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3D LiDAR Based SLAM System Evaluation with Low-Cost Real-Time Kinematics GPS Solution

  • Positioning mobile systems with high accuracy is a prerequisite for intelligent autonomous behavior, both in industrial environments and in field robotics. This paper describes the setup of a robotic platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. A configuration using a mobile robot Husky A200, and a LiDAR (light detection and ranging) sensorPositioning mobile systems with high accuracy is a prerequisite for intelligent autonomous behavior, both in industrial environments and in field robotics. This paper describes the setup of a robotic platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. A configuration using a mobile robot Husky A200, and a LiDAR (light detection and ranging) sensor was used to implement the setup. For verification of the proposed setup, different scan matching methods for odometry determination in indoor and outdoor environments are tested. An assessment of the accuracy of the baseline 3D-SLAM system and the selected evaluation system is presented by comparing different scenarios and test situations. It was shown that the hdl_graph_slam in combination with the LiDAR OS1 and the scan matching algorithms FAST_GICP and FAST_VGICP achieves good mapping results with accuracies up to 2 cm.zeige mehrzeige weniger

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Metadaten
Dokumentart:Zeitschriftenartikel, wissenschaftlich
Review-Status:Begutachtet (reviewed)
Zitierlink: https://opus.hs-offenburg.de/6162
Bibliografische Angaben
Titel (Englisch):3D LiDAR Based SLAM System Evaluation with Low-Cost Real-Time Kinematics GPS Solution
Verfasserangaben:Stefan HenselStaff MemberORCiDGND, Marin B. MarinovORCiD, Markus Obert
Erscheinungsjahr:2022
Datum der Erstveröffentlichung:04.09.2022
Verlagsort:Basel
Verlag:MDPI
Erste Seite:1
Letzte Seite:19
Aufsatznummer:154
Titel des übergeordneten Werkes (Englisch):Computation
Herausgeber*in:Xiaoqiang Hua
Jahrgang (Band):10
Heft (Ausgabe):9
ISSN:2079-3197
DOI:https://doi.org/10.3390/computation10090154
URN:https://urn:nbn:de:bsz:ofb1-opus4-61629
Sprache:Englisch
Inhaltliche Informationen
Fakultäten / Einrichtungen:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Sammlungen der Hochschule Offenburg:Bibliografie
Freies Schlagwort / Tag:LOAM (LiDAR odometry and mapping); LiDAR; ROS; RTK; SLAM; machine learning; mapping; mobile robotics; navigation; stochastic approach
Formale Angaben
Relevanz für "Jahresbericht über Forschungsleistungen":5-fach | Wiss. Zeitschriftenartikel reviewed: AGQ-Positivlisten
Open-Access-Status: Open Access 
 Gold 
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International