Localization initialization for multi-beam LiDAR considering indoor scene feature

Abstract

For the problem of localization initialization (LI) of robot in indoor large-scale scene, a localization initialization method based on feature pattern is proposed. Firstly, with feature analysis of indoor scene structure, the proposed method explores robust man-made structures (e.g., walls, columns and some other structures with spatial location indication function), which are defined as feature patterns to improve robustness of scene feature expression. Then, with characteristics of multi-beam light detection and ranging (LiDAR) point cloud, a feature pattern extraction method in real-time data is proposed to improve efficiency of feature expression with a hierarchical management. Next, a semi-automatic processing method is proposed to extract feature patterns from point cloud map, and an efficient data management pipeline is designed to avoid repeatedly redundant operations on map data during multiple times initialization to improve efficiency of LI. Finally, two kinds of error equations are constructed for different feature patterns. With L-M gradient descent solution and hit ratio of map grid as metric, an adaptive matching and registration strategy is proposed to accomplish LI of robot in large-scale indoor scene. In order to verify feasibility of this method, a low-cost 16-line LiDAR was used in the experiment in three typical indoor scenes i.e., corridor, hall and underground parking lot. The experimental results show that LI is accomplished quickly and accurately with proposed method in large-scale indoor scene, and it basically meets the localization accuracy and efficiency requirements of indoor robot in practical application.