Mixed Reality and Remote Sensing Application of Unmanned Aerial Vehicle in Fire and Smoke Detection

Sadeghi Esfahlani, Shabnam (2019) Mixed Reality and Remote Sensing Application of Unmanned Aerial Vehicle in Fire and Smoke Detection. Journal of Industrial Information Integration. ISSN 2452-414X

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Official URL: https://doi.org/10.1016/j.jii.2019.04.006

Abstract

This paper proposes the development of a system incorporating inertial measurement unit (IMU), a consumer-grade digital camera and a fire detection algorithm simultaneously with a nano Unmanned Aerial Vehicle (UAV) for inspection purposes. The video streams are collected through the monocular camera and navigation relied on the state-of-the-art indoor/outdoor Simultaneous Localisation and Mapping (SLAM) system. It implements the robotic operating system (ROS) and computer vision algorithm to provide a robust, accurate and unique inter-frame motion estimation. The collected onboard data are communicated to the ground station and used the SLAM system to generate a map of the environment. A robust and efficient re-localization was performed to recover from tracking failure, motion blur, and frame lost in the data received. The fire detection algorithm was deployed based on the colour, movement attributes, temporal variation of fire intensity and its accumulation around a point. The cumulative time derivative matrix was utilized to analyze the frame-by-frame changes and to detect areas with high-frequency luminance flicker (random characteristic). Colour, surface coarseness, boundary roughness, and skewness features were perceived as the quadrotor flew autonomously within the clutter and congested area. Mixed Reality system was adopted to visualize and test the proposed system in a physical environment, and the virtual simulation was conducted through the Unity game engine. The results showed that the UAV could successfully detect fire and flame, autonomously fly towards and hover around it, communicate with the ground station and simultaneously generate a map of the environment. There was a slight error between the real and virtual UAV calibration due to the ground truth data and the correlation complexity of tracking real and virtual camera coordinate frames.

Item Type: Journal Article
Keywords: Fire Detection, Crazyflie 2.0, Monocular Camera, Computer Vision, Autonomous Flight
Faculty: Faculty of Science & Engineering
Depositing User: Dr Shabnam Sadeghi Esfahlani
Date Deposited: 30 Apr 2019 09:40
Last Modified: 14 Nov 2019 16:08
URI: http://arro.anglia.ac.uk/id/eprint/703794

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