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.
Abstract (translated)
视觉同时定位与地图构建（vSLAM）系统在动态环境中面临重大挑战，因为移动物体会影响跟踪精度和地图一致性。本文介绍了PCR-ORB（点云精炼ORB），这是一种增强版的ORB-SLAM3框架，集成了基于深度学习的点云精炼技术，以减轻动态对象干扰的影响。我们的方法结合使用YOLOv8进行语义分割以及CUDA加速处理来实现实时性能。该系统实施了多级过滤策略，包括地面平面估计、天空区域移除、边缘滤波和时间一致性验证。 在KITTI数据集（序列00-09）上的全面评估显示，在不同环境条件和场景类型下系统的性能特征。特别是在某些序列中观察到了显著改进：序列04的ATE RMSE提高了25.9%，ATE中位数提高了30.4%。然而，结果显示在不同的序列上表现不一，表明其有效性取决于具体的应用场景。 该实现为动态物体过滤挑战提供了见解，并揭示了在复杂环境中进行稳健导航的机会。
URL
https://arxiv.org/abs/2512.23318
