An IMU/Sonar-based Extended Kalman Filter for Mini-UAV Localization in Indoor Environment

Micro Aerial Vehicles (MAV) have been seen rapid progress in the indoor entertaining, security monitoring, as well as search and rescue activities. The indoor localization with lightweight sensors in a Global Positioning System (GPS-denied) environment is a challenging topic for MAVs autonomous flight and path planning. This paper proposes a novel indoor localization approach relying on only the IMU and four ultrasonic sensors. Four mutually perpendicular installed ultrasonic sensors are used to provide distances of each direction. A prior map and an improved multiple rays model are constructed to approximate the measurement of the ultrasonic sensor. A fast algorithm to calculate the Jacobian matrix of the measurement function is given, then an Extended Kalman Filter (EKF) is conducted to fuse the information from IMU and the sonar sensor. The proposed algorithm is validated by the simulation and the results indicate good localization performance and robustness against compass measurement noise.