Efficient simultaneous localization and mapping algorithms using submap networks

Autonomous mapping of large-scale environments has been a critical challenge confronting researchers in mobile robotics. This thesis investigates two aspects of the large-scale simultaneous localization and mapping (SLAM) problem: (1) the behavior of the covariance matrix in the Kalman filter solution to the linear Gaussian SLAM problem, and (2) the development of new algorithms for efficient large-scale mapping. The key issue motivating study of the linear Gaussian SLAM problem is to understand the behavior of the uncertainty estimates with time. In this thesis, we provide an analysis of the asymptotic behavior of the full covariance SLAM solution. We present a novel generalized closed-form solution to the single degree-of-freedom SLAM problem (known as the MonoRob problem). We examine the cross correlation behavior for the case of observed and non-observed features, and show that a feature must be repeatedly reobserved for it to become fully correlated with other features. Additionally, we provide a new "tight" lower bound for the map uncertainty for a certain class of the MonoRob problem. The second part of the thesis develops new techniques for attacking the scaling problem in SLAM.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Ocean Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2004

Author dc:creator

• Kim, SungJoon, 1970-

Advisor dc:contributor.advisor

• John J. Leonard.

• Ocean Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/33448

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/33448