NDLI: Development of a Parametric Model for the Environment of a Mobile Robot

Content Provider	IEEE Xplore Digital Library
Author	Yaqub, T. Eaton, R. Katupitiya, J.
Copyright Year	2006
Description	Author affiliation: ARC Centre of Excellence for Autonomous Syst., Univ. of New South Wales, Sydney, NSW (Yaqub, T.)
Abstract	We present a new approach for reducing the computational complexity of grid-based localization methods by suggesting the development of a parametric model of the environment of a mobile robot. This model is based on the sensor perception and obtained by applying the nonparametric bootstrapping technique. Particle filter based localization methods assume that map is available but are computationally very expensive mainly due to the measurement models. This method reduces the computational complexity of these methods by extracting a parametric model from the given map to be used for measurement update. We assign multinomial probabilities to the grid cells for future inferences. Having the cell probabilities, we can update the particles obtained from action model. The likelihood calculations become straight-forward and hence complex measurement models can be simplified. Our method can exploit any feature extraction algorithm to get the multinomial approximation of the environment. The offline extraction of few vital geometrical feature is carried out at some known locations on the map. This is then followed by the statistical technique to get the model parameters and essential cell statistics. The extracted model is used for measurement updates using a simulated pioneer robot and results show a significant increase in the update rate of the particle filter
Starting Page	1285
Ending Page	1290
File Size	351506
Page Count	6
File Format	PDF
ISBN	1424402581
DOI	10.1109/IROS.2006.281891
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parametric statistics Mobile robots Computational complexity Particle filters Particle measurements Simultaneous localization and mapping Robot sensing systems Sensor phenomena and characterization Sampling methods Intelligent robots
Content Type	Text
Resource Type	Article

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