融合激光雷达与视觉信息，实现跨模态高精度定位与重定位。
SOLVR: Submap Oriented LiDAR-Visual Re-Localisation
- 用双目图像生成带位姿的深度图，扩展相机视域。
- 在KITTI和KITTI360上实现当前最佳的定位与重定位性能。
- 改进了低内点率场景下的注册效果，适合复杂城市环境使用。
本文提出SOLVR，一种基于学习的统一激光雷达-视觉重定位框架，可实现跨模态的位置识别与6-自由度注册。通过利用双目图像流生成带有位姿信息的度量深度预测，对输入传感器模态进行对齐；随后采用概率占用框架融合局部窗口内的多视角场景，扩展相机有限的视野。SOLVR采用灵活的正例定义方式，使位置识别与注册性能可同时优化。此外，用基于稀疏关键点对应内点概率加权的最小二乘拟合替代RANSAC，提升了在查询与检索场景间内点比例较低时的注册性能。在KITTI与KITTI360数据集上的实验表明，SOLVR在激光雷达-视觉位置识别与注册任务中达到当前最优表现，尤其在查询与检索位置距离较远时显著提升注册精度。
原文摘要 · Abstract (English)
This paper proposes SOLVR, a unified pipeline for learning based LiDAR-Visual re-localisation which performs place recognition and 6-DoF registration across sensor modalities. We propose a strategy to align the input sensor modalities by leveraging stereo image streams to produce metric depth predictions with pose information, followed by fusing multiple scene views from a local window using a probabilistic occupancy framework to expand the limited field-of-view of the camera. Additionally, SOLVR adopts a flexible definition of what constitutes positive examples for different training losses, allowing us to simultaneously optimise place recognition and registration performance. Furthermore, we replace RANSAC with a registration function that weights a simple least-squares fitting with the estimated inlier likelihood of sparse keypoint correspondences, improving performance in scenarios with a low inlier ratio between the query and retrieved place. Our experiments on the KITTI and KITTI360 datasets show that SOLVR achieves state-of-the-art performance for LiDAR-Visual place recognition and registration, particularly improving registration accuracy over larger distances between the query and retrieved place.
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