Camera-LiDAR Calibration Using a Board-Based Common Reference
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Solution Overview
Problem
Various sensors used for Simultaneous Localization and Mapping (SLAM) cannot be physically installed at the same point, necessitating prior calibration of their data for accurate mapping.
Innovation Solution
A method and computer program for automatically calibrating a camera and LiDAR using a calibration board, involving the detection of flat regions, matching image and point cloud data, and determining the camera's pose based on registered coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sensors are installed at different physical points, then device installation flexibility is improved, but calibration complexity increases
Solution Approach 1:
A calibration board serves as an intermediary object between the camera and LiDAR sensors. The board contains known geometric features (circles, lines, patterns) that both sensors can detect and use to establish coordinate transformations. This mediator enables automatic calibration by providing a common reference frame that resolves the complexity of calibrating sensors at different physical locations.
2Measurement precision
If manual calibration is performed, then calibration precision can be controlled, but time consumption increases
Solution Approach 1:
The calibration system performs self-calibration by automatically detecting features on the calibration board through both camera images and LiDAR point clouds. The system independently computes extrinsic parameters through feature matching and coordinate transformation without requiring manual intervention, thereby reducing calibration time while maintaining precision through algorithmic accuracy.
Solution Approach 2:
The calibration board is pre-designed with known geometric configurations (circle radii, line positions, pattern dimensions) before the calibration process. This preliminary preparation of reference data enables the system to perform rapid automatic calibration by comparing sensor measurements against the pre-established ground truth, eliminating the need for time-consuming manual measurements.
3Productivity
If automatic calibration is implemented, then calibration time is reduced, but algorithm complexity increases
Solution Approach 1:
The automatic calibration algorithm is divided into distinct modular steps: feature detection in images, feature detection in point clouds, feature matching between modalities, coordinate transformation computation, and parameter optimization. This segmentation allows each module to be independently optimized and executed efficiently, reducing overall calibration time while managing algorithmic complexity through structured organization.
Data Source
AI summary
The present invention proposes a method for automatically performing calibration between a camera and a LiDAR using a calibration board. The method may include: detecting flat regions corresponding to the calibration board from each of two or more point cloud data acquired by a LiDAR; identifying regions corresponding to the calibration board from each of two or more images captured by a camera at the time the respective point cloud data was acquired, and matching the identified regions with the flat regions for estimating initial positions of the calibration board in three-dimensional coordinates; registering coordinates of the initial positions of the calibration board with coordinates of the flat regions; and determining a pose of the camera based on the registered coordinates.