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Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments
In this paper, we introduce a real-time parallel-serial algorithm for autonomous robot positioning for GPS-denied, dark environments, such as caves and mine galleries. To achieve a good complexity-accuracy trade-off, we fuse data from light detection and ranging (LiDAR) and an inertial measurement u...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764368/ https://www.ncbi.nlm.nih.gov/pubmed/33322587 http://dx.doi.org/10.3390/s20247123 |
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author | Niedzwiedzki, Jakub Niewola, Adam Lipinski, Piotr Swaczyna, Piotr Bobinski, Aleksander Poryzala, Pawel Podsedkowski, Leszek |
author_facet | Niedzwiedzki, Jakub Niewola, Adam Lipinski, Piotr Swaczyna, Piotr Bobinski, Aleksander Poryzala, Pawel Podsedkowski, Leszek |
author_sort | Niedzwiedzki, Jakub |
collection | PubMed |
description | In this paper, we introduce a real-time parallel-serial algorithm for autonomous robot positioning for GPS-denied, dark environments, such as caves and mine galleries. To achieve a good complexity-accuracy trade-off, we fuse data from light detection and ranging (LiDAR) and an inertial measurement unit (IMU). The proposed algorithm’s main novelty is that, unlike in most algorithms, we apply an extended Kalman filter (EKF) to each LiDAR scan point and calculate the location relative to a triangular mesh. We also introduce three implementations of the algorithm: serial, parallel, and parallel-serial. The first implementation verifies the correctness of our innovative approach, but is too slow for real-time execution. The second approach implements a well-known parallel data fusion approach, but is still too slow for our application. The third and final implementation of the presented algorithm along with the state-of-the-art GPU data structures achieves real-time performance. According to our experimental findings, our algorithm outperforms the reference Gaussian mixture model (GMM) localization algorithm in terms of accuracy by a factor of two. |
format | Online Article Text |
id | pubmed-7764368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77643682020-12-27 Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments Niedzwiedzki, Jakub Niewola, Adam Lipinski, Piotr Swaczyna, Piotr Bobinski, Aleksander Poryzala, Pawel Podsedkowski, Leszek Sensors (Basel) Article In this paper, we introduce a real-time parallel-serial algorithm for autonomous robot positioning for GPS-denied, dark environments, such as caves and mine galleries. To achieve a good complexity-accuracy trade-off, we fuse data from light detection and ranging (LiDAR) and an inertial measurement unit (IMU). The proposed algorithm’s main novelty is that, unlike in most algorithms, we apply an extended Kalman filter (EKF) to each LiDAR scan point and calculate the location relative to a triangular mesh. We also introduce three implementations of the algorithm: serial, parallel, and parallel-serial. The first implementation verifies the correctness of our innovative approach, but is too slow for real-time execution. The second approach implements a well-known parallel data fusion approach, but is still too slow for our application. The third and final implementation of the presented algorithm along with the state-of-the-art GPU data structures achieves real-time performance. According to our experimental findings, our algorithm outperforms the reference Gaussian mixture model (GMM) localization algorithm in terms of accuracy by a factor of two. MDPI 2020-12-11 /pmc/articles/PMC7764368/ /pubmed/33322587 http://dx.doi.org/10.3390/s20247123 Text en © 2020 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 Niedzwiedzki, Jakub Niewola, Adam Lipinski, Piotr Swaczyna, Piotr Bobinski, Aleksander Poryzala, Pawel Podsedkowski, Leszek Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments |
title | Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments |
title_full | Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments |
title_fullStr | Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments |
title_full_unstemmed | Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments |
title_short | Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments |
title_sort | real-time parallel-serial lidar-based localization algorithm with centimeter accuracy for gps-denied environments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764368/ https://www.ncbi.nlm.nih.gov/pubmed/33322587 http://dx.doi.org/10.3390/s20247123 |
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