Scene Embedding Navigation Using Visual Triplet Learning
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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 to environmental changes due to the use of customized computer vision algorithms that rely on feature signatures.
Innovation Solution
Utilizing deep convolutional neural networks to extract feature vectors from visual sensory data, generating a topologically consistent representation of the environment through scene embedding, which facilitates localization and goal-directed navigation without the need for customized algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional computer vision algorithms with customized feature signatures are used, then navigation can be achieved, but the system complexity and engineering effort increase significantly
Solution Approach 1:
The patent applies universality by using a single trained neural network model that can perform multiple navigation tasks including localization, landmark recognition, and path planning across diverse environments. The model learns generalizable features from training data that enable it to handle various navigation scenarios without requiring environment-specific customization, thereby reducing system complexity while maintaining reliable navigation capability.
2Measurement precision
If customized algorithms with specific situational knowledge are employed, then accurate navigation is achieved, but adaptability to environmental changes deteriorates
Solution Approach 1:
The patent applies dynamics by implementing a neural network model that can dynamically adapt to new environments through continuous learning from training data. The system maintains navigation accuracy by learning environment-specific features during training while preserving the ability to generalize to unseen environments. This dynamic adaptation capability allows the system to maintain both precision and versatility across changing conditions.
Solution Approach 2:
The patent applies parameter changes by training the neural network with adjustable parameters (weights and biases) that are optimized during training to achieve accurate navigation. The model's internal parameters are tuned to capture environmental features while maintaining generalizability, allowing the system to adapt to different environments by modifying these parameters based on training data rather than requiring complete algorithm redesign.
3Adaptability or versatility
If deep convolutional neural networks are used for scene embedding, then adaptability to environmental changes improves, but computational requirements increase
Solution Approach 1:
The patent applies preliminary action by performing computationally intensive neural network training offline before deployment. The model learns environmental representations and navigation strategies during the training phase, storing this knowledge in its weights and biases. During actual navigation, the pre-trained model requires minimal computational resources to process new inputs and make decisions, thereby reducing real-time energy consumption while maintaining high adaptability through the previously acquired knowledge.
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.