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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
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.
References
Singhal, G., Bansod, B., Mathew, L.: Unmanned aerial vehicle classification, applications and challenges: a review (2018). https://doi.org/10.20944/preprints201811.0601.v1
Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping (SLAM): part 1 – the essential algorithms; part 2 – state of the art. IEEE Robot. Autom. Mag. 3, 99–117 (2006)
Petritoli, E., Leccese, F.: High accuracy attitude and navigation system for an autonomous underwater vehicle (AUV). ACTA IMEKO (2018). https://doi.org/10.21014/acta_imeko.v7i2.535
Vasiliev, K.K.: The use of statistical methods in designing ship communication systems and automatic motion control. Autom. Control Processes 1(23), 72–77 (2011)
Theodoridis, S.: Probability and stochastic processes. machine learning: a Bayesian and optimization perspective, pp. 9–51. Academic Press (2015). (Chapter 1)
Danilov, A.N., Andriyanov, N.A., Azanov, P.T.: Ensuring the effectiveness of the taxi order service by mathematical modeling and machine learning. J. Phys: Conf. Ser. 1096, 012188 (2018)
Dimitriou-Fakalou, C.: Yule-Walker estimation for the moving-average model. Int. J. Stochast. Anal. (2011). https://doi.org/10.1155/2011/151823
Andriyanov, N.A., Gavrilina, Y.N.: Image models and segmentation algorithms based on discrete doubly stochastic autoregressions with multiple roots of characteristic equations. In: CEUR Workshop Proceedings, vol. 2076, pp. 19–29 (2018)
Andriyanov, N.A., Vasiliev, K.K.: Use autoregressions with multiple roots of the characteristic equations to image representation and filtering. In: CEUR Workshop Proceedings, vol. 2210, pp. 273–281 (2018)
Acknowledgment
This work was supported by Grants RFBR No. 17-01-00179, No. 18-31-00056.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-29513-4_74
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29512-7
Online ISBN: 978-3-030-29513-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)