Abstract
Simultaneous Localization And Map** (SLAM) algorithm allows the robot to map the environment while locating it in the space. SLAM algorithm is the more efficient and more accredited system by autonomous vehicle navigation and robotic application in current research. However, it has not yet adopted a complete end-to-end hardware implementation. Our work is aimed at hardware/software optimization of a time-consuming and expensive functional block of a SLAM-based autonomous navigation application, precisely monocular ORB-SLAM. Thus, the proposed optimization is achieved through the implementation of a heterogeneous FPGA-based embedded architecture. A comparative study is then conducted with parallel architectures such as automotive embedded architecture TX1 and a high-end machine. This work shows that heterogeneous FPGA-based embedded architectures are appealing embedded platforms for the heavy computational algorithm, achieving a 16% improvement of the original, whereas high-end machines achieve 45% improvement.
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Mamri, A., Abouzahir, M., Ramzi, M., Sbihi, M. (2021). High-Level Synthesis Implementation of Monocular SLAM on Low-Cost Parallel Platforms. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_37
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