On-Vehicle Camera Extrinsic Calibration With Self-Inspection
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Solution Overview
Problem
Conventional camera calibration methods for autonomous vehicles are inaccurate and inefficient, leading to perception errors due to miscalibration, and there is a need for timely detection of changes in camera position or pose to prevent such errors.
Innovation Solution
A system and method for calibrating extrinsic parameters of on-vehicle cameras by capturing images of feature points on a ground plane with known coordinates, using a homography matrix to estimate initial parameters, and applying the Levenberg-Marquardt algorithm to minimize inverse-projection errors, with self-inspection capabilities to detect deviations while the vehicle is in motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional camera calibration methods are used, then the calibration process is simple, but the calibration accuracy is low leading to perception errors
Solution Approach 1:
The patent introduces a ground plane with known feature points as an intermediary calibration target. This ground plane serves as a mediator between the camera and the calibration process, enabling accurate extrinsic parameter estimation through homography matrix computation. The known coordinates of feature points on the ground plane provide a reference framework that improves measurement precision without requiring complex calibration equipment.
Solution Approach 2:
The patent replaces conventional mechanical calibration methods with a computational approach using homography matrix estimation and Levenberg-Marquardt optimization. Instead of relying on complex mechanical positioning systems or specialized calibration equipment, the method uses image processing and mathematical optimization to achieve high-precision calibration, thereby improving accuracy while managing complexity through software-based solutions.
2Reliability
If conventional calibration methods are used, then the calibration process is fast, but the reliability of object detection is low due to miscalibration
Solution Approach 1:
The patent performs preliminary calibration of the camera using the ground plane feature points before actual object detection tasks. By pre-computing the homography matrix and optimizing extrinsic parameters in advance, the system ensures high reliability for subsequent detection tasks. This preliminary action prevents perception errors during operation, and the calibration can be performed offline or during initial setup, minimizing impact on operational time.
Solution Approach 2:
The patent implements a feedback mechanism where the calibration system uses the known ground plane feature points to continuously verify and adjust camera parameters. The optimization process minimizes reprojection errors by iteratively refining extrinsic parameters based on the discrepancy between projected and actual feature point positions. This feedback loop ensures high detection reliability while the efficient optimization algorithm keeps calibration time acceptable.
3Measurement precision
If the camera is installed and initially calibrated, then the setup is complete, but position/pose changes occur leading to perception errors
Solution Approach 1:
The patent implements a self-inspection mechanism that continuously monitors camera position and pose by comparing detected feature points against the calibrated model. When deviations exceed thresholds, the system triggers recalibration. This feedback loop maintains measurement precision despite environmental changes or vibrations, as the system actively detects and corrects position instability rather than relying solely on initial calibration.
Solution Approach 2:
The patent employs periodic self-inspection and recalibration cycles to maintain camera positioning accuracy. Instead of assuming permanent stability after initial calibration, the system periodically verifies camera parameters using the ground plane reference and performs recalibration when necessary. This periodic action compensates for gradual drift or sudden changes in camera position, ensuring sustained measurement precision throughout operation.
Data Source
AI summary
The disclosure provides a system and method for calibrating extrinsic parameters of a camera mounted on a vehicle. While the vehicle is parked, the camera captures an image of a plurality of feature points with known world coordinates on a ground plane. The system determines pixel coordinates of the plurality of feature points in an image plane and estimates a homography matrix between the ground plane and the image plane based on a subset of the plurality of feature points. The system generates an initial estimate of the extrinsic parameters based on the estimated homography matrix and applies an optimization technique to obtain calibrated extrinsic parameters based on the initial estimate, the world coordinates, and the pixel coordinates of the plurality of feature points. Applying the optimization technique can include minimizing inverse-projection errors of the plurality of feature points from the image plane to the ground plane.