Cargando…

Landmark-Based Drift Compensation Algorithm for Inertial Pedestrian Navigation

The navigation of pedestrians based on inertial sensors, i.e., accelerometers and gyroscopes, has experienced a great growth over the last years. However, the noise of medium- and low-cost sensors causes a high error in the orientation estimation, particularly in the yaw angle. This error, called dr...

Descripción completa

Detalles Bibliográficos
Autores principales: Munoz Diaz, Estefania, Caamano, Maria, Fuentes Sánchez, Francisco Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539485/
https://www.ncbi.nlm.nih.gov/pubmed/28671622
http://dx.doi.org/10.3390/s17071555
_version_ 1783254489318293504
author Munoz Diaz, Estefania
Caamano, Maria
Fuentes Sánchez, Francisco Javier
author_facet Munoz Diaz, Estefania
Caamano, Maria
Fuentes Sánchez, Francisco Javier
author_sort Munoz Diaz, Estefania
collection PubMed
description The navigation of pedestrians based on inertial sensors, i.e., accelerometers and gyroscopes, has experienced a great growth over the last years. However, the noise of medium- and low-cost sensors causes a high error in the orientation estimation, particularly in the yaw angle. This error, called drift, is due to the bias of the z-axis gyroscope and other slow changing errors, such as temperature variations. We propose a seamless landmark-based drift compensation algorithm that only uses inertial measurements. The proposed algorithm adds a great value to the state of the art, because the vast majority of the drift elimination algorithms apply corrections to the estimated position, but not to the yaw angle estimation. Instead, the presented algorithm computes the drift value and uses it to prevent yaw errors and therefore position errors. In order to achieve this goal, a detector of landmarks, i.e., corners and stairs, and an association algorithm have been developed. The results of the experiments show that it is possible to reliably detect corners and stairs using only inertial measurements eliminating the need that the user takes any action, e.g., pressing a button. Associations between re-visited landmarks are successfully made taking into account the uncertainty of the position. After that, the drift is computed out of all associations and used during a post-processing stage to obtain a low-drifted yaw angle estimation, that leads to successfully drift compensated trajectories. The proposed algorithm has been tested with quasi-error-free turn rate measurements introducing known biases and with medium-cost gyroscopes in 3D indoor and outdoor scenarios.
format Online
Article
Text
id pubmed-5539485
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-55394852017-08-11 Landmark-Based Drift Compensation Algorithm for Inertial Pedestrian Navigation Munoz Diaz, Estefania Caamano, Maria Fuentes Sánchez, Francisco Javier Sensors (Basel) Article The navigation of pedestrians based on inertial sensors, i.e., accelerometers and gyroscopes, has experienced a great growth over the last years. However, the noise of medium- and low-cost sensors causes a high error in the orientation estimation, particularly in the yaw angle. This error, called drift, is due to the bias of the z-axis gyroscope and other slow changing errors, such as temperature variations. We propose a seamless landmark-based drift compensation algorithm that only uses inertial measurements. The proposed algorithm adds a great value to the state of the art, because the vast majority of the drift elimination algorithms apply corrections to the estimated position, but not to the yaw angle estimation. Instead, the presented algorithm computes the drift value and uses it to prevent yaw errors and therefore position errors. In order to achieve this goal, a detector of landmarks, i.e., corners and stairs, and an association algorithm have been developed. The results of the experiments show that it is possible to reliably detect corners and stairs using only inertial measurements eliminating the need that the user takes any action, e.g., pressing a button. Associations between re-visited landmarks are successfully made taking into account the uncertainty of the position. After that, the drift is computed out of all associations and used during a post-processing stage to obtain a low-drifted yaw angle estimation, that leads to successfully drift compensated trajectories. The proposed algorithm has been tested with quasi-error-free turn rate measurements introducing known biases and with medium-cost gyroscopes in 3D indoor and outdoor scenarios. MDPI 2017-07-03 /pmc/articles/PMC5539485/ /pubmed/28671622 http://dx.doi.org/10.3390/s17071555 Text en © 2017 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
Munoz Diaz, Estefania
Caamano, Maria
Fuentes Sánchez, Francisco Javier
Landmark-Based Drift Compensation Algorithm for Inertial Pedestrian Navigation
title Landmark-Based Drift Compensation Algorithm for Inertial Pedestrian Navigation
title_full Landmark-Based Drift Compensation Algorithm for Inertial Pedestrian Navigation
title_fullStr Landmark-Based Drift Compensation Algorithm for Inertial Pedestrian Navigation
title_full_unstemmed Landmark-Based Drift Compensation Algorithm for Inertial Pedestrian Navigation
title_short Landmark-Based Drift Compensation Algorithm for Inertial Pedestrian Navigation
title_sort landmark-based drift compensation algorithm for inertial pedestrian navigation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539485/
https://www.ncbi.nlm.nih.gov/pubmed/28671622
http://dx.doi.org/10.3390/s17071555
work_keys_str_mv AT munozdiazestefania landmarkbaseddriftcompensationalgorithmforinertialpedestriannavigation
AT caamanomaria landmarkbaseddriftcompensationalgorithmforinertialpedestriannavigation
AT fuentessanchezfranciscojavier landmarkbaseddriftcompensationalgorithmforinertialpedestriannavigation