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Implementation of SLAM Algorithms in a Small-Scale Vehicle Using Model-Based Development
Linköping University, Department of Electrical Engineering, Computer Engineering.
Linköping University, Department of Electrical Engineering, Computer Engineering.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Implementation av SLAM-algoritmer i småskaligt fordon genom modellbaserad utveckling (Swedish)
Abstract [en]

As autonomous driving is rapidly becoming the next major challenge in the auto- motive industry, the problem of Simultaneous Localization And Mapping (SLAM) has never been more relevant than it is today. This thesis presents the idea of examining SLAM algorithms by implementing such an algorithm on a radio con- trolled car which has been fitted with sensors and microcontrollers. The software architecture of this small-scale vehicle is based on the Robot Operating System (ROS), an open-source framework designed to be used in robotic applications.

This thesis covers Extended Kalman Filter (EKF)-based SLAM, FastSLAM, and GraphSLAM, examining these algorithms in both theoretical investigations, simulations, and real-world experiments. The method used in this thesis is model- based development, meaning that a model of the vehicle is first implemented in order to be able to perform simulations using each algorithm. A decision of which algorithm to be implemented on the physical vehicle is then made backed up by these simulation results, as well as a theoretical investigation of each algorithm.

This thesis has resulted in a dynamic model of a small-scale vehicle which can be used for simulation of any ROS-compliant SLAM-algorithm, and this model has been simulated extensively in order to provide empirical evidence to define which SLAM algorithm is most suitable for this application. Out of the algo- rithms examined, FastSLAM was proven to the best candidate, and was in the final stage, through usage of the ROS package gMapping, successfully imple- mented on the small-scale vehicle.

Place, publisher, year, edition, pages
2017. , p. 93
Keywords [en]
SLAM, Robotics, Particle Filter, Bayes, Kalman Filter. Navigation, Localization, Mapping, ROS, Model-based development
Keywords [sv]
SLAM, Robotik, Partikelfilter, Bayes, Kalmanfilter, Navigering, Lokalisering, Kartläggning, ROS, Modellbaserad utveckling
National Category
Robotics
Identifiers
URN: urn:nbn:se:liu:diva-148612ISRN: LiTH-ISY-EX--17/5101--SEOAI: oai:DiVA.org:liu-148612DiVA, id: diva2:1218791
External cooperation
ÅF Consult AB
Subject / course
Computer Engineering
Presentation
2017-11-17, Linköping, 22:14 (Swedish)
Supervisors
Examiners
Available from: 2018-06-20 Created: 2018-06-14 Last updated: 2018-06-20Bibliographically approved

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CiteExportLink to record
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