ICRA'09 Paper Abstract

Close

Paper FrB2.4

Munoz, Daniel (Carnegie Mellon University), vandapel, nicolas (Carnegie Mellon University), hebert, martial (CMU)

Onboard Contextual Classification of 3-D Point Clouds with Learned High-Order Markov Random Fields

Scheduled for presentation during the Regular Sessions "Computer Vision for Robotics and Automation - III" (FrB2), Friday, May 15, 2009, 11:30−11:50, 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 24, 2022

Keywords Computer Vision for Robotics and Automation, Range Sensing, Mapping

Abstract

Abstract—Contextual reasoning through graphical models such as Markov Random Fields often show superior performance against local classifiers in many domains. Unfortunately, this performance increase is often at the cost of time consuming, memory intensive learning and slow inference at testing time. Structured prediction for 3-D point cloud classification is one example of such an application. In this paper we present two contributions. First we show how efficient learning of a random field with higher-order cliques can be achieved using subgradient optimization. Second, we present a context approximation using random fields with high-order cliques designed to make this model usable online, onboard a mobile vehicle for environment modeling. We obtained results with the mobile vehicle on a variety of terrains, at 1/3 Hz for a map 25 x 50 meters and a vehicle speed of 1-2 m/s.

 

 

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 2022-01-24  07:07:54 PST  Terms of use