Multimodal Visual Localization in GPS-Denied Agent Navigation
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Solution Overview
Problem
Mobile agents operating in GPS-denied environments face challenges in determining their location without access to Global Positioning System (GPS) signals, particularly when constrained by limitations on two-way communication and the need to operate day and night without relying on GPS.
Innovation Solution
Utilizing a system comprising an image encoder and location encoder trained on images from visible and thermal cameras, along with a location decoder and a multimodal model, to generate accurate location pairs without GPS, enhancing self-localization capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS signals are used for location determination, then location accuracy is improved, but the system cannot operate in GPS-denied environments
Solution Approach 1:
The patent introduces an intermediary system consisting of image encoders, location encoders, and location decoders that mediate between visual input and location determination. This intermediary architecture enables the system to function without direct GPS signals by translating visual scene information into location data through learned mappings, thus resolving the contradiction between GPS dependency and GPS-denied operation capability.
Solution Approach 2:
The patent replaces the GPS satellite-based electromagnetic signal system with a local visual processing system using neural networks and encoders. By substituting the external GPS infrastructure with an onboard visual recognition and encoding system, the agent can determine its location through processing visual scenes captured by cameras, enabling operation in environments where GPS signals are unavailable.
2Measurement precision
If multiple encoders and decoders are used to improve self-localization accuracy, then location precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the location determination system into distinct functional modules: image encoders for processing visual input, location encoders for transforming image representations into location space, and location decoders for extracting final location pairs. This segmentation allows each component to be optimized independently while working together to achieve high self-localization accuracy, managing complexity through modular functional division.
Solution Approach 2:
The patent merges multiple encoding functions (image encoding and location encoding) into a unified neural network architecture that operates within a shared latent space. By combining these functions into an integrated system rather than separate independent modules, the patent reduces overall system complexity while maintaining the benefits of multiple encoding stages for improved location precision.
Data Source
AI summary
An apparatus can comprise an image encoder and a location encoder. The image encoder can be configured to be trained using a plurality of images including at least one image captured by a visible sensor and at least one image captured by a thermal camera. Further, the image encoder can be configured to output image encoder values based on the plurality of images. The location encoder can be configured to be trained using a plurality of location pairs, the location encoder configured to output location encoder values, each location pair from the plurality of location pairs uniquely associated with at least one image from the plurality of images. Further, the image encoder values and the location encoder values can collectively define a shared latent space.