Real-Time Obstacle Detection and Field Mapping System Using LIDAR-ToF Sensors for Small UAS

UASs (Unmanned Aircraft Systems) are becoming increasingly popular, for both military and civil applications. They are widely used in various tasks, such as search and rescue, disaster assessment, urban traffic monitoring, 3D mapping, etc., that would be risky or impossible to perform for a human. DAA (Detect and Avoid) is a new UAS technology necessary to safely avoid obstacles or other UASs and aircrafts. In this work low-cost sensors, namely, a DAA architecture based on a LIDAR (Light Detection and Ranging), and a ToF (Time of Flight) sensor, will be installed on a small unmanned rotorcraft to estimate its distance from an obstacle and for field mapping. To correct the data from systematic errors (bias) and measurement noise, Kalman filtering and a criterion of optimal estimation have been implemented. Collected data are sent to a microcontroller (Arduino Mega 2560), which allows for low-cost hardware implementations of multiple sensors for use in aerospace applications.