Integrating SLAM-DUNK and variable rate particle observers for fast multi-hypothesis SLAM

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Bhattacharjee, Sanchit.

Advisor dc:contributor.advisor

• Jean-Jacques Slotine.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124234

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124234