用深度学习过滤动态物体，提升ORB-SLAM3在复杂环境中的定位精度
PCR-ORB: Enhanced ORB-SLAM3 with Point Cloud Refinement Using Deep Learning-Based Dynamic Object Filtering
- 引入YOLOv8与多阶段滤波，实时剔除动态障碍物点云
- KITTI数据集上序列04的定位误差降低25.9%（ATE RMSE）
- 适合需要高鲁棒性的自动驾驶与机器人导航场景
视觉同时定位与地图构建（vSLAM）系统在动态环境中面临严重挑战，移动物体导致跟踪精度和地图一致性下降。本文提出PCR-ORB（Point Cloud Refinement ORB），一种增强版ORB-SLAM3框架，通过基于深度学习的点云精修来缓解动态物体干扰。该方法结合YOLOv8进行语义分割，并利用CUDA加速实现实时处理。系统采用多阶段滤波策略，包括地面平面估计、天空区域去除、边缘过滤及时间一致性验证。在KITTI数据集（序列00-09）上的全面评估显示，不同环境条件和场景类型下表现各异；其中序列04的ATE RMSE降低25.9%，ATE中位数降低30.4%。但各序列结果表现不一，表明效果具有场景依赖性。该实现为动态物体过滤提供了新思路，有助于复杂环境下鲁棒导航。
原文摘要 · Abstract (English)
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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