Simultaneous Object Class and Pose Estimation for Mobile Robotic Applications with Minimalistic Recognition
2010 (English)In: 2010 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) / [ed] Rakotondrabe M; Ivan IA, 2010, 2020-2027 p.Conference paper (Refereed)
In this paper we address the problem of simultaneous object class and pose estimation using nothing more than object class label measurements from a generic object classifier. We detail a method for designing a likelihood function over the robot configuration space. This function provides a likelihood measure of an object being of a certain class given that the robot (from some position) sees and recognizes an object as being of some (possibly different) class. Using this likelihood function in a recursive Bayesian framework allows us to achieve a kind of spatial averaging and determine the object pose (up to certain ambiguities to be made precise). We show how inter-class confusion from certain robot viewpoints can actually increase the ability to determine the object pose. Our approach is motivated by the idea of minimalistic sensing since we use only class label measurements albeit we attempt to estimate the object pose in addition to the class.
Place, publisher, year, edition, pages
2010. 2020-2027 p.
, IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-27695DOI: 10.1109/ROBOT.2010.5509304ISI: 000284150001035ScopusID: 2-s2.0-77955835614ISBN: 978-1-4244-5040-4OAI: oai:DiVA.org:kth-27695DiVA: diva2:381159
IEEE International Conference on Robotics and Automation (ICRA) Anchorage, AK, MAY 03-08, 2010
QC 201012232010-12-232010-12-202011-12-09Bibliographically approved