Inter-Sensor Calibration for Radar and Camera Tracking
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Solution Overview
Problem
Existing methods for calibrating sensors in multi-sensor tracking systems are time-consuming, manually intensive, and often require downtime, leading to inaccurate data fusion and inconsistent tracking results due to sensor misalignment.
Innovation Solution
A method and system for inter-sensor calibration that automatically adjusts internal and external sensor parameters in near real-time using redundant data from multiple sensors, optimizing sensor orientations and parameters to achieve consistent and accurate tracking without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used to determine sensor parameters, then calibration accuracy can be achieved, but the process becomes time-consuming and requires sensor downtime
Solution Approach 1:
The system performs self-calibration by automatically comparing tracks from multiple sensors and computing correction parameters without manual intervention. The calibration process is executed autonomously using redundant measurements from the sensor network itself, eliminating the need for external calibration equipment and manual operations.
Solution Approach 2:
The calibration process occurs continuously in the background during normal tracking operations. Correction parameters are computed and applied without interrupting sensor data collection, allowing the system to maintain both calibration accuracy and continuous operational capability.
2Measurement precision
If manual calibration procedures are implemented, then sensor parameters can be determined, but the complexity and manual intensity increase
Solution Approach 1:
The system automatically determines sensor parameters by comparing redundant tracks from multiple sensors and computing correction parameters. The entire calibration process, including track generation, comparison, and parameter computation, is performed autonomously without requiring manual setup, data entry, or analysis.
Solution Approach 2:
Manual mechanical calibration procedures are replaced with an automated computational system that uses mathematical optimization to determine sensor parameters. The system substitutes human operators with algorithms that automatically process sensor data and compute correction parameters.
3Measurement precision
If sensors are calibrated frequently to account for drift, then tracking accuracy is maintained, but operational downtime increases
Solution Approach 1:
The calibration process runs continuously in the background during normal tracking operations. The system computes correction parameters from redundant measurements without interrupting data collection, allowing frequent calibration updates without operational downtime.
Solution Approach 2:
The calibration system is dynamic and adaptive, continuously updating correction parameters as sensor drift occurs. Rather than periodic static calibration, the system adapts in real-time to maintain tracking accuracy while minimizing disruption to operational productivity.
Data Source
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AI summary
A method includes generating an initial radar track using radar data and initial radar parameters and an initial camera track using image angular position data, initial camera parameters, and the radar range data in combination with calculating correction parameters to be applied to one of the radar data and the image data by comparing positions for the object from the initial radar track and the initial camera track, the first correction parameters being selected so that, when applied to the data from the other of the radar and the camera, to generate a first corrected track, a degree of correspondence between the first corrected track and the track of the other of the radar and camera is higher than a degree of correspondence between the initial radar track and the initial camera track.