運動と深層点対応を用いたターゲット不要のカメラ-LiDAR自動キャリブレーション
Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences
視覚・LiDARオドメトリの運動推定と深層学習による2D-3D点対応を組み合わせ、専用ターゲットや人手を要さずにカメラ-LiDAR間の外部キャリブレーションを最適化で求める手法を提案。
著者: Kürsat Petek, Niclas Vödisch, Johannes Meyer, Daniele Cattaneo, Abhinav Valada, Wolfram Burgard
分類: cs.RO
原文アブストラクト
Sensor setups of robotic platforms commonly include both camera and LiDAR as they provide complementary information. However, fusing these two modalities typically requires a highly accurate calibration between them. In this paper, we propose MDPCalib which is a novel method for camera-LiDAR calibration that requires neither human supervision nor any specific target objects. Instead, we utilize sensor motion estimates from visual and LiDAR odometry as well as deep learning-based 2D-pixel-to-3D-point correspondences that are obtained without in-domain retraining. We represent camera-LiDAR calibration as an optimization problem and minimize the costs induced by constraints from sensor motion and point correspondences. In extensive experiments, we demonstrate that our approach yields highly accurate extrinsic calibration parameters and is robust to random initialization. Additionally, our approach generalizes to a wide range of sensor setups, which we demonstrate by employing it on various robotic platforms including a self-driving perception car, a quadruped robot, and a UAV. To make our calibration method publicly accessible, we release the code on our project website at http://calibration.cs.uni-freiburg.de.
