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conference paper · Signal Processing and Communications Applications Conference (SIU) · 2009

Simultaneous localization and mapping using Extended Kalman Filter

Sırma Yavuz, Zeyneb Kurt, Mehmet Biçer

Cited by 11Abstract and details only

Abstract

In this study, an offline statistical estimation algorithm based on extended Kalman filter method is developed to solve the SLAM (simultaneous localization and map building) problem. For the application, a robot equipped with only simple and cheap sensors is used. Two of the most frequent problems in SLAM algorithms which are known as loop closing and data association are effectively solved by extended Kalman filter method.

Cite this paper

APA 7

Yavuz, S., Kurt, Z., & Biçer, M. (2009). Simultaneous localization and mapping using Extended Kalman Filter. Signal Processing and Communications Applications Conference (SIU), 700–703. https://doi.org/10.1109/siu.2009.5136492

MLA 9

Yavuz, Sırma, et al. “Simultaneous localization and mapping using Extended Kalman Filter.” Signal Processing and Communications Applications Conference (SIU), 2009, pp. 700–03, https://doi.org/10.1109/siu.2009.5136492.

Chicago

Yavuz, Sırma, Zeyneb Kurt, and Mehmet Biçer. 2009. “Simultaneous localization and mapping using Extended Kalman Filter.” Signal Processing and Communications Applications Conference (SIU): 700–03. https://doi.org/10.1109/siu.2009.5136492.

Harvard

Yavuz, S., Kurt, Z. and Biçer, M. (2009) ‘Simultaneous localization and mapping using Extended Kalman Filter’, Signal Processing and Communications Applications Conference (SIU), pp. 700–703. Available at: https://doi.org/10.1109/siu.2009.5136492.

BibTeX
@article{yavuz2009simultaneous,
  title = {{Simultaneous localization and mapping using Extended Kalman Filter}},
  author = {Yavuz, Sırma and Kurt, Zeyneb and Biçer, Mehmet},
  journal = {Signal Processing and Communications Applications Conference (SIU)},
  pages = {700--703},
  year = {2009},
  doi = {10.1109/siu.2009.5136492}
}

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