TY - GEN
T1 - Dynamical Alignment and Estimation for Horizontal Attitude of UAV Based on Vision and IMU.
AU - Wu, Xue Yong
AU - Li, Jie
AU - Zhang, Cheng
AU - Yang, Yu
AU - Yang, Ya Chao
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/12/24
Y1 - 2020/12/24
N2 - Exact attitude estimation of an unmanned aerial vehicle (UAV) is the critical requirement for an autonomous and stable flight. Under dynamic conditions, the autopilot of a UAV cannot complete self-alignment and calculate its accurate attitude only by relying on an inertial measurement unit (IMU). This paper presents a dynamical alignment and estimation method for the horizontal attitude (the pitch and roll Euler angle) based on vision and inertial measurement units (IMU). Firstly, the horizontal attitude is estimated through image information of a visible light camera (hereinafter referred to as visual pose), and is used as the initial alignment input for IMU. Then the visual pose is corrected according to the normalized accelerometer output when UAV is stationary. Finally, Sage-Husa Adaptive Kalman Filter (SHAKF) is used for the fusion of the visual pose and the inertial attitude (the attitude calculated by the IMU). The simulation results show that the maximum estimation error of the UAV's horizontal attitude is within 3°, and the average estimation absolute error is less than 1°, which verifies the effectiveness of this method.
AB - Exact attitude estimation of an unmanned aerial vehicle (UAV) is the critical requirement for an autonomous and stable flight. Under dynamic conditions, the autopilot of a UAV cannot complete self-alignment and calculate its accurate attitude only by relying on an inertial measurement unit (IMU). This paper presents a dynamical alignment and estimation method for the horizontal attitude (the pitch and roll Euler angle) based on vision and inertial measurement units (IMU). Firstly, the horizontal attitude is estimated through image information of a visible light camera (hereinafter referred to as visual pose), and is used as the initial alignment input for IMU. Then the visual pose is corrected according to the normalized accelerometer output when UAV is stationary. Finally, Sage-Husa Adaptive Kalman Filter (SHAKF) is used for the fusion of the visual pose and the inertial attitude (the attitude calculated by the IMU). The simulation results show that the maximum estimation error of the UAV's horizontal attitude is within 3°, and the average estimation absolute error is less than 1°, which verifies the effectiveness of this method.
KW - Data fusion
KW - Dynamical alignment
KW - Horizontal attitude
KW - Sage-Husa Adaptive Kalman Filter
UR - http://www.scopus.com/inward/record.url?scp=85102971863&partnerID=8YFLogxK
U2 - 10.1145/3446132.3446161
DO - 10.1145/3446132.3446161
M3 - Conference contribution
AN - SCOPUS:85102971863
T3 - ACM International Conference Proceeding Series
BT - Conference Proceeding - ACAI 2020
PB - Association for Computing Machinery
T2 - 3rd International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2020
Y2 - 24 December 2020 through 26 December 2020
ER -