DXQ-Net：品質認識フローを用いた微分可能なLiDAR-カメラ外部キャリブレーション
DXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using Quality-aware Flow
LiDARとカメラの外部キャリブレーションを、不確実性を考慮したフロー推定と微分可能な姿勢推定モジュールでエンドツーエンドに行う手法を提案し、回転成分で最先端性能と高い汎化性能を示した。
著者: Xin Jing, Xiaqing Ding, Rong Xiong, Huanjun Deng, Yue Wang
分類: cs.RO
原文アブストラクト
Accurate LiDAR-camera extrinsic calibration is a precondition for many multi-sensor systems in mobile robots. Most calibration methods rely on laborious manual operations and calibration targets. While working online, the calibration methods should be able to extract information from the environment to construct the cross-modal data association. Convolutional neural networks (CNNs) have powerful feature extraction ability and have been used for calibration. However, most of the past methods solve the extrinsic as a regression task, without considering the geometric constraints involved. In this paper, we propose a novel end-to-end extrinsic calibration method named DXQ-Net, using a differentiable pose estimation module for generalization. We formulate a probabilistic model for LiDAR-camera calibration flow, yielding a prediction of uncertainty to measure the quality of LiDAR-camera data association. Testing experiments illustrate that our method achieves a competitive with other methods for the translation component and state-of-the-art performance for the rotation component. Generalization experiments illustrate that the generalization performance of our method is significantly better than other deep learning-based methods.
