ICRA'09 Paper Abstract


Paper FrB2.3

Agrawal, Anuraag (Osaka University), Nakazawa, Atsushi (Osaka University), Takemura, Haruo (Osaka University)

MMM-Classification of 3D Range Data

Scheduled for presentation during the Regular Sessions "Computer Vision for Robotics and Automation - III" (FrB2), Friday, May 15, 2009, 11:10−11:30, Room: ICR

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 21, 2022

Keywords Computer Vision for Robotics and Automation, Recognition, Visual Learning


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 “related points”. The algorithm was tested against five outdoor scenes and provided accurate classification even in the presence of many classes of interest.



Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2022 PaperCept, Inc.
Page generated 2022-01-21  08:25:21 PST  Terms of use