CNN Feature Extraction for Robotic Location Recognition
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Solution Overview
Problem
Existing robotic navigation systems require powerful computational resources for efficient location recognition, limiting their application to larger robotic platforms due to the heavy computational load of traditional image feature extraction methods.
Innovation Solution
A system utilizing a convolutional neural network (CNN) feature extractor and a k-Nearest Neighbor (k-NN) with majority voting to efficiently convert images into high-dimensional feature vectors, allowing for robust location recognition and learning on mobile platforms with reduced computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image feature extraction methods are used, then location recognition accuracy is improved, but computational load increases significantly
Solution Approach 1:
The patent extracts only the essential discriminative features from images using CNNs, rather than processing entire high-resolution images. The system extracts local image features and converts them into compact feature vectors, reducing computational requirements while maintaining location recognition accuracy.
Solution Approach 2:
The patent uses pre-trained CNN models that have been copied and deployed on robotic platforms. These pre-trained models contain learned feature extraction capabilities that can be directly applied without requiring extensive computational resources for training during operation, enabling efficient location recognition.
2Productivity
If powerful computers are used for location recognition, then processing speed is improved, but device size and complexity increase
Solution Approach 1:
The patent employs lightweight CNN architectures and optimized feature extraction pipelines that can run on embedded systems with limited resources. Instead of relying on powerful standalone computers, the system uses efficient, resource-constrained implementations that provide sufficient processing speed for robotic navigation applications.
Solution Approach 2:
The patent segments the location recognition task into distinct stages: image capture, feature extraction using CNNs, feature matching, and location identification. This segmentation allows each stage to be optimized independently, enabling efficient processing on platforms with limited computing resources while maintaining overall system performance.
Data Source
AI summary
Described is a system for location recognition for mobile platforms, such as autonomous robotic exploration. In operation, an image in front of the platform is converted into a high-dimensional feature vector. The image reflects a scene proximate the mobile platform. A candidate location identification of the scene is then determined. The candidate location identification is then stored in a history buffer. Upon receiving a cue, the system then determines if the candidate location identification is a known location or a new location.