Robust non-Gaussian semantic simultaneous localization and mapping

The recent success of object detection systems motivates object-based representations for robot navigation; i.e. semantic simultaneous localization and mapping (SLAM), in which we aim to jointly estimate the pose of the robot over time as well as the location and semantic class observed objects. A solution to the semantic SLAM problem necessarily addresses the continuous inference problems where am I? and where are the objects?, but also the discrete inference problem what are the objects?. We consider the problem of semantic SLAM under non-Gaussian uncertainty. The most prominent case in which this arises is from data association uncertainty, where we do not know with certainty what objects in the environment caused the measurement made by our sensor. The semantic class of an object can help to inform data association; a detection classified as a door is unlikely to be associated to a chair object.

Name thesis:degree_name

Master

Department dc:contributor.department

Joint Program in Applied Ocean Science and Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Doherty, Kevin J.(Automated vehicles software expert)(Kevin Joseph)Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• John J. Leonard.

• Joint Program in Applied Ocean Science and Engineering.
• Aeronautics and Astronautics.
• Woods Hole Oceanographic Institution.

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/1721.1/124176

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124176