RKHS-BA: A Semantic Correspondence-Free Multi-View Registration Framework with Global Tracking
<|reference_start|>RKHS-BA: A Semantic Correspondence-Free Multi-View Registration Framework with Global Tracking: This work reports a novel Bundle Adjustment (BA) formulation using a Reproducing Kernel Hilbert Space (RKHS) representation called RKHS-BA. The proposed formulation is correspondence-free, enables the BA to use RGB-D/LiDAR and semantic labels in the optimization directly, and provides a generalization for the photometric loss function commonly used in direct methods. RKHS-BA can incorporate appearance and semantic labels within a continuous spatial-semantic functional representation that does not require optimization via image pyramids. We demonstrate its applications in sliding-window odometry and global LiDAR mapping, which show highly robust performance in extremely challenging scenes and the best trade-off of generalization and accuracy.<|reference_end|>
@article{zhang2024rkhs-ba:,
    title={RKHS-BA: A Semantic Correspondence-Free Multi-View Registration
  Framework with Global Tracking},
    author={Ray Zhang, Jingwei Song, Xiang Gao, Junzhe Wu, Tianyi Liu, Jinyuan
  Zhang, Ryan Eustice, Maani Ghaffari},
    journal={arXiv preprint arXiv:2403.01254},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01254},
    primaryClass={cs.RO}
}