Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: D’Adamo, Timothy, Phillips, Tyson, McAree, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784675026250760192
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
work_keys_str_mv AT dadamotimothy lidarstabilisedgnssimuplatformposetracking
AT phillipstyson lidarstabilisedgnssimuplatformposetracking
AT mcareepeter lidarstabilisedgnssimuplatformposetracking