TY - GEN
T1 - Feedback State Estimation for Multi-rotor Drones Stabilisation Using Low-Pass Filter and a Complementary Kalman Filter
AU - Kangunde, Vemema
AU - Mohutsiwa, Lucky O.
AU - Jamisola, Rodrigo S.
N1 - Funding Information:
The authors would like to acknowledge the funding support on this work from the Botswana International University of Science and Technology (BIUST) Drones Project with project number P00015.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - This paper presents a low-pass filter and a complementary Kalman filter for improving the quality of UAV feedback control information by means of onboard UAV sensor fusion. The control of UAVs requires real-time feedback from onboard sensors to update the status of the UAV. Localisation is achieved by determining the UAV attitude and position. While the UAV position can be provided by sensors such as the GPS receiver, attitude estimation for UAVs is provided, mostly, by an IMU. IMU sensors are mainly the accelerometer, gyroscope, and magnetometer. These sensors have limitations in the accuracy caused by sensor drift and noise and thus needed post-processing for sensor data improvement. This paper focuses on correctly estimating roll and pitch angles using a Kalman filter and tests the performance of the estimated feedback angle information on a quadrotor platform.
AB - This paper presents a low-pass filter and a complementary Kalman filter for improving the quality of UAV feedback control information by means of onboard UAV sensor fusion. The control of UAVs requires real-time feedback from onboard sensors to update the status of the UAV. Localisation is achieved by determining the UAV attitude and position. While the UAV position can be provided by sensors such as the GPS receiver, attitude estimation for UAVs is provided, mostly, by an IMU. IMU sensors are mainly the accelerometer, gyroscope, and magnetometer. These sensors have limitations in the accuracy caused by sensor drift and noise and thus needed post-processing for sensor data improvement. This paper focuses on correctly estimating roll and pitch angles using a Kalman filter and tests the performance of the estimated feedback angle information on a quadrotor platform.
UR - http://www.scopus.com/inward/record.url?scp=85111409752&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111409752&partnerID=8YFLogxK
U2 - 10.1109/ICUAS51884.2021.9476809
DO - 10.1109/ICUAS51884.2021.9476809
M3 - Conference contribution
AN - SCOPUS:85111409752
T3 - 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021
SP - 188
EP - 194
BT - 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021
Y2 - 15 June 2021 through 18 June 2021
ER -