LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909317820579840 |
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| author | Li, Rundong Liu, Xiyuan Li, Haotian Liu, Zheng Lin, Jiarong Cai, Yixi Zhang, Fu |
| author_facet | Li, Rundong Liu, Xiyuan Li, Haotian Liu, Zheng Lin, Jiarong Cai, Yixi Zhang, Fu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10868 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping Li, Rundong Liu, Xiyuan Li, Haotian Liu, Zheng Lin, Jiarong Cai, Yixi Zhang, Fu Robotics 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. |
| title | LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.10868 |