改进ORB-SLAM3，用光流应对颠簸路面的帧匹配丢失问题。
ORB-SLAM3AB: Augmenting ORB-SLAM3 to Counteract Bumps with Optical Flow Inter-frame Matching
- 融合特征点与光流，提升复杂地形下的匹配鲁棒性。
- 在高振动时自动增强光流依赖，保持定位精度不下降。
- 专为颠簸路面设计，适合越野或自动驾驶场景使用。
本文提出对ORB-SLAM3算法的改进，专用于崎岖路面场景。新方法结合特征点匹配与光流技术，利用光流在复杂地形中的高鲁棒性与特征点在平坦表面的高精度优势。通过优化ORB-SLAM3的帧间匹配逻辑，有效缓解了不平路面导致的帧匹配丢失问题。引入自适应匹配机制，在高振动阶段增加光流点权重，避免精度下降。由于缺乏适用于颠簸路面的多传感器数据集，研究团队采集了含激光雷达与相机的实测数据。将改进后的ORB-SLAM3AB与多个仅依赖激光或视觉的先进开源SLAM算法进行对比，基于绝对轨迹误差（ATE）与相对位姿误差（RPE）评估，结果表明其在崎岖路面上具有更强的鲁棒性与更高精度。
原文摘要 · Abstract (English)
This paper proposes an enhancement to the ORB-SLAM3 algorithm, tailored for applications on rugged road surfaces. Our improved algorithm adeptly combines feature point matching with optical flow methods, capitalizing on the high robustness of optical flow in complex terrains and the high precision of feature points on smooth surfaces. By refining the inter-frame matching logic of ORB-SLAM3, we have addressed the issue of frame matching loss on uneven roads. To prevent a decrease in accuracy, an adaptive matching mechanism has been incorporated, which increases the reliance on optical flow points during periods of high vibration, thereby effectively maintaining SLAM precision. Furthermore, due to the scarcity of multi-sensor datasets suitable for environments with bumpy roads or speed bumps, we have collected LiDAR and camera data from such settings. Our enhanced algorithm, ORB-SLAM3AB, was then benchmarked against several advanced open-source SLAM algorithms that rely solely on laser or visual data. Through the analysis of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) metrics, our results demonstrate that ORB-SLAM3AB achieves superior robustness and accuracy on rugged road surfaces.
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