CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes

• URL: http://arxiv.org/abs/2103.04558v1
• Date: Mon, 8 Mar 2021 06:02:44 GMT
• Title: CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes
• Authors: Tao Ma, Zhizheng Liu, Guohang Yan, Yikang Li
• Abstract summary: We propose a novel method to calibrate the extrinsic parameter for LiDAR and camera in road scenes. Our method introduces line features from static straight-line-shaped objects such as road lanes and poles in both image and point cloud. We conduct extensive experiments on KITTI and our in-house dataset, quantitative and qualitative results demonstrate the robustness and accuracy of our method.
• Score: 16.201111055979453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For autonomous vehicles, an accurate calibration for LiDAR and camera is a prerequisite for multi-sensor perception systems. However, existing calibration techniques require either a complicated setting with various calibration targets, or an initial calibration provided beforehand, which greatly impedes their applicability in large-scale autonomous vehicle deployment. To tackle these issues, we propose a novel method to calibrate the extrinsic parameter for LiDAR and camera in road scenes. Our method introduces line features from static straight-line-shaped objects such as road lanes and poles in both image and point cloud and formulates the initial calibration of extrinsic parameters as a perspective-3-lines (P3L) problem. Subsequently, a cost function defined under the semantic constraints of the line features is designed to perform refinement on the solved coarse calibration. The whole procedure is fully automatic and user-friendly without the need to adjust environment settings or provide an initial calibration. We conduct extensive experiments on KITTI and our in-house dataset, quantitative and qualitative results demonstrate the robustness and accuracy of our method.

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