Open AccessResearch on SLAM Drift Reduction Mechanism Based on Point Cloud Segmentation Semantic Information
Author(s) -
Junqin Lin,
Zhihong Chen,
Han Li,
Yu Liao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1570/1/012095
Subject(s) - point cloud , computer science , segmentation , artificial intelligence , computer vision , bundle adjustment , frame (networking) , feature (linguistics) , key (lock) , orb (optics) , point (geometry) , reduction (mathematics) , image (mathematics) , mathematics , telecommunications , linguistics , philosophy , geometry , computer security
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%.