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Author
Title Particle Filter based SLAM to map random environments using âiRobot Roombaâ
URL
Publication Date
Degree MS
Discipline/Department Computer Science
Degree Level masters
University/Publisher Vanderbilt University
Abstract For any mobile robot application it is important that a robot knows its location in an operating environment. The map for the operating environment may not be available every time, so the robot needs to build a map as it explores its surroundings. As a result, robot must simultaneously localize and map the operating environment. This is "Simultaneous Localization And Mapping" (SLAM) problem. SLAM finds its applications in various real life situations where automated vehicles need to map the environment during disaster relief, underwater navigation, airborne systems, minimally invasive surgery, visual tracking, etc. Statistical techniques like Kalman filters or Particle filters provide a robust framework to map an environment. Based on particle filtering, this work presents a working prototype and analysis for a SLAM implementation using an iRobot Roomba and simulations of it using MATLAB and Blender.
Subjects/Keywords Particle Filter; Monte Carlo; Mapping; SLAM; Localization
Contributors Dr. Alan Peters (committee_member); Dr. Gabor Karsai (chair)
Language en
Rights unrestricted
Country of Publication us
Format application/pdf
Record ID oai:VANDERBILTETD:etd-12062011-193408
Repository vandy
Date Retrieved
Date Indexed 2019-06-05
Grantor VANDERBILT

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