Autonomous Vehicle Sensor Calibration via Epipolar Geometry
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Solution Overview
Problem
Current sensor calibration techniques for autonomous vehicles require infrastructure like fiducial markers, are manual, slow, and potentially imprecise, leading to unsafe conditions and downtime due to the need for physical transportation to calibration locations, and are computationally expensive.
Innovation Solution
The use of epipolar geometry to determine calibration data by analyzing point pairs in images from overlapping camera fields of view, combined with lidar data to constrain camera alignment relative to the vehicle, allowing for infrastructure-free calibration of both extrinsic and intrinsic sensor characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If infrastructure-based calibration techniques are used, then calibration accuracy can be improved, but system downtime increases due to the need to transport the system to calibration locations
Solution Approach 1:
The patent introduces natural environmental features (trees, buildings, road markings) as intermediary calibration targets instead of requiring specialized infrastructure. These naturally available features serve as mediators between the sensor system and the calibration process, eliminating the need to transport the system to specific calibration locations while maintaining calibration accuracy through feature detection and geometric relationship analysis.
Solution Approach 2:
The system performs self-calibration by utilizing environmental features that are naturally present in the operational environment. The sensor system calibrates itself by detecting and analyzing geometric relationships between naturally occurring features, eliminating the need for external infrastructure or manual intervention, and allowing calibration to be performed in-situ without system transport.
2Ease of operation
If manual calibration with human operators is used, then calibration can be performed, but the process becomes slow and potentially imprecise
Solution Approach 1:
The calibration system operates autonomously by automatically detecting environmental features, computing geometric relationships, and determining calibration parameters without human operator involvement. The system serves itself by processing sensor data through algorithms that identify natural features and calculate calibration transformations, eliminating manual operations while increasing calibration speed and consistency.
Solution Approach 2:
The patent replaces manual mechanical calibration operations with automated computational processes. Instead of human operators physically adjusting components or manually operating calibration equipment, the system uses image processing algorithms and geometric computations to automatically determine calibration parameters, substituting mechanical/manual processes with electronic and computational systems.
3Measurement precision
If traditional calibration techniques are used, then calibration can be achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent extracts and utilizes only the essential geometric information from environmental features that is necessary for calibration, rather than processing all sensor data comprehensively. By identifying and focusing on specific geometric relationships (epipolar constraints, vanishing points, feature correspondences) that directly contribute to calibration, the system reduces computational overhead while maintaining calibration accuracy.
Solution Approach 2:
The system performs partial calibration by focusing computational resources on the specific geometric constraints and features that are most critical for calibration accuracy, rather than exhaustively analyzing all possible sensor data. This selective approach applies calibration computations only where geometric relationships provide the most valuable information, reducing overall computational energy consumption while achieving sufficient calibration precision.
Data Source
AI summary
This disclosure is directed to calibrating sensors mounted on an autonomous vehicle. First image data and second image data representing an environment can be captured by first and second cameras, respectively (and or a single camera at different points in time). Point pairs comprising a first point in the first image data and a second point in the second image data can be determined and projection errors associated with the points can be determined. A subset of point pairs can be determined, e.g., by excluding point pairs with the highest projection error. Calibration data associated with the subset of points can be determined and used to calibrate the cameras without the need for calibration infrastructure.