Author:
Du Hao,Wang Wei,Xu Chaowen,Xiao Ran,Sun Changyin
Abstract
The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference33 articles.
1. State Estimation for Robotics;Timothy,2018
2. Multi-Sensor Data Fusion with MATLAB;Jitendra,2010
3. Heterogeneous Multisensor Fusion for Mobile Platform Three-Dimensional Pose Estimation;Hanieh;J. Dyn. Syst. Meas. Control,2017
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