Predicting the performance of ORB-SLAM3 on embedded platforms
Simultaneous Localization and Mapping (SLAM) is a crucial component to the push towards full autonomy of robotic systems, yet it is computationally expensive and can rarely achieve real-time execution speeds on embedded platforms. Therefore, a need exists to evaluate the performance of SLAM algorit...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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South African Institute of Computer Scientists and Information Technologists
2024-12-01
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| Series: | South African Computer Journal |
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| Online Access: | https://sacj.org.za/article/view/20099 |
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| _version_ | 1850127174996066304 |
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| author | Jacques Matthee Kenneth Uren George van Schoor Corne van Daalen |
| author_facet | Jacques Matthee Kenneth Uren George van Schoor Corne van Daalen |
| author_sort | Jacques Matthee |
| collection | DOAJ |
| description | Simultaneous Localization and Mapping (SLAM) is a crucial component to the push towards full autonomy of robotic systems, yet it is computationally expensive and can rarely achieve real-time execution speeds on embedded platforms. Therefore, a need exists to evaluate the performance of SLAM algorithms in practical embedded environments – this paper addresses this need by creating prediction models to estimate the performance that ORB-SLAM3 can achieve on embedded platforms. The paper uses three embedded platforms: Nvidia Jetson TX2, Raspberry Pi 3B+ and the Raspberry Pi 4B, to generate a dataset that is used in training and testing performance prediction models. The process of profiling ORB-SLAM3 aids in the selection of inputs to the prediction model as well as benchmarking the embedded platforms’ performances by using PassMark. The EuRoC micro aerial vehicle (MAV) dataset is used to generate the average tracking time that the embedded platforms can achieve when executing ORB-SLAM3, which is the target of the prediction model. The best-performing model has the following results 2.84%, 3.93%, and 0.95 for MAE, RMSE and R2 score respectively. The results show the feasibility of predicting the performance that SLAM applications can achieve on embedded platforms. |
| format | Article |
| id | doaj-art-00ed506f8f294a99ad769ffa36eeb35e |
| institution | OA Journals |
| issn | 1015-7999 2313-7835 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | South African Institute of Computer Scientists and Information Technologists |
| record_format | Article |
| series | South African Computer Journal |
| spelling | doaj-art-00ed506f8f294a99ad769ffa36eeb35e2025-08-20T02:33:44ZengSouth African Institute of Computer Scientists and Information TechnologistsSouth African Computer Journal1015-79992313-78352024-12-0136210.18489/sacj.v36i2/2009924517Predicting the performance of ORB-SLAM3 on embedded platformsJacques Matthee0https://orcid.org/0000-0002-2139-0648Kenneth Uren1https://orcid.org/0000-0002-0561-0735George van Schoor2https://orcid.org/0000-0001-5702-1812Corne van Daalen3https://orcid.org/0000-0002-9849-586XNorth-West UniversityNorth-West UniversityNorth-West UniversityStellenbosch UniversitySimultaneous Localization and Mapping (SLAM) is a crucial component to the push towards full autonomy of robotic systems, yet it is computationally expensive and can rarely achieve real-time execution speeds on embedded platforms. Therefore, a need exists to evaluate the performance of SLAM algorithms in practical embedded environments – this paper addresses this need by creating prediction models to estimate the performance that ORB-SLAM3 can achieve on embedded platforms. The paper uses three embedded platforms: Nvidia Jetson TX2, Raspberry Pi 3B+ and the Raspberry Pi 4B, to generate a dataset that is used in training and testing performance prediction models. The process of profiling ORB-SLAM3 aids in the selection of inputs to the prediction model as well as benchmarking the embedded platforms’ performances by using PassMark. The EuRoC micro aerial vehicle (MAV) dataset is used to generate the average tracking time that the embedded platforms can achieve when executing ORB-SLAM3, which is the target of the prediction model. The best-performing model has the following results 2.84%, 3.93%, and 0.95 for MAE, RMSE and R2 score respectively. The results show the feasibility of predicting the performance that SLAM applications can achieve on embedded platforms.https://sacj.org.za/article/view/20099monocular-inertial slamorb-slam3embedded platformnvidia jetson tx2raspberry pi |
| spellingShingle | Jacques Matthee Kenneth Uren George van Schoor Corne van Daalen Predicting the performance of ORB-SLAM3 on embedded platforms South African Computer Journal monocular-inertial slam orb-slam3 embedded platform nvidia jetson tx2 raspberry pi |
| title | Predicting the performance of ORB-SLAM3 on embedded platforms |
| title_full | Predicting the performance of ORB-SLAM3 on embedded platforms |
| title_fullStr | Predicting the performance of ORB-SLAM3 on embedded platforms |
| title_full_unstemmed | Predicting the performance of ORB-SLAM3 on embedded platforms |
| title_short | Predicting the performance of ORB-SLAM3 on embedded platforms |
| title_sort | predicting the performance of orb slam3 on embedded platforms |
| topic | monocular-inertial slam orb-slam3 embedded platform nvidia jetson tx2 raspberry pi |
| url | https://sacj.org.za/article/view/20099 |
| work_keys_str_mv | AT jacquesmatthee predictingtheperformanceoforbslam3onembeddedplatforms AT kennethuren predictingtheperformanceoforbslam3onembeddedplatforms AT georgevanschoor predictingtheperformanceoforbslam3onembeddedplatforms AT cornevandaalen predictingtheperformanceoforbslam3onembeddedplatforms |