LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping
Rundong Li, Xiyuan Liu, Haotian Li, Zheng Liu, Jiarong Lin, Yixi Cai, Fu Zhang
Abstract
Point cloud maps with accurate color are crucial in robotics and mapping applications. Existing approaches for producing RGB-colorized maps are primarily based on realtime 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., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{3}$</tex> LIVE and FAST-LIVO). Our results prove that LVBA can proficiently reconstruct high-fidelity, accurate RGB point cloud maps, outperforming its counterparts.
BibTeX
@inproceedings{icra2025_lvbalidarvisualb,
title = {LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping},
author = {Rundong Li and Xiyuan Liu and Haotian Li and Zheng Liu and Jiarong Lin and Yixi Cai and Fu Zhang},
booktitle = {ICRA 2025},
year = {2025}
}