Research on SLAM Drift Reduction Mechanism Based on Point Cloud Segmentation Semantic Information

This paper combines the semantic segmentation of scenes with Simultaneous localization and Mapping (SLAM) technology to build a three-dimensional semantic map. The input sequence is selected by ORB-SLAM for key frame selection, and the scene's semantic segmentation is performed in the corresponding point cloud data. We use a new 3D segmentation framework, which can effectively simulate the local structure of point cloud. A drift reduction mechanism based on semantic constraints and Bundle Adjustment (BA) constraints was proposed. This mechanism considers the three-dimensional objects, feature points and camera pose for semantic recognition in the scene, and integrates them into the back-end BA to optimize them. The final experimental results show that compared with the current popular ORB-SLAM, this mechanism can reduce the system's translation drift error by 18.8%.