Robot Object Modeling for Unrecognized Object Pose Estimation
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Solution Overview
Problem
Robots face challenges in detecting and estimating the pose of objects in their environment when these objects do not match existing 3D models, leading to recognition failures.
Innovation Solution
A method is implemented where a robot captures vision sensor data from multiple angles to generate a model of the unrecognized object, which is then used to create rendered images with varying content. These images are used to train a machine learning model, such as a CNN, to enable detection and pose estimation of the object, with the model being tailored to the robot's environment for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a robot uses existing 3D models to detect objects, then detection accuracy is improved for known objects, but the robot cannot detect unrecognized objects that do not match existing models
Solution Approach 1:
The system enables the robot to autonomously generate its own 3D models for unrecognized objects by capturing images from multiple viewpoints, processing them through neural networks to create mesh models, and adding these models to its database for future recognition
Solution Approach 2:
The system performs preliminary 3D modeling and model database expansion when unrecognized objects are detected, preparing models in advance before they are needed for routine detection tasks
2Manufacturing precision
If a robot captures vision sensor data from multiple viewpoints to model unrecognized objects, then model accuracy is improved, but the time and computational resources required are increased
Solution Approach 1:
The system captures images from a sufficient number of viewpoints needed to create accurate 3D models without requiring exhaustive coverage of all possible angles, balancing model quality with time efficiency
Solution Approach 2:
The system replaces traditional manual or mechanical 3D scanning methods with neural network-based image processing to automatically generate accurate 3D mesh models from 2D images, significantly reducing the time and computational resources required
Data Source
AI summary
Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.