Laser Radar Camera Calibration via Line Feature Scoring
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Solution Overview
Problem
Current multisensor calibration methods for unmanned driving require manual calibration and a strict environment, leading to potential inaccuracies and the need for real-time correction of spatial position errors between laser radars and camera sensors.
Innovation Solution
An automatic calibration method and system that adjusts the spatial position of laser radars relative to camera sensors using line feature extraction from point cloud and image data, calculating scores to determine accurate positions without manual calibration objects and correcting errors in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration method is adopted, then calibration can be performed with existing methods, but calibration requires strict environment and selected calibration objects, affecting calibration accuracy
Solution Approach 1:
The system performs self-calibration by automatically selecting calibration objects from the environment and executing calibration procedures without human intervention. The calibration object selection module autonomously identifies suitable objects, and the calibration execution module automatically adjusts sensor positions and orientations based on detected object features.
Solution Approach 2:
The system changes the parameter of calibration environment requirements by transitioning from strict controlled environments to flexible real-world environments. This is achieved by implementing adaptive calibration object selection that works with various object types and environmental conditions, making the calibration process parameter-independent regarding environmental constraints.
2Reliability
If manual calibration is performed periodically, then calibration can be maintained, but spatial position errors accumulate over time and cannot be corrected in real-time
Solution Approach 1:
The system implements continuous feedback by periodically detecting calibration objects and recalibrating sensor spatial relationships in real-time. The calibration object detection module continuously monitors the environment, and when calibration objects are detected, the system automatically executes recalibration procedures to correct accumulated spatial position errors immediately.
Solution Approach 2:
Instead of periodic manual calibration, the system maintains continuous calibration readiness by automatically detecting calibration objects and performing calibration operations whenever conditions are suitable. This continuous calibration process prevents error accumulation by constantly adjusting sensor positions and orientations based on real-time environmental feedback.
3Extent of automation
If automatic calibration is implemented, then real-time correction is possible, but the system requires automated calibration object selection and calibration execution capabilities
Solution Approach 1:
The system achieves automation through multi-functional modules that perform multiple tasks. The calibration object selection module not only identifies objects but also extracts their feature information. The calibration execution module handles both position adjustment and orientation calibration. This universal design reduces overall system complexity despite high automation levels.
Solution Approach 2:
The system introduces calibration objects as intermediaries between the sensors and the calibration process. These objects serve as mediators that enable automatic calibration by providing detectable features for the selection module and serving as reference points for the execution module, thereby simplifying the automation architecture.
4Measurement precision
If calibration objects are manually selected, then specific calibration objects can be chosen for precision, but the process requires human intervention and is not suitable for real-time calibration
Solution Approach 1:
The calibration object selection module autonomously performs the task of selecting appropriate calibration objects without human intervention. It automatically scans the environment, identifies suitable objects based on predefined criteria, and selects them for calibration purposes, thereby maintaining precision while eliminating manual operation requirements.
Solution Approach 2:
The system implements feedback mechanisms where the selection module continuously monitors environmental conditions and calibration object availability. Based on this feedback, it dynamically selects the most appropriate calibration objects, ensuring precision is maintained while the process remains fully automated and simple to operate.
Data Source
AI summary
An automatic calibration method and system for spatial positions of a laser radar and a camera sensor is provided. The method includes: adjusting a spatial position of the laser radar relative to the camera sensor to obtain a plurality of spatial position relationships of the laser radar and the camera sensor; for a spatial position relationship, calculating a gray value of each laser radar point conforming to the line features after projection as a score, and accumulating scores of all laser radar points as a total score; traversing all the spatial position relationships to obtain a plurality of total scores; and selecting a spatial position relationship of the laser radar and the camera sensor corresponding to a highest total score from the plurality of total scores to serve as a calibrated position relationship of the laser radar and the camera sensor.