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Jean-Jacques Slotine. Bhattacharjee, Sanchit. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2020-03-24T15:35:36Z 2020-03-24T15:35:36Z 2019 2019 https://hdl.handle.net/1721.1/124234 1144933730 This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 73-74). In this thesis, the problem of SLAM with some set of prior hypotheses about the map, called Multi-Hypothesis SLAM, was tackled using a combination of an existing landmark-based SLAM algorithm called SLAM-DUNK and a particle filter inspired approach. SLAM-DUNK is a recent Kalman Filter-based algorithm for landmark tracking that scales linearly with the number of landmarks and converges globally to true landmark coordinates, thus far outperforming standard Kalman filter methods such as EKF-SLAM. This was combined with the Hybrid Variable Rate Particle Observer, a particle-filter inspired approach used here for hypothesis discrimination and particularly suited to tracking applications. An algorithm based on combining these two methods was formulated and shown to solve the problem quite successfully, with good resistance to complicating factors such as noise and number of hypotheses. The limitations and peak performance of this algorithm were also investigated by considering the ability of the algorithm to discriminate between very similar hypotheses and the conditions under which the algorithm performed best. Finally, the ability to extend this algorithm to dierent types of hypotheses, including Dense Maps owing to SLAM-DUNK's versatility, was considered and discussed. by Sanchit Bhattacharjee. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 74 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Integrating SLAM-DUNK and variable rate particle observers for fast multi-hypothesis SLAM Thesis application/pdf 2020-03-24T15:35:35Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="9efac0cd-286a-4244-8fc2-2161c87e0334"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Integrating SLAM-DUNK and variable rate particle observers for fast multi-hypothesis SLAM</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Bhattacharjee, Sanchit.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>In this thesis, the problem of SLAM with some set of prior hypotheses about the map, called Multi-Hypothesis SLAM, was tackled using a combination of an existing landmark-based SLAM algorithm called SLAM-DUNK and a particle filter inspired approach. SLAM-DUNK is a recent Kalman Filter-based algorithm for landmark tracking that scales linearly with the number of landmarks and converges globally to true landmark coordinates, thus far outperforming standard Kalman filter methods such as EKF-SLAM. This was combined with the Hybrid Variable Rate Particle Observer, a particle-filter inspired approach used here for hypothesis discrimination and particularly suited to tracking applications. An algorithm based on combining these two methods was formulated and shown to solve the problem quite successfully, with good resistance to complicating factors such as noise and number of hypotheses. The limitations and peak performance of this algorithm were also investigated by considering the ability of the algorithm to discriminate between very similar hypotheses and the conditions under which the algorithm performed best. Finally, the ability to extend this algorithm to dierent types of hypotheses, including Dense Maps owing to SLAM-DUNK&apos;s versatility, was considered and discussed.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>