Towards MAV Navigation in Underground Mine Using Deep Learning

Document identifier: oai:DiVA.org:ltu-76967
Access full text here:10.1109/ROBIO.2018.8665290
Keyword: Engineering and Technology, Electrical Engineering, Electronic Engineering, Information Engineering, Control Engineering, Teknik och teknologier, Elektroteknik och elektronik, Reglerteknik
Publication year: 2018
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructure
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

The usage of Micro Aerial Vehicles (MAVs) is rapidly emerging in the mining industry to increase overall safety and productivity. However, the mine environment is especially challenging for the MAV's operation due to the lack of illumination, narrow passages, wind gusts, dust, and other factors that can affect the MAV's overall flying capability. This article presents a method to assist the navigation of MAVs by using a method from the field of Deep Learning (DL), while considering a low-cost platform without high-end sensor suits. The presented DL scheme can be further utilized as a supervised image classifier that has the ability to process the image frames from a single on-board camera and to provide mine tunnel wall collision prevention. The efficiency of the proposed scheme has been experimentally evaluated in two underground tunnel environments that were used for data collection, training, and corresponding testing under multiple flying scenarios with different cameras configurations and illuminations.

Authors

Sina Sharif Mansouri

Luleå tekniska universitet; Signaler och system
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Christoforos Kanellakis

Luleå tekniska universitet; Signaler och system
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George Georgoulas

Luleå tekniska universitet; Signaler och system
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George Nikolakopoulos

Luleå tekniska universitet; Signaler och system
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