UASs (Unmanned Aerial Systems) have become increasingly popular for both military and civil applications, thanks to improved batteries, motors, and rapidly falling prices. They are mostly implemented during various operation tasks, as search and rescue, disaster assessment, urban traffic monitoring, 3D mapping, etc., that would be risky or impossible for a human to perform. DAA (Detect and Avoid) is a new technology necessary for UASs to safely avoid obstacles or other aircrafts. In this paper low-cost sensors, such as a 10-DOF (Degree Of Freedom) MEMS (Micro Electro-Mechanical System) IMU (Inertial Measurement Unit) and a LIDAR (Light Detection and Ranging), synchronized at 10-Hz measurement rate, were installed on a small unmanned rotorcraft to estimate the position of the platform and its distance from an obstacle or a landing field. To correct the IMU data from systematics errors (bias) and measurement noise, and to derive estimated positions from accelerometer data, Kalman filtering was implemented. The optimal estimation algorithm, developed on an onboard microcontroller (Arduino Mega 2560), allows for low-cost hardware implementations of multiple sensors for use in aerospace applications.
UAS for positioning and field mapping using LIDAR and IMU sensors data: Kalman filtering and integration
ARIANTE, GENNARO;Papa, Umberto;Ponte, Salvatore;Del Core, Giuseppe
2019-01-01
Abstract
UASs (Unmanned Aerial Systems) have become increasingly popular for both military and civil applications, thanks to improved batteries, motors, and rapidly falling prices. They are mostly implemented during various operation tasks, as search and rescue, disaster assessment, urban traffic monitoring, 3D mapping, etc., that would be risky or impossible for a human to perform. DAA (Detect and Avoid) is a new technology necessary for UASs to safely avoid obstacles or other aircrafts. In this paper low-cost sensors, such as a 10-DOF (Degree Of Freedom) MEMS (Micro Electro-Mechanical System) IMU (Inertial Measurement Unit) and a LIDAR (Light Detection and Ranging), synchronized at 10-Hz measurement rate, were installed on a small unmanned rotorcraft to estimate the position of the platform and its distance from an obstacle or a landing field. To correct the IMU data from systematics errors (bias) and measurement noise, and to derive estimated positions from accelerometer data, Kalman filtering was implemented. The optimal estimation algorithm, developed on an onboard microcontroller (Arduino Mega 2560), allows for low-cost hardware implementations of multiple sensors for use in aerospace applications.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
