Dynamic environment modelling and prediction for autonomous systems
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Proceedings of the 2016 13th Workshop on Positioning, Navigation and Communication, WPNC 2016: WPNC 2016. IEEE - Institute of Electrical and Electronics Engineers Inc., 2017. S. 1-6 7822847 (Proceedings of the 2016 13th Workshop on Positioning, Navigation and Communication, WPNC 2016).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - Dynamic environment modelling and prediction for autonomous systems
AU - Papadoudis, Jan
AU - Georgiadis, Anthimos
N1 - Conference code: 13
PY - 2017/1/17
Y1 - 2017/1/17
N2 - This work describes a method to extend classical maps in terms of additional information and a prediction about objects within the environment. The prediction system is based on the behaviour of the observed objects and influences accordingly the updating of the map. First all objects are classified due to their size and further parameters characterizing the ability to move. Furthermore the velocity and orientation of objects within the visible area of the autonomous system are extracted from a vision sensor. In case of a mobile autonomous system they help to adjust its path in real time. Additionally all objects are tracked. In order to generate the statistical map a statistical indicator is introduced describing the possible future positions of objects. Thus the conventional maps can be improved by adding information about the status of the considered space. Furthermore the status of objects can be predicted even when they are not visible anymore. In the case of a mobile system, it will improve the awareness drastically enabling it to act pre-emptively and improve the human-machine interaction in e.g. a production environment.
AB - This work describes a method to extend classical maps in terms of additional information and a prediction about objects within the environment. The prediction system is based on the behaviour of the observed objects and influences accordingly the updating of the map. First all objects are classified due to their size and further parameters characterizing the ability to move. Furthermore the velocity and orientation of objects within the visible area of the autonomous system are extracted from a vision sensor. In case of a mobile autonomous system they help to adjust its path in real time. Additionally all objects are tracked. In order to generate the statistical map a statistical indicator is introduced describing the possible future positions of objects. Thus the conventional maps can be improved by adding information about the status of the considered space. Furthermore the status of objects can be predicted even when they are not visible anymore. In the case of a mobile system, it will improve the awareness drastically enabling it to act pre-emptively and improve the human-machine interaction in e.g. a production environment.
KW - Engineering
KW - Intelligent systems
KW - Maps
KW - Autonomous systems
KW - Dynamic environments
KW - Human machine interaction
KW - Mobile autonomous
KW - Parameters characterizing
KW - Prediction systems
KW - Production Environments
KW - Statistical indicators
UR - http://ieeexplore.ieee.org/document/7822847/
UR - http://www.scopus.com/inward/record.url?scp=85015221052&partnerID=8YFLogxK
U2 - 10.1109/WPNC.2016.7822847
DO - 10.1109/WPNC.2016.7822847
M3 - Article in conference proceedings
SN - 978-1-5090-5441-1
T3 - Proceedings of the 2016 13th Workshop on Positioning, Navigation and Communication, WPNC 2016
SP - 1
EP - 6
BT - Proceedings of the 2016 13th Workshop on Positioning, Navigation and Communication, WPNC 2016
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th Workshop on Positioning, Navigation and Communications (WPNC)
Y2 - 19 October 2016 through 20 October 2016
ER -