Depth Sensor Calibration Using Planar Reference Object
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Solution Overview
Problem
Integrating and calibrating data from multiple depth sensors in dynamic and complex environments, such as homes or shopping centers, to provide accurate user interaction feedback is challenging due to differences in sensor orientations and environmental factors, requiring effective translation and rotation transformations to reconcile data across sensors.
Innovation Solution
The implementation of a user-assisted calibration process using a planar calibration object to determine transformation matrices for each depth sensor, allowing data from multiple sensors to be aligned and integrated into a unified frame of reference, facilitating advanced gesture recognition and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple depth sensors are used to cover larger environments, then the coverage area and interaction capabilities are improved, but the complexity of calibrating and integrating data from different sensors increases
Solution Approach 1:
A planar calibration object serves as an intermediary between multiple depth sensors, providing a common reference frame. The calibration object with identifiable features allows each sensor to independently measure the same physical plane, enabling the system to compute transformation matrices that reconcile data from different sensors without requiring complex direct pairwise calibration between all sensor combinations.
2Adaptability or versatility
If sensors are positioned at different orientations to cover dynamic environments, then the adaptability and field of view are improved, but the difficulty of aligning and transforming data between sensors increases
Solution Approach 1:
The system computes transformation matrices that parameterize the spatial relationship between sensors with different orientations. By representing sensor transformations as mathematical parameters (rotation and translation components), the system can efficiently align data from sensors positioned at various angles while maintaining adaptability to dynamic environments. The calibration object provides reference measurements that determine these transformation parameters.
3Measurement precision
If a unified frame of reference is established for multiple sensors, then the accuracy of gesture recognition and user interaction is improved, but the processing time and computational resources required for calibration increase
Solution Approach 1:
The system performs calibration computations to establish transformation matrices in advance, before actual user interaction begins. By pre-computing the spatial relationships between all sensors and a unified reference frame using the planar calibration object, the system prepares the coordinate transformation data structure ahead of time, enabling accurate gesture recognition during operation without real-time calibration overhead.
Data Source
AI summary
Various of the disclosed embodiments provide Human Computer Interfaces (HCI) that incorporate depth sensors at multiple positions and orientations. The depth sensors may be used in conjunction with a display screen to permit users to interact dynamically with the system, e.g., via gestures. Calibration methods for orienting depth values between sensors are also presented. The calibration methods may generate both rotation and translation transformations that can be used to determine the location of a depth value acquired in one sensor from the perspective of another sensor. The calibration process may itself include visual feedback to direct a user assisting with the calibration. In some embodiments, floor estimation techniques may be used alone or in conjunction with the calibration process to facilitate data processing and gesture identification.