LiDAR Calibration Using Point-Cloud Planes for Vehicle Alignment
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Solution Overview
Problem
Existing LiDAR calibration techniques for vehicles are time-consuming, complex, and prone to errors due to the need for manual verification of panel positions, which can lead to misalignment and skewing of point cloud data, affecting automated driving and driver assist systems.
Innovation Solution
A method involving a single fixed environmental feature to generate a set of calibration planes using a LiDAR system, where a line or structure is detected to define vectors and planes, allowing for precise alignment of the LiDAR system relative to the vehicle body, reducing the need for manual verification and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of panel positions is performed in existing LiDAR calibration techniques, then measurement precision can be maintained, but the calibration process becomes time-consuming and complex
Solution Approach 1:
The system performs self-calibration by automatically detecting calibration features in the point cloud and computing the LiDAR-to-vehicle-body transformation matrix without requiring manual verification. The processor identifies calibration features, computes transformation parameters, and applies corrections automatically, eliminating the need for manual panel position verification while maintaining calibration accuracy.
Solution Approach 2:
The patent replaces manual mechanical verification processes with automated computational methods. Instead of physically verifying panel positions, the system uses image processing and point cloud analysis to automatically determine calibration parameters and compute transformation matrices, substituting manual mechanical operations with automated digital processing.
2Measurement precision
If manual verification of panel positions is performed, then calibration accuracy can be maintained, but the process becomes prone to errors
Solution Approach 1:
The system performs self-calibration by automatically detecting calibration features in the point cloud and computing the LiDAR-to-vehicle-body transformation matrix without requiring manual verification. The processor identifies calibration features, computes transformation parameters, and applies corrections automatically, eliminating the need for manual panel position verification while maintaining calibration accuracy.
Solution Approach 2:
The system uses the detected calibration features and computed transformation parameters to automatically adjust and correct LiDAR data. The feedback loop involves comparing the detected calibration features against the expected positions, computing transformation matrices, and applying corrections to the point cloud data, thereby improving reliability without manual intervention.
3Measurement precision
If multiple calibration panels are used in existing techniques, then measurement precision can be maintained, but device complexity increases
Solution Approach 1:
The patent extracts and utilizes only the essential calibration features from the environment rather than requiring multiple physical calibration panels. By identifying and using salient features such as corners, edges, or distinctive markers already present in the surroundings, the system reduces the calibration system complexity while maintaining accuracy through automated feature detection and transformation computation.
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 method significantly speeds up the calibration process, reduces errors, and ensures accurate alignment of the LiDAR system, improving the reliability of extrinsic environmental data for automated vehicle operations.
Implementation Method 1
operates by targeting an object or a surface with a laser and measuring a time for reflected light to return to the receiver
Implementation Method 2
A first linear structure and a ground plane is detected using the LiDAR point cloud
Data Source
AI summary
A process for calibrating a LiDAR system includes physically aligning a body on a calibration structure, wherein a LiDAR system is mounted on the body and centering the body using the calibration structure. A LiDAR point cloud is generated using the LiDAR system. A first linear structure and a ground plane is detected using the LiDAR point cloud and a first vector aligned with the first linear structure is determined. A first plane normal to the first vector is identified, and a second vector normal to the ground plane and in the first plane is identified. A second plane normal to the second vector is identified. A third vector at an intersection of the first plane and the second plane is identified. A third plane normal to the third vector is identified, and an orientation of the LiDAR system relative to the body using the planes and vectors is calibrated.