Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data Association
Simultaneous Localization and Mapping (SLAM) algorithms are crucial for enabling agents to estimate their position in unknown environments. In autonomous navigation systems, these algorithms need to operate in real-time on devices with limited resources, emphasizing the importance of reducing comple...
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MDPI AG
2024-12-01
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| author | Jessica Giovagnola Manuel Pegalajar Cuéllar Diego Pedro Morales Santos |
| author_facet | Jessica Giovagnola Manuel Pegalajar Cuéllar Diego Pedro Morales Santos |
| author_sort | Jessica Giovagnola |
| collection | DOAJ |
| description | Simultaneous Localization and Mapping (SLAM) algorithms are crucial for enabling agents to estimate their position in unknown environments. In autonomous navigation systems, these algorithms need to operate in real-time on devices with limited resources, emphasizing the importance of reducing complexity and ensuring efficient performance. While SLAM solutions aim at ensuring accurate and timely localization and mapping, one of their main limitations is their computational complexity. In this scenario, particle filter-based approaches such as FastSLAM 2.0 can significantly benefit from parallel programming due to their modular construction. The parallelization process involves identifying the parameters affecting the computational complexity in order to distribute the computation among single multiprocessors as efficiently as possible. However, the computational complexity of methodologies such as FastSLAM 2.0 can depend on multiple parameters whose values may, in turn, depend on each specific use case scenario ( ingi.e., the context), leading to multiple possible parallelization designs. Furthermore, the features of the hardware architecture in use can significantly influence the performance in terms of latency. Therefore, the selection of the optimal parallelization modality still needs to be empirically determined. This may involve redesigning the parallel algorithm depending on the context and the hardware architecture. In this paper, we propose a CUDA-based adaptable design for FastSLAM 2.0 on GPU, in combination with an evaluation methodology that enables the assessment of the optimal parallelization modality based on the context and the hardware architecture without the need for the creation of separate designs. The proposed implementation includes the parallelization of all the functional blocks of the FastSLAM 2.0 pipeline. Additionally, we contribute a parallelized design of the data association step through the Joint Compatibility Branch and Bound (JCBB) method. Multiple resampling algorithms are also included to accommodate the needs of a wide variety of navigation scenarios. |
| format | Article |
| id | doaj-art-01b5e7c453064992a31b20ddcc921226 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
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| spelling | doaj-art-01b5e7c453064992a31b20ddcc9212262025-08-20T01:55:44ZengMDPI AGApplied Sciences2076-34172024-12-0114231146610.3390/app142311466Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data AssociationJessica Giovagnola0Manuel Pegalajar Cuéllar1Diego Pedro Morales Santos2Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, GermanyDepartment of Computer Science and Artificial Intelligence, University of Granada, Calle Periodista Daniel Saucedo Aranda s/n, 18071 Granada, SpainDepartment of Electronic and Computer Technology, University of Granada, Avenida de Fuente Nueva s/n, 18071 Granada, SpainSimultaneous Localization and Mapping (SLAM) algorithms are crucial for enabling agents to estimate their position in unknown environments. In autonomous navigation systems, these algorithms need to operate in real-time on devices with limited resources, emphasizing the importance of reducing complexity and ensuring efficient performance. While SLAM solutions aim at ensuring accurate and timely localization and mapping, one of their main limitations is their computational complexity. In this scenario, particle filter-based approaches such as FastSLAM 2.0 can significantly benefit from parallel programming due to their modular construction. The parallelization process involves identifying the parameters affecting the computational complexity in order to distribute the computation among single multiprocessors as efficiently as possible. However, the computational complexity of methodologies such as FastSLAM 2.0 can depend on multiple parameters whose values may, in turn, depend on each specific use case scenario ( ingi.e., the context), leading to multiple possible parallelization designs. Furthermore, the features of the hardware architecture in use can significantly influence the performance in terms of latency. Therefore, the selection of the optimal parallelization modality still needs to be empirically determined. This may involve redesigning the parallel algorithm depending on the context and the hardware architecture. In this paper, we propose a CUDA-based adaptable design for FastSLAM 2.0 on GPU, in combination with an evaluation methodology that enables the assessment of the optimal parallelization modality based on the context and the hardware architecture without the need for the creation of separate designs. The proposed implementation includes the parallelization of all the functional blocks of the FastSLAM 2.0 pipeline. Additionally, we contribute a parallelized design of the data association step through the Joint Compatibility Branch and Bound (JCBB) method. Multiple resampling algorithms are also included to accommodate the needs of a wide variety of navigation scenarios.https://www.mdpi.com/2076-3417/14/23/11466FastSLAM2.0CUDAGPGPUJCBB |
| spellingShingle | Jessica Giovagnola Manuel Pegalajar Cuéllar Diego Pedro Morales Santos Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data Association Applied Sciences FastSLAM2.0 CUDA GPGPU JCBB |
| title | Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data Association |
| title_full | Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data Association |
| title_fullStr | Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data Association |
| title_full_unstemmed | Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data Association |
| title_short | Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data Association |
| title_sort | context adaptable deployment of fastslam 2 0 on graphic processing unit with unknown data association |
| topic | FastSLAM2.0 CUDA GPGPU JCBB |
| url | https://www.mdpi.com/2076-3417/14/23/11466 |
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