Embbedded System-on-Chip 3D Localization and Mapping—eSoC-SLAM
This paper discusses a novel embedded system-on-chip 3D localization and mapping (eSoC-LAM) implementation, that followed a co-design approach with the primary aim of being deployed in a small system on a programmable chip (SoPC), the Intel’s (a.k.a Altera) Cyclone V 5CSEMA5F31C6N, available in the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/12/1378 |
| _version_ | 1797530862135279616 |
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| author | Eduardo A. Gerlein Gabriel Díaz-Guevara Henry Carrillo Carlos Parra Enrique Gonzalez |
| author_facet | Eduardo A. Gerlein Gabriel Díaz-Guevara Henry Carrillo Carlos Parra Enrique Gonzalez |
| author_sort | Eduardo A. Gerlein |
| collection | DOAJ |
| description | This paper discusses a novel embedded system-on-chip 3D localization and mapping (eSoC-LAM) implementation, that followed a co-design approach with the primary aim of being deployed in a small system on a programmable chip (SoPC), the Intel’s (a.k.a Altera) Cyclone V 5CSEMA5F31C6N, available in the Terasic’s board DE1-SoC. This computer board incorporates an 800 MHz Dual-core ARM Cortex-A9 and a Cyclone V FPGA with 85k programmable logic elements and 4450 Kbits of embedded memory running at 50 MHz. We report experiments of the eSoC-LAM implementation using a Robosense’s 3D LiDAR RS-16 sensor in a Robotis’ TurtleBot2 differential robot, both controlled by a Terasic’s board DE1-SoC. This paper presents a comprehensive description of the designed architecture, design constraints, resource optimization, HPS-FPGA exchange of information, and co-design results. The eSoC-LAM implementation reached an average speed-up of 6.5× when compared with a version of the algorithm running in a the hard processor system of the Cyclone V device, and a performance of nearly 32 fps, while keeping high map accuracy. |
| first_indexed | 2024-03-10T10:35:04Z |
| format | Article |
| id | doaj.art-4c7ebc38f67e4e89bda0e97dcb9ed03e |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T10:35:04Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-4c7ebc38f67e4e89bda0e97dcb9ed03e2023-11-21T23:19:56ZengMDPI AGElectronics2079-92922021-06-011012137810.3390/electronics10121378Embbedded System-on-Chip 3D Localization and Mapping—eSoC-SLAMEduardo A. Gerlein0Gabriel Díaz-Guevara1Henry Carrillo2Carlos Parra3Enrique Gonzalez4Department of Electronics, Pontificia Universidad Javeriana, Bogotá 110231, ColombiaDepartment of Electronics, Pontificia Universidad Javeriana, Bogotá 110231, ColombiaGenius Sports, Medellin 050022, ColombiaDepartment of Electronics, Pontificia Universidad Javeriana, Bogotá 110231, ColombiaDepartment of Systems Engineering, Pontificia Universidad Javeriana, Bogotá 110231, ColombiaThis paper discusses a novel embedded system-on-chip 3D localization and mapping (eSoC-LAM) implementation, that followed a co-design approach with the primary aim of being deployed in a small system on a programmable chip (SoPC), the Intel’s (a.k.a Altera) Cyclone V 5CSEMA5F31C6N, available in the Terasic’s board DE1-SoC. This computer board incorporates an 800 MHz Dual-core ARM Cortex-A9 and a Cyclone V FPGA with 85k programmable logic elements and 4450 Kbits of embedded memory running at 50 MHz. We report experiments of the eSoC-LAM implementation using a Robosense’s 3D LiDAR RS-16 sensor in a Robotis’ TurtleBot2 differential robot, both controlled by a Terasic’s board DE1-SoC. This paper presents a comprehensive description of the designed architecture, design constraints, resource optimization, HPS-FPGA exchange of information, and co-design results. The eSoC-LAM implementation reached an average speed-up of 6.5× when compared with a version of the algorithm running in a the hard processor system of the Cyclone V device, and a performance of nearly 32 fps, while keeping high map accuracy.https://www.mdpi.com/2079-9292/10/12/1378system-on-chipSoCSoPCFPGASLAMrobot localization |
| spellingShingle | Eduardo A. Gerlein Gabriel Díaz-Guevara Henry Carrillo Carlos Parra Enrique Gonzalez Embbedded System-on-Chip 3D Localization and Mapping—eSoC-SLAM Electronics system-on-chip SoC SoPC FPGA SLAM robot localization |
| title | Embbedded System-on-Chip 3D Localization and Mapping—eSoC-SLAM |
| title_full | Embbedded System-on-Chip 3D Localization and Mapping—eSoC-SLAM |
| title_fullStr | Embbedded System-on-Chip 3D Localization and Mapping—eSoC-SLAM |
| title_full_unstemmed | Embbedded System-on-Chip 3D Localization and Mapping—eSoC-SLAM |
| title_short | Embbedded System-on-Chip 3D Localization and Mapping—eSoC-SLAM |
| title_sort | embbedded system on chip 3d localization and mapping esoc slam |
| topic | system-on-chip SoC SoPC FPGA SLAM robot localization |
| url | https://www.mdpi.com/2079-9292/10/12/1378 |
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