Camera-LiDAR Calibration Panel for Precise Sensor Alignment
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Solution Overview
Problem
Existing methods for calibrating camera and lidar sensors in autonomous vehicles are inefficient and prone to errors due to mechanical tolerances during installation, leading to significant deviations in obstacle detection, which can result in inaccurate collision detection.
Innovation Solution
A method using a calibration board with known patterns and additional reflective areas, allowing simultaneous or parallel image capture by the camera and lidar sensor to determine their poses relative to each other, followed by conversion into a common coordinate system, enabling precise alignment and fusion of images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If mechanical assembly with standard tolerances is used for installing obstacle detection systems, then device complexity is reduced and ease of manufacture is improved, but manufacturing precision deteriorates leading to large deviations in obstacle detection at distance
Solution Approach 1:
The patent replaces mechanical alignment methods with optical field-based calibration. A calibration board with known geometric patterns is captured by the camera, and its 3D point cloud is captured by the lidar. The transformation between coordinate systems is calculated based on the known positions of calibration features, eliminating the need for precise mechanical assembly while achieving sub-pixel alignment accuracy.
Solution Approach 2:
The calibration board serves as an intermediary object between the camera and lidar sensors. It provides known geometric features that both sensors can detect, enabling the calculation of transformation parameters between their coordinate systems. This intermediary facilitates precise relative positioning without requiring direct mechanical coupling between sensors.
2Measurement precision
If a three-dimensional calibration cube with multiple surfaces is used, then measurement precision is improved, but device complexity increases and ease of operation deteriorates due to large size and unwieldiness
Solution Approach 1:
The calibration board is segmented into multiple distinct calibration features (circles, squares, triangles) with known geometric relationships. These segmented features are distributed across the board surface, allowing the system to calculate transformation parameters from multiple independent feature pairs, improving precision while keeping the overall board size manageable.
Solution Approach 2:
The patent uses 2D geometric patterns on the calibration board that can be captured as images by the camera and converted to 3D point clouds by the lidar. These 2D patterns serve as simplified copies of 3D calibration features, reducing the physical complexity and size of the calibration object while maintaining calibration accuracy through mathematical reconstruction.
3Measurement precision
If camera calibration is performed before lidar-camera calibration, then measurement precision is improved, but loss of time increases due to sequential calibration steps
Solution Approach 1:
The patent merges the camera calibration and lidar-camera calibration processes into a single unified operation. The calibration board contains features that enable both intrinsic camera parameter calibration and extrinsic transformation calculation simultaneously. By capturing the calibration board with both sensors and processing the data together, the system eliminates sequential calibration steps while maintaining accuracy.
Solution Approach 2:
The calibration board is designed with multi-functional features that serve dual purposes: calibrating the camera's intrinsic parameters and determining the transformation between camera and lidar coordinate systems. This universal calibration object eliminates the need for separate calibration procedures, reducing calibration time while maintaining measurement precision.
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 efficiency, reduces errors, and facilitates robust obstacle detection by aligning sensor data accurately, improving collision avoidance and object recognition in various environments.
Implementation Method 1
areas with high reflectivity are determined from intensity values of laser light reflected by the calibration board
Data Source
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AI summary
The position calibration method is used to fuse images (13, 15) from a camera (1) and a lidar sensor (2). The camera (1) captures an image (13) of a calibration target (11), whereby the pose of the calibration target (11) relative to the camera (1) can be determined using known patterns (12). The lidar sensor (2) captures an image (15) of the calibration target (11), whereby the pose of the calibration target (11) relative to the lidar sensor (2) can be determined using additional reflection areas (14) on the calibration target (11). Based on both poses, images (13, 15) captured by the camera (1) and/or the lidar sensor (2) can subsequently be converted into a common coordinate system or into the coordinate system of the other image (13, 15). This allows objects (7) detected in one image (13, 15) to be verified in another image (15, 13).