Robot Object Modeling From Multi-View Vision for Unknown Objects
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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 involving a vision sensor capturing data from multiple vantages, generating a machine learning model, and creating rendered images with varying content to train a CNN model for object detection and pose estimation, tailored to the robot's environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a robot uses existing 3D models for object detection, then detection accuracy is improved for known objects, but the robot cannot recognize new or unrecognized objects
Solution Approach 1:
The system enables robots to autonomously generate their own object models by capturing images with vision sensors, processing them through neural networks, and creating 3D representations without human intervention. This self-service capability allows robots to independently adapt to new objects in their environment
Solution Approach 2:
The system performs preliminary actions by pre-processing captured images through neural network inference before model generation. This preliminary processing extracts key features and prepares data structures that facilitate efficient 3D model construction from multiple viewpoint images
2Adaptability or versatility
If a robot captures images from multiple vantages to create comprehensive object models, then object recognition capability is improved, but the time and computational resources required increase
Solution Approach 1:
The system applies partial action by selecting a sufficient subset of viewpoints rather than capturing all possible angles. The neural network determines when enough views have been captured to generate an accurate model, avoiding unnecessary image captures and processing time
Solution Approach 2:
The system creates simplified 3D model representations that capture essential object characteristics without requiring complete geometric fidelity. These copied representations are sufficient for recognition tasks and can be generated more quickly from limited viewpoint data
3Reliability
If a robot generates custom 3D models for unrecognized objects, then object detection capability is improved for those objects, but the complexity of the system increases
Solution Approach 1:
The system employs a universal neural network architecture that handles multiple functions: image processing, feature extraction, and 3D model generation. This multi-functional approach consolidates complexity into a single versatile component rather than requiring separate specialized systems
Solution Approach 2:
The system replaces complex mechanical 3D scanning and modeling hardware with software-based neural network processing. Instead of physical measurement devices, the system uses computational inference to generate 3D models from standard 2D images, significantly reducing hardware complexity
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.