TY - GEN
T1 - A monocular vision localization algorithm based on maximum likelihood estimation
AU - Chen, Shiyu
AU - Li, Yanjie
AU - Chen, Haoyao
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In this paper, we present a method that uses the theory of maximum likelihood estimation to improve the precision of the unmanned aerial vehicle (UAV) localization algorithm based on monocular vision. The main goal of this work is to obtain the accurate position information of UAV and achieve the autonomous navigation in complex indoor and outdoor environments. An embedded camera mounted on the UAV platform is used to provide real-time video streams to the vision-based localization algorithm. All the algorithms run in the onboard computer to ensure the real-time property of the system. Simulation of the UAV platform with monocular camera is performed to verify the feasibility of the improved localization algorithm firstly. After the simulation verification, a series of real-time experiments are implemented to demonstrate the precision of the algorithm.
AB - In this paper, we present a method that uses the theory of maximum likelihood estimation to improve the precision of the unmanned aerial vehicle (UAV) localization algorithm based on monocular vision. The main goal of this work is to obtain the accurate position information of UAV and achieve the autonomous navigation in complex indoor and outdoor environments. An embedded camera mounted on the UAV platform is used to provide real-time video streams to the vision-based localization algorithm. All the algorithms run in the onboard computer to ensure the real-time property of the system. Simulation of the UAV platform with monocular camera is performed to verify the feasibility of the improved localization algorithm firstly. After the simulation verification, a series of real-time experiments are implemented to demonstrate the precision of the algorithm.
UR - https://www.scopus.com/pages/publications/85050616500
U2 - 10.1109/RCAR.2017.8311922
DO - 10.1109/RCAR.2017.8311922
M3 - 会议稿件
AN - SCOPUS:85050616500
T3 - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
SP - 561
EP - 566
BT - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
Y2 - 14 July 2017 through 18 July 2017
ER -