ORB-SLAM accelerated on heterogeneous parallel architectures
SLAM algorithm permits the robot to cartography the desired environment while positioning it in space. It is a more efficient system and more accredited by autonomous vehicle navigation and robotic application in the ongoing research. Except it did not adopt any complete end-to-end hardware implemen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2021-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/05/e3sconf_iccsre2021_01055.pdf |
| Summary: | SLAM algorithm permits the robot to cartography the desired environment while positioning it in space. It is a more efficient system and more accredited by autonomous vehicle navigation and robotic application in the ongoing research. Except it did not adopt any complete end-to-end hardware implementation yet. Our work aims to a hardware/software optimization of an expensive computational time functional block of monocular ORB-SLAM2. Through this, we attempt to implement the proposed optimization in FPGA-based heterogeneous embedded architecture that shows attractive results. Toward this, we adopt a comparative study with other heterogeneous architecture including powerful embedded GPGPU (NVIDIA Tegra TX1) and high-end GPU (NVIDIA GeForce 920MX). The implementation is achieved using high-level synthesis-based OpenCL for FPGA and CUDA for NVIDIA targeted boards. |
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| ISSN: | 2267-1242 |