Using the CONDENSATION Algorithm for Robust, Vision-Based Mobile Robot Localization

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/21565

Title: Using the CONDENSATION Algorithm for Robust, Vision-Based Mobile Robot Localization
Author: Burgard, Wolfram ; Dellaert, Frank ; Fox, Dieter ; Thrun, Sebastian
Abstract: To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot’s position once its location is known. Vision has long been advertised as providing a solution to these problems, but we still lack efficient solutions in unmodified environments. Many existing approaches require modification of the environment to function properly, and those that work within unmodified environments seldomly address the problem of global localization. In this paper we present a novel, vision-based localization method based on the CONDENSATION algorithm [17, 18], a Bayesian filtering method that uses a sampling-based density representation. We show how the CONDENSATION algorithm can be used in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition, it can also be used to globally localize the camera platform, given a visual map of the environment. Based on these two observations, we present a vision-based robot localization method that provides a solution to a difficult and open problem in the mobile robotics community. As evidence for the viability of our approach, we show both global localization and tracking results in the context of a state of the art robotics application.
Type: Paper
URI: http://hdl.handle.net/1853/21565
Date: 1999
Contributor: Carnegie-Mellon University. Computer Science Dept.
Universität Bonn. Institut für Informatik III
Publisher: Georgia Institute of Technology
Subject: Autonomous
Condensation algorithm
Densities
Global localization
Tracking

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