Cargando…

A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm

In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we considered...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Yun-Ting, Peng, Chao-Chung, Ravankar, Ankit A., Ravankar, Abhijeet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5949039/
https://www.ncbi.nlm.nih.gov/pubmed/29690624
http://dx.doi.org/10.3390/s18041294
_version_ 1783322677135540224
author Wang, Yun-Ting
Peng, Chao-Chung
Ravankar, Ankit A.
Ravankar, Abhijeet
author_facet Wang, Yun-Ting
Peng, Chao-Chung
Ravankar, Ankit A.
Ravankar, Abhijeet
author_sort Wang, Yun-Ting
collection PubMed
description In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we considered a light detection and ranging (LiDAR) device as the only sensor for detecting surroundings and propose an efficient indoor localization algorithm. To attenuate the computation effort and preserve localization robustness, a weighted parallel iterative closed point (WP-ICP) with interpolation is presented. As compared to the traditional ICP, the point cloud is first processed to extract corners and line features before applying point registration. Later, points labeled as corners are only matched with the corner candidates. Similarly, points labeled as lines are only matched with the lines candidates. Moreover, their ICP confidence levels are also fused in the algorithm, which make the pose estimation less sensitive to environment uncertainties. The proposed WP-ICP architecture reduces the probability of mismatch and thereby reduces the ICP iterations. Finally, based on given well-constructed indoor layouts, experiment comparisons are carried out under both clean and perturbed environments. It is shown that the proposed method is effective in significantly reducing computation effort and is simultaneously able to preserve localization precision.
format Online
Article
Text
id pubmed-5949039
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-59490392018-05-17 A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm Wang, Yun-Ting Peng, Chao-Chung Ravankar, Ankit A. Ravankar, Abhijeet Sensors (Basel) Article In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we considered a light detection and ranging (LiDAR) device as the only sensor for detecting surroundings and propose an efficient indoor localization algorithm. To attenuate the computation effort and preserve localization robustness, a weighted parallel iterative closed point (WP-ICP) with interpolation is presented. As compared to the traditional ICP, the point cloud is first processed to extract corners and line features before applying point registration. Later, points labeled as corners are only matched with the corner candidates. Similarly, points labeled as lines are only matched with the lines candidates. Moreover, their ICP confidence levels are also fused in the algorithm, which make the pose estimation less sensitive to environment uncertainties. The proposed WP-ICP architecture reduces the probability of mismatch and thereby reduces the ICP iterations. Finally, based on given well-constructed indoor layouts, experiment comparisons are carried out under both clean and perturbed environments. It is shown that the proposed method is effective in significantly reducing computation effort and is simultaneously able to preserve localization precision. MDPI 2018-04-23 /pmc/articles/PMC5949039/ /pubmed/29690624 http://dx.doi.org/10.3390/s18041294 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Yun-Ting
Peng, Chao-Chung
Ravankar, Ankit A.
Ravankar, Abhijeet
A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm
title A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm
title_full A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm
title_fullStr A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm
title_full_unstemmed A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm
title_short A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm
title_sort single lidar-based feature fusion indoor localization algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5949039/
https://www.ncbi.nlm.nih.gov/pubmed/29690624
http://dx.doi.org/10.3390/s18041294
work_keys_str_mv AT wangyunting asinglelidarbasedfeaturefusionindoorlocalizationalgorithm
AT pengchaochung asinglelidarbasedfeaturefusionindoorlocalizationalgorithm
AT ravankarankita asinglelidarbasedfeaturefusionindoorlocalizationalgorithm
AT ravankarabhijeet asinglelidarbasedfeaturefusionindoorlocalizationalgorithm
AT wangyunting singlelidarbasedfeaturefusionindoorlocalizationalgorithm
AT pengchaochung singlelidarbasedfeaturefusionindoorlocalizationalgorithm
AT ravankarankita singlelidarbasedfeaturefusionindoorlocalizationalgorithm
AT ravankarabhijeet singlelidarbasedfeaturefusionindoorlocalizationalgorithm