Lidar SLAM Based on Particle Filter and Graph Optimization for Substation Inspection

Abstract

Simultaneous Localization and Mapping (SLAM) is the core technology of intelligent substation inspection robot. Because of lightweight computation, Rao-Blackwellized Particle Filter (RBPF) is widely used in two-dimensional SLAM. However, it suffers from poor positioning accuracy, low robustness and rapid cumulative errors despite recent improvement. This paper presents a lidar SLAM system based on RBPF and graph optimization that can adapt to unstructured operating environment of substation. Firstly, the diversity of particles is increased by rebuilding the resample algorithm to improve the robustness of the system, and high-quality poses are estimated in submaps. Secondly, the multi-submap system is established to construct odometry constraints (one pose corresponds to two submaps). Furthermore, loop detector is an important part of optimization algorithm, and the branch-bound method is used to reduce computation burden and accelerate the loop detection. Finally, global poses of robot are optimized by the whole odometry and loop constraints in real time. Experiment results show that the proposed method is more accurate than other methods, and can maintain and produce high-precision positioning and mapping in complex substation operation and maintenance environment. It provides a new idea for intelligent substation inspection and positioning method.

Keywords

• Graph optimization
• loop closure detection
• RBPF
• SLAM
• substation inspection