Camera-to-Vehicle Alignment Using IMU and Reprojection Error
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Solution Overview
Problem
Existing technologies require a calibration target for camera-to-vehicle alignment, which is time-consuming, costly, and not suitable for real-time calibration during normal vehicle operation.
Innovation Solution
A method and apparatus that use an inertial measurement unit (IMU) to calculate the IMU-to-vehicle alignment and then employ reprojection error to determine the IMU-to-camera alignment, allowing for camera-to-vehicle alignment calibration without a calibration target during normal vehicle operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a calibration target is used for camera-to-vehicle alignment, then alignment accuracy is improved, but calibration time and operational disruption increase
Solution Approach 1:
The patent extracts and removes the calibration target from the calibration process, replacing it with natural environment features detected by the camera. This allows calibration to occur during normal vehicle operation without requiring external calibration equipment, thereby reducing calibration time while maintaining alignment accuracy through image-based feature detection and reprojection error minimization
Solution Approach 2:
The system performs self-calibration by using the camera to detect and track features in the natural environment. The vehicle's own motion data from the IMU is combined with image data to automatically compute camera-to-vehicle alignment parameters without external intervention or calibration targets, enabling calibration during normal operation
2Measurement precision
If a calibration target is used for camera-to-vehicle alignment, then alignment accuracy is improved, but system complexity and cost increase
Solution Approach 1:
The patent removes the calibration target and associated positioning equipment from the system, replacing them with software-based feature detection and tracking algorithms. This simplifies the hardware configuration while maintaining calibration accuracy through computational methods that use natural environment features and IMU data fusion
Solution Approach 2:
The patent replaces the mechanical calibration target system with an optical and computational approach. Instead of using physical calibration targets with known geometries, the system uses image processing to detect natural features and combines this with IMU data through reprojection error minimization to achieve alignment, thereby reducing hardware complexity
3Productivity
If calibration is performed during normal vehicle operation, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The patent enables continuous calibration during normal vehicle operation by constantly tracking environmental features and updating alignment parameters in real-time. The system processes image data and IMU data continuously, allowing calibration to occur without interrupting vehicle operations, thereby maintaining both productivity and precision through ongoing optimization
Solution Approach 2:
The system implements feedback through reprojection error calculation, where detected feature positions in images are compared against expected positions based on IMU data. The alignment parameters are iteratively adjusted to minimize reprojection error, providing continuous feedback that maintains high measurement precision even during dynamic vehicle operation
Data Source
AI summary
A method is provided calibration of alignment between a vehicle and a camera of the vehicle. Methods may include: receiving location information associated with a vehicle; receive measurement data from an inertial measurement unit associated with the vehicle; calculating, from the measurement data and the location information, a position of the inertial measurement unit relative to the vehicle; receiving a first image and a second image from a camera associated with the vehicle; calculating a position of the camera based, at least in part, on the first image and the second image; calculating, from the measurement data and the position of the camera, a position of the inertial measurement unit relative to the camera; and determining alignment of the camera with the vehicle based on the position of the inertial measurement unit relative to the vehicle and the position of the inertial measurement unit relative to the camera.