Semantic labeling of places using information extracted from laser and vision sensor data

Martinez Mozos, Oscar and Rottmann, Axel and Triebel, Rudolph and Jensfelt, Patric and Burgard, Wolfram (2006) Semantic labeling of places using information extracted from laser and vision sensor data. In: IEEE/RSJ IROS Workshop: From sensors to human spatial concepts, October 10, 2006, Beijing, China.

Documents
mozos2006iros_w.pdf
[img]
[Download]
[img]
Preview
PDF
mozos2006iros_w.pdf - Whole Document

1MB
Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Indoor environments can typically be divided into places with different functionalities like corridors, kitchens,
offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction withhumans. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we firrst propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from range data and vision into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. Secondly,
we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor
environments.

Keywords:mobile robotics
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:9580
Deposited On:30 May 2013 07:41

Repository Staff Only: item control page