Multi-Camera Scene Perception with Pose-Tracked Data Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional scene perception systems lack accuracy and reliability in dynamic and complex environments, such as surgical procedures, due to obstructions and changing viewpoints.
Innovation Solution
A dynamic multi-device architecture with imaging devices that are dynamically movable, utilizing tracking data to fuse data sets and adjust scene perception in real-time, optimizing the perception process through a feedback control loop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional scene perception systems are used, then the system structure is simple, but the scene perception accuracy and reliability deteriorate in dynamic and complex environments
Solution Approach 1:
The patent implements a dynamic multi-device architecture where imaging devices can change their poses dynamically during scene capture. The system tracks pose changes in real-time and adjusts the fusion process accordingly, allowing the configuration to adapt to complex environments rather than remaining static. This dynamic capability enables the system to maintain high perception accuracy even when objects are obstructed or when viewpoints need to change.
Solution Approach 2:
The patent combines data from multiple imaging devices through a fusion process that integrates tracking data with image data. By merging information from multiple sources and temporal points, the system achieves higher reliability and accuracy in scene perception, particularly in dynamic environments where single-device or static multi-device systems would fail due to obstructions.
2Adaptability or versatility
If imaging devices are fixed in position, then the device complexity is reduced, but the ability to maintain optimal viewpoints deteriorates in dynamic environments
Solution Approach 1:
The system employs dynamically moveable imaging devices that can adjust their positions and orientations during the imaging process. This dynamic configuration allows the devices to maintain optimal viewpoints as the scene or objects within it change, providing adaptability that fixed systems cannot achieve.
Solution Approach 2:
The patent implements a feedback control loop that continuously monitors the scene and adjusts the imaging device configurations accordingly. The system uses tracking data to detect changes in the scene or obstructions, then feeds this information back to adjust device positions and maintain optimal viewing angles, creating a closed-loop adaptive system.
3Reliability
If data fusion is performed without tracking pose changes, then the processing complexity is reduced, but the scene perception reliability deteriorates when devices move
Solution Approach 1:
The system incorporates tracking data that monitors pose changes of imaging devices during data capture. This tracking information is fed back into the fusion process, allowing the system to compensate for device movements and maintain accurate scene perception. The feedback mechanism ensures that even when devices move, the fused data remains reliable by adjusting for the changed viewpoints.
Solution Approach 2:
The patent performs tracking of device poses concurrently with data capture, rather than as a separate post-processing step. By preliminarily tracking and recording pose changes during the imaging process, the system has the necessary information ready when fusion occurs, enabling reliable perception without adding significant processing complexity at the fusion stage.
Data Source
AI summary
Scene perception systems and methods are described herein. In certain illustrative examples, a system combines data sets associated with imaging devices included in a dynamic multi-device architecture and uses the combined data sets to perceive a scene (e.g., a surgical scene) imaged by the imaging devices. To illustrate, the system may access tracking data for imaging devices capturing images of a scene and fuse, based on the tracking data, data sets respectively associated with the imaging devices to generate fused sets of data for the scene. The tracking data may represent a change in a pose of at least one of the image devices that occurs while the imaging devices capture images of the scene. The fused sets of data may represent or be used to generate perceptions of the scene. In certain illustrative examples, scene perception is dynamically optimized using a feedback control loop.