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conference paper · 2008

Strong Tracking Filter Simultaneous Localization and Mapping Algorithm

Huiping Li, Demin Xu, Yao Yao, Fubin Zhang

Cited by 1Abstract and details only

Abstract

Simultaneous localization and mapping (SLAM) is a central and complex problem in robot research community. In SLAM, extended Kalman filter (EKF) implementation is widely used to localize the robot and build the environment map incrementally. In this paper, we propose a strong tracking filter (STF) SLAM algorithm. This algorithm applies STF to deal with the non-linear estimated problem in SLAM instead of EKF. It can make the performance of the nonlinear filter approximate to that of optimal linear Kalman Filter (KF), so it can construct high accuracy maps and locate the robot more accurately than EKF SLAM. Simulation experiments illustrate the superior performance of our approach compared to EKF SLAM algorithm.

Cite this paper

APA 7

Li, H., Xu, D., Yao, Y., & Zhang, F. (2008). Strong Tracking Filter Simultaneous Localization and Mapping Algorithm. https://doi.org/10.1109/csse.2008.487

MLA 9

Li, Huiping, et al. “Strong Tracking Filter Simultaneous Localization and Mapping Algorithm.” vol. 17, 2008, pp. 1085–88, https://doi.org/10.1109/csse.2008.487.

Chicago

Li, Huiping, Demin Xu, Yao Yao, and Fubin Zhang. 2008. “Strong Tracking Filter Simultaneous Localization and Mapping Algorithm.” https://doi.org/10.1109/csse.2008.487.

Harvard

Li, H. et al. (2008) ‘Strong Tracking Filter Simultaneous Localization and Mapping Algorithm’. Available at: https://doi.org/10.1109/csse.2008.487.

BibTeX
@article{li2008strong,
  title = {{Strong Tracking Filter Simultaneous Localization and Mapping Algorithm}},
  author = {Li, Huiping and Xu, Demin and Yao, Yao and Zhang, Fubin},
  volume = {17},
  pages = {1085--1088},
  year = {2008},
  doi = {10.1109/csse.2008.487}
}

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