Loop Closure Detection Method of Laser SLAM Based on Global Feature Descriptor

Abstract

To solve the problem that localization error of the underground inspection system continues to accumulate over time, a loop closure detection algorithm based on point cloud global feature descriptor is proposed, which is suitable for laser simultaneous localization and mapping (SLAM). The feature vector of each point in point cloud is calculated by curvature, then the global feature descriptor of point cloud is constructed based on the angle distribution and scale distribution relationship between the feature vector and center point coordinate system. In addition, the pose transformation of two similar frames is calculated by feature point registration to improve computing efficiency. The proposed algorithm is verified by simulation experiments and open-source data experiments. The experimental results show that the proposed algorithm has a significant improvement in localization accuracy and real-time performance, which can effectively solve the problems of increased cumulative error and poor global consistency of the localization algorithm during long-term inspections.

Keywords

• underground inspection
• laser localization
• loop closure detection
• global feature descriptor