Kalman Filters Based Robot Kinematics Estimation for 2D-SLAM Pose Pre-compensation

Abstract

Nowadays robots are widely used in several industries As far as mobile robots are concerned how mobile robots go to the goal from the origin becomes a quite important issue Nowadays the technique that is most widely used for mobile robots is Simultaneous Localization And Mapping (SLAM) SLAM can build the ambient map online and localize the relative position through the point cloud scanned from LiDAR But there are still some shortcomings in this technology the algorithm will go wrong in certain environment conditions such as out of scan limit cloister(not enough characteristic feature) The vision of this research is to improve the above problems by combining other sensors which can provide SLAM algorithm additional information to make SLAM algorithm doesn’t go wrong in mentioned situations Even the pre-compensation mechanism can help SLAM to turn the update frequency down and downgrade the hardware so that the final goal of saving budget can be expected The sensors which are used in this research are encoders gyros and accelerometers However the data measured by different sensors often can be used to calculate same state For example the data measured from gyro and encoder both can be used to calculate the yawing rate of Wheeled Mobile Robot (WMR) As for which one should be trusted more How to decide the weighting of trust is also the main purpose of this research Therefore this research will decide an optimal weighting through Kalman Filter (KF) to estimate the optimal state After that this research will combine Adaptive Kalman Filter (AKF) and proposed slippage detection algorithm to decide the weighting when slippage happens so that the estimated posture of WMR will not be influence by the effect of slippage At last this research will confirm the theory through simulation and experiment

Date of Award	2020
Original language	English
Supervisor	Chao-Chung Peng (Supervisor)