Citation: Dai, J.; Hao, X.; Liu, S.; Ren,
Z. Research on UAV Robust Adaptive
Positioning Algorithm Based on
IMU/GNSS/VO in Complex Scenes.
Sensors 2022, 22, 2832. https://
doi.org/10.3390/s22082832
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Received: 8 March 2022
Accepted: 4 April 2022
Published: 7 April 2022
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Article
Research on UAV Robust Adaptive Positioning Algorithm
Based on IMU/GNSS/VO in Complex Scenes
Jun Dai
1,2
, Xiangyang Hao
1,
*, Songlin Liu
1
and Zongbin Ren
1
1
Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China;
daijun502@163.com (J.D.); lsl759514@126.com (S.L.); rzb13017600350@163.com (Z.R.)
2
School of Aerospace Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450001, China
* Correspondence: xiangyanghao2004@163.com; Tel.: +86-135-9804-9970
Abstract:
As an important component of autonomous intelligent systems, the research on au-
tonomous positioning algorithms used by UAVs is of great significance. In order to resolve the
problem whereby the GNSS signal is interrupted, and the visual sensor lacks sufficient feature points
in complex scenes, which leads to difficulties in autonomous positioning, this paper proposes a new
robust adaptive positioning algorithm that ensures the robustness and accuracy of autonomous navi-
gation and positioning in UAVs. On the basis of the combined navigation model of vision/inertial
navigation and satellite/inertial navigation, based on ESKF, a multi-source fusion model based on a
federated Kalman filter is here established. Furthermore, a robust adaptive localization algorithm is
proposed, which uses robust equivalent weights to estimate the sub-filters, and then uses the sub-filter
state covariance to adaptively assign information sharing coefficients. After simulation experiments
and dataset verification, the results show that the robust adaptive algorithm can effectively limit
the impact of gross errors in observations and mathematical model deviations and can automati-
cally update the information sharing coefficient online according to the sub-filter equivalent state
covariance. Compared with the classical federated Kalman algorithm and the adaptive federated
Kalman algorithm, our algorithm can meet the real-time requirements of navigation, and the accuracy
of position, velocity, and attitude measurement is improved by 2–3 times. The robust adaptive
localization algorithm proposed in this paper can effectively improve the reliability and accuracy
of autonomous navigation systems in complex scenes. Moreover, the algorithm is general—it is not
intended for a specific scene or a specific sensor combination– and is applicable to individual scenes
with varied sensor combinations.
Keywords:
UAV; robust adaptation filter; multi-source fusion; error state Kalman filter (ESKF);
information sharing coefficient
1. Introduction
With the rapid development of research on autonomous and intelligent unmanned
systems, UAVs can now operate in high-risk and complex environments, thus expanding
the scope for human activities by virtue of their flexibility, low cost, and strong adaptability.
Therefore, research on their application is of great significance to the military and civilian
fields [1–3].
At present, sensors that can be used for autonomous navigation and positioning
include inertial sensors, visual sensors, satellite navigation sensors, and so on [
4
]. As
the heart and eyes of autonomous navigation systems, these sensors are intrinsic to the
realization of autonomous and intelligent drones. However, satellite signals are interrupted
by urban canyons and complex environments; in fog, heavy snow, and disaster scenarios,
visual sensors lack sufficient feature points; inertial sensors face problems such as long-term
error accumulation. Therefore, a single type of sensor alone cannot meet the autonomous
navigation requirements of UAVs used in complex scenarios; multi-source sensors need
Sensors 2022, 22, 2832. https://doi.org/10.3390/s22082832 https://www.mdpi.com/journal/sensors
