Multicamera 3D Workspace Imaging to Resolve Robotic Blind Spots
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Single cameras struggle to generate comprehensive 3D image data for all objects in a workspace due to obscuration, necessitating improved systems for robotic operations to handle changing conditions within operational timeframes.
Innovation Solution
A multicamera image processing system merges and segments data from multiple sensors to generate a complete 3D view of the workspace, enabling robust robotic operations through calibration and recalibration techniques, and allows for human intervention when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single camera is used to capture workspace data, then the system complexity is reduced, but the completeness of 3D image data deteriorates due to object obscuration
Solution Approach 1:
The workspace is divided into multiple fields of view captured by different cameras. Each camera captures a specific portion of the workspace, and the system segments and processes data from multiple camera views to reconstruct complete 3D information about all objects, eliminating blind spots and obscuration issues.
Solution Approach 2:
Data from multiple cameras is merged and integrated to form a comprehensive 3D representation of the workspace. The system combines image data, depth data, and spatial information from multiple camera sources to create a complete and accurate model of all objects, resolving the limitations of single-camera observation.
2Loss of information
If multiple cameras are deployed to capture complete workspace data, then the completeness of 3D image data is improved, but the device complexity increases
Solution Approach 1:
The system employs a unified processing architecture that handles data from multiple cameras through common processing pipelines. The same software modules and algorithms process data from any camera source, making the system multi-functional and reducing operational complexity despite having multiple physical cameras.
Solution Approach 2:
The system introduces intermediate processing layers including calibration modules, coordinate transformation systems, and data fusion algorithms that mediate between multiple camera inputs and the final 3D reconstruction. These intermediaries standardize and simplify the integration of multi-camera data, reducing overall system complexity.
3Ease of operation
If traditional single-camera systems are used, then the system is easier to operate, but the ability to respond to changing conditions deteriorates
Solution Approach 1:
The system implements dynamic camera calibration and real-time coordinate transformation capabilities that automatically adapt to changing workspace conditions. The calibration data and transformation parameters are updated dynamically as objects move or new objects are introduced, enabling the system to respond to changing conditions while maintaining ease of operation through automated adjustments.
Data Source
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.