用类人导航思路实现媲美GPS的视觉定位，精度高且省资源。
STRMs: Spatial Temporal Reasoning Models for Vision-Based Localization Rivaling GPS Precision
- 基于生物导航机制，将第一视角图像生成全局坐标表示。
- 在校园与城市场景中分别达到2.29米和4.45米中位误差，优于现有方法。
- 模型轻量高效，推理速度更快、显存占用更低，适合实际部署。
本文提出一种受生物空间导航启发的视觉定位方法，通过两个序列生成模型VAE-RNN与VAE-Transformer，将第一人称视角（FPP）观测转换为全局地图视角（GMP）表示及精确地理坐标。不同于依赖密集卫星图库的检索方法，该方法将定位视为生成任务，直接学习视角间的映射关系。在杰卡尔机器人导航的大学校园与特斯拉轿车行驶的城市街区中评估，VAE-Transformer分别实现2.29米（占环境大小1.37%）与4.45米（0.35%）的中位偏差，显著优于VAE-RNN及现有跨视图地理定位方法。综合定位性能特征（LPC）分析显示，其AUC达0.777，远超VIGOR 200（0.295）与TransGeo（0.225），创下新基准。部分场景下，系统性能接近商用手机GPS（AUC 0.797），同时仅需1/5 GPU内存，推理速度提升3倍，证明生物启发模型可在低算力下实现高精度复杂环境定位。
原文摘要 · Abstract (English)
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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