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

Article scientifique 2016 Anglais

Résumé

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.

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Abouzahir, M., Elouardi, A., Bouaziz, S., Latif, R., Tajer, A. (2016). Large-scale monocular FastSLAM2.0 acceleration on an embedded heterogeneous architecture. https://doi.org/10.1186/s13634-016-0386-3

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Auteur(s)

Mohamed Abouzahir Abdelhafid Elouardi Samir Bouaziz Rachid Latif Abdelouahed Tajer

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