LiDAR mini-matching positioning method based on constraint of lightweight point cloud feature map

Abstract

In the global navigation satellite positioning denial environment, the matching localization based on a high-definition map is a well-recognized method to assist autonomous vehicles in achieving continuous, stable, and reliable navigation and positioning in complex scenes. However, due to the complexity, density, and richness of the typical high-definition point cloud map, there are high technical thresholds in the process of localization production, application, and maintenance. As a result, referring to the idea of traverse survey in geodesy, we design a priori lightweight point cloud feature map structure based on road mileage nodes as the main line to increase the flexibility of map segmentation, splicing, retrieval, and communication transmission. At the same time, an efficient and practical mini-matching positioning method is proposed. Finally, using the real datasets of various urban canyon scenes for verification, the proposed method can achieve stable sub-meter level localization.