Affiliation:
1. School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China
2. School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China
Abstract
Motion estimation is a major issue in applications of Unmanned Aerial Vehicles (UAVs). This paper proposes an entire solution to solve this issue using information from an Inertial Measurement Unit (IMU) and a monocular camera. The solution includes two steps: visual location and multisensory data fusion. In this paper, attitude information provided by the IMU is used as parameters in Kalman equations, which are different from pure visual location methods. Then, the location of the system is obtained, and it will be utilized as the observation in data fusion. Considering the multiple updating frequencies of sensors and the delay of visual observation, a multi-rate delay-compensated optimal estimator based on the Kalman filter is presented, which could fuse the information and obtain the estimation of 3D positions as well as translational speed. Additionally, the estimator was modified to minimize the computational burden, so that it could run onboard in real time. The performance of the overall solution was assessed using field experiments on a quadrotor system, compared with the estimation results of some other methods as well as the ground truth data. The results illustrate the effectiveness of the proposed method.
Funder
the Natural Science Foundation of Anhui Province
the grant of Scientific Research and Talent Development Foundation of the Hefei University
the Program for Scientific Research Innovation Team in Colleges and Universities of Anhui Province
the Educational Teaching Research Project of Anhui Province
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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