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Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model

  • Random matrices are used to filter the center of gravity (CoG) and the covariance matrix of measurements. However, these quantities do not always correspond directly to the position and the extent of the object, e.g. when a lidar sensor is used.In this paper, we propose a Gaussian processes regression model (GPRM) to predict the position and extension of the object from the filtered CoG and covariance matrix of the measurements. Training data for the GPRM are generated by a sampling method and a virtual measurement model (VMM). The VMM is a function that generates artificial measurements using ray tracing and allows us to obtain the CoG and covariance matrix that any object would cause. This enables the GPRM to be trained without real data but still be applied to real data due to the precise modeling in the VMM. The results show an accurate extension estimation as long as the reality behaves like the modeling and e.g. lidar measurements only occur on the side facing the sensor.

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Author:Patrick HoherORCiD, Johannes ReuterORCiD, Daniel Dold, Dennis Griesser, Felix GovaersORCiD, Wolfgang Koch
Parent Title (English):26th International Conference on Information Fusion (FUSION), 27-30 June 2023, Charleston, SC, USA
Document Type:Conference Proceeding
Year of Publication:2023
Release Date:2023/09/25
Tag:Extended object tracking; Random matrices; Lidar; Gaussian processes; Extension estimation
Page Number:8
Volltext im Campusnetz der Hochschule Konstanz via Datenbank IEEE Xplore abrufbar.
Institutes:Institut für Optische Systeme - IOS
Institut für Systemdynamik - ISD
DDC functional group:000 Allgemeines, Informatik, Informationswissenschaft
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (German):License LogoUrheberrechtlich geschützt