Memorias de investigación
Ponencias en congresos:
Deep Learning Based Semantic Situation Awareness System for Multirotor Aerial Robots using LIDAR
Año:2019
Áreas de investigación
  • Robots aéreos,
  • Robots autónomos
Datos
Descripción
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.
Internacional
Si
Nombre congreso
2019 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS' 19)
Tipo de participación
960
Lugar del congreso
Atlanta, GA, USA
Revisores
Si
ISBN o ISSN
978-1-7281-0333-4
DOI
10.1109/ICUAS.2019.8797770
Fecha inicio congreso
11/06/2019
Fecha fin congreso
14/06/2019
Desde la página
899
Hasta la página
908
Título de las actas
Deep learning based semantic situation awareness system for multirotor aerial robots using LIDAR
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Jose Luis Sanchez Lopez UPM
  • Autor: Carlos Sampedro Perez UPM
  • Autor: Dario Cazzato
  • Autor: Holger Voos
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Visión por Computador y Robótica Aérea
  • Departamento: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
  • Centro o Instituto I+D+i: Centro de Automática y Robótica (CAR). Centro Mixto UPM-CSIC