Efficiently learning metric and topological maps with autonomous service robots

Stachniss, Cyrill, Grisetti, Giorgio, Martinez Mozos, Oscar and Burgard, Wolfram (2007) Efficiently learning metric and topological maps with autonomous service robots. it--Information Technology, 49 (4). pp. 232-237. ISSN 1611-2776

Full content URL: http://www.oldenbourg-link.com/doi/abs/10.1524/iti...

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Models of the environment are needed for a wide range of robotic applications, from search and rescue to automated vacuum cleaning. Learning maps has therefore been a major research focus in the robotics community over the last decades. In general, one distinguishes between metric and topological maps. Metric maps model the environment based on grids or geometric representations whereas topological maps model the structure of the environment using a graph. The contribution of this paper is an approach that learns a metric as well as a topological map based on laser range data obtained with a mobile robot. Our approach consists of two steps. First, the robot solves the simultaneous localization and mapping problem using an efficient probabilistic filtering technique. In a second step, it acquires semantic information about the environment using machine learning techniques. This semantic information allows the robot to distinguish between different types of places like, e. g., corridors or rooms. This enables the robot to construct annotated metric as well as topological maps of the environment. All techniques have been implemented and thoroughly tested using real mobile robot in a variety of environments.

Keywords:SLAM, metric map, topological map, particle filter, semantic information
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:9569
Deposited On:20 May 2013 17:04

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