用全景激光雷达增强视觉里程计，提升大视野下的定位精度。
Panoramic Direct LiDAR-assisted Visual Odometry
- 融合360度全景图像与激光雷达点云，实现全向信息匹配
- 在公开数据集上优于现有方法，尤其在纹理不足场景下表现更稳
- 适合自动驾驶、机器人导航等需要广域感知的场景
通过利用激光雷达提供的稀疏深度信息来增强视觉里程计，是提升定位精度的有前景方案。现有多数工作采用单目针孔相机，但受限于视场角（FOV）较小，易因纹理不足或运动模糊导致鲁棒性差。本文提出一种全景直接激光雷达辅助视觉里程计，将360度视场角的激光雷达点云与360度视场角的全景图像完全对齐。全景图像提供更丰富信息，可弥补单视角下因纹理缺失或运动模糊造成的姿态估计误差。除了不同时刻同一视角间的约束外，还可建立同一时刻不同视角间的约束。在多个公开数据集上的实验表明，本方法在大视场角优势下显著优于当前先进方法。
原文摘要 · Abstract (English)
Enhancing visual odometry by exploiting sparse depth measurements from LiDAR is a promising solution for improving tracking accuracy of an odometry. Most existing works utilize a monocular pinhole camera, yet could suffer from poor robustness due to less available information from limited field-of-view (FOV). This paper proposes a panoramic direct LiDAR-assisted visual odometry, which fully associates the 360-degree FOV LiDAR points with the 360-degree FOV panoramic image datas. 360-degree FOV panoramic images can provide more available information, which can compensate inaccurate pose estimation caused by insufficient texture or motion blur from a single view. In addition to constraints between a specific view at different times, constraints can also be built between different views at the same moment. Experimental results on public datasets demonstrate the benefit of large FOV of our panoramic direct LiDAR-assisted visual odometry to state-of-the-art approaches.
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