Indoor robot localization combining feature clustering with wireless sensor network

Xiaoming Dong,Benyue Su,Rong Jiang

doi:10.1186/s13638-018-1179-1

Abstract

Indoor robot localization is an indispensable ingredient for robots to perform autonomous services because GPS (Global Position System) information is not available. Natural features are usually used to implement this task, but it is difficult to solve the problem of localization robustness. A solution is proposed combining feature clustering and wireless sensor network to improve the effectiveness of robot localization: firstly, the SIFT (scalable invariable feature transform) features are extracted with feature clustering algorithm to estimate the robot position; secondly, the wireless sensor network is constructed to localize the robot from another independent way; finally, EKF (extended Kalman filter) is utilized to fuse the two kinds of localization results. The experiments demonstrate that this proposed method is effective and robust.

Highlights

GPS is commonly used to implement robot localization, but GPS cannot be used in some conditions such as indoor environments [1]
Wireless sensors are used in mobile robot navigation as reference nodes; the methods can be divided into two categories [6,7,8]: One is to implement robot localization utilizing wireless sensor networks on their own sensor nodes [9], and the other is to carry out
The object of this paper is to develop a method integrating distinguished features of indoor environment and wireless sensor network to improve the effect of robot localization

Summary

Introduction

GPS is commonly used to implement robot localization, but GPS cannot be used in some conditions such as indoor environments [1]. A common way to implement indoor localization is to estimate robots’ pose utilizing inertial sensors; this method is difficult to resolve the problem of robot’s wheel slor robot localization combining feature clusteipping, so the accumulated errors impact the estimating accuracy greatly [2]. The main problems existing in visual localization research are it is difficult to implement feature extraction and landmark recognition quickly and accurately through robot vision systems in complex environments [17, 18]; the amount of image features required for landmark recognition is too large, especially in a complex and large-scale environment [19, 20]. The object of this paper is to develop a method integrating distinguished features of indoor environment and wireless sensor network to improve the effect of robot localization.

Methods

Observation model

Conclusions