Robot Camera Repositioning for Multi-View Object Detection
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Solution Overview
Problem
Current robotic systems face challenges in accurately detecting and interacting with objects in dynamic environments, particularly in determining object types and planning effective interaction strategies based on limited or varied viewpoints.
Innovation Solution
A computing system that uses a camera mounted on a robot arm to generate and process multiple sets of image information, identifying object corners and types by comparing sensed structure information with object recognition templates, and determining robot interaction locations for gripping and movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single viewpoint is used for object detection, then the detection process is simple and fast, but the accuracy of object type determination and interaction planning is insufficient
Solution Approach 1:
The detection process is divided into multiple stages: initial object detection from a first viewpoint, identification of object corners, and subsequent detailed imaging from second viewpoints. This segmentation allows the system to progressively refine object characterization without requiring all views simultaneously, balancing accuracy with manageable complexity
Solution Approach 2:
The system transitions from two-dimensional image data to three-dimensional structure information by incorporating depth data and multiple viewpoints. This dimensional enhancement enables more accurate object type determination and interaction planning by providing comprehensive spatial understanding
2Measurement precision
If multiple viewpoints are captured for object detection, then the object structure information is more accurate, but the detection time and processing complexity increase
Solution Approach 1:
The system performs preliminary object detection and corner identification from a first viewpoint before capturing detailed images from second viewpoints. This preliminary action filters out unnecessary processing and prepares the system to focus computational resources only on relevant object regions, reducing overall detection time
Solution Approach 2:
The system captures more viewpoint data than minimally required by selectively imaging only object corners and relevant portions from second viewpoints rather than complete object views. This partial action approach achieves sufficient accuracy while minimizing capture and processing time
3Measurement precision
If object corners are identified for targeted imaging, then the interaction planning precision is improved, but the processing complexity increases
Solution Approach 1:
The system extracts and identifies object corners from the initial image data as key features for subsequent targeted imaging. By taking out these critical corner points, the system focuses computational effort on determining interaction locations at these specific points rather than processing the entire object surface, improving precision while managing complexity
Data Source
AI summary
A method and computing system for performing object detection are presented. The computing system may be configured to: receive first image information that represents at least a first portion of an object structure of an object in a camera's field of view, wherein the first image information is associate with a first camera pose; generate or update, based on the first image information, sensed structure information representing the object structure; identify an object corner associated with the object structure; cause the robot arm to move the camera to a second camera pose in which the camera is pointed at the object corner; receive second image information associated with the second camera pose; update the sensed structure information based on the second image information; determine, based on the updated sensed structure information, an object type associated with the object; determine one or more robot interaction locations based on the object type.