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 color, 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). Color, 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.

本文提出了一种系统的开发，该系统将惯性测量单元（IMU），消费级数码相机和火灾探测算法与纳米无人飞行器（UAV）同时用于检查目的。视频流通过单眼相机收集，并依靠最先进的室内/室外同时定位和制图（SLAM）系统进行导航。它实现了机器人操作系统（ROS）和计算机视觉算法，以提供可靠，准确和独特的帧间运动估计。收集的机载数据被传送到地面站，并使用SLAM系统生成环境图。执行了强大而有效的重新定位，以从接收到的数据中的跟踪失败，运动模糊和帧丢失中恢复过来。根据颜色，运动属性，火灾强度的时间变化及其在一个点周围的积累，部署了火灾探测算法。累积时间导数矩阵用于分析逐帧变化并检测具有高频亮度闪烁（随机特性）的区域。当四旋翼在杂乱和拥挤的区域内自主飞行时，颜色，表面粗糙度，边界粗糙度和偏斜度特征被感知。采用混合现实系统在物理环境中可视化和测试了所提出的系统，并通过Unity游戏引擎进行了虚拟仿真。结果表明，无人机可以成功地探测到火和火焰，自动向其飞行并在其周围盘旋，与地面站通信，并同时生成环境图。由于地面真实数据以及跟踪真实和虚拟摄像机坐标系的相关复杂性，真实和虚拟UAV校准之间存在轻微误差。