Next Article in Journal
Natural Frequencies of Rectangular Laminated Plates—Introduction to Optimal Design in Aeroelastic Problems
Previous Article in Journal
Effect of Ramie Fabric Chemical Treatments on the Physical Properties of Thermoset Polylactic Acid (PLA) Composites
Previous Article in Special Issue
Multi-UAV Doppler Information Fusion for Target Tracking Based on Distributed High Degrees Information Filters
Article Menu
Issue 3 (September) cover image

Export Article

Open AccessArticle
Aerospace 2018, 5(3), 94; https://doi.org/10.3390/aerospace5030094

Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

1
Computer Vision and Aerial Robotics (CVAR) Group, Centre for Automation and Robotics (UPM-CSIC), Universidad Politécnica de Madrid, Calle José Gutiérrez Abascal 2, 28006 Madrid, Spain
2
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 29, Avenue J. F. Kenedy, 1855 Luxembourg, Luxembourg
*
Author to whom correspondence should be addressed.
Received: 10 August 2018 / Revised: 30 August 2018 / Accepted: 4 September 2018 / Published: 6 September 2018
(This article belongs to the Collection Unmanned Aerial Systems)
Full-Text   |   PDF [8196 KB, uploaded 7 September 2018]   |  

Abstract

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack. View Full-Text
Keywords: flight altitude estimation; UAVs; multirotor aerial robots; K-means clustering; 3D point cloud; kalman filter; SLAM flight altitude estimation; UAVs; multirotor aerial robots; K-means clustering; 3D point cloud; kalman filter; SLAM
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Bavle, H.; Sanchez-Lopez, J.L.; de la Puente, P.; Rodriguez-Ramos, A.; Sampedro, C.; Campoy, P. Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors. Aerospace 2018, 5, 94.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Aerospace EISSN 2226-4310 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top