SLAM with Object Discovery, Modeling and Mapping
Alexander J. B. Trevor,
Henrik I. Christensen,
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, USA
Object discovery and modeling have been widely
studied in the computer vision and robotics communities.
SLAM approaches that make use of objects and higher level
features have also recently been proposed. Using higher level
features provides several benefits: these can be more discrim-
inative, which helps data association, and can serve to inform
service robotic tasks that require higher level information,
such as object models and poses. We propose an approach
for online object discovery and object modeling, and extend a
SLAM system to utilize these discovered and modeled objects as
landmarks to help localize the robot in an online manner. Such
landmarks are particularly useful for detecting loop closures
in larger maps. In addition to the map, our system outputs
a database of detected object models for use in future SLAM
or service robotic tasks. Experimental results are presented to
demonstrate the approach’s ability to detect and model objects,
as well as to improve SLAM results by detecting loop closures.
This work was financially supported by
ARL MAST CTA, project 36666A9, and Boeing Corporation.
Send any comments or questions to Siddharth Choudhary (Email : siddharth [dot] choudhary [at] gatech.edu)