3D Extended Object Tracking Using Recursive Gaussian Processes

Date

2018-07-10

Author

Kumru, Murat
Özkan, Emre

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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

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Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

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.