OpenCL and OpenGL Implementation of Simultaneous Localization and Mapping Algorithm using High-End GPU
Simultaneous Localization And Mapping (SLAM) algorithms are being used in many robotic applications and autonomous navigation systems. The FastSLAM2.0 addresses an issue of the SLAM problem and allows a robot to navigate in an unknown environment. Several works have presented many algorithmic optimi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2022/06/itmconf_iceas2022_04001.pdf |
| _version_ | 1818252369827201024 |
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| author | Abouzahir Mohamed Latif Rachid Ramzi Mustapha Sbihi Mohammed |
| author_facet | Abouzahir Mohamed Latif Rachid Ramzi Mustapha Sbihi Mohammed |
| author_sort | Abouzahir Mohamed |
| collection | DOAJ |
| description | Simultaneous Localization And Mapping (SLAM) algorithms are being used in many robotic applications and autonomous navigation systems. The FastSLAM2.0 addresses an issue of the SLAM problem and allows a robot to navigate in an unknown environment. Several works have presented many algorithmic optimizations to reduce the computational complexity of such algorithm. In this paper, a GPGPU (general-purpose computing on graphics processing units) is exploited to achieve a parallel implementation of the FastSLAM2.0. The GPGPU acceleration is done using two different implementations for parallel programming. The first implementation used OpenGL shading language which is based on the characteristics of graphics hardwares. The second implementation used OpenCL which allows hardware acceleration across heterogeneous architectures. We also explored the impact of the two approaches on the the resulting GPGPU implementation. A comparison related to processing-time and localization accuracy is made using a real indoor dataset. Our results show a significant speedup of the GPGPU implementation over a Quad-Core CPU. We show also that, by adopting the same optimization methodology using the two approachs, the OpenCL implementation is faster and suitable for GPGPU accelerated SLAM algorithms. |
| first_indexed | 2024-12-12T16:23:05Z |
| format | Article |
| id | doaj.art-b2d0d2aa41ee4299ae3a56ac9d28e608 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-12-12T16:23:05Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-b2d0d2aa41ee4299ae3a56ac9d28e6082022-12-22T00:18:56ZengEDP SciencesITM Web of Conferences2271-20972022-01-01460400110.1051/itmconf/20224604001itmconf_iceas2022_04001OpenCL and OpenGL Implementation of Simultaneous Localization and Mapping Algorithm using High-End GPUAbouzahir Mohamed0Latif Rachid1Ramzi Mustapha2Sbihi Mohammed3Systems Analysis, Information Processing and Industrial Management Laboratory (LASTIMI), High School of Technology, Mohammed V University in RabatInformation Systems and Technology Engineering Laboratory (LISTI), National School of Applied Sciences of Agadir, Ibn Zohr University in AgadirSystems Analysis, Information Processing and Industrial Management Laboratory (LASTIMI), High School of Technology, Mohammed V University in RabatSystems Analysis, Information Processing and Industrial Management Laboratory (LASTIMI), High School of Technology, Mohammed V University in RabatSimultaneous Localization And Mapping (SLAM) algorithms are being used in many robotic applications and autonomous navigation systems. The FastSLAM2.0 addresses an issue of the SLAM problem and allows a robot to navigate in an unknown environment. Several works have presented many algorithmic optimizations to reduce the computational complexity of such algorithm. In this paper, a GPGPU (general-purpose computing on graphics processing units) is exploited to achieve a parallel implementation of the FastSLAM2.0. The GPGPU acceleration is done using two different implementations for parallel programming. The first implementation used OpenGL shading language which is based on the characteristics of graphics hardwares. The second implementation used OpenCL which allows hardware acceleration across heterogeneous architectures. We also explored the impact of the two approaches on the the resulting GPGPU implementation. A comparison related to processing-time and localization accuracy is made using a real indoor dataset. Our results show a significant speedup of the GPGPU implementation over a Quad-Core CPU. We show also that, by adopting the same optimization methodology using the two approachs, the OpenCL implementation is faster and suitable for GPGPU accelerated SLAM algorithms.https://www.itm-conferences.org/articles/itmconf/pdf/2022/06/itmconf_iceas2022_04001.pdf |
| spellingShingle | Abouzahir Mohamed Latif Rachid Ramzi Mustapha Sbihi Mohammed OpenCL and OpenGL Implementation of Simultaneous Localization and Mapping Algorithm using High-End GPU ITM Web of Conferences |
| title | OpenCL and OpenGL Implementation of Simultaneous Localization and Mapping Algorithm using High-End GPU |
| title_full | OpenCL and OpenGL Implementation of Simultaneous Localization and Mapping Algorithm using High-End GPU |
| title_fullStr | OpenCL and OpenGL Implementation of Simultaneous Localization and Mapping Algorithm using High-End GPU |
| title_full_unstemmed | OpenCL and OpenGL Implementation of Simultaneous Localization and Mapping Algorithm using High-End GPU |
| title_short | OpenCL and OpenGL Implementation of Simultaneous Localization and Mapping Algorithm using High-End GPU |
| title_sort | opencl and opengl implementation of simultaneous localization and mapping algorithm using high end gpu |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2022/06/itmconf_iceas2022_04001.pdf |
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