Simultaneous Localization and Mapping
Date
2013
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Swarthmore College. Dept. of Engineering
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Thesis (B.A.)
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en_US
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Full copyright to this work is retained by the student author. It may only be used for non-commercial, research, and educational purposes. All other uses are restricted.
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Abstract
In mobile robot navigation, Simultaneous Localization and Mapping (SLAM) involves
building a map of an unknown environment and determining the robot's position
in the map at the same time. Several variations of the SLAM algorithm have
been proposed, but implementation of SLAM is notoriously difficult. We focus on the
FastSLAM algorithm that uses a Rao-Blackwellized particle filter to improve SLAM's
accuracy and runtime performance. Our project is twofold: we successfully simulate
the FastSLAM algorithm and we implement the algorithm on the Turtlebot platform.
Through our work in simulation, we were able to fine tune several intrinsic parameters
of the FastSLAM algorithm. Our implementation work is a proof of concept that
FastSLAM can be used to help the Turtlebots build a map of their surroundings.