Multi-Sensor Tracking Calibration Using Known Surface Constraints
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Solution Overview
Problem
Existing object tracking systems struggle with maintaining accurate sensor parameters in real-time, leading to errors in object positioning, especially in environments with known surfaces, as practical methods to update these parameters during events are not feasible.
Innovation Solution
A method and apparatus that utilize a multi-sensor tracking system to calculate and constrain sensor parameters by analyzing interactions of objects with known bounding surfaces, minimizing overall system error through bundle adjustment techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor parameters are updated in real-time during events, then tracking accuracy is improved, but system complexity and computational requirements increase significantly
Solution Approach 1:
The system performs preliminary calibration by capturing images of the calibration pattern before the event begins, establishing initial sensor parameters and coordinate transformations. This preliminary action separates the complex parameter determination from real-time operation, reducing computational burden during events while maintaining accuracy
Solution Approach 2:
A calibration pattern serves as an intermediary object between the sensors and the tracking system. This pattern provides known geometric features that facilitate parameter determination without requiring complex real-time computations. The calibration pattern acts as a mediator that simplifies the parameter estimation process
2Device complexity
If sensor parameters are fixed and not updated, then system complexity is reduced, but tracking accuracy deteriorates over time
Solution Approach 1:
The system performs calibration at periodic intervals (before events, between events, or at scheduled times) rather than continuously during events. This periodic recalibration maintains tracking accuracy while avoiding the computational complexity of real-time updates. The calibration process can be repeated as needed to account for drift or changes in sensor parameters
3Measurement precision
If calibration is performed continuously during events, then parameter accuracy is maintained, but event time is lost and productivity decreases
Solution Approach 1:
Calibration is performed in advance before events begin, so that accurate sensor parameters are established without interrupting or delaying the actual event. This preliminary calibration ensures parameter accuracy is maintained while preserving event time and productivity
Solution Approach 2:
The system dynamically adapts its calibration schedule based on event requirements. Calibration can be performed before events, between events, or at scheduled intervals, allowing the system to maintain accuracy while minimizing impact on event throughput. The calibration timing is flexible rather than rigid
4Reliability
If multiple sensors are used to track objects, then measurement reliability is improved, but determining consistent positions across sensors becomes more difficult
Solution Approach 1:
The calibration pattern serves as a common reference frame that mediates between multiple sensors. By capturing images of the same known pattern, all sensors can be calibrated to a consistent coordinate system, enabling reliable position determination across the sensor array without complex coordination
Solution Approach 2:
The system merges data from multiple sensors by establishing a common coordinate transformation through calibration. The calibration process combines information from all sensors viewing the calibration pattern, creating a unified reference frame that simplifies subsequent position determination across the sensor network
Data Source
AI summary
A sensor system and a method of operating a sensor system including a plurality of sensors tracking a moving object in an area having known bounding surfaces. The apparatus and method calculate a time-specific position of the object based on data and sensor parameters from at least two of the plurality of sensors and determine errors between the calculated time-specific positions calculated. The method and apparatus calculate a minimum system error attributable to the at least two sensors by constraining at least one dimension in the data of the sensor used in the calculated time-specific position of the object associated with the sensor, the constraining based on an object/surface interaction, the minimum system error calculated by solving for modified sensor parameters for each sensor.