AutoCompass: Accurate Visual Localization on Public Maps by Learning from Weak Labels

2026-09-02

AI Summary

为解决地图匹配中位置和方向标签噪声问题，AutoCompass通过弱标签学习，结合GPS容忍区及相对位姿信息，提高视觉定位精度。

Abstract

Neural map matchers estimate an image's 3-DoF pose relative to a 2D map. These models are trained on large-scale datasets of geo-referenced images, whose position and heading labels often contain noise that affects the trained models. To address this, we present AutoCompass, a supervision approach for training neural map matchers from inaccurate absolute pose labels. First, we show that heading labels are unnecessary: trained from raw GPS labels, models learn to predict accurate headings, automatically. Second, defining a tolerance region around raw GPS improves positional accuracy. Third, if available, our supervision uses relative poses between training images, obtained via SLAM or SfM, which provide a more accurate training signal. Across driving and egocentric benchmarks, AutoCompass consistently outperforms counterparts trained with the usual strong reliance on absolute pose labels.

Problem

• neural map matchers
• geo-referenced images
• pose labels
• noise
• inaccurate

Innovation

• AutoCompass
• Weak Labels
• Neural Map Matchers
• Tolerance Region
• Relative Poses