NDLI: Humanoid robot localization in complex indoor environments

Content Provider	IEEE Xplore Digital Library
Author	Hornung, A. Wurm, K.M. Bennewitz, M.
Copyright Year	2010
Description	Author affiliation: Dept. of Computer Science, University of Freiburg, Germany (Hornung, A.; Wurm, K.M.; Bennewitz, M.)
Abstract	In this paper, we present a localization method for humanoid robots navigating in arbitrary complex indoor environments using only onboard sensing. Reliable and accurate localization for humanoid robots operating in such environments is a challenging task. First, humanoids typically execute motion commands rather inaccurately and odometry can be estimated only very roughly. Second, the observations of the small and lightweight sensors of most humanoids are seriously affected by noise. Third, since most humanoids walk with a swaying motion and can freely move in the environment, e.g., they are not forced to walk on flat ground only, a 6D torso pose has to be estimated. We apply Monte Carlo localization to globally determine and track a humanoid's 6D pose in a 3D world model, which may contain multiple levels connected by staircases. To achieve a robust localization while walking and climbing stairs, we intergrate 2D laser range measurements as well as attitude data and information from the joint encoders. We present simulated as well as real-word experiments with our humanoid and thoroughly evaluate our approach. As the experiments illustrate, the robot is able to globally localize itself and accurately track its 6D pose over time.
Starting Page	1690
Ending Page	1695
File Size	1570269
Page Count	6
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5649751
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot sensing systems Variable speed drives Navigation Filtering Torso Tracking
Content Type	Text
Resource Type	Article

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