Camera-LiDAR Calibration Board for Precise Sensor Registration
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Solution Overview
Problem
Existing methods for calibrating camera and LIDAR sensors in autonomously driving vehicles face challenges due to mechanical tolerances, leading to inaccuracies in obstacle detection, as small alignment errors result in significant deviations in obstacle positioning, potentially causing false collision alerts or missed hazards.
Innovation Solution
A method using a calibration board with known patterns and additional reflection regions of higher reflectivity, allowing for efficient pose determination of the board relative to both camera and LIDAR sensors, enabling image conversion into a common coordinate system and facilitating precise alignment and error diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a three-dimensional calibration cube is used for LIDAR-camera calibration, then the registration between LIDAR and camera coordinate systems can be determined, but the calibration cube is large and unwieldy, making it difficult to use in practice
Solution Approach 1:
The patent replaces the physical three-dimensional calibration cube with a two-dimensional calibration board that contains encoded spatial information. The calibration board uses known patterns (circles, lines, or grids) that can be detected by both LIDAR and camera, effectively creating a simplified copy of the calibration functionality without the bulk of a 3D cube structure.
Solution Approach 2:
The patent extracts the essential calibration information from a complex 3D cube structure and condenses it into a flat 2D board. By removing the third dimension's physical bulk while retaining the calibration functionality through encoded patterns, the solution achieves ease of handling while maintaining measurement precision.
2Ease of manufacture
If mechanical tolerances are present during installation of obstacle detection systems, then assembly is easier, but small alignment errors cause large deviations in obstacle positioning at distance
Solution Approach 1:
The patent implements a feedback mechanism where the calibration board provides known reference patterns that are detected by both LIDAR and camera. The system measures the actual positions of these patterns in both sensor coordinate systems, calculates the transformation matrix that maps between them, and uses this feedback to correct for mechanical tolerances and alignment errors in the obstacle detection system.
Solution Approach 2:
The patent changes the calibration approach from relying on precise mechanical assembly parameters to using detectable geometric patterns with known parameters. By transforming the calibration problem from a mechanical parameter problem to a geometric pattern recognition problem, the system can tolerate mechanical variations while maintaining positioning accuracy through mathematical transformation.
3Measurement precision
If the camera is pre-calibrated before LIDAR calibration, then camera intrinsic parameters are known, but the overall calibration process becomes more complex and time-consuming
Solution Approach 1:
The patent merges the camera and LIDAR calibration processes into a single unified operation. Instead of performing camera calibration separately and then LIDAR calibration separately, the system uses the same calibration board to simultaneously determine both camera extrinsic parameters (pose) and LIDAR-camera transformation parameters in one calibration session, significantly reducing total calibration time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances calibration robustness and efficiency, improves obstacle detection accuracy, and allows for real-time object identification and risk assessment, enabling more effective anti-collision systems and object recognition in diverse environments.
Implementation Method 1
at least one image is recorded with the LIDAR sensor and the regions of high reflectivity are determined based on intensity values of the laser light reflected by the calibration board
Data Source
AI summary
The method for position calibration serves to fuse images of a camera and a LIDAR sensor. The camera records an image of a calibration board, wherein the pose of the calibration board relative to the camera can be determined based on known patterns. The LIDAR sensor records an image of the calibration board, wherein a pose of the calibration board relative to the LIDAR sensor can be determined based on additional reflection regions on the calibration board. Based on both poses, images that are recorded by the camera and/or the LIDAR sensor can respectively be converted into a common coordinate system or into the coordinate system of the other image in the following. Objects that are detected in one image can thereby be verified in another image.