Enhanced ORB-SLAM3 with Point-Cloud Refinement
Integrated YOLOv8-based dynamic filtering and CUDA-accelerated point cloud refinement. Achieved 25.9% reduction in ATE RMSE and 30.4% improvement in trajectory accuracy on KITTI dataset.
Advisor
Team
Sheng-Kai Chen
Project Developer & Paper Author
Jie-Yu Chao
Paper Co-Author (Responsible for Related Work)
Jr-Yu Chang
Paper Co-Author (Responsible for Related Work)
Po-Lien Wu
Paper Co-Author (Responsible for Related Work)
Abstract
Visual Simultaneous Localization and Mapping (vSLAM) systems encounter substantial challenges in dynamic environments where moving objects compromise tracking accuracy and map consistency. This paper introduces PCR-ORB (Point Cloud Refinement ORB), an enhanced ORB-SLAM3 framework that integrates deep learning-based point cloud refinement to mitigate dynamic object interference. Our approach employs YOLOv8 for semantic segmentation combined with CUDA-accelerated processing to achieve real-time performance. The system implements a multi-stage filtering strategy encompassing ground plane estimation, sky region removal, edge filtering, and temporal consistency validation. Comprehensive evaluation on the KITTI dataset (sequences 00-09) demonstrates performance characteristics across different environmental conditions and scene types. Notable improvements are observed in specific sequences, with sequence 04 achieving 25.9% improvement in ATE RMSE and 30.4% improvement in ATE median. However, results show mixed performance across sequences, indicating scenario-dependent effectiveness. The implementation provides insights into dynamic object filtering challenges and opportunities for robust navigation in complex environments.