Visual analytics of 3D LiDAR point clouds in robotics operating systems
This paper presents visual analytics of 3D LiDAR point clouds in robotics operating system. In this study, experiment on Simultaneous Localization and Mapping (SLAM) using point cloud data derived from the Light Detection and Ranging (LiDAR) technology is conducted. We argue that one of the weakness...
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Institute of Advanced Engineering and Science
2020
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2-s2.0-85083981498 Azri A.M.; Abdul-Rahman S.; Hamzah R.; Aziz Z.A.; Bakar N.A. Visual analytics of 3D LiDAR point clouds in robotics operating systems 2020 Bulletin of Electrical Engineering and Informatics 9 2 10.11591/eei.v9i2.2061 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083981498&doi=10.11591%2feei.v9i2.2061&partnerID=40&md5=3ea89812f8a96206c2b04e024b34ebe7 This paper presents visual analytics of 3D LiDAR point clouds in robotics operating system. In this study, experiment on Simultaneous Localization and Mapping (SLAM) using point cloud data derived from the Light Detection and Ranging (LiDAR) technology is conducted. We argue that one of the weaknesses of the SLAM algorithm is in the localization process of the landmarks. Existing algorithms such as Grid Mapping and Monte Carlo have limitations in dealing with 3D environment data that have led to less accurate estimation. Therefore, this research proposes the SLAM algorithm based on Real-Time Appearance-Based (RTAB) and makes use of the Red Green Blue (RGB) camera for visualisation. The algorithm was tested by using the map data that was collected and simulated on the Robot Operating System (ROS) in Linux environment. We present the results and demonstrates that the map produced by RTAB is better compared to its counterparts. In addition,the probability for the estimated location is improved which allows for better vehicle maneuverability. © 2020, Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 20893191 English Article All Open Access; Gold Open Access |
author |
Azri A.M.; Abdul-Rahman S.; Hamzah R.; Aziz Z.A.; Bakar N.A. |
spellingShingle |
Azri A.M.; Abdul-Rahman S.; Hamzah R.; Aziz Z.A.; Bakar N.A. Visual analytics of 3D LiDAR point clouds in robotics operating systems |
author_facet |
Azri A.M.; Abdul-Rahman S.; Hamzah R.; Aziz Z.A.; Bakar N.A. |
author_sort |
Azri A.M.; Abdul-Rahman S.; Hamzah R.; Aziz Z.A.; Bakar N.A. |
title |
Visual analytics of 3D LiDAR point clouds in robotics operating systems |
title_short |
Visual analytics of 3D LiDAR point clouds in robotics operating systems |
title_full |
Visual analytics of 3D LiDAR point clouds in robotics operating systems |
title_fullStr |
Visual analytics of 3D LiDAR point clouds in robotics operating systems |
title_full_unstemmed |
Visual analytics of 3D LiDAR point clouds in robotics operating systems |
title_sort |
Visual analytics of 3D LiDAR point clouds in robotics operating systems |
publishDate |
2020 |
container_title |
Bulletin of Electrical Engineering and Informatics |
container_volume |
9 |
container_issue |
2 |
doi_str_mv |
10.11591/eei.v9i2.2061 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083981498&doi=10.11591%2feei.v9i2.2061&partnerID=40&md5=3ea89812f8a96206c2b04e024b34ebe7 |
description |
This paper presents visual analytics of 3D LiDAR point clouds in robotics operating system. In this study, experiment on Simultaneous Localization and Mapping (SLAM) using point cloud data derived from the Light Detection and Ranging (LiDAR) technology is conducted. We argue that one of the weaknesses of the SLAM algorithm is in the localization process of the landmarks. Existing algorithms such as Grid Mapping and Monte Carlo have limitations in dealing with 3D environment data that have led to less accurate estimation. Therefore, this research proposes the SLAM algorithm based on Real-Time Appearance-Based (RTAB) and makes use of the Red Green Blue (RGB) camera for visualisation. The algorithm was tested by using the map data that was collected and simulated on the Robot Operating System (ROS) in Linux environment. We present the results and demonstrates that the map produced by RTAB is better compared to its counterparts. In addition,the probability for the estimated location is improved which allows for better vehicle maneuverability. © 2020, Institute of Advanced Engineering and Science. All rights reserved. |
publisher |
Institute of Advanced Engineering and Science |
issn |
20893191 |
language |
English |
format |
Article |
accesstype |
All Open Access; Gold Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1809677897866149888 |