X-View: Graph-Based Semantic Multi-View Localization
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Global registration of multi-view robot data is a challenging task. Appearance-based global localization approaches often fail under drastic view-point changes, as representations have limited view-point invariance. This work is based on the idea that human-made environments contain rich semantics which can be used to disambiguate global localization. Here, we present X-View, a Multi-View Semantic Global Localization system. X-View leverages semantic graph descriptor matching for global localization, enabling localization under drastically different view-points. While the approach is general in terms of the semantic input data, we present and evaluate an implementation on visual data. We demonstrate the system in experiments on the publicly available SYNTHIA dataset, and a realistic urban dataset recorded with a simulator. On these data, our findings show that X-View is able to globally localize aerial-to-ground, and ground-to-ground robot data of drastically different view-points. Our approach achieves an accuracy of up to 85 localizations in the multi-view case, while the benchmarked traditional appearance based method reaches up to 50
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