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LiDAR-Stabilised GNSS-IMU Platform Pose Tracking
The requirement to estimate the six degree-of-freedom pose of a moving platform frequently arises in automation applications. It is common to estimate platform pose by the fusion of global navigation satellite systems (GNSS) measurements and translational acceleration and rotational rate measurement...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949951/ https://www.ncbi.nlm.nih.gov/pubmed/35336417 http://dx.doi.org/10.3390/s22062248 |
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author | D’Adamo, Timothy Phillips, Tyson McAree, Peter |
author_facet | D’Adamo, Timothy Phillips, Tyson McAree, Peter |
author_sort | D’Adamo, Timothy |
collection | PubMed |
description | The requirement to estimate the six degree-of-freedom pose of a moving platform frequently arises in automation applications. It is common to estimate platform pose by the fusion of global navigation satellite systems (GNSS) measurements and translational acceleration and rotational rate measurements from an inertial measurement unit (IMU). This paper considers a specific situation where two GNSS receivers and one IMU are used and gives the full formulation of a Kalman filter-based estimator to do this. A limitation in using this sensor set is the difficulty of obtaining accurate estimates of the degree of freedom corresponding to rotation about the line passing through the two GNSS receiver antenna centres. The GNSS-aided IMU formulation is extended to incorporate LiDAR measurements in both known and unknown environments to stabilise this degree of freedom. The performance of the pose estimator is established by comparing expected LiDAR range measurements with actual range measurements. Distributions of the terrain point-to-model error are shown to improve from [Formula: see text] [Formula: see text] mean error to [Formula: see text] [Formula: see text] when the GNSS-aided IMU estimator is augmented with LiDAR measurements. This precision is marginally degraded to [Formula: see text] [Formula: see text] when the pose estimator is operated in an a prior unknown environment. |
format | Online Article Text |
id | pubmed-8949951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89499512022-03-26 LiDAR-Stabilised GNSS-IMU Platform Pose Tracking D’Adamo, Timothy Phillips, Tyson McAree, Peter Sensors (Basel) Article The requirement to estimate the six degree-of-freedom pose of a moving platform frequently arises in automation applications. It is common to estimate platform pose by the fusion of global navigation satellite systems (GNSS) measurements and translational acceleration and rotational rate measurements from an inertial measurement unit (IMU). This paper considers a specific situation where two GNSS receivers and one IMU are used and gives the full formulation of a Kalman filter-based estimator to do this. A limitation in using this sensor set is the difficulty of obtaining accurate estimates of the degree of freedom corresponding to rotation about the line passing through the two GNSS receiver antenna centres. The GNSS-aided IMU formulation is extended to incorporate LiDAR measurements in both known and unknown environments to stabilise this degree of freedom. The performance of the pose estimator is established by comparing expected LiDAR range measurements with actual range measurements. Distributions of the terrain point-to-model error are shown to improve from [Formula: see text] [Formula: see text] mean error to [Formula: see text] [Formula: see text] when the GNSS-aided IMU estimator is augmented with LiDAR measurements. This precision is marginally degraded to [Formula: see text] [Formula: see text] when the pose estimator is operated in an a prior unknown environment. MDPI 2022-03-14 /pmc/articles/PMC8949951/ /pubmed/35336417 http://dx.doi.org/10.3390/s22062248 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article D’Adamo, Timothy Phillips, Tyson McAree, Peter LiDAR-Stabilised GNSS-IMU Platform Pose Tracking |
title | LiDAR-Stabilised GNSS-IMU Platform Pose Tracking |
title_full | LiDAR-Stabilised GNSS-IMU Platform Pose Tracking |
title_fullStr | LiDAR-Stabilised GNSS-IMU Platform Pose Tracking |
title_full_unstemmed | LiDAR-Stabilised GNSS-IMU Platform Pose Tracking |
title_short | LiDAR-Stabilised GNSS-IMU Platform Pose Tracking |
title_sort | lidar-stabilised gnss-imu platform pose tracking |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949951/ https://www.ncbi.nlm.nih.gov/pubmed/35336417 http://dx.doi.org/10.3390/s22062248 |
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