UAV indoor localization fusing IMU UWB and optical flow sensor

Abstract

In the past, limited by the accuracy of indoor localization technology, UAVs were mainly used in outdoor environment with GPS signal. With the increasing demand for UAVs in indoor rescue, surveillance, and performance missions, the research on UAV indoor localization technology has become important. In recent years, UWB equipment has developed rapidly and has become a suitable substitute for GPS due to its advantages of high localization accuracy, large data transmission capacity and strong obstacle penetration ability. However, its robustness is poor so that the appearance of outliers in use is inevitable.

Traditional IMU and optical flow sensors cannot be used alone to get position due to the large drift of the sensor itself and the lack of absolute coordinate information. At present, the main solution is to fuse the data of different sensors to meet the requirements of UAV indoor localization system.

In this thesis, the data of IMU, optical flow sensor and UWB are fused by Kalman filter algorithm that can circumvent the shortcomings of single sensor localization. And some algorithms are also used to improve the performance of cheap embedded IMU. The effect of Kalman filter is verified by static and dynamic experiments. Compared with the single sensor, the variance of localization error of fusion algorithm reduces 34%-98%, and the defects of each sensor are effectively avoided.