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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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 |
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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 |
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