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, on a realistic urban dataset recorded with a simulator, and on
real-world StreetView 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 % on
global localizations in the multi-view case, while the benchmarked baseline
appearance-based methods reach up to 75 %.

本研究提出一种基于语义图描述符匹配的多视野语义全局化系统 X-View，实现了在不同视角条件下的全局本体识别，可应用于地面至空中、以及地面至地面搬运机器人数据，准确率达 85%。