Abstract
The paper deals with the task of positioning an autonomous mobile object. At the beginning, attention was paid to popular modern methods used in robotics. Then equations for estimating coordinates in the case of the SLAM algorithm are described briefly. At the same time, models of movement, observations and filtering in the case of movement without specified landmarks are described in detail. These models are based on autoregressive equations, and Kalman vector filter is implemented for filtering. In addition, the models are modified when the first landmark is highlighted in the visibility zone, as well as during the subsequent change (appearance and disappearance) of new landmarks. The task of building a path on a plane along selected landmarks is considered. On the basis of autoregression with multiple roots of characteristic equations, the SLAM algorithm was modified, which allowed smoothing the trajectory of the mobile object and get a better characteristics when estimating the trajectory using this model.
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Notes
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Dawei Du et al., The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking. arXiv:1804.00518. (2018) https://arxiv.org/abs/1804.00518, last accessed 2019/01/08.
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Acknowledgment
This work was supported by Grants RFBR No. 17-01-00179, No. 18-31-00056.
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Andriyanov, N., Vasiliev, K. (2020). Using Local Objects to Improve Estimation of Mobile Object Coordinates and Smoothing Trajectory of Movement by Autoregression with Multiple Roots. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_74
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