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3D Extended Object Tracking Using Recursive Gaussian Processes
Date
2018-07-10
Author
Kumru, Murat
Özkan, Emre
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In this study, we consider the challenging task of tracking dynamic 3D objects with unknown shapes by using sparse point cloud measurements gathered from the surface of the objects. We propose a Gaussian process based algorithm that is capable of tracking the dynamic behavior of the object and learn its shape in 3D simultaneously. Our solution does not require any parametric model assumption for the unknown shape. The shape of the objects is learned online via a Gaussian process. The proposed method can jointly estimate the position, orientation, and the shape of the object. The inference is performed by an extended Kalman filter which is suitable for online real-time applications. Lastly, we demonstrate the initial results of a promising approach, which aims at reducing the computational complexity.
Subject Keywords
Extended target tracking
,
Object tracking
,
Gaussian process
,
Learning
,
Contour estimation
,
Lidar
,
3d
URI
https://hdl.handle.net/11511/34474
DOI
https://doi.org/10.23919/icif.2018.8455480
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
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M. Kumru and E. Özkan, “3D Extended Object Tracking Using Recursive Gaussian Processes,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34474.