An Improved Particle Filtering Algorithm for Indoor Robot Localization Using a Prior Map

This paper focuses on the indoor robot localization problem in the presence of accumulated errors from odometry and IMU. Considering that the motion of the robot is constrained by the prior map, employing the map as a position reference is beneficial for reducing accumulated errors. Therefore, a particle filtering method with map constraints is proposed to improve indoor robot localization accuracy. First, a LiDAR measurement model is constructed to provide the distance from the robot to map obstacles. Since each particle represents a possible state of the robot, the residual between its virtual measurement and the actual LiDAR measurement is used to update the corresponding particle weight. Additionally, an iterative movement strategy is designed to guide the sampling particles toward the high-probability region for enhancing the effectiveness of the particles. Finally, the experimental results show that the proposed method achieves higher localization accuracy than traditional particle filter methods.