マルチビューポイントクラウド位置合わせのためのバンドル調整
On Bundle Adjustment for Multiview PointCloud Registration
複数スキャンの剛体変換を推定するマルチビュー位置合わせにおいて、計測ノイズと計算コストを考慮した目的関数と局所分布集約による特徴更新を提案し、ボクセル量子化を用いた実用的なシステムを構築した。
著者: Huaiyang Huang, Yuxiang Sun, Jin Wu, Jiaohao Jiao, Xiangcheng Hu, Linwei Zheng, Lujia Wang, Ming Liu
分類: cs.RO
原文アブストラクト
Multiview registration is used to estimate Rigid Body Transformations (RBTs) from multiple frames and reconstruct a scene with corresponding scans. Despite the success of pairwise registration and pose synchronization, the concept of Bundle Adjustment (BA) has been proven to better maintain global consistency. So in this work, we make the multiview point-cloud registration more tractable from a different perspective in resolving range-based BA. Based on this analysis, we propose an objective function that takes both measurement noises and computational cost into account. For the feature parameter update, instead of calculating the global distribution parameters from the raw measurements, we aggregate the local distributions upon the pose update at each iteration. The computational cost of feature update is then only dependent on the number of scans. Finally, we develop a multiview registration system using voxel-based quantization that can be applied in real-world scenarios. The experimental results demonstrate our superiority over the baselines in terms of both accuracy and speed. Moreover, the results also show that our average positioning errors achieve the centimeter level.
