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    BreezySLAM: A Simple, efficient, cross-platform Python package for Simultaneous Localization and Mapping (thesis)

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    Honors Thesis (1.014Mb)
    Date
    2014
    Author
    Bajracharya, Suraj
    Subject
    Washington and Lee University -- Honors in Computer Science
    Computer algorithms
    Application program interfaces (Computer software)
    Robotics
    Digital mapping -- Computer programs
    Robots -- Control systems
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    Description
    Thesis; [FULL-TEXT FREELY AVAILABLE ONLINE]
     
    Suraj Bajracharya is a member of the Class of 2014 of Washington and Lee University.
     
    BreezySLAM is a simple, efficient, multiplatform, open--‐source Python library for Simultaneous Localization and Mapping. By using Python C extensions to wrap existing implementations of existing SLAM algorithms, BreezySLAM provides a Python API for SLAM that runs nearly as fast as the original C code. By making a SLAM API available in Python, students and other interested users will be able to get their hands on SLAM very quickly and efficiently. BreezySLAM has been tested with a number of robots in V--‐REP Simulator, as well as on a Neato XV--‐11, with promising results.
     
    Suraj Bajracharya
     
    URI
    http://hdl.handle.net/11021/27338
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    • W&L Dept. of Computer Science

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