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arXiv · 2512.23318

PCR-ORB: Enhanced ORB-SLAM3 with Point Cloud Refinement Using Deep Learning-Based Dynamic Object Filtering

Sheng-Kai Chen·Jie-Yu Chao·Jr-Yu Chang·Po-Lien Wu·Po-Chiang Lin
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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.

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Sheng-Kai Chen, Jie-Yu Chao, Jr-Yu Chang, Po-Lien Wu, Po-Chiang Lin. 2025-12-29. PCR-ORB: Enhanced ORB-SLAM3 with Point Cloud Refinement Using Deep Learning-Based Dynamic Object Filtering. https://arxiv.org/abs/2512.23318

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