Scene Embedding for Visual Navigation Without Explicit Coordinates
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Solution Overview
Problem
Conventional autonomous navigation systems for vehicles and robots are complex and require specific situational knowledge, limiting their adaptability and efficiency in changing environments due to the need for customized algorithms and feature signature identification.
Innovation Solution
The use of scene embedding techniques, leveraging deep convolutional neural networks to generate topologically consistent representations of environments from sensory data, allowing for intelligent navigation and cognitive mapping without the need for explicit coordinate calculations or extensive supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer vision algorithms with customized feature signatures are used, then navigation accuracy can be achieved, but system complexity and engineering effort increase significantly
Solution Approach 1:
The patent replaces conventional computer vision algorithms with a deep learning-based embedding network that automatically learns visual features from raw pixel data. This substitution eliminates the need for hand-crafted feature signatures and complex customized algorithms, reducing system complexity while maintaining navigation accuracy through automated feature extraction and scene embedding.
2Reliability
If customized algorithms with specific situational knowledge are implemented, then navigation performance improves, but adaptability to changing environments deteriorates
Solution Approach 1:
The patent implements a dynamic embedding network that can be continuously trained and adapted to new environments. The system learns visual representations from training data specific to each environment and can update its knowledge base as conditions change, providing both reliable navigation performance and adaptability to changing environments through continuous learning rather than static customized algorithms.
Solution Approach 2:
The embedding network serves multiple functions: it performs scene representation, localization, and navigation simultaneously. The same trained network can be applied across different environments and conditions, providing universal adaptability while maintaining specialized performance through environment-specific training data.
3Measurement precision
If extensive supervision and explicit coordinate calculations are used, then navigation precision is maintained, but computational efficiency and processing speed decrease
Solution Approach 1:
The patent extracts and eliminates the need for explicit coordinate calculations and extensive supervision by using the embedding network to directly map visual scenes to navigational decisions. The network learns implicit spatial relationships during training and can perform navigation tasks without requiring explicit coordinate computations, significantly improving computational efficiency while maintaining precision.
Data Source
AI summary
Navigation instructions are determined using visual data or other sensory information. Individual frames can be extracted from video data, captured from passes through an environment, to generate a sequence of image frames. The frames are processed using a feature extractor to generate frame-specific feature vectors. Image triplets are generated, including a representative image frame (or corresponding feature vector), a similar image frame adjacent in the sequence, and a disparate image frame that is separated by a number of frames in the sequence. The embedding network is trained using the triplets. Image data for a current position and a target destination can then be provided as input to the trained embedding model, which outputs a navigation vector indicating a direction and distance over which the vehicle is to be navigated in the physical environment.