NDLI: Using prioritized relaxations to locate objects in points clouds for manipulation

Content Provider	IEEE Xplore Digital Library
Author	Truax, R. Platt, R. Leonard, J.
Copyright Year	2011
Description	Author affiliation: Computer Science and Artificial Intelligence Laboratory at MIT, USA (Truax, R.; Platt, R.; Leonard, J.)
Abstract	This paper considers the problem of identifying objects of interest in laser range point clouds for the purposes of manipulation. One of the characteristics of perception for manipulation is that while it is unnecessary to label all objects in the scene, it may be very important to maximize the likelihood of correctly locating a desired object. This paper leverages this and proposes an approach for locating the most likely object configurations given an object parameterization and a point cloud. While many other approaches to object localization need to explicitly associate points with hypothesized objects, our proposed method avoids this by optimizing relaxations of the likelihood function rather than the exact likelihood. The result is a simple, efficient, and robust method for locating objects that makes few assumptions beyond the desired object parameterization and with few parameters that require tuning.
Starting Page	2091
Ending Page	2097
File Size	1488263
Page Count	7
File Format	PDF
ISBN	9781612843865
ISSN	10504729
e-ISBN	9781612843858
DOI	10.1109/ICRA.2011.5980255
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Equations Mathematical model Object recognition Robot sensing systems Shape Data models
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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