SOLVR: Submap Oriented LiDAR-Visual Re-Localisation
<|reference_start|>SOLVR: Submap Oriented LiDAR-Visual Re-Localisation: 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.<|reference_end|>
@article{knights2024solvr:,
    title={SOLVR: Submap Oriented LiDAR-Visual Re-Localisation},
    author={Joshua Knights, Sebasti'an Barbas Laina, Peyman Moghadam, Stefan
  Leutenegger},
    journal={arXiv preprint arXiv:2409.10247},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10247},
    primaryClass={cs.CV}
}