the tilt φ from the roll angle φ ₀ of the vehicle body (step S105). In the averaging process described above, the vehicle-mounted device 1 executes the processes of steps S101 to S104 for multiple detection target features Ftag to obtain multiple tilts φ, and calculates the average value of the obtained multiple tilts φ as the tilt _Lφ .

[Pitch angle estimation]
Next, a method for estimating the pitch angle _Lθ of the LIDAR 2 will be described. The vehicle-mounted device 1 regards a rectangular road sign having a surface perpendicular to the width direction of the road (i.e., a surface oriented horizontally to the road) as a detection target feature Ftag, and estimates the pitch angle of the LIDAR 2 by calculating the inclination of the road sign based on the measurement point cloud output by the LIDAR 2.

Figure 10 shows an overhead view of the area around the vehicle when the vehicle is traveling near a road sign 24 that is a target feature Ftag when estimating the pitch angle of the lidar 2. In Figure 10, a dashed circle 20 indicates the lidar detection range in which the lidar 2 can detect the target feature Ftag, and a dashed frame 21 indicates a detection window set for the road sign 24 that is a target feature Ftag.

In this case, the vehicle-mounted device 1 refers to the map DB 10 to recognize that a rectangular road sign 24 having a surface perpendicular to the width direction of the road is present within the lidar detection range shown in the dashed circle 20, and sets a detection window shown in the dashed frame 21 based on position information and the like related to the road sign 24. In this case, the vehicle-mounted device 1 pre-stores, for example, type information and the like of the road sign that is the detection target feature Ftag, and determines whether or not a road sign of the type indicated by the type information is present within the lidar detection range by referring to the feature data in the map DB 10. Then, the vehicle-mounted device 1 extracts the measurement point group that is present within the set detection window from the measurement point group output by the lidar 2 as the target measurement point group Ptag.

Next, the vehicle-mounted device 1 estimates the pitch angle of the rider 2 by performing a process similar to the roll angle estimation method on the extracted target measurement point cloud Ptag.

Specifically, the vehicle-mounted device 1 first determines the light receiving intensity for each measurement point of the target measurement point group Ptag based on a predetermined threshold, and excludes measurement points corresponding to light receiving intensities equal to or less than the predetermined threshold from the target measurement point group Ptag. The vehicle-mounted device 1 then extracts an outer edge point group Pout that constitutes the outer edge from the target measurement point group Ptag after the exclusion process based on the light receiving intensity. Next, the vehicle-mounted device 1 calculates the slopes "θ ₁ " to "θ ₄ " of the approximated straight lines of each side of the quadrangle constituted by the outer edge point group Pout using a regression analysis method such as the least squares method. The vehicle-mounted device 1 then calculates the slope "θ" of the road sign 22 by performing an averaging process based on the slopes θ ₁ to θ ₄ . In addition, the vehicle-mounted device 1 may calculate the inclination θ by a weighted average that takes into account at least one of the numbers _n1 to _n4 of points constituting each side of the quadrangle and the vertical interval _IV and the horizontal interval _IH , similar to equations (6) to (8).

The vehicle-mounted device 1 also calculates the pitch angle "θ ₀ " of the vehicle body based on the outputs of the gyro sensor 3 and the acceleration sensor 4, and calculates the difference between this and the inclination θ to calculate the pitch angle L _θ of the rider 2. In this case, the vehicle-mounted device 1 calculates the inclination L _θ of the rider 2 in the pitch direction based on the following equation (11).

This allows the vehicle-mounted device 1 to suitably calculate the pitch angle L _θ of the LIDAR 2, excluding the influence of the vehicle tilt. In addition, preferably, taking into consideration the possibility that the road sign 24, which is the detection target feature Ftag, is tilted with respect to the horizontal direction, measurement errors, etc., the vehicle-mounted device 1 performs the above-mentioned calculation of the pitch angle L _θ of the LIDAR 2 for many road signs that are the detection target feature Ftag, and averages them. This allows the vehicle-mounted device 1 to suitably calculate a likely pitch angle L _θ of the LIDAR 2. Here, since the pitch angle θ ₀ of the vehicle body can be regarded as 0° on average, it becomes unnecessary to acquire the pitch angle θ ₀ of the vehicle body by lengthening the above-mentioned averaging time. For example, when N tilts θ are acquired, the pitch angle L _θ of the LIDAR 2 is expressed by the following formula (12).

Figure 11 is a flowchart showing the steps of the pitch angle estimation process.

First, the vehicle-mounted device 1 refers to the map DB 10 and sets a detection window for the detection target feature Ftag around the vehicle (step S201). In this case, the vehicle-mounted device 1 selects a rectangular road sign having a perpendicular surface to the width direction of the road as the detection target feature Ftag, and identifies the above-mentioned road sign that exists within a predetermined distance from the current position from the map DB 10. Then, the vehicle-mounted device 1 sets the detection window by acquiring position information and the like related to the identified road sign from the map DB 10.

Next, the vehicle-mounted device 1 acquires the measurement point cloud of the lidar 2 within the detection window set in step S201 as a point cloud of the detection target feature Ftag (i.e., the target measurement point cloud Ptag) (step S202).Then, the vehicle-mounted device 1 extracts an outer edge point cloud Pout that forms the outer edge of the point cloud from the point cloud of the detection target feature Ftag acquired in step S202 (step S203).

Then, the vehicle-mounted device 1 calculates the tilt θ in the longitudinal direction of the outer edge point group Pout extracted in step S203 (step S204). After that, the vehicle-mounted device 1 calculates the tilt L _θ in the pitch direction of the rider 2 by averaging the tilt θ and/or by subtracting the tilt θ from the pitch angle θ ₀ of the vehicle body (step S205). In the above-mentioned averaging process, the vehicle-mounted device 1 executes the processes of steps S201 to S204 for multiple target measurement point groups Ptag to calculate multiple tilts θ, and calculates the average value of these as the tilt L _θ .

[Yaw angle estimation]
Next, a description will be given of a method for estimating the yaw angle _Lψ of the LIDAR 2. The vehicle-mounted device 1 regards a lane marking such as a white line as a detection target feature Ftag, and estimates the yaw angle _Lψ of the LIDAR 2 by calculating the direction of the center line of the lane marking based on a cloud of measurement points measured by the LIDAR 2.

FIG. 12 shows an overhead view of the vehicle periphery when the vehicle travels along a demarcation line that is a detection target feature Ftag when estimating the yaw angle of the LIDAR 2. In this example, a continuous line 30, which is a continuous demarcation line, exists on the left side of the vehicle, and a broken line 31, which is a demarcation line that exists intermittently, exists on the right side of the vehicle. The arrow 25 indicates the reference direction in the yaw direction when no deviation in the yaw direction occurs in the LIDAR 2, and is the direction in which the coordinate axis "x _L " of the LIDAR 2 is rotated by the yaw angle L _Ψ of the LIDAR 2, and coincides with the coordinate axis "x _b " along the traveling direction of the vehicle. That is, the yaw angle L _Ψ of the LIDAR 2 indicates a yaw angle (also called the "yaw direction reference angle") that is considered to be the traveling direction of the vehicle if no deviation occurs. On the other hand, the arrow 26 indicates the yaw direction reference angle when a deviation in the yaw direction ΔL _Ψ occurs in the LIDAR 2, and if the yaw direction reference angle is not corrected, it will not coincide with the traveling direction of the vehicle. The yaw direction reference angle is stored in advance in, for example, the storage unit 12 or the like.

In this case, for example, the map DB 10 contains demarcation line information indicating the discrete coordinate positions of the continuous line 30 and the discrete coordinate positions of the dashed line 31. The vehicle-mounted device 1 then references this demarcation line information, extracts the closest coordinate position from a position a predetermined distance (e.g., 5 m) away from the vehicle in each of the directions of the left front, left rear, right front, and right rear of the vehicle, and sets a rectangular area centered on the extracted coordinate position as the detection window shown in the dashed frame 21A-21D.

Next, the vehicle-mounted device 1 extracts, from the measurement point clouds measured by the lidar 2, a point cloud that is on the road surface within the detection window and has a reflection intensity equal to or greater than a predetermined threshold, as the target measurement point cloud Ptag. The vehicle-mounted device 1 then finds the center point of the target measurement point cloud Ptag for each scanning line, and calculates the straight line (see arrows 27A to 27D) that passes through these center points for each detection window as the center line of the lane marking.

Figure 13(A) shows the center point of the detection window of dashed frame 21A with a circle, and Figure 13(B) shows the center point of the detection window of dashed frame 21B with a circle. Also, Figures 13(A) and (B) clearly show the scanning line 28 of the laser light from the lidar 2. Note that because the laser light from the lidar 2 that is incident on the lane marking is emitted in a downward diagonal direction, the spacing of the scanning lines becomes shorter as they get closer to the vehicle.

As shown in Figures 13(A) and (B), the vehicle-mounted device 1 calculates a center point for each scanning line 28. The center point may be a position obtained by averaging the coordinate positions indicated by the measurement points for each scanning line 28, or may be a midpoint between the measurement points at the left and right ends of each scanning line 28, or may be a measurement point located midway on each scanning line 28. Then, the vehicle-mounted device 1 calculates the center line of the division line (see arrows 27A and 27B) for each detection window from the calculated center point using a regression analysis method such as the least squares method.

The vehicle-mounted device 1 then calculates the angles between the center lines of the lane lines calculated for each detection window and the yaw direction reference angle (see arrow 26 in FIG. 12) as inclinations "ψ ₁ " to "ψ ₄ ". FIGS. 14(A) to (D) are diagrams showing the inclinations ψ ₁ to ψ ₄ , respectively. As shown in FIGS. 14(A) to (D), the inclinations ψ ₁ to ψ ₄ correspond to the angles between the arrows 27A to 27D indicating the center lines of the lane lines and the arrow 26 indicating the yaw direction reference angle.

Then, the vehicle-mounted device 1 calculates the inclination ψ by averaging the inclinations ψ ₁ to ψ ₄ as shown in the following equation (13).

Furthermore, the vehicle-mounted device 1 may weight the slopes ψ ₁ to ψ ₄ based on the number of center points used in calculating the center lines of the lane lines. In this case, assuming that the number of center points used in calculating the center line of the lane line with slope ψ ₁ is "n _{1 "} , the number of center points used in calculating the center line of the lane line with slope ψ ₂ is "n ₂ ", the number of center points used in calculating the center line of the lane line with slope ψ 3 is "n ₃ ", and the number of center points used in calculating the center line of the lane line with slope ψ ₄ is "n ₄ ", the vehicle-mounted device 1 calculates the slope ψ based on the following formula (14):

As a result, the vehicle-mounted device 1 can accurately determine the slope ψ by relatively lowering the weight of the slope of the center line of the lane marking (dashed line 31 in Fig. 12) calculated using fewer center points. Therefore, the weight is lower in the detection situation of Fig. 13(B) than in the detection situation of Fig. 13(A). Note that, in the explanation of Figs. 12 to 14, an example has been shown in which the slopes ψ ₁ to ψ ₄ relative to four lane markings are calculated simultaneously, but it is sufficient to calculate the slope relative to at least one lane marking.

Next, a method for calculating the yaw angle L _ψ of the rider 2 from the inclination ψ will be described. Since the lane markings such as white lines are drawn along the road, the yaw angle of the vehicle body can be considered to be almost constant (i.e., parallel to the road). Therefore, unlike the method for estimating the roll angle or pitch angle, the vehicle-mounted device 1 calculates the inclination ψ as the deviation ΔL _ψ of the yaw angle of the rider 2 (i.e., "ΔL _ψ = ψ"). However, in normal driving, lane changes and wobbling of the vehicle body within the lane exist. Therefore, the vehicle-mounted device 1 may execute a process for calculating the inclination of the center line for many lane marks and average them. This allows the vehicle-mounted device 1 to suitably calculate the deviation ΔL _ψ of the yaw angle of the rider 2 that is likely to occur. For example, when N inclinations ψ are acquired, the yaw angle L _ψ of the rider 2 is expressed by the following formula (15).

Here, "L _ψ0 " indicates the yaw angle L _ψ (i.e., the yaw direction reference angle) when no deviation occurs in the yaw angle.

Figure 15 is a flowchart showing the steps of the yaw angle estimation process.

First, the vehicle-mounted device 1 refers to the map DB 10 and sets one or more detection windows for the lane markings (step S301). Then, the vehicle-mounted device 1 acquires the measurement point cloud of the lidar 2 within the detection window set in step S301 as the point cloud of the lane markings (i.e., the target measurement point cloud Ptag) (step S302).

Then, the vehicle-mounted device 1 calculates the center line of the demarcation line in each detection window that has been set (step S303). In this case, the vehicle-mounted device 1 obtains the center point of the target measurement point group Ptag for each scanning line of the lidar 2, and calculates the center line of the demarcation line for each detection window from the center point based on the least squares method or the like. Then, the vehicle-mounted device 1 calculates the angle ψ between the yaw angle reference angle previously stored in the memory unit 12 or the like and the center line of the demarcation line (step S304). In this case, if two or more detection windows are set in step S301, the vehicle-mounted device 1 obtains the angle between the yaw angle reference angle and the center line of the demarcation line for each detection window, and calculates the above-mentioned angle ψ by averaging these angles or averaging them weighted by the number of center points.

Then, the vehicle-mounted device 1 averages the multiple angles ψ calculated by executing steps S301 to S304 for different lane lines, and calculates the yaw angle L _ψ of the rider 2 based on equation (15) (step S305).

Here, a specific example of processing based on the estimated values of the roll angle, pitch angle, and yaw angle described above will be described. FIG. 16 is a flowchart showing a specific example of processing based on the estimated values of the roll angle, pitch angle, and yaw angle.

First, the vehicle-mounted device 1 executes the process of the flowchart of Fig. 9, Fig. 11, or Fig. 15 to determine whether or not any of the roll angle _Lφ , pitch angle _Lθ , or yaw angle _Lψ of the rider 2 has been calculated (step S401). Then, when the vehicle-mounted device 1 calculates any of the roll angle _Lφ , pitch angle _Lθ , or yaw angle _Lψ of the rider 2 (step S401; Yes), it determines whether or not the calculated angle has changed by a predetermined angle or more from the angle recorded in the rider installation information IL (step S402). The above-mentioned threshold value is a threshold value for determining whether or not the measurement data of the rider 2 can be used continuously by performing a correction process of the measurement data of the rider 2 in step S404 described later, and is set in advance based on, for example, experiments.

If the calculated angle has changed by a predetermined angle or more from the angle recorded in the lidar installation information IL (step S402; Yes), the vehicle-mounted device 1 stops using the output data of the target lidar 2 (i.e., use for obstacle detection, vehicle position estimation, etc.), and outputs a warning from the information output unit 16 that the target lidar 2 needs to be realigned (step S403). This reliably prevents a decrease in safety, etc., caused by using measurement data from a lidar 2 whose attitude or position has shifted significantly due to an accident, etc.

On the other hand, if the calculated angle has not changed by a predetermined angle or more from the angle recorded in the LIDAR installation information IL (step S402; No), the vehicle-mounted device 1 corrects each measurement value of the point cloud data output by the LIDAR 2 based on the amount of change in the calculated angle from the angle recorded in the LIDAR installation information IL (step S404). In this case, the vehicle-mounted device 1 stores, for example, a map or the like indicating the amount of correction of the measurement value for the amount of change described above, and corrects the above measurement value by referring to the map or the like. The measurement value may also be corrected by setting a value of a predetermined percentage of the amount of change as the amount of correction of the measurement value.

As described above, the vehicle-mounted device 1 in this embodiment acquires a target measurement point cloud Ptag measured by the lidar 2 attached to the vehicle of road signs having a surface perpendicular to the traveling direction of the vehicle's roadway or a surface perpendicular to the width direction of the roadway. The vehicle-mounted device 1 then estimates the mounting attitude of the lidar 2 to the vehicle based on the longitudinal tilt of the road sign calculated from the target measurement point cloud Ptag with reference to a coordinate system based on the installation information of the lidar 2. This allows the vehicle-mounted device 1 to accurately estimate the mounting attitude of the lidar 2 to the vehicle in the roll direction and pitch direction.

As described above, the vehicle-mounted device 1 in this embodiment acquires a cloud of measurement points obtained by measuring the lane markings using the lidar 2 attached to the vehicle, and calculates the center line of the lane markings along the vehicle's traveling direction based on the cloud of measurement points. The vehicle-mounted device 1 then estimates the mounting attitude of the lidar 2 to the vehicle based on the direction of the center line of the lane markings relative to the yaw direction reference angle based on which the lidar 2 is based. This allows the vehicle-mounted device 1 to preferably estimate the mounting attitude of the lidar 2 to the vehicle in the yaw direction.

[Modification]
Preferred modified examples of the embodiment will be described below. The following modified examples may be applied to the embodiment in combination.

(Variation 1)
In the description of step S303 in Figures 13 and 15, the vehicle-mounted device 1 calculates the center line of the lane line by calculating the center point of the lane line in the width direction within the detection window. Alternatively, the vehicle-mounted device 1 may extract the right end point or the left end point of the scanning line within the detection window and calculate a straight line passing through the right end or the left end of the lane line. This also allows the vehicle-mounted device 1 to preferably identify a line that is parallel to the road and calculate the angle ψ.

(Variation 2)
In the yaw angle estimation process described in Figures 12 to 15, the vehicle-mounted device 1 regards the lane markings as the detection target feature Ftag. Alternatively, or in addition, the vehicle-mounted device 1 may regard a curb or the like as the detection target feature Ftag. In this way, the vehicle-mounted device 1 may execute the yaw angle estimation process described in Figures 12 to 15 by regarding, as the detection target feature Ftag, any feature whose measurement surface is parallel to the traveling road surface and is formed along the traveling road surface, not limited to the lane markings.

(Variation 3)
In step S404 of FIG. 16, instead of correcting each measurement value of the point cloud data output by the LIDAR 2, the vehicle-mounted device 1 may convert each measurement value of the point cloud data output by the LIDAR 2 into the vehicle coordinate system based on the roll angle, pitch angle, and yaw angle calculated by the processing of the flowchart of FIG. 9, FIG. 11, or FIG. 15.

In this case, the vehicle-mounted device 1 may use the calculated roll angle _Lφ , pitch angle _Lθ , and yaw angle _Lψ to convert each measurement value of the point cloud data output by the LIDAR 2 from the LIDAR coordinate system to the vehicle body coordinate system based on equation (4), and perform vehicle position estimation, automatic driving control, and the like based on the converted data.

In another example, if each rider 2 is equipped with an adjustment mechanism such as an actuator for correcting the attitude of the rider 2, the vehicle-mounted device 1 may control the actuation of the adjustment mechanism to correct the attitude of the rider 2 by the deviation from the angle recorded in the rider installation information IL, instead of the processing of step S404 in FIG. 16.

(Variation 4)
The configuration of the vehicle system shown in Fig. 1 is an example, and the configuration of a vehicle system to which the present invention can be applied is not limited to the configuration shown in Fig. 1. For example, instead of having an on-board device 1, the vehicle system may have an electronic control device of the vehicle execute the processes shown in Figs. 9, 11, 15, 16, etc. In this case, the LIDAR installation information IL is stored in, for example, a storage unit in the vehicle, and the electronic control device of the vehicle is configured to be able to receive output data from various sensors such as the LIDAR 2.

(Variation 5)
The vehicle system may include a plurality of LIDARs 2. In this case, the vehicle-mounted device 1 executes the processes of the flowcharts in Fig. 9, Fig. 11, and Fig. 15 for each LIDAR 2 to estimate the roll angle, pitch angle, and yaw angle of each LIDAR 2.

REFERENCE SIGNS LIST 1 Vehicle-mounted device 2 Lidar 3 Gyro sensor 4 Acceleration sensor 5 GPS receiver 10 Map DB

Claims (10)

an acquisition means for acquiring a cloud of measurement points obtained by measuring features having a surface perpendicular to a direction of travel of a travel path of a moving body or a surface perpendicular to a width direction of the travel path by a measuring device attached to the moving body;
an estimation means for estimating an attachment attitude of the measurement device to the moving body based on an inclination of the vertical plane with respect to a coordinate system based on installation information of the measurement device, the inclination being calculated from the measurement point cloud;
A posture estimation device having the following configuration. The posture estimation device according to claim 1, wherein the acquisition means recognizes the position of the feature based on feature information about the feature, and acquires a measurement point cloud measured by the measurement device at that position as the measurement point cloud of the feature. The estimation means includes:
estimating an attitude of the measurement device in a roll direction based on the inclination calculated from a measurement point cloud of a feature having a surface perpendicular to a traveling direction of the travel path;
3. The attitude estimation device according to claim 1, further comprising: estimating an attitude of the measurement device in a pitch direction based on the inclination calculated from a cloud of measurement points of a feature having a surface perpendicular to a width direction of the road. The posture estimation device according to any one of claims 1 to 3, wherein the feature is a road sign provided on the travel path. The posture estimation device according to any one of claims 1 to 4, wherein the estimation means extracts an outer edge point cloud that forms the edge of the measurement point cloud, and calculates the inclination of the feature based on the outer edge point cloud. the vertical surface is rectangular;
The attitude estimation device according to claim 5 , wherein the estimation means calculates the inclination of the feature by calculating an inclination corresponding to each side of the rectangle from the outer point cloud. The posture estimation device according to claim 6, wherein the inclination of the feature is calculated by weighting the inclination corresponding to each side based on at least one of the number and spacing of the points constituting each side. A control method executed by a posture estimation device, comprising:
an acquisition step of acquiring a cloud of measurement points obtained by measuring features having a surface perpendicular to the direction of travel of the travel path of the moving body or a surface perpendicular to the width direction of the travel path by a measuring device attached to the moving body;
an estimation step of estimating an attachment attitude of the measurement device to the moving body based on an inclination of the vertical plane with respect to a coordinate system based on installation information of the measurement device, the inclination being calculated from the measurement point cloud;
The control method includes: A program executed by a computer,
an acquisition means for acquiring a cloud of measurement points obtained by measuring features having a surface perpendicular to a direction of travel of a travel path of a moving body or a surface perpendicular to a width direction of the travel path by a measuring device attached to the moving body;
A program that causes the computer to function as an estimation means for estimating the mounting attitude of the measuring device to the moving body, based on the inclination of the vertical plane relative to a coordinate system based on installation information of the measuring device, which is calculated from the measurement point cloud. A storage medium storing the program according to claim 9.