We propose a multisensor fusion framework for onboard real-time navigation of a quadrotor in an indoor environment, by integrating sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localization system. The sensor readings from the camera-based object detection algorithm and the UWB localization system arrive intermittently, since the measurements are not readily available. We design a Kalman filter that manages intermittent observations in order to handle and fuse the readings and estimate the pose of the quadrotor for tracking a predefined trajectory. The system is implemented via a Hardware-in-the-loop (HIL) simulation technique, in which the dynamic model of the quadrotor is simulated in an open-source 3D robotics simulator tool, and the whole navigation system is implemented on Artificial Intelligence (AI) enabled edge GPU. The simulation results show that our proposed framework offers low positioning and trajectory errors, while handling intermittent sensor measurements.

翻译：我们建议为室内环境中的二次钻探器的机载实时导航建立一个多传感器聚合框架,将惰性测量股(IMU)的传感器读数、一个基于摄像的物体探测算法和Ultra-WideBand(UWB)本地化系统(UWB)的传感器读数整合在一起,从基于相机的物体探测算法和UWB本地化系统(UWB)本地化系统(UWB)的传感器读数断断续续续地到达。我们设计了一个卡尔曼过滤器,管理间歇性观测,以便处理和连接读数,并估计二次钻探器的形状,以跟踪预定的轨迹。该系统是通过硬盘在极地(HIL)模拟技术实施的,在这种技术中,在开放源3D机器人机器人模拟器工具中模拟了孔晶体的动态模型,整个导航系统是在人工智能智能(AI)启用边缘GPU上实施的。模拟结果表明,我们提议的框架在处理间断传感测测量时存在低定位和轨误差。