STRMs: Spatial Temporal Reasoning Models for Vision-Based Localization Rivaling GPS Precision

• URL: http://arxiv.org/abs/2503.07939v1
• Date: Tue, 11 Mar 2025 00:38:54 GMT
• Title: STRMs: Spatial Temporal Reasoning Models for Vision-Based Localization Rivaling GPS Precision
• Authors: Hin Wai Lui, Jeffrey L. Krichmar,
• Abstract summary: We introduce two sequential generative models, VAE-RNN and VAE-Transformer, which transform first-person perspective observations into global map perspective representations.<n>We evaluate these models across two real-world environments: a university campus navigated by a Jackal robot and an urban downtown area navigated by a Tesla sedan.
• Score: 3.671692919685993
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper explores vision-based localization through a biologically-inspired approach that mirrors how humans and animals link views or perspectives when navigating their world. We introduce two sequential generative models, VAE-RNN and VAE-Transformer, which transform first-person perspective (FPP) observations into global map perspective (GMP) representations and precise geographical coordinates. Unlike retrieval-based methods, our approach frames localization as a generative task, learning direct mappings between perspectives without relying on dense satellite image databases. We evaluate these models across two real-world environments: a university campus navigated by a Jackal robot and an urban downtown area navigated by a Tesla sedan. The VAE-Transformer achieves impressive precision, with median deviations of 2.29m (1.37% of environment size) and 4.45m (0.35% of environment size) respectively, outperforming both VAE-RNN and prior cross-view geo-localization approaches. Our comprehensive Localization Performance Characteristics (LPC) analysis demonstrates superior performance with the VAE-Transformer achieving an AUC of 0.777 compared to 0.295 for VIGOR 200 and 0.225 for TransGeo, establishing a new state-of-the-art in vision-based localization. In some scenarios, our vision-based system rivals commercial smartphone GPS accuracy (AUC of 0.797) while requiring 5x less GPU memory and delivering 3x faster inference than existing methods in cross-view geo-localization. These results demonstrate that models inspired by biological spatial navigation can effectively memorize complex, dynamic environments and provide precise localization with minimal computational resources.

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