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Relative Localization Approach for Combined Aerial and Ground Robotic System

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Abstract

The objective of this paper is to develop a relative localization method for coordinated control of multi-robotic systems equipped with both aerial and ground vehicles. To overcome the issues of filter initialization and state bias at hard linearization in the general EKF approach, this paper proposes a pseudo-linear measurement-based technique for relative localization where true nonlinear measurements are algebraically transformed into pseudo-linear measurements. These pseudo-linear measurements are then employed for sensor fusion. A nonlinear observability analysis is performed to identify the sufficient conditions under which the proposed pseudo-linear measurement-based relative localization scheme is locally weakly observable. The estimation consistency is analyzed in order to verify that the proposed estimator is consistent and that none of the covariance matrixes lead to ill conditions during the estimation process. The performance of the proposed relative localization scheme is compared with traditional EKF-based methods for unknown filter initialization. The results demonstrate that the proposed method is capable of achieving both the acceptable positional and orientational accuracy within 12 iterations, whereas traditional methods require more than 250 iterations to achieve the same accuracy. This observation verified that the proposed relative localization approach has fast convergence property for unknown filter initialization compared to traditional EKF-based methods.

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Authors and Affiliations

  1. IS Lab, Faculty of Engineering and Applied Science Memorial, University of Newfoundland, St. John’s, NL, A1B 3X5, Canada

    Thumeera R. Wanasinghe, George K. I. Mann & Raymond G. Gosine

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  1. Thumeera R. Wanasinghe
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  2. George K. I. Mann
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  3. Raymond G. Gosine
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Correspondence to Thumeera R. Wanasinghe.

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Wanasinghe, T.R., I. Mann, G.K. & Gosine, R.G. Relative Localization Approach for Combined Aerial and Ground Robotic System. J Intell Robot Syst 77, 113–133 (2015). https://doi.org/10.1007/s10846-014-0094-x

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