Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Comparative Analysis of Integrated Filtering Method Using UWB Localization in Indoor Environments

Rahul Ranjan
ORCID logo ,
Donggyu Shin
,
Yoonsik Jung
,
Sanghyun Kim
,
Jong-Hwan Yun
ORCID logo ,
Chang-Hyun Kim
ORCID logo ,
Seung-jae Lee
* ,
Joongeup Kye
Version 1 : Received: 20 October 2023 / Approved: 20 October 2023 / Online: 23 October 2023 (09:43:47 CEST)

A peer-reviewed article of this Preprint also exists.

Ranjan, R.; Shin, D.; Jung, Y.; Kim, S.; Yun, J.-H.; Kim, C.-H.; Lee, S.; Kye, J. Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment. Sensors 2024, 24, 1052, doi:10.3390/s24041052. Ranjan, R.; Shin, D.; Jung, Y.; Kim, S.; Yun, J.-H.; Kim, C.-H.; Lee, S.; Kye, J. Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment. Sensors 2024, 24, 1052, doi:10.3390/s24041052.

Abstract

This research paper investigates ultra-wideband (UWB) localization systems by focusing on the use of average filter (AVG), Kalman filter (KF), and extended Kalman filter (EKF) algorithms, as well as a novel integrated filtering method that incorporates low-pass filter (LPF) into AVG, KF, and EKF. The study aims to improve localization loss in indoor environments using a TurtleBot robot equipped with a camera to observe ground truth positions. To evaluate the effectiveness of the proposed algorithms, a comprehensive comparison of the raw and filtered data with the camera-based ground truth observations is performed. Quantitative analyses of the results, including max, min, max-min, and mean error, are performed to evaluate the localization performance of the algorithms and the integrated filtering method. The results reveal that the integrated filtering method has performed better accuracy in comparison with existing methods.

Keywords

Ultra-Wideband (UWB); Average Filter (AVG); Kalman Filter (KF); Extended Kalman Filter (EKF); Robot Operating System (ROS); LiDAR; Robot Navigation

Subject

Engineering, Other

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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