Reference : Deep learning based semantic situation awareness system for multirotor aerial robots ...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/41301
Deep learning based semantic situation awareness system for multirotor aerial robots using LIDAR
English
Sanchez Lopez, Jose Luis mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Sampedro, Carlos []
Cazzato, Dario [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Voos, Holger [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Jun-2019
2019
2019 International Conference on Unmanned Aircraft Systems (ICUAS)
899-908
Yes
No
International
2019 International Conference on Unmanned Aircraft Systems (ICUAS)
from 12-06-2019 to 14-06-2019
[en] Aerial robots ; situation awareness ; deep learning
[en] In this work, we present a semantic situation awareness system for multirotor aerial robots, based on 2D LIDAR measurements, targeting the understanding of the environment and assuming to have a precise robot localization as an input of our algorithm. Our proposed situation awareness system calculates a semantic map of the objects of the environment as a list of circles represented by their radius, and the position and the velocity of their center in world coordinates. Our proposed algorithm includes three main parts. First, the LIDAR measurements are preprocessed and an object segmentation clusters the candidate objects present in the environment. Secondly, a Convolutional Neural Network (CNN) that has been designed and trained using an artificially generated dataset, computes the radius and the position of the center of individual circles in sensor coordinates. Finally, an indirect-EKF provides the estimate of the semantic map in world coordinates, including the velocity of the center of the circles in world coordinates.We have quantitative and qualitative evaluated the performance of our proposed situation awareness system by means of Software-In-The-Loop simulations using VRep with one and multiple static and moving cylindrical objects in the scene, obtaining results that support our proposed algorithm. In addition, we have demonstrated that our proposed algorithm is capable of handling real environments thanks to real laboratory experiments with non-cylindrical static (i.e. a barrel) and moving (i.e. a person) objects.
http://hdl.handle.net/10993/41301
10.1109/ICUAS.2019.8797770
https://ieeexplore.ieee.org/abstract/document/8797770

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