ORD-WM: A two-stage loop closure detection algorithm for dense scenes

Abstract

Loop closure detection is a crucial technique supporting localization and navigation in autonomous vehicles. Existing research focuses on feature extraction in global scenes while neglecting considerations for local dense environments. In such local scenes, there are a large number of buildings, vehicles, and traffic signs, characterized by abundant objects, dense distribution, and interlaced near and far. The current methods only employ a single strategy for constructing descriptors, which fails to provide a detailed representation of the feature distribution in dense scenes, leading to inadequate discrimination of descriptors. Therefore, this paper proposes a multi-information point cloud descriptor to address the aforementioned issues. This descriptor integrates three types of environmental features: object density, region density, and distance, enhancing the recognition capability in local dense scenes. Additionally, we incorporated wavelet transforms and invariant moments from the image domain, designing wavelet invariant moments with rotation and translation invariance. This approach resolves the issue of point cloud mismatch caused by LiDAR viewpoint variations. In the experimental part, We collected data from dense scenes and conducted targeted experiments, demonstrating that our method achieves excellent loop closure detection performance in these scenes. Finally, the method is applied to a complete SLAM system, achieving accurate mapping.