Deep learning based semantic situation awareness system for multirotor aerial robots using LIDAR

Sánchez López, José Luis and Sampedro Pérez, Carlos and Cazzato, Dario and Voos, Holger (2019). Deep learning based semantic situation awareness system for multirotor aerial robots using LIDAR. In: "ICUAS'19 The 2019 International Conference on Unmanned Aircraft Systems", 11-14 Jun 2019, Atlanta, USA. ISBN 978-1-7281-0332-7. pp. 899-908. https://doi.org/10.1109/ICUAS.2019.8797770.

Description

Title: Deep learning based semantic situation awareness system for multirotor aerial robots using LIDAR
Author/s:
  • Sánchez López, José Luis
  • Sampedro Pérez, Carlos
  • Cazzato, Dario
  • Voos, Holger
Item Type: Presentation at Congress or Conference (Article)
Event Title: ICUAS'19 The 2019 International Conference on Unmanned Aircraft Systems
Event Dates: 11-14 Jun 2019
Event Location: Atlanta, USA
Title of Book: 2019 International Conference on Unmanned Aircraft Systems (ICUAS)
Date: 2019
ISBN: 978-1-7281-0332-7
Subjects:
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 64575
DC Identifier: http://oa.upm.es/64575/
OAI Identifier: oai:oa.upm.es:64575
DOI: 10.1109/ICUAS.2019.8797770
Official URL: http://uasconferences.com/2019_icuas/index.php
Deposited by: Memoria Investigacion
Deposited on: 30 Oct 2020 14:10
Last Modified: 30 Oct 2020 14:10
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