Simultaneous Localization and Mapping based on Lidar

To solve these problems of low accuracy of the proposed distribution, large number of particles required and particle degradation of traditional Rao-Blackwillised particle filter(RBPF), an improved RBPF simultaneous localization and mapping (SLAM) method based on lidar is proposed. The SLAM method uses the Point to Line Iterative closest point (PL-ICP) to register the laser scan data of two adjacent frames, and replaces the inter-frame matching result with the odometer reading to optimize the proposal distribution. Introducing a particle weight balancing strategy during sampling reduces the loss of particle diversity. The performance of the algorithm is verified by the robot operating system(ROS) and the differential robot platform. The experimental results show that the improved RBPF-SLAM method reduces the number of particles needed for mapping, and alleviates the problem of particle dissipation while improving the accuracy of interior mapping and location.