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We propose a multisensor fusion framework for onboard real-time navigation ofa quadrotor in an indoor environment, by integrating sensor readings from anInertial Measurement Unit (IMU), a camera-based object detection algorithm, andan Ultra-WideBand (UWB) localization system. The sensor readings from thecamera-based object detection algorithm and the UWB localization system arriveintermittently, since the measurements are not readily available. We design aKalman filter that manages intermittent observations in order to handle andfuse the readings and estimate the pose of the quadrotor for tracking apredefined trajectory. The system is implemented via a Hardware-in-the-loop(HIL) simulation technique, in which the dynamic model of the quadrotor issimulated in an open-source 3D robotics simulator tool, and the wholenavigation system is implemented on Artificial Intelligence (AI) enabled edgeGPU. The simulation results show that our proposed framework offers lowpositioning and trajectory errors, while handling intermittent sensormeasurements.

Onboard Real-Time Multi-Sensor Pose Estimation for Indoor Quadrotor Navigation with Intermittent Communication