LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping
<|reference_start|>LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping: Point cloud maps with accurate color are crucial in robotics and mapping applications. Existing approaches for producing RGB-colorized maps are primarily based on real-time localization using filter-based estimation or sliding window optimization, which may lack accuracy and global consistency. In this work, we introduce a novel global LiDAR-Visual bundle adjustment (BA) named LVBA to improve the quality of RGB point cloud mapping beyond existing baselines. LVBA first optimizes LiDAR poses via a global LiDAR BA, followed by a photometric visual BA incorporating planar features from the LiDAR point cloud for camera pose optimization. Additionally, to address the challenge of map point occlusions in constructing optimization problems, we implement a novel LiDAR-assisted global visibility algorithm in LVBA. To evaluate the effectiveness of LVBA, we conducted extensive experiments by comparing its mapping quality against existing state-of-the-art baselines (i.e., R$^3$LIVE and FAST-LIVO). Our results prove that LVBA can proficiently reconstruct high-fidelity, accurate RGB point cloud maps, outperforming its counterparts.<|reference_end|>
@article{li2024lvba:,
    title={LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping},
    author={Rundong Li, Xiyuan Liu, Haotian Li, Zheng Liu, Jiarong Lin, Yixi Cai,
  and Fu Zhang},
    journal={arXiv preprint arXiv:2409.10868},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10868},
    primaryClass={cs.RO}
}