Localisation is the process of determining the position or the state of a robot with respect to the enviroment.
Data provided in the data.pickle:
| Variable | Description |
|---|---|
| t | timestamps for the measurements provided |
| x_init | initial x position of the bot |
| y_init | initial y position of the bot |
| th_init | initial θ of the bot |
| v | Odometry reading of the velocity |
| v_var | Variance in the odometry reading of the velocity |
| om | Odometry Reading of the angular velocity |
| om_var | Variance in the odometry reading of the angular velocity |
| l | Location of the landmarks [a b]. Where a is the x coordinate of the landmark and b is the y coordinate of the landmark |
| d | Distance between the center of the bot and the LiDAR sensor |
| b | Bearing measurement provided by the LiDAR sensor |
| b_var | Variance in the Bearing measurement of the LiDAR sensor |
| r | Range measurement provided by the LiDAR sensor |
| r_var | Variance in the Range measurement provided by the LiDAR sensor |
import pickle
import numpy
with open('./data.pickle', 'rb') as f:
data = pickle.load(f)Refer to Bayes Filter and Kalman Filter Algorithm if you are unfimiliar with Bayes Filter and Kalman Filter
Extended Kalman Filter is a Recursive Bayes Filter used for estimating the state of a robot by obtaining linear appoximation of the non-linear system.
State Prediction involves predicting the state of the robot using only odmetry commands given to the motors of the robot.
The above equation is the motion model. This is used to predict the position of robot given the position (x, y, θ)T at time t-1 and control commands (u,w)T given to the robot at time t.
(x', y', θ')T is the predicted pose of the robot at time t.