A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles

Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault d...

Descripción completa

Detalles Bibliográficos
Autores principales: Meng, Xiaoli, Wang, Heng, Liu, Bingbing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5621051/
https://www.ncbi.nlm.nih.gov/pubmed/28926996
http://dx.doi.org/10.3390/s17092140
_version_ 1783267675062927360
author Meng, Xiaoli
Wang, Heng
Liu, Bingbing
author_facet Meng, Xiaoli
Wang, Heng
Liu, Bingbing
author_sort Meng, Xiaoli
collection PubMed
description Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization.
format Online
Article
Text
id pubmed-5621051
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-56210512017-10-03 A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles Meng, Xiaoli Wang, Heng Liu, Bingbing Sensors (Basel) Article Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization. MDPI 2017-09-18 /pmc/articles/PMC5621051/ /pubmed/28926996 http://dx.doi.org/10.3390/s17092140 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
Meng, Xiaoli
Wang, Heng
Liu, Bingbing
A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles
title A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles
title_full A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles
title_fullStr A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles
title_full_unstemmed A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles
title_short A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles
title_sort robust vehicle localization approach based on gnss/imu/dmi/lidar sensor fusion for autonomous vehicles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5621051/
https://www.ncbi.nlm.nih.gov/pubmed/28926996
http://dx.doi.org/10.3390/s17092140
work_keys_str_mv AT mengxiaoli arobustvehiclelocalizationapproachbasedongnssimudmilidarsensorfusionforautonomousvehicles
AT wangheng arobustvehiclelocalizationapproachbasedongnssimudmilidarsensorfusionforautonomousvehicles
AT liubingbing arobustvehiclelocalizationapproachbasedongnssimudmilidarsensorfusionforautonomousvehicles
AT mengxiaoli robustvehiclelocalizationapproachbasedongnssimudmilidarsensorfusionforautonomousvehicles
AT wangheng robustvehiclelocalizationapproachbasedongnssimudmilidarsensorfusionforautonomousvehicles
AT liubingbing robustvehiclelocalizationapproachbasedongnssimudmilidarsensorfusionforautonomousvehicles

Ejemplares similares