Where to look: a collection of methods forMAV heading correction in underground tunnels. Issue 10 (7th July 2020)

• Record Type: Journal Article
• Authors: Kanellakis, Christoforos; Sharif Mansouri, Sina; Castaño, Miguel; Karvelis, Petros; Kominiak, Dariusz; Nikolakopoulos, G.
• Is Part Of: IET image processing. Volume 14:Issue 10(2020)
• Page Start: 2020
• Page End: 2027
• Publication Date: 2020-07-07
• Subjects: sensors -- optical radar -- image sensors -- mobile robots -- aircraft control -- remotely operated vehicles -- aerospace robotics -- neural nets; MAV heading correction -- underground tunnel -- miniature aerial vehicles -- forefront -- application breakthroughs -- flying capabilities -- traversability issues -- ground robots -- degraded subterranean environments -- attractive case -- safety operations -- underground mines -- mining process -- sensor data -- MAVs heading regulation -- dark tunnel areas -- textureless tunnel areas -- open space -- align the MAV heading -- visual sensor -- single image depth estimation -- Convolutional Neural Network regression -- 2D Lidar sensor -- dark scene -- yaw rate command -- aerial robotics community; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1423
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Degraded Subterranean environments are an attractive case for miniature aerial vehicles, since there is a constant need to increase the safety operations in underground mines. The starting point for integrating aerial vehicles in the mining process is the capability to reliably navigate along tunnels. Inspired by recent advancements, this paper presents a collection of different, experimentally verified, methods tackling the problem of MAVs heading regulation while navigating in dark and textureless tunnel areas. More specifically, four different methods are presented in this work with the common goal to identify open space in the tunnel and align the MAV heading using either visual sensor in methods a) single image depth estimation, b) darkness contour detection, c) Convolutional Neural Network (CNN) regression and 2D Lidar sensor in method d) range geometry. For the works a)‐c) the dark scene in the middle of the tunnel is considered as open space and is processed and converted to yaw rate command, while d) examines the geometry of the range measurements to calculate the yaw rate command. Experimental results from real underground tunnel demonstrate the performance of the methods in the field, while setting the ground for further developments in the aerial robotics community.