Camera-Based IMU Calibration With Secure Optical Target Validation
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Solution Overview
Problem
Existing vehicle guidance systems face challenges in accurately calibrating inertial measurement units (IMUs) due to mounting errors, which can lead to unsafe vehicle behavior, especially in autonomous and semi-autonomous driving, as current calibration methods are either complex, time-consuming, or susceptible to hacking.
Innovation Solution
A secure camera-based IMU calibration system that uses multiple cameras with opposing field of views to capture encoded optical patterns from calibration targets, allowing for precise alignment of IMU data with the vehicle coordinate system, thereby compensating for mounting errors and enhancing security by restricting calibration to controlled environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional IMU mounting methods are used with existing manufacturing techniques, then the mounting process is simple, but the mounting precision cannot achieve half-degree accuracy due to coplanarity limits
Solution Approach 1:
The patent replaces mechanical mounting precision requirements with an optical calibration system. Instead of relying solely on precise mechanical mounting within half-degree tolerances, the system uses camera-based optical patterns and coordinate transformations to measure and compensate for mounting errors, achieving the required precision through measurement and software compensation rather than mechanical precision alone.
2Ease of operation
If IMU calibration is made accessible for field updates, then ease of operation improves, but security risks increase due to potential hacking attempts
Solution Approach 1:
The patent introduces multiple intermediary security layers between the calibration system and external access attempts. These include encoded optical patterns that must be correctly decoded, coordinate system transformations that validate proper setup, and verification protocols that ensure only authorized calibration procedures can modify IMU parameters. These intermediaries enable field calibration while preventing unauthorized hacking attempts.
3Measurement precision
If complex calibration procedures are used to achieve high precision, then measurement precision improves, but time consumption increases
Solution Approach 1:
The patent performs preliminary actions by pre-encoding calibration patterns with known coordinate transformations and pre-establishing the relationship between camera views and the IMU coordinate system. The calibration targets contain pre-encoded data about their position and orientation, allowing the system to quickly compute transformation matrices without requiring time-consuming iterative adjustments during the actual calibration process.
4Measurement precision
If single-camera calibration systems are used, then device complexity is reduced, but measurement precision decreases due to limited viewing angles
Solution Approach 1:
The patent segments the calibration task across multiple cameras with different viewing angles. Each camera captures calibration patterns from its specific perspective, and the system integrates these segmented views through coordinate transformations to build a complete three-dimensional understanding of the IMU's orientation relative to the vehicle. This segmentation enables precise multi-axis calibration that a single camera cannot achieve alone.
Data Source
AI summary
Described are techniques and systems for secure camera based IMU calibration for stationary systems, including vehicles. Existing vehicle camera systems are employed, with enhanced security to prevent malicious attempts by hackers to try and cause a vehicle to enter IMU calibration mode. IMU calibration occurs when a calibration system determines the vehicle is parked in a controlled environment; calibration targets are positioned at different viewing angles to vehicle cameras to act as sources of optical patterns of encoded data. Features of the patterns are for security as well as for alignment functionality. Images of the calibration targets enable inference of a vehicle coordinate system, from which calculations for IMU mounting error compensations are performed. A relative rotation between the IMU and the vehicle coordinate system are applied to IMU data to compensate for relative rotations between the vehicle and the IMU, thereby improving vehicle slope and bank metrics.