Robot Vision Dataset Mapping for Multi-Position Object Learning
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Solution Overview
Problem
Current robotic systems require significant effort and computational resources to identify and locate objects, especially in dynamic environments, due to challenges in translating 2D image positions to 3D coordinates and handling outdoor light and image resolution issues, necessitating specialized processing and calibration for each object.
Innovation Solution
A method involving a robotic system that captures images of objects from multiple positions, extracts visual features, and stores associations between these features and positional information in a dataset, allowing for efficient identification and location of objects using pre-trained datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current image analysis tools are used to identify objects, then object identification can be performed, but significant computational effort and specialized processing are required for each particular object
Solution Approach 1:
The system performs preliminary action by pre-collecting images of objects from multiple viewpoints and storing them in a database before actual object identification is needed. This pre-processing eliminates the need for complex real-time analysis, as the system can directly compare captured images with pre-stored reference images.
Solution Approach 2:
The system creates copies of objects by capturing images from multiple positions and angles, storing these visual copies in a database. During operation, the system identifies objects by comparing captured images against these pre-stored visual copies, avoiding complex analytical processing.
2Measurement precision
If sensors are calibrated to translate object positions to robot-relative coordinates, then accurate positioning is achieved, but additional significant computational effort is required
Solution Approach 1:
The system replaces complex computational coordinate transformation with a simpler image-matching approach. Instead of calibrating sensors and performing mathematical transformations to translate positions between coordinate systems, the system uses visual pattern matching to directly identify object positions and orientations.
3Adaptability or versatility
If depth maps sensors are used to translate 2D image positions to 3D positions, then translation capability is improved, but outdoor light effects and image resolution limitations remain challenges
Solution Approach 1:
The system segments the object recognition task into two parts: first, capturing images from multiple fixed positions to build a comprehensive visual database; second, comparing new captured images against this database. This segmentation allows the system to avoid real-time 2D-to-3D translation challenges by relying on pre-captured multi-angle images that already encode spatial information.
4Measurement precision
If robotic systems perform specialized processing for each object, then accurate object identification is achieved, but qualified personnel effort and time are significantly increased
Solution Approach 1:
The system performs all necessary object analysis and database building in advance, before actual operational use. This preliminary action eliminates the need for time-consuming calibration and specialized processing during actual object identification tasks, as the system simply compares images against pre-analyzed reference data.
Data Source
AI summary
According to an aspect of some embodiments of the present invention there is provided a method for generating a dataset mapping visual features of each of a plurality of objects, comprising: for each of a plurality of different objects: instructing a robotic system to move an arm holding a respective the object to a plurality of positions, and when the arm is in each of the plurality of positions: acquiring at least one image depicting the respective object in the position, receiving positional information of the arm in respective the position, analyzing the at least one image to identify at least one visual feature of the object in the respective position, and storing, in a mapping dataset, an association between the at least one visual feature and the positional information, and outputting the mapping dataset.