Multicamera 3D Workspace Reconstruction for Occlusion Handling
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Solution Overview
Problem
Existing robotics systems face challenges in generating comprehensive 3D image data for all objects in a workspace due to occlusions, requiring efficient perception methods to update changing environments and plan operations within operational timeframes.
Innovation Solution
Utilizing multiple cameras and sensors to generate and merge 3D views of a workspace, incorporating techniques like registration, segmentation, and recalibration to create a complete and accurate 3D model for robotic operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single camera is used to capture workspace data, then the system is simple and easy to operate, but it cannot generate complete 3D image data for all objects due to occlusions
Solution Approach 1:
The patent merges image data from multiple cameras (first camera and second camera) to generate a comprehensive 3D representation of the workspace. By combining the field of view and depth information from multiple camera sources, the system overcomes occlusion limitations and achieves complete object detection while maintaining manageable system complexity through integrated processing.
2Reliability
If multiple cameras are deployed to capture all objects, then complete 3D coverage is achieved, but processing and merging the data becomes more complex
Solution Approach 1:
The patent replaces complex mechanical coordination of multiple cameras with a computational approach using neural networks. The machine learning model automatically processes and integrates image data from multiple cameras, performing registration, segmentation, and 3D reconstruction through software-based methods rather than requiring complex mechanical synchronization systems.
Solution Approach 2:
The system transforms 2D image parameters from multiple cameras into unified 3D spatial parameters through neural network processing. By changing the parameter representation from separate 2D images to integrated 3D point clouds and depth maps, the system simplifies the merging process and achieves comprehensive workspace coverage with manageable computational complexity.
3Measurement precision
If comprehensive 3D data is collected from multiple sources, then object detection accuracy improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary processing of image data from multiple cameras simultaneously during data acquisition. The neural network begins segmentation, registration, and 3D reconstruction processes in parallel as images are captured, rather than waiting for all data to be collected first. This preliminary action reduces the final processing time while maintaining high detection accuracy through comprehensive data analysis.
Data Source
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AI summary
A multicamera image processing system is disclosed. In various embodiments, image data is received from each of a plurality of sensors associated with a workspace, the image data comprising for each sensor in the plurality of sensors one or both of visual image information and depth information. Image data from the plurality of sensors is merged to generate a merged point cloud data. Segmentation is performed based on visual image data from at least a subset of the sensors in the plurality of sensors to generate a segmentation result. One or both of the merged point cloud data and the segmentation result is/are used to generate a merged three dimensional and segmented view of the workspace.