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 -