LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping

TL;DR

LVBA is a novel global bundle adjustment method that enhances RGB point cloud mapping accuracy by integrating LiDAR and visual data, addressing occlusions and improving global consistency in robotics applications.

Contribution

The paper introduces LVBA, a new global LiDAR-Visual bundle adjustment technique that combines LiDAR and visual data for improved RGB point cloud mapping accuracy.

Findings

LVBA outperforms state-of-the-art baselines in mapping quality.

LVBA produces high-fidelity, accurate RGB point cloud maps.

The method effectively handles occlusions with a LiDAR-assisted visibility algorithm.

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 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.