Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment
Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment
复制标题
基于语义直方图的大规模环境下实时多机器人全局定位图匹配
DOI：
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发表时间：
2020
影响因子：
5.2
通讯作者：
Tin Lun Lam
中科院分区：
文献类型：
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作者：
Xiyue Guo;Junjie Hu;Junfeng Chen;Fuqin Deng;Tin Lun Lam
视觉多机器人同时定位与建图（MR-SLAM）的核心问题是如何高效、准确地进行多机器人全局定位（MR-GL）。困难是双重的。第一个是全局定位的困难，显著的视点差异。基于外观的定位方法往往会在大的视点变化下失败。最近，语义图已经被用来克服视点变化问题。然而，这些方法非常耗时，特别是在大规模环境中。这导致了第二个困难，即如何执行实时全局定位。在这封信中，我们提出了一个基于语义直方图的图匹配方法，是强大的视点变化，可以实现实时的全局定位。在此基础上，我们开发了一个系统，可以准确，有效地执行MR-GL的同质和异构的机器人。实验结果表明，我们的方法比基于随机游走的语义描述符快30倍左右。此外，它实现了95%的全球定位准确度，而最先进的方法的准确度为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 letter, 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%.