WiFi signal strength-based robot indoor localization

2014 IEEE International Conference on Information and Automation (ICIA) Pub Date : 2014-07-28 DOI:10.1109/ICINFA.2014.6932662

Yuxiang Sun, Ming Liu, M. Meng

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引用次数: 74

Abstract

Due to the unavailable GPS signals in indoor environments, indoor localization has become an increasingly heated research topic in recent years. Researchers in robotics community have tried many approaches, but this is still an unsolved problem considering the balance between performance and cost. The widely deployed low-cost WiFi infrastructure provides a great opportunity for indoor localization. In this paper, we develop a system for WiFi signal strength-based indoor localization and implement two approaches. The first is improved KNN algorithm-based fingerprint matching method, and the other is the Gaussian Process Regression (GPR) with Bayes Filter approach. We conduct experiments to compare the improved KNN algorithm with the classical KNN algorithm and evaluate the localization performance of the GPR with Bayes Filter approach. The experiment results show that the improved KNN algorithm can bring enhancement for the fingerprint matching method compared with the classical KNN algorithm. In addition, the GPR with Bayes Filter approach can provide about 2m localization accuracy for our test environment.

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基于WiFi信号强度的机器人室内定位

由于室内环境中GPS信号不可用，室内定位成为近年来日益热门的研究课题。机器人学界的研究人员已经尝试了许多方法，但考虑到性能和成本之间的平衡，这仍然是一个未解决的问题。广泛部署的低成本WiFi基础设施为室内定位提供了巨大的机会。本文开发了一种基于WiFi信号强度的室内定位系统，并实现了两种定位方法。一种是基于改进KNN算法的指纹匹配方法，另一种是基于贝叶斯滤波的高斯过程回归(GPR)方法。通过实验将改进的KNN算法与经典的KNN算法进行了比较，并对采用贝叶斯滤波方法的探地雷达定位性能进行了评价。实验结果表明，与传统的KNN算法相比，改进的KNN算法对指纹匹配方法有增强作用。此外，采用贝叶斯滤波方法的探地雷达在我们的测试环境下可以提供约2m的定位精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2014 IEEE International Conference on Information and Automation (ICIA)

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