Workload Partitioning of a Bio-inspired Simultaneous Localization and Mapping Algorithm on an Embedded Architecture

Many algorithms were developed to perform visual localization and mapping (SLAM) for robotic applications. These algorithms used monocular or stereovision systems to solve constraints related to the navigation in unknown or dynamic environment. The requirement of SLAM systems in terms of processing...

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Vydané v:International journal of advanced computer science & applications Ročník 12; číslo 5
Hlavní autori: Mounir, Amraoui, Rachid, Latif, Ouardi, Abdelhafid El, Tajer, Abdelouahed
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: West Yorkshire Science and Information (SAI) Organization Limited 2021
The Science and Information Organization
Predmet:
Algorithms
Computer architecture
Embedded systems
Engineering Sciences
Image processing
Localization
Partitioning
Robotics
Simultaneous localization and mapping
Workload
Workloads
ISSN:2158-107X, 2156-5570
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Shrnutí:Many algorithms were developed to perform visual localization and mapping (SLAM) for robotic applications. These algorithms used monocular or stereovision systems to solve constraints related to the navigation in unknown or dynamic environment. The requirement of SLAM systems in terms of processing time and precision is a factor that limits their use in many embedded applications like UAVs or autonomous vehicles. Meanwhile, trends towards low-cost and low-power processing require massive parallelism on hardware architectures. The emergence of recent heterogeneous embedded architectures should help design embedded systems dedicated to Visual SLAM applications. It was demonstrated in a previous work that bio-inspired algorithms are competitive compared to classical methods based on image processing and environment perception. This paper is a study of a bio-inspired SLAM algorithm with the aim of making it suitable for an implementation on a heterogeneous architecture dedicated for embedded applications. An algorithm-architecture adequation approach is used to achieve a workload partitioning on CPU-GPU architecture and hence speeding up processing tasks.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2021.0120528