Vision-Based Multirotor Following Using Synthetic Learning Techniques

Rodriguez Ramos, Alejandro and Álvarez Fernández, Adrián and Bavle, Hriday and Campoy Cervera, Pascual and How, Jonathan P. (2019). Vision-Based Multirotor Following Using Synthetic Learning Techniques. "Sensors", v. 19 (n. 21); pp.. ISSN 1424-8220.


Title: Vision-Based Multirotor Following Using Synthetic Learning Techniques
  • Rodriguez Ramos, Alejandro
  • Álvarez Fernández, Adrián
  • Bavle, Hriday
  • Campoy Cervera, Pascual
  • How, Jonathan P.
Item Type: Article
Título de Revista/Publicación: Sensors
Date: November 2019
ISSN: 1424-8220
Volume: 19
Freetext Keywords: multirotor; UAV; following; synthetic learning; reinforcement learning; deep learning
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
UPM's Research Group: Computer Vision CVG
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview


Deep- and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or motion-control strategies. On this subject, the research community lacks robust approaches to overcome unavailable real-world extensive data by means of realistic synthetic-information and domain-adaptation techniques. In this work, synthetic-learning strategies have been used for the vision-based autonomous following of a noncooperative multirotor. The complete maneuver was learned with synthetic images and high-dimensional low-level continuous robot states, with deep- and reinforcement-learning techniques for object detection and motion control, respectively. A novel motion-control strategy for object following is introduced where the camera gimbal movement is coupled with the multirotor motion during the multirotor following. Results confirm that our present framework can be used to deploy a vision-based task in real flight using synthetic data. It was extensively validated in both simulated and real-flight scenarios, providing proper results (following a multirotor up to 1.3 m/s in simulation and 0.3 m/s in real flights).

Funding Projects

Government of SpainRTI2018-100847-B-C21UnspecifiedUnspecifiedCOMplex Coordinated Inspection and Security missions by UAVs in cooperation with UGV

More information

Item ID: 64118
DC Identifier:
OAI Identifier:
DOI: 10.3390/s19214794
Official URL:
Deposited by: Memoria Investigacion
Deposited on: 28 Sep 2020 16:33
Last Modified: 28 Sep 2020 16:33
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM