Abstract
In this paper, the extended Kalman filter (EKF) is used to estimate the position of the feature points when data missing occurs, taking the feature extraction of plane moving robot ceiling-based positioning as background and the coordinates of the feature points in the image plane as objects. Firstly, the acceleration model of the feature points in the image plane is established as the motion equation, and the motion information of the feature points is extracted by filter. Then, the equality constraints of the feature points are added to the filter scheme to increase the measurement information. In the case where there is a loss of data, that is, the feature points are lost partly, and the predicted values of the lost points are estimated as the true value. By comparing the filtering results, it shows that the addition of equality constraints can not only enhance the filtering effect, but also can estimate the loss points more effectively. Finally, the validity of the filtering scheme is verified by a numerical example.
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
References
Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60(2):91–110.
Chen X, Jia Y. Indoor localization for mobile robots using lampshade corners as landmarks: visual system calibration, feature extraction and experiments. Int J Control Autom Syst. 2014;12(6):1313–22.
Guan X. A method of object tracking based on feature point matching. Infrared Technol. 2016.
Xu D, Han L, Tan M, et al. Ceiling-based visual positioning for an indoor mobile robot with monocular vision. IEEE Trans Industr Electron. 2009;56(5):1617–28.
Caceres MA, Sottile F, Spirito MA. Adaptive location tracking by Kalman filter in wireless sensor networks. In: IEEE International conference on wireless and mobile computing, networking and communications. IEEE;2009. p. 123–28.
Yan Y, Shi YC, Ma ZQ. Moving vehicle tracking based on Kalman filter. Appl Mech Mater. 2011;71–78:3950–3.
Zhang YJ, Jin YY, Peng X. Adaptive Kalman filter in integrated navigation system. J Qingdao Univ Sci Technol. 2005.
Alonge F, Cangemi T, D’Ippolito F, et al. Convergence analysis of extended Kalman filter for sensorless control of induction motor. IEEE Trans Industr Electron. 2015;62(4):2341–52.
Chia TL, Simon D, Chizeck HJ. Kalman filtering with statistical state constraints. Control Intell Syst. 2006;34(1):73–9.
Duan Z, Li XR. The role of pseudo measurements in equality-constrained state estimation. IEEE Trans Aerosp Electron Syst. 2013;49(3):1654–66.
Potmesil M, Chakravarty I. A lens and aperture camera model for synthetic image generation. In: Conference on computer graphics and interactive techniques. ACM;1981. p. 297–305.
Zhang Z. A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell. 2000;22(11):1330–4.
Duan Z, Li XR. Best linear unbiased state estimation with noisy and noise-free measurements. In: International conference on information fusion. IEEE Xplore;2009. p. 2193–200.
Acknowledgements
This work was supported by the NSFC (61327807, 61521091, 61520106010, 61134005) and the National Basic Research Program of China (973 Program: 2012CB821200, 2012CB821201).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, H., Jia, Y. (2018). Data Missing Process by Extended Kalman Filter with Equality Constraints. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-6496-8_19
Download citation
DOI: https://doi.org/10.1007/978-981-10-6496-8_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6495-1
Online ISBN: 978-981-10-6496-8
eBook Packages: EngineeringEngineering (R0)