Authors:
Fan Zhou
1
;
Wei Zheng
1
and
Zengfu Wang
2
Affiliations:
1
University of Science and Technology of China, China
;
2
Chinese Academy of Sciences, China
Keyword(s):
Adaptive Noise Variance Identification, Vision Location, Motion Estimation, Kalman Filter.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Robotics
;
Sensors and Early Vision
;
Software Engineering
Abstract:
Vision location methods have been widely used in the motion estimation of unmanned aerial vehicles (UAVs). The noise of the vision location result is usually modeled as the white gaussian noise so that this result could be utilized as the observation vector in the kalman filter to estimate the motion of the vehicle. Since the noise of the vision location result is affected by external environment, the variance of the noise is uncertain. However, in previous researches the variance is usually set as a fixed empirical value, which will lower the
accuracy of the motion estimation. In this paper, a novel adaptive noise variance identification (ANVI) method is proposed, which utilizes the special kinematic property of the UAV for frequency analysis and adaptively identify the variance of the noise. Then, the adaptively identified variance are used in the kalman filter for accurate motion estimation. The performance of the proposed method is assessed by simulations and field experiments on
a quadrotor system. The results illustrate the effectiveness of the method.
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