Camera Field Occlusion Detection for Reliable Robot Recognition
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Solution Overview
Problem
In automation environments, such as warehousing and manufacturing, robots face challenges in accurately interacting with objects due to occlusion within the camera field of view, which is caused by noise and inaccuracies in recognizing objects from camera data.
Innovation Solution
A computing system and method that determines occlusion by analyzing camera data to identify target features, calculating occluding regions, and adjusting object recognition confidence parameters to improve robot interaction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robot interaction is controlled based on camera data, then automation capability is improved, but object recognition accuracy deteriorates due to occlusion and noise
Solution Approach 1:
The patent introduces an occlusion detection module as an intermediary between the camera data acquisition and robot control systems. This module analyzes camera images to identify occluding objects and determines occlusion regions, providing corrected object location information to the robot control system, thereby resolving the contradiction between automation and recognition accuracy
Solution Approach 2:
The system implements feedback by using camera data to detect occlusion conditions and adjust robot interaction operations accordingly. The occlusion detection results feed back into the control decisions, allowing the system to adapt its behavior based on the actual visual environment and maintain high recognition accuracy
2Measurement precision
If multiple cameras are used to reduce occlusion, then object recognition accuracy is improved, but system complexity increases
Solution Approach 1:
Instead of adding more cameras (spatial dimension), the patent processes the same camera data through an additional analytical dimension - occlusion detection and region analysis. This approach improves recognition accuracy by extracting more information from existing data without increasing hardware complexity
Data Source
AI summary
A system and method for determining occlusion are presented. The system receives camera data generated by at least one camera, which includes a first camera having a first camera field of view. The camera data is generated when a stack having a plurality of objects is in the first camera field of view, and describes a stack structure formed from at least an object structure for a first object of the plurality of objects. The system identifies a target feature of or disposed on the object structure, and determines a 2D region that is co-planar with and surrounds the target feature. The system determines a 3D region defined by connecting a location of the first camera and the 2D region. The system determines, based on the camera data and the 3D region, a size of an occluding region, and determines a value of an object recognition confidence parameter.