Open-Set Object Based Data Association

Representing the world using sparse objects allows for compact and semantically meaningful maps in simultaneous localization and mapping (SLAM). Traditionally, object detectors trained on a specific set of objects, such as the YCB objects, are used to provide input to the data association problem, which limits the scope of the system to environments that it has been trained on. With advancements in foundational models, we can extend this representation for objects that are not known a priori and do not have a labeled category during training. This thesis explores a system that creates data associations between open-set objects using an RGB-D camera and how it is used in a sparse object SLAM system. We show comparable trajectory performance to traditional SLAM systems while being more adaptable to out-of-distribution objects.

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

2024

Author dc:creator

• Magoun, Tim Y.

Advisor dc:contributor.advisor

• Leonard, John J.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/153827

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/153827