TY - GEN
T1 - An IμSonar-based Extended Kalman Filter for Mini-UAV Localization in Indoor Environment
AU - Shu, Xiangqian
AU - Yang, Lingyu
AU - Feng, Xiaoke
AU - Zhang, Jing
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8
Y1 - 2018/8
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85074410385
U2 - 10.1109/GNCC42960.2018.9018955
DO - 10.1109/GNCC42960.2018.9018955
M3 - 会议稿件
AN - SCOPUS:85074410385
T3 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
BT - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
Y2 - 10 August 2018 through 12 August 2018
ER -