An EKF-based multiple data fusion for mobile robot indoor localization

Guangbing Zhou, Jing Luo*, Shugong Xu, Shunqing Zhang, Shige Meng, Kui Xiang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

Purpose: Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments. Design/methodology/approach: Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments. Findings: The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation. Originality/value: Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

Original languageEnglish
Pages (from-to)274-282
Number of pages9
JournalAssembly Automation
Volume41
Issue number3
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • EKF-based multiple sensors fusion
  • Indoor localization
  • Mobile robot
  • SLAM

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