Using Negative Information in simultaneous localization and mapping
Name
765975067-MIT.pdf
Description
Full printable version
Size
5.27 MB
Format
Adobe PDF
Checksum (MD5)
42134f8442aa79dcd842d09de86f7d2c
Author(s)
McClelland, Hunter (Hunter Grant)
Advisor(s)
John J. Leonard.
Alternative Title
Using NI in simultaneous localization and mapping
Date Issued
2011
Publisher
Massachusetts Institute of Technology
Abstract
The problem of autonomous navigation is one of efficiently utilizing available information from sensors and intelligently processing that information to determine the state of the robot and its environment. This thesis explores a topic often ignored in the Simultaneous Localization And Mapping (SLAM) literature: the utility of including Negative Information as a means of aiding state-estimation decisions and successfully re-localizing the autonomous agent. The work is motivated by a low-cost underwater mine neutralization project, which requires that an Autonomous Underwater Vehicle (AUV) successfully localizes itself in a difficult SLAM environment. This thesis presents a new Negative And Positive Scoring (NAPS) algorithm for comparing multiple localization hypotheses and then uses a large number of simulations to quantify the effect of including the often ignored Negative Information (NI). The ultimate conclusion of this thesis, that careful inclusion of Negative Information increases the chances of successful localization across a wide variety of difficult SLAM situations, extends beyond the intended target reacquisition application and is generally applicable to robotic navigation problems.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 67-71).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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