Speckle Sensing for 3D Motion Tracking
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Solution Overview
Problem
Existing motion tracking systems for 3D position and orientation, such as time of flight camera systems and structured light camera systems, are often cost-prohibitive and unsuitable for fine-grained, high-speed tracking, lacking the precision needed for applications like robotics and gaming.
Innovation Solution
The use of speckle sensing technology, combining laser speckle patterns with additional sources like structured light systems or time of flight systems, to compute 3D motion and orientation with high accuracy, employing multiple speckle sensors and projectors to enhance tracking precision and reduce ambiguity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing motion tracking systems (time of flight, structured light) are used for 3D position tracking, then coarse-level tracking is achieved, but the systems are cost-prohibitive and lack fine-grained precision
Solution Approach 1:
The system segments the tracking function by using a speckle sensor array that captures multiple speckle patterns from different spatial locations simultaneously. Each sensor element processes local speckle disparities to contribute to the overall 3D position calculation, enabling fine-grained tracking without requiring a single complex system
Solution Approach 2:
The invention uses multiple speckle sensors to capture multiple copies of the speckle pattern from slightly different perspectives. By processing the disparities between these copied patterns, the system achieves precise 3D position measurement without needing expensive single-point tracking infrastructure
2Productivity
If existing motion tracking systems are used for fast tracking, then some tracking capability is provided, but fine-grained high-speed tracking with precise 6-DOF pose estimation is not achieved
Solution Approach 1:
The system performs preliminary action by capturing multiple speckle patterns simultaneously across the sensor array before processing. The speckle correlations are computed in parallel from pre-captured images, enabling fast tracking speeds while maintaining precision through subsequent disparity analysis of the pre-acquired patterns
Solution Approach 2:
The invention transitions from 2D speckle pattern analysis to 3D position estimation by incorporating temporal dimension (multiple time points) and spatial dimension (multiple sensor elements). This multi-dimensional approach enables simultaneous achievement of high tracking speed and precise 6-DOF pose estimation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate, fine-grained, and fast 3D motion tracking, overcoming the limitations of existing systems by providing precise 6-degree-of-freedom pose estimation and improving tracking accuracy, especially at high speeds and with small objects.
Implementation Method 1
A speckle pattern is a micro-pattern of illumination generated by a coherent light source, such as a laser, when it passes through a diffuser or when it scatters from a surface which has irregularities larger than the wavelength of the illumination
Implementation Method 2
a coherent light source, such as a laser
Implementation Method 3
when it scatters from a surface which has irregularities larger than the wavelength of the illumination
Data Source
AI summary
Speckle sensing for motion tracking is described, for example, to track a user's finger or head in an environment to control a graphical user interface, to track a hand-held device, to track digits of a hand for gesture-based control, and to track 3D motion of other objects or parts of objects in a real-world environment. In various examples a stream of images of a speckle pattern from at least one coherent light source illuminating the object, or which is generated by a light source at the object to be tracked, is used to compute an estimate of 3D position of the object. In various examples the estimate is transformed using information about position and/or orientation of the object from another source. In various examples the other source is a time of flight system, a structured light system, a stereo system, a sensor at the object, or other sources.