By Topic

MMM-classification of 3D range data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Agrawal, A. ; Grad. Sch. of Inf. Sci. & Technol., Osaka Univ., Toyonaka, Japan ; Nakazawa, A. ; Takemura, H.

This paper presents a method for accurately segmenting and classifying 3D range data into particular object classes. Object classification of input images is necessary for applications including robot navigation and automation, in particular with respect to path planning. To achieve robust object classification, we propose the idea of an object feature which represents a distribution of neighboring points around a target point. In addition, rather than processing raw points, we reconstruct polygons from the point data, introducing connectivity to the points. With these ideas, we can refine the Markov Random Field (MRF) calculation with more relevant information with regards to determining ldquorelated pointsrdquo. The algorithm was tested against five outdoor scenes and provided accurate classification even in the presence of many classes of interest.

Published in:

Robotics and Automation, 2009. ICRA '09. IEEE International Conference on

Date of Conference:

12-17 May 2009