RPV-Map: A novel Reflection-Planes Voxel Map for glass and mirror reconstruction on SLAM System

Abstract

This paper presents a novel reflection-plane voxel map approach that effectively detects and reconstructs reflective objects during SLAM by integrating geometric and visual information from 3D LiDAR and camera. The method first estimates rough reflection planes from the voxel map and extracts 3D boundary and surface points through intensity-based point cloud classification. Using 2D Alpha Shape boundary extraction and multi-view reflection mask validation, we achieve reliable geometric feature extraction. Our proposed plane optimization method considers weighted distributions of different point types and employs boundary refinement to improve reconstruction accuracy. Experimental results demonstrate that our approach outperforms existing methods in terms of plane accuracy, reconstruction completeness, and point cloud classification while achieving real-time performance.