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Cited by 9Inertial Sensor and NavigationTarget Tracking and Data Fusion in Sensor NetworksRobotics and Sensor-Based Localization

The DOI matches its Crossref record.

Sensors data fusion via Bayesian filter

Stanislav, Vechet, Jiri, Krejsa, Vit, Ondrousek·2010·Proceedings of 14th International Power Electronics and Motion Control Conference EPE-PEMC 2010
DOI 10.1109/epepemc.2010.5606874
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Abstract

Presented paper deals with the data fusion of measured environment attributes obtained from different kinds of sensors used by autonomous mobile robot. The method is based on algorithm called Bayesian filter. Implementation details and simulation experiment that fuses three different sensors measurement to determine robot orientation are given in the paper.

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Crossref · 10.1109/epepemc.2010.5606874

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Research works printed but not linked: 6

  1. 10.1109/JPROC.1997.554212 · DOI
  2. 10.1017/s0263574703005381 · DOI
  3. 10.1177/02783640022067869 · DOI
  4. 10.1117/12.287628 · DOI
  5. 10.1007/978-1-4757-3502-4 · DOI
  6. Jensen, 1996, An Introduction to Bayesian Networks, UCL Press

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Metadata

Authors
Stanislav, V., Jiri, K., & Vit, O.
Affiliations
Brno University of Technology (CZ)
Venue
Proceedings of 14th International Power Electronics and Motion Control Conference EPE-PEMC 2010
Publisher
IEEE
Published
2010-09
DOI
10.1109/epepemc.2010.5606874
OpenAlex
W2044555354
Cited by
9
References
0
Citations by year
2023: 1 · 2022: 2 · 2017: 1 · 2014: 3
FWCI
1.27
Type
conference paper
Language
en
Open access
closed
Locations
International Power Electronics and Motion Control Conference (EPE-PEMC) (publishedVersion)
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
Keywords
sensor fusion, Bayesian filter, autonomous mobile robots, robot orientation estimation, multi-sensor measurement, robot localization

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