Fail-aware LIDAR-based odometry for autonomous vehicles
Authors
García Daza, Iván; Rentero Alonso de Linaje, Mónica; Salinas Maldonado, Carlota; Izquierdo Gonzalo, Rubén; Hernández Parra, Noelia; [et al.]Identifiers
Permanent link (URI): http://hdl.handle.net/10017/59662DOI: 10.3390/s20154097
ISSN: 1424-8220
Publisher
MDPI
Date
2020-07-15Affiliation
Universidad de Alcalá. Deparamento de AutomáticaFunders
Universidad de Alcalá
Comunidad de Madrid
Agencia Estatal de Investigación
European Commission
Ministerio de Economía y Competitividad
Bibliographic citation
García Daza, I. [et al.], 2020, "Fail-aware LIDAR-based odometry for autonomous vehicles", Sensors, vol. 20, no. 15, art. no. 4097, pp. 1-30.
Keywords
LiDAR odometry
Fail-operational systems
Fail-aware
Automated driving
Project
info:eu-repo/grantAgreement/UAH//CCG2018%2FEXP-065
info:eu-repo/grantAgreement/CAM//S2018%2FEMT-4362/SEGURIDAD DE VEHÍCULOS PARA UNA MOVILIDAD INTELIGENTE, SOSTENIBLE, SEGURA E INTEGRADORA/SEGVAUTO 4.0-CM
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-90035-R/ES/INTERACCION PREDICTIVA ENTRE VEHICULOS AUTONOMOS COOPERATIVOS Y USUARIOS VULNERABLES DE CARRETERA ORIENTADA AL USUARIO FINAL/
info:eu-repo/grantAgreement/EC/H2020/723021/BRIDGING GAPS FOR THE ADOPTION OF AUTOMATED VEHICLES/BRAVE
info:eu-repo/grantAgreement/EC/H2020/737469//AutoDrive
info:eu-repo/grantAgreement/MINECO//TRA2017-90620-REDT/ES//RETEVI-II
info:eu-repo/grantAgreement/EC/H2020/754382/EU/GOT Energy Talent/GET
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.3390/s20154097Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2020 The authors
Access rights
info:eu-repo/semantics/openAccess
Abstract
Autonomous driving systems are set to become a reality in transport systems and, so,
maximum acceptance is being sought among users. Currently, the most advanced architectures
require driver intervention when functional system failures or critical sensor operations take place,
presenting problems related to driver state, distractions, fatigue, and other factors that prevent safe
control. Therefore, this work presents a redundant, accurate, robust, and scalable LiDAR odometry
system with fail-aware system features that can allow other systems to perform a safe stop manoeuvre
without driver mediation. All odometry systems have drift error, making it difficult to use them
for localisation tasks over extended periods. For this reason, the paper presents an accurate LiDAR
odometry system with a fail-aware indicator. This indicator estimates a time window in which the
system manages the localisation tasks appropriately. The odometry error is minimised by applying a
dynamic 6-DoF model and fusing measures based on the Iterative Closest Points (ICP), environment
feature extraction, and Singular Value Decomposition (SVD) methods. The obtained results are
promising for two reasons: First, in the KITTI odometry data set, the ranking achieved by the
proposed method is twelfth, considering only LiDAR-based methods, where its translation and
rotation errors are 1.00% and 0.0041 deg/m, respectively. Second, the encouraging results of the
fail-aware indicator demonstrate the safety of the proposed LiDAR odometry system. The results
depict that, in order to achieve an accurate odometry system, complex models and measurement
fusion techniques must be used to improve its behaviour. Furthermore, if an odometry system is
to be used for redundant localisation features, it must integrate a fail-aware indicator for use in a safe manner.
Files in this item
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Files | Size | Format |
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Fal_aware_Sensors_2020.pdf | 5.407Mb |
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