ICRA 2011 Paper Abstract

Close

Paper WeA210.1

Liu, Ming (ETH Zurich), Colas, Francis (ETH Zürich), Siegwart, Roland (ETH Zurich)

Regional Topological Segmentation Based on Mutual Information Graphs

Scheduled for presentation during the Regular Sessions "Mapping and Navigation II" (WeA210), Wednesday, May 11, 2011, 10:05−10:20, Room 5E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 10, 2019

Keywords Mapping, AI Reasoning Methods

Abstract

When people communicate with robots, the most intuitive mean is by naming the different regions in the environment. The capability that robots are able to identify different regions highly depends on the unsupervised topological segmentation results. This paper addresses the problem of segmenting a metric map into regions. Nowadays many researches in this direction develop approaches based on spectral clustering. However there are inherent drawbacks of spectral clustering algorithms. In this paper, we first discuss these drawbacks using several testing results; then we propose our approach based on information theory which uses Chow-Liu tree to segment the composed graph according to the weight differences. The results show that our method provides more flexible and faster results in the sense of facilitating semantic mapping or further applications.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2019 PaperCept, Inc.
Page generated 2019-12-10  04:39:46 PST  Terms of use