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Multi-Sensor Integrated Navigation System Aided by Continuous Error Map for autonomous vehicle in Urban Areas

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Continuous Error Map for Autonomous Vehicles in Urban Areas

This repo will contain the implementation for our ITSC 2023 paper: Adaptive Multi-Sensor Integrated Navigation System Aided by Continuous Error Map from RSU for Autonomous Vehicles in Urban Areas. It is part of the project V2X Cooperative Navigation.

Videos:

Checkout our demo at Video Link

Sensor Setup

We collect 10 Hz LiDAR point cloud, 20 Hz images, 100 Hz IMU and 100 Hz ground truth positioning that explicitly considers the real-world noise in urban scenarios under a maximum speed of 30 km/h. Furthermore, we simulated the GNSS measurements using the realist RUMS simulator, which considers the impact of signal reflection and diffraction based on the 3D building model, as the CARLA can only simulate GNSS solutions with Gaussian noise. You can also collect your data using the tools here

Sensor Description
1x LiDAR 360° Horizontal FOV, +10°~-30° vertical FOV, 100 meters, Velodyne HDL 32 noise model, 5cm standard deviation on range measurement
1 x RGB Camera FoV of 90°, 960x600, forward, default noise setting in CARLA
1 x IMU 100 Hz, 9-axis, Xsens MTi 10 noise model according to UrbanNav
1 x GNSS 10 Hz, Ublox M8T noise model with sign reflection from buildings

Data Details

The dataset is released as rosbag while the ground truth is released both in TUM format and the raw output from CARLA simulator using CarlaFLCAV

name Description size link
urban-noon 1 simulation data to generate error maps at noon 32.0 GB ROSBAG, GT
urban-noon 2 test data for error map-aided sensor fusion at noon 32.0 GB ROSBAG, GT
urban-sunset 1 simulation data to generate error maps at sunset 32.0 GB ROSBAG, GT
urban-sunset 2 test data for error map-aided sensor fusion at sunset 32.0 GB ROSBAG, GT
urban-night 1 simulation data to generate error maps at sunset 32.6 GB ROSBAG, GT
urban-night 2 test data for error map-aided sensor fusion at sunset 32.0 GB ROSBAG, GT

The topics within the rosbag are listed below:

topic type frequency description
/carla/ego_vehicle/lidar sensor_msgs/PointCloud2 10Hz 32 line LiDAR
/carla/ego_vehicle/image sensor_msgs/Image 20Hz FoV of 90° 960x600 image
/carla/ego_vehicle/gnss nav_msgs/Odometry 10Hz GNSS data
/carla/ego_vehicle/gnss_path nav_msgs/Path 10Hz GNSS path, for vis use
/carla/ego_vehicle/gt_path nav_msgs/Path 10Hz GT path, for vis use
/carla/ego_vehicle/imu sensor_msgs/Imu 100Hz IMU data

Run

Error map estimation

You can refer the config folder of the sensor parameter for running LOAM, LIOSAM, VINS and LVISAM with this data

Error-map-aided multi-sensor fusion

We are arranging the code, which will come soon!!

Acknowledge

We acknowledge the CarlaFLCAV for the useful tools to collect the data in CARLA. The authors also thank the valuable comments from ITSC 2023 reviewer, we will include the speed profile in the future.

Citation

If you use this work for your research, you may want to cite

@INPROCEEDINGS{rsalio2023huang,
  author={Huang, Feng and Wen, Weisong and Zhang, Guohao and Su, Dongzhe and Hsu, Li-Ta},
  booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, 
  title={Adaptive Multi-Sensor Integrated Navigation System Aided by Continuous Error Map from RSU for Autonomous Vehicles in Urban Areas
}, 
  year={2023},
  volume={},
  number={}
}

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