Unmanned Aerial Vehicles (UAV) with on-board augmentation systems (UAS, Unmanned\nAircraft System) have penetrated into civil and general-purpose applications, due to advances\nin battery technology, control components, avionics and rapidly falling prices. This paper\ndescribes the conceptual design and the validation campaigns performed for an embedded\nprecision Positioning, field mapping, Obstacle Detection and Avoiding (PODA) platform, which\nuses commercial-off-the-shelf sensors, i.e., a 10-Degrees-of-Freedom Inertial Measurement Unit\n(10-DoF IMU) and a Light Detection and Ranging (LiDAR), managed by an Arduino Mega 2560\nmicrocontroller with Wi-Fi capabilities. The PODA system, designed and tested for a commercial\nsmall quadcopter (Parrot Drones SAS Ar.Drone 2.0, Paris, France), estimates position, attitude and\ndistance of the rotorcraft from an obstacle or a landing area, sending data to a PC-based ground\nstation. The main design issues are presented, such as the necessary corrections of the IMU data\n(i.e., biases and measurement noise), and Kalman filtering techniques for attitude estimation, data\nfusion and position estimation from accelerometer data. The real-time multiple-sensor optimal state\nestimation algorithm, developed for the PODA platform and implemented on the Arduino, has been\ntested in typical aerospace application scenarios, such as General Visual Inspection (GVI), automatic\nlanding and obstacle detection. Experimental results and simulations of various missions show the\neffectiveness of the approach.
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