论文标题
基于语义直方图的图形匹配，用于实时多机器人全局本地化的大规模环境
Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment
论文作者
论文摘要
视觉多机器人同时定位和映射（MR-SLAM）的核心问题是如何有效，准确地执行多机器人全球定位（MR-GL）。困难是两个方面。首先是全球本地化方面的难度差异。基于外观的本地化方法往往会在大型观点变化下失败。最近，使用语义图来克服观点变化问题。但是，这些方法非常耗时，尤其是在大规模环境中。这导致了第二个困难，这是执行实时全球本地化的方法。在本文中，我们提出了一种基于语义直方图的图形匹配方法，该方法对观点变化很强，可以实现实时全局本地化。基于此，我们开发了一个可以准确有效地对均质和异质机器人进行MR-GL的系统。实验结果表明，我们的方法比基于步行的语义描述符快30倍。此外，全球本地化的准确性为95％，而最新方法的准确性为85％。
The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL). The difficulties are two-fold. The first is the difficulty of global localization for significant viewpoint difference. Appearance-based localization methods tend to fail under large viewpoint changes. Recently, semantic graphs have been utilized to overcome the viewpoint variation problem. However, the methods are highly time-consuming, especially in large-scale environments. This leads to the second difficulty, which is how to perform real-time global localization. In this paper, we propose a semantic histogram-based graph matching method that is robust to viewpoint variation and can achieve real-time global localization. Based on that, we develop a system that can accurately and efficiently perform MR-GL for both homogeneous and heterogeneous robots. The experimental results show that our approach is about 30 times faster than Random Walk based semantic descriptors. Moreover, it achieves an accuracy of 95% for global localization, while the accuracy of the state-of-the-art method is 85%.
