Autonomous Vehicle Sensor Calibration Using Entropy Minimization
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Solution Overview
Problem
Current sensor calibration techniques are time-consuming, require offline processes, and involve human operators, and are inefficient, failing to detect, and result in inaccurate sensor data integration, especially in autonomous vehicles.
Innovation Solution
A combinatorial search algorithm is used to project sensor data onto a grid, determining the lowest entropy value to achieve optimal sensor calibration, adjusting for both intrinsic and extrinsic characteristics, and utilizing a gradient descent algorithm as an alternative.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current calibration techniques are used, then sensor calibration can be performed, but the process is time-consuming and computationally expensive
Solution Approach 1:
The system performs self-calibration automatically without requiring human operators. The calibration component uses sensor data from multiple sensors to automatically determine calibration parameters through optimization algorithms, eliminating the need for manual calibration processes while maintaining high accuracy.
Solution Approach 2:
The system continuously collects and stores sensor data in advance for calibration purposes. By maintaining a database of pre-collected sensor data from normal operation, the system can perform calibration without requiring dedicated calibration time or taking the vehicle out of service.
2Measurement precision
If current calibration techniques are used, then sensor calibration can be performed, but the process requires offline operations and vehicle downtime
Solution Approach 1:
The calibration system operates continuously in the background during normal vehicle operation without interrupting useful actions. Multiple sensors continuously collect data during regular driving, and calibration computations are performed continuously or periodically without requiring the vehicle to be taken out of service.
Solution Approach 2:
The system performs self-calibration automatically during normal operation using onboard computing resources. The calibration component processes sensor data and updates calibration parameters without human intervention or vehicle downtime, maintaining continuous productivity.
3Measurement precision
If current calibration techniques are used, then sensor calibration can be performed, but human operators are required making the process manual and imprecise
Solution Approach 1:
The system performs self-calibration automatically without requiring human operators. The calibration component uses sensor data from multiple sensors to automatically determine calibration parameters through optimization algorithms, eliminating the need for manual calibration processes while maintaining high accuracy.
Solution Approach 2:
The system replaces manual mechanical adjustment processes with automated computational algorithms. Instead of human operators physically adjusting sensor mounting positions, the system uses optimization algorithms to compute calibration parameters that align sensor data, achieving higher precision through computational methods.
4Measurement precision
If current calibration techniques are used, then sensor calibration can be performed, but the computational cost is high
Solution Approach 1:
The system performs calibration computations at different levels of detail as needed. Rather than continuously performing full computational optimization, the system can perform quicker partial calibrations or use pre-computed calibration data when conditions allow, reducing computational energy consumption while maintaining sufficient accuracy.
Data Source
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Figure 3A~3B
AI summary
This disclosure is directed to calibrating sensors for an autonomous vehicle. First sensor data and second sensor data can be captured by one or more sensors representing an environment. The first sensor data and the second sensor data can be associated with a grid in a plurality of combinations to generate a plurality of projected data. A number of data points of the projected data occupying a cell of the grid can be summed to determine a spatial histogram. An amount of error (such as an entropy value) can be determined for each of the projected data, and the projected data corresponding to the lowest entropy value can be selected as representing a calibrated configuration of the one or more sensors. Calibration data associated with the lowest entropy value can be determined and used to calibrate the one or more sensors, respectively.