Robot Object Modeling From Multi-View Vision for Pose Estimation
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Solution Overview
Problem
Robots face challenges in detecting and estimating the pose of unfamiliar objects in their environment, as existing 3D models and vision sensors are insufficient for recognition.
Innovation Solution
A method involving a vision sensor capturing data from multiple angles, generating a machine learning model, and creating rendered images with varying content to train a CNN for object detection and pose estimation, tailored to the robot's environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robot uses existing 3D models and vision sensors to detect objects, then it can recognize familiar objects, but it fails to recognize unfamiliar objects that do not match existing models
Solution Approach 1:
The system enables robots to autonomously generate their own 3D models for unrecognized objects by capturing multi-view images, processing them through neural networks to create depth maps and 3D representations, and storing these models for future recognition. This self-service mechanism eliminates the need for pre-existing comprehensive object databases.
Solution Approach 2:
The system performs preliminary 3D model generation and storage for objects encountered in the environment before they are needed for recognition tasks. By proactively building and maintaining a library of 3D models through continuous environmental scanning and processing, the system ensures that models are ready when needed for detection and pose estimation.
2Adaptability or versatility
If a robot captures vision sensor data from multiple vantages to improve object recognition, then it can better understand unfamiliar objects, but it increases data processing time and computational complexity
Solution Approach 1:
The system replaces traditional mechanical 3D reconstruction methods with neural network-based processing. The neural network efficiently processes multi-view images to generate depth maps and 3D models, significantly reducing computational time compared to conventional geometric processing approaches while maintaining high accuracy.
Solution Approach 2:
The system transforms the processing approach by changing from direct geometric computation to neural network inference. This parameter change in the processing methodology allows for faster computation of 3D models from multi-view images, as the neural network has learned efficient transformation patterns during training.
3Measurement precision
If a robot uses complete 3D object models for detection, then it can accurately estimate poses of known objects, but it cannot detect objects that do not match existing models
Solution Approach 1:
The system transitions from static pre-defined 3D models to dynamic model generation. When an object is encountered, the system dynamically creates a 3D model specific to that object instance, allowing the detection system to adapt to any object type while maintaining precise pose estimation capabilities through the generated model.
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.