MAV indoor navigation based on a closed-form solution for absolute scale velocity estimation using Optical Flow and inertial data

Vincenzo Lippiello, Giuseppe Loianno, Bruno Siciliano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A new vision-based obstacle avoidance technique for indoor navigation of Micro Aerial Vehicles (MAVs) is presented in this paper. The vehicle trajectory is modified according to the obstacles detected through the Depth Map of the surrounding environment, which is computed online using the Optical Flow provided by a single onboard omnidirectional camera. An existing closed-form solution for the absolute-scale velocity estimation based on visual correspondences and inertial measurements is generalized and here employed for the Depth Map estimation. Moreover, a dynamic region-of-interest for image features extraction and a self-limitation control for the navigation velocity are proposed to improve safety in view of the estimated vehicle velocity. The proposed solutions are validated by means of simulations.

Original languageEnglish (US)
Title of host publication2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Pages3566-3571
Number of pages6
DOIs
StatePublished - 2011
Event2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
Duration: Dec 12 2011Dec 15 2011

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
CountryUnited States
CityOrlando, FL
Period12/12/1112/15/11

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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    Lippiello, V., Loianno, G., & Siciliano, B. (2011). MAV indoor navigation based on a closed-form solution for absolute scale velocity estimation using Optical Flow and inertial data. In 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 (pp. 3566-3571). [6160577] (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2011.6160577