Practical Visual Localization for Autonomous Driving: Why Not Filter?
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A major focus of current research on place recognition is visual localization for autonomous driving. However, while many visual localization algorithms for autonomous driving have achieved impressive results, it seems not all previous works have been set in a realistic setting for the problem, namely using training and testing videos that were collected in a distributed manner from multiple vehicles, all traversing through a road network in an urban area under different environmental conditions (weather, lighting, etc.). More importantly, in this setting, we show that exploiting temporal continuity in the testing sequence significantly improves visual localization - qualitatively and quantitatively. Although intuitive, this idea has not been fully explored in recent works. Our main contribution is a novel particle filtering technique that works in conjunction with a visual localization method to achieve accurate city-scale localization that is robust against environmental variations. We provide convincing results on synthetic and real datasets.
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