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A Non-Linear Least Squares Approach to SLAM using a Dynamic Likelihood Field

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Abstract

This paper presents a fast scan matching approach to online SLAM supported by a dynamic likelihood field. The dynamic likelihood field plays a central role in the approach: it avoids the necessity to establish direct correspondences; it is the connection link between scan matching and the online SLAM; and it has a low computational complexity. Scan matching is formulated as a non-linear least squares problem that allows us to solve it using Gauss-Newton or Levenberg-Marquardt methods. Furthermore, to reduce the influence of outliers during optimization, a loss function is introduced. The proposed solution was evaluated using an objective benchmark designed to compare different SLAM solutions. Additionally, the execution times of our proposal were also analyzed. The obtained results show that the proposed approach provides a fast and accurate online SLAM, suitable for real-time operation.

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Acknowledgements

This research is supported by: National Funds through FCT - Foundation for Science and Technology, in the context of the project UID/CEC/00127/2013; and by European Union’s FP7 under EuRoC grant agreement CP-IP 608849.

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Authors and Affiliations

  1. Department of Electronics, Telecommunications and Informatics (DETI), Intelligent Robotics and Intelligent Systems Laboratory (IRIS), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal

    Eurico Pedrosa, Artur Pereira & Nuno Lau

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  1. Eurico Pedrosa
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  2. Artur Pereira
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  3. Nuno Lau
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Correspondence to Eurico Pedrosa.

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Pedrosa, E., Pereira, A. & Lau, N. A Non-Linear Least Squares Approach to SLAM using a Dynamic Likelihood Field. J Intell Robot Syst 93, 519–532 (2019). https://doi.org/10.1007/s10846-017-0763-7

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