Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences
arxiv(2024)
摘要
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 the camera-LiDAR calibration as a graph
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.
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