Inter-Sensor Calibration Using Radar-Camera Track Matching
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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 of moving objects.
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, such as cameras and radars, to optimize tracking accuracy without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used with fixtures and known objects, then sensor parameters can be determined, but the process becomes time-consuming and requires sensor downtime
Solution Approach 1:
The system performs self-calibration by automatically determining sensor parameters using data from multiple sensors tracking moving objects. The calibration process does not require manual intervention with fixtures or known objects, but rather uses the natural operating data of the sensors themselves to compute parameters through automated mathematical operations.
Solution Approach 2:
The system performs calibration in advance or continuously in the background using redundant sensor data, so that when calibration is needed, the parameters are already updated. This allows calibration to occur without stopping sensor operation, as the system prepares calibration data and computations beforehand using naturally occurring moving objects in the sensor field.
2Measurement precision
If manual calibration methods are used with fixtures and known objects, then sensor parameters can be determined, but the process becomes manually intensive
Solution Approach 1:
The system performs self-calibration by automatically determining sensor parameters using data from multiple sensors tracking moving objects. The calibration process does not require manual intervention with fixtures or known objects, but rather uses the natural operating data of the sensors themselves to compute parameters through automated mathematical operations.
Solution Approach 2:
The patent replaces manual mechanical calibration procedures with automated computational methods. Instead of physically positioning sensors in fixtures and manually identifying features, the system uses automated track matching algorithms and mathematical operations to determine sensor parameters from digital data.
3Productivity
If sensors are calibrated in situ using moving objects, then downtime is minimized, but parameter drift over time reduces tracking accuracy
Solution Approach 1:
The system performs calibration continuously or periodically in the background using ongoing sensor data from moving objects. Rather than performing discrete calibration events that stop operation, the calibration process runs continuously alongside normal tracking operations, constantly updating parameters to compensate for drift while maintaining uninterrupted sensor functionality.
4Area of stationary object
If multiple sensors are used to expand capture volume, then tracking coverage is improved, but calibration complexity increases
Solution Approach 1:
The system uses a universal calibration approach that works across multiple sensor types (radar, optical, infrared) and configurations. The same automated track-matching algorithm and parameter determination process applies regardless of how many sensors are used or what types they are, providing a multi-functional calibration solution that handles diverse sensor arrays without requiring different procedures.
Data Source
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.