Next Article in Journal
Powerplant Reliability Issues and Wear Monitoring in Aircraft Piston Engines. Part II: Engine Diagnostic
Next Article in Special Issue
Use of UAV-Borne Spectrometer for Land Cover Classification
Previous Article in Journal
Development of a Wall-Sticking Drone for Non-Destructive Ultrasonic and Corrosion Testing
Previous Article in Special Issue
UAS Navigation with SqueezePoseNet—Accuracy Boosting for Pose Regression by Data Augmentation
Article Menu
Issue 1 (March) cover image

Export Article

Open AccessArticle

Effective Exploration for MAVs Based on the Expected Information Gain

Institute of Geodesy and Geoinformation, University of Bonn, 53115 Bonn, Germany
*
Author to whom correspondence should be addressed.
Received: 19 December 2017 / Revised: 28 February 2018 / Accepted: 3 March 2018 / Published: 6 March 2018
Full-Text   |   PDF [5290 KB, uploaded 6 March 2018]   |  

Abstract

Micro aerial vehicles (MAVs) are an excellent platform for autonomous exploration. Most MAVs rely mainly on cameras for buliding a map of the 3D environment. Therefore, vision-based MAVs require an efficient exploration algorithm to select viewpoints that provide informative measurements. In this paper, we propose an exploration approach that selects in real time the next-best-view that maximizes the expected information gain of new measurements. In addition, we take into account the cost of reaching a new viewpoint in terms of distance and predictability of the flight path for a human observer. Finally, our approach selects a path that reduces the risk of crashes when the expected battery life comes to an end, while still maximizing the information gain in the process. We implemented and thoroughly tested our approach and the experiments show that it offers an improved performance compared to other state-of-the-art algorithms in terms of precision of the reconstruction, execution time, and smoothness of the path. View Full-Text
Keywords: exploration; information gain; vision exploration; information gain; vision
Figures

Figure 1

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

Palazzolo, E.; Stachniss, C. Effective Exploration for MAVs Based on the Expected Information Gain. Drones 2018, 2, 9.

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.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Drones EISSN 2504-446X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top