LiDAR-Camera Coordinate Calibration via Lane Matching
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Solution Overview
Problem
Current methods for calibrating vehicle-mounted camera and LiDAR systems require manual operation and equipment, leading to high costs and time consumption, especially when the vehicle is in motion or when equipment is replaced or repositioned.
Innovation Solution
A method and apparatus for acquiring coordinate system conversion information using a vehicle-mounted LiDAR and camera, which identifies level ground and matches lane information from three-dimensional data with surrounding images to perform real-time calibration without manual intervention, utilizing communication methods like CDMA or Wi-Fi for data exchange.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual calibration methods are used for vehicle-mounted camera and LiDAR systems, then calibration accuracy can be achieved, but the process requires manual operation and equipment, leading to high costs and time consumption
Solution Approach 1:
The system performs self-calibration by automatically capturing images of calibration patterns (such as chessboards or circular markers) using the vehicle-mounted camera and LiDAR, then computes coordinate conversion information through image processing and feature matching algorithms without requiring manual intervention or external calibration equipment
Solution Approach 2:
The patent replaces manual mechanical calibration operations with automated computational methods, using computer vision algorithms to detect feature points in images and calculate spatial transformation parameters, thereby eliminating the need for manual measurement and physical adjustment tools
2Adaptability or versatility
If equipment is replaced or repositioned in vehicle-mounted systems, then system adaptability is improved, but calibration must be redone, increasing time and operational requirements
Solution Approach 1:
The system pre-stores multiple coordinate conversion information sets corresponding to different mounting positions and postures of camera and LiDAR devices. When equipment is replaced or repositioned, the system automatically selects or switches to the appropriate pre-computed conversion parameters without requiring recalibration, enabling quick adaptation to configuration changes
3Measurement precision
If traditional calibration methods are used, then coordinate conversion information can be acquired, but the process requires specialized equipment and manual operations, increasing overall system cost
Solution Approach 1:
The system uses virtual calibration patterns displayed on screens or projected surfaces as substitutes for physical calibration targets. The camera captures images of these virtual patterns, and the LiDAR scans the surrounding environment to identify corresponding 3D features, allowing coordinate calibration without requiring specialized physical calibration equipment while maintaining measurement precision through digital image processing and 3D point cloud analysis
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
Enables accurate and cost-effective calibration of camera and LiDAR systems on a driving vehicle, reducing the need for manual operations and equipment, thereby streamlining the calibration process and enhancing efficiency.
Implementation Method 1
acquiring three-dimensional information including first lane information corresponding to a lane adjacent to a vehicle, through a LiDAR installed at the vehicle
Implementation Method 2
a surrounding image including second lane information corresponding to the lane, through a camera installed at the vehicle
Data Source
AI summary
In accordance with an aspect of the present disclosure, there is provided a method for acquiring coordinate system conversion information, the method comprising: acquiring three-dimensional information including first lane information corresponding to a lane adjacent to a vehicle, through a LiDAR installed at the vehicle, and a surrounding image including second lane information corresponding to the lane, through a camera installed at the vehicle; acquiring first coordinate system conversion information on the LiDAR and the camera by matching the second lane information with the first lane information; and acquiring second coordinate system conversion information on the vehicle and the camera by using top view image conversion information acquired based on the surrounding image, and the driving direction of the vehicle.