LCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction

Abstract

A globally consistent map is the basis of indoor robot localization and navigation. However, map built by Rao-Blackwellized Particle Filter (RBPF) doesn’t have high global consistency which is not suitable for long-term application in large scene. To address the problem, we present an improved RBPF Lidar SLAM system with loop detection and correction named LCPF. The efficiency and accuracy of loop detection depend on the segmentation of submaps. Instead of dividing the submap at fixed number of laser scan like existing method, Dynamic Submap Segmentation is proposed in LCPF. This segmentation algorithm decreases the error inside the submap by splitting the submap where there is high scan match error and later rectifies the error by an improved pose graph optimization between submaps. In order to segment the submap at appropriate point, when to create a new submap is determined by both the accumulation of scan match error and the particle distribution. Furthermore, LCPF uses branch and bound algorithm as basic detector for loop detection and multiple criteria to judge the reliability of a loop. In the criteria, a novel parameter called usable ratio was proposed to measure the useful information that a laser scan containing. Finally, comparisons to existing 2D-Lidar mapping algorithm are performed with a series of open dataset simulations and real robot experiments to demonstrate the effectiveness of LCPF.

Keywords

• Simultaneous localization and mapping
• mobile robots
• indoor navigation
• particle filter
• loop detection
• dynamic submap segementation