Large-scale monocular FastSLAM2.0 acceleration on an embedded heterogeneous architecture

Mohamed Abouzahir,Rachid Latif,Samir Bouaziz,Abdelouahed Tajer,Abdelhafid Elouardi

doi:10.1186/s13634-016-0386-3

Abstract

Simultaneous localization and mapping (SLAM) is widely used in many robotic applications and autonomous navigation. This paper presents a study of FastSLAM2.0 computational complexity based on a monocular vision system. The algorithm is intended to operate with many particles in a large-scale environment. FastSLAM2.0 was partitioned into functional blocks allowing a hardware software matching on a CPU-GPGPU-based SoC architecture. Performances in terms of processing time and localization accuracy were evaluated using a real indoor dataset. Results demonstrate that an optimized and efficient CPU-GPGPU partitioning allows performing accurate localization results and high-speed execution of a monocular FastSLAM2.0-based embedded system operating under real-time constraints.

Highlights

Simultaneous localization and mapping (SLAM) algorithms are computationally intensive
We report the mean of processing time tFBx and tFBx respectively, computed relatively for the MOTS and for a single occurrence (Section 3.4)
6 Conclusions This article proposed an efficient matching of monocular FastSLAM2.0 algorithm on a heterogeneous embedded architecture

Summary

Introduction

Simultaneous localization and mapping (SLAM) algorithms are computationally intensive. In the early days of their creation, have contained one kind of processors designed to run general computing tasks Performance improvement of such computers was relied to Moore’s law which predicts doubling transistor density every 18 months. In order to surpass these issues and to reach high performances, a new trend now is to include other processing elements in a single chip area These new systems gain performance not by adding only the same type of processing units but implementing dissimilar processors incorporating specialized capabilities dedicated for handling specific tasks. These systems are referred to as heterogeneous system architectures (HSAs).

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Conclusion