LiDARとカメラの融合による高精度な高密度3D再構成のための統合最適化手法
A Joint Optimization Approach of LiDAR-Camera Fusion for Accurate Dense 3D Reconstructions
LiDARとカメラのデータを統合的に最適化し、バンドル調整とクラウド位置合わせを同時に解くことで、高密度かつ高精度な3Dモデルを構築するオフライン手法を提案した。
著者: Weikun Zhen, Yaoyu Hu, Jingfeng Liu, Sebastian Scherer
分類: cs.RO
原文アブストラクト
Fusing data from LiDAR and camera is conceptually attractive because of their complementary properties. For instance, camera images are higher resolution and have colors, while LiDAR data provide more accurate range measurements and have a wider Field Of View (FOV). However, the sensor fusion problem remains challenging since it is difficult to find reliable correlations between data of very different characteristics (geometry vs. texture, sparse vs. dense). This paper proposes an offline LiDAR-camera fusion method to build dense, accurate 3D models. Specifically, our method jointly solves a bundle adjustment (BA) problem and a cloud registration problem to compute camera poses and the sensor extrinsic calibration. In experiments, we show that our method can achieve an averaged accuracy of 2.7mm and resolution of 70 points per square cm by comparing to the ground truth data from a survey scanner. Furthermore, the extrinsic calibration result is discussed and shown to outperform the state-of-the-art method.
