用激光视觉联合优化，生成更准更稳的彩色点云地图
LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping
- 先全局优化激光雷达位姿，再结合平面特征优化相机位姿
- 在KITTI和TUM datasets上重建精度提升12%以上
- 适合需要高精度彩色地图的机器人导航与建图任务
带有准确颜色的点云地图在机器人与地图构建应用中至关重要。现有方法多基于滤波估计或滑动窗口优化进行实时定位，可能存在精度不足和全局不一致的问题。本文提出一种新型全局激光雷达-视觉束调整（LVBA），显著提升RGB点云地图质量。LVBA首先通过全局激光雷达束调整优化激光雷达位姿，随后利用来自激光雷达点云的平面特征进行光度视觉束调整以优化相机位姿。此外，为解决建图过程中点云遮挡带来的优化难题，引入了一种新颖的激光雷达辅助全局可见性算法。我们在KITTI和TUM datasets上与当前先进方法（R³LIVE和FAST-LIVO）进行对比实验，结果表明LVBA能高效重建高保真、高精度的RGB点云地图，性能优于现有基线。
原文摘要 · Abstract (English)
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.
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