HSC: a multi-hierarchy descriptor for loop closure detection in overhead occlusion scenes

Abstract

Abstract Loop closure detection is a key technology for robotic navigation. Existing research primarily focuses on feature extraction from global scenes but often neglects local overhead occlusion scenes. In these local scenes, objects such as vehicles, trees, and buildings vary in height, creating a complex multi-layered structure with vertical occlusions. Current methods predominantly employ a single-level extraction strategy to construct descriptors, which fails to capture the characteristics of occluded objects. This limitation results in descriptors with restricted descriptive capabilities. This paper introduces a descriptor named Hierarchy Scan Context (HSC) to address this shortfall. HSC effectively extracts height feature information of objects at different levels in overhead occlusion scenes through hierarchical division, demonstrating enhanced descriptive capabilities. Additionally, a time series enhancement strategy is proposed to reduce the number of algorithmic missed detections. In the experiments, the proposed method is validated using a self-collected dataset and the public KITTI and NCLT datasets, demonstrating superior performance compared to competitive methods. Furthermore, the proposed method also achieves an average maximum F1 score of 0.92 in experiments conducted on nine selected road segments with overhead occlusion.

Keywords

• Robotics
• LiDAR
• Simultaneous localization and mapping (SLAM)
• Loop closure detection (LCD)