YOCO: You Only Calibrate Once for Accurate Extrinsic Parameter in LiDAR-Camera Systems

TL;DR

The paper tackles automatic extrinsic calibration for LiDAR–Camera systems by abandoning automatic correspondence-point registration. It introduces a plane-based LiDAR-point extraction pipeline and co-planar constraint optimization to estimate $R_{CL}$ and $t_{CL}$ in a single step, following an initial checkerboard camera calibration. Across simulations and real-world data, the method achieves superior accuracy (e.g., rotation below $0.05^{\circ}$ and translation below $0.015\,\mathrm{m}$ in some cases) while reducing calibration data requirements. This approach enhances automation, robustness, and practicality for long-term sensor fusion deployments in diverse environments.

Abstract

In a multi-sensor fusion system composed of cameras and LiDAR, precise extrinsic calibration contributes to the system's long-term stability and accurate perception of the environment. However, methods based on extracting and registering corresponding points still face challenges in terms of automation and precision. This paper proposes a novel fully automatic extrinsic calibration method for LiDAR-camera systems that circumvents the need for corresponding point registration. In our approach, a novel algorithm to extract required LiDAR correspondence point is proposed. This method can effectively filter out irrelevant points by computing the orientation of plane point clouds and extracting points by applying distance- and density-based thresholds. We avoid the need for corresponding point registration by introducing extrinsic parameters between the LiDAR and camera into the projection of extracted points and constructing co-planar constraints. These parameters are then optimized to solve for the extrinsic. We validated our method across multiple sets of LiDAR-camera systems. In synthetic experiments, our method demonstrates superior performance compared to current calibration techniques. Real-world data experiments further confirm the precision and robustness of the proposed algorithm, with average rotation and translation calibration errors between LiDAR and camera of less than 0.05 degree and 0.015m, respectively. This method enables automatic and accurate extrinsic calibration in a single one step, emphasizing the potential of calibration algorithms beyond using corresponding point registration to enhance the automation and precision of LiDAR-camera system calibration.

Figures (11)

• Figure 1: A LiDAR-camera system, $O_{\text{L}}$ and $O_{\text{C}}$ denotes the coordinate system of LiDAR and camera. The $xoy$ plane of word coordinate system $O_{\text{W}}$ is attached to the plane of checkerboard in this paper.
• Figure 2: Pipeline of proposed calibration method
• Figure 3: Illustration of the camera pinhole model. The distance between the checkerboard and the camera can be estimated by employing the pinhole model of the camera and the camera parameters
• Figure 4: Most of the irrelevant point clouds in the left figure are filtered out, leaving only the point clouds mainly face to the $xoy$ plane in the right figure.
• Figure 5: Plane point cloud extraction in different scenario.