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Cartographer local SLAM optimization using multistage distance scan scheduler

This paper presents the utilization of Google’s simultaneous localization and mapping (SLAM) called Cartographer, and improvement of the existing processing speed using multistage distance scheduler. The presented approach optimizes the Local SLAM part in Cartographer to correct local pose based fro...

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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Dwijotomo, Abdurahman, Abdul Rahman, Mohd. Azizi, Mohammed Ariff, Mohd. Hatta, Zamzuri, Hairi
التنسيق: Conference or Workshop Item
منشور في: 2020
الموضوعات:
TA Engineering (General). Civil engineering (General)
الوصول للمادة أونلاين:http://eprints.utm.my/id/eprint/89792/
http://dx.doi.org/10.1007/978-981-15-4481-1_20
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id my.utm.89792
record_format eprints
spelling my.utm.897922021-03-04T02:45:49Z http://eprints.utm.my/id/eprint/89792/ Cartographer local SLAM optimization using multistage distance scan scheduler Dwijotomo, Abdurahman Abdul Rahman, Mohd. Azizi Mohammed Ariff, Mohd. Hatta Zamzuri, Hairi TA Engineering (General). Civil engineering (General) This paper presents the utilization of Google’s simultaneous localization and mapping (SLAM) called Cartographer, and improvement of the existing processing speed using multistage distance scheduler. The presented approach optimizes the Local SLAM part in Cartographer to correct local pose based from Ceres scan matcher by integrating scheduling software, which controls the distance of light detection and ranging (LiDAR) sensor and scan matcher’s search window size. In preceding work, the multistage distance scheduler was successfully tested in the actual vehicle to map the road in real-time. Multistage distance scheduler means that local pose correction is done by limiting the distance scan of LiDAR and search window with the help of scheduling algorithm. The scheduling algorithm manages the SLAM to swap between small scan size (25 m) and large scan size (60 m) LiDAR at a fixed time during map data collection; thus it can improve performance speed efficiently better than full-sized LiDAR while maintaining the accuracy of full distance LiDAR. By swapping the scan distance of sensor between small and long-range scan, and adaptively limit search size of scan matcher to handle difference scan size, it can improve pose generation performance time around 15% as opposed against fixed scan distance 60 m while maintaining similar pose accuracy and large map size. 2020-06 Conference or Workshop Item PeerReviewed Dwijotomo, Abdurahman and Abdul Rahman, Mohd. Azizi and Mohammed Ariff, Mohd. Hatta and Zamzuri, Hairi (2020) Cartographer local SLAM optimization using multistage distance scan scheduler. In: 6th International Conference and Exhibition on Sustainable Energy and Advanced Materials, 16 October 2019 through 17 October 2019, Surakarta, Indonesia. http://dx.doi.org/10.1007/978-981-15-4481-1_20
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Dwijotomo, Abdurahman
Abdul Rahman, Mohd. Azizi
Mohammed Ariff, Mohd. Hatta
Zamzuri, Hairi
Cartographer local SLAM optimization using multistage distance scan scheduler
description This paper presents the utilization of Google’s simultaneous localization and mapping (SLAM) called Cartographer, and improvement of the existing processing speed using multistage distance scheduler. The presented approach optimizes the Local SLAM part in Cartographer to correct local pose based from Ceres scan matcher by integrating scheduling software, which controls the distance of light detection and ranging (LiDAR) sensor and scan matcher’s search window size. In preceding work, the multistage distance scheduler was successfully tested in the actual vehicle to map the road in real-time. Multistage distance scheduler means that local pose correction is done by limiting the distance scan of LiDAR and search window with the help of scheduling algorithm. The scheduling algorithm manages the SLAM to swap between small scan size (25 m) and large scan size (60 m) LiDAR at a fixed time during map data collection; thus it can improve performance speed efficiently better than full-sized LiDAR while maintaining the accuracy of full distance LiDAR. By swapping the scan distance of sensor between small and long-range scan, and adaptively limit search size of scan matcher to handle difference scan size, it can improve pose generation performance time around 15% as opposed against fixed scan distance 60 m while maintaining similar pose accuracy and large map size.
format Conference or Workshop Item
author Dwijotomo, Abdurahman
Abdul Rahman, Mohd. Azizi
Mohammed Ariff, Mohd. Hatta
Zamzuri, Hairi
author_facet Dwijotomo, Abdurahman
Abdul Rahman, Mohd. Azizi
Mohammed Ariff, Mohd. Hatta
Zamzuri, Hairi
author_sort Dwijotomo, Abdurahman
title Cartographer local SLAM optimization using multistage distance scan scheduler
title_short Cartographer local SLAM optimization using multistage distance scan scheduler
title_full Cartographer local SLAM optimization using multistage distance scan scheduler
title_fullStr Cartographer local SLAM optimization using multistage distance scan scheduler
title_full_unstemmed Cartographer local SLAM optimization using multistage distance scan scheduler
title_sort cartographer local slam optimization using multistage distance scan scheduler
publishDate 2020
url http://eprints.utm.my/id/eprint/89792/
http://dx.doi.org/10.1007/978-981-15-4481-1_20
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score 13.251813

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