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World modeling and position estimation for a mobile robot using ultrasonic ranging

Crowley, J.L.·2003·Proceedings, 1989 International Conference on Robotics and Automation

Abstract

The author describes a system for dynamically maintaining a description of the limits to free space for a mobile robot using a belt of ultrasonic range sensors. A model is presented for the uncertainty inherent in such sensors, and the projection of range measurements into external Cartesian coordinates is described. Line segments are then expressed by a set of parameters represented by an estimate and a precision. A process is presented for extracting line segments from adjacent collinear range measurements, and a fast algorithm is presented for matching these line segments to a model of the limits to free space of the robot. A side effect of matching observations to a local model is a correction to the estimated position of the robot at the time that the observation was made. A Kalman filter update equation is developed to permit the correspondence of a line segment to the model to be applied as a correction to estimated position. Examples of segment extraction, position correction and modeling are presented using real ultrasonic data.>

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Crossref · 10.1109/robot.1989.100062

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Metadata

Authors
Crowley, J. L.
Venue
Proceedings, 1989 International Conference on Robotics and Automation
Published
2003-01-07
Pages
674-680
ISSN
1050-4729 (ISSN-L)
OpenAlex
W2113786136
Cited by
466
References
8
FWCI
57.75
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: World modeling and position estimation for a mobile robot using ultrasonic ranging. https://app.cochat.ai/papers/10.1109/robot.1989.100062
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Crowley, J. L. (2003). World modeling and position estimation for a mobile robot using ultrasonic ranging. Proceedings, 1989 International Conference on Robotics and Automation. https://doi.org/10.1109/robot.1989.100062

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.