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FPGA Implementation of Low Power SLAM Accelerated Core

by Mohammad Nazma Sultana, S. Ravi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 27
Year of Publication: 2025
Authors: Mohammad Nazma Sultana, S. Ravi
10.5120/ijca2025925467

Mohammad Nazma Sultana, S. Ravi . FPGA Implementation of Low Power SLAM Accelerated Core. International Journal of Computer Applications. 187, 27 ( Aug 2025), 25-30. DOI=10.5120/ijca2025925467

@article{ 10.5120/ijca2025925467,
author = { Mohammad Nazma Sultana, S. Ravi },
title = { FPGA Implementation of Low Power SLAM Accelerated Core },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2025 },
volume = { 187 },
number = { 27 },
month = { Aug },
year = { 2025 },
issn = { 0975-8887 },
pages = { 25-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number27/fpga-implementation-of-low-power-slam-accelerated-core/ },
doi = { 10.5120/ijca2025925467 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-08-02T01:56:29.614270+05:30
%A Mohammad Nazma Sultana
%A S. Ravi
%T FPGA Implementation of Low Power SLAM Accelerated Core
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 27
%P 25-30
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Simultaneous Localization and Mapping (SLAM) is critical for autonomous systems because it enables real-time environmental mapping and navigation. Implementing SLAM algorithms in hardware, particularly on low-resource platforms, poses challenges owing to the computational complexity of operations such as matrix multiplications and quaternion transformations. This study introduces a novel accelerated core for SLAM algorithms that is optimized for hardware resource efficiency and high computational performance. By leveraging dedicated instruction set and memory reuse strategies, this core supports various SLAM approaches. The experimental results demonstrate the coprocessor's high precision, low resource consumption, and adaptability to multiple SLAM algorithms.

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Index Terms

Computer Science
Information Sciences

Keywords

Simultaneous Localization and Mapping (SLAM) Quaternion Matrix multiplications Rotation matrix

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