ArticleSensors (Basel, Switzerland)2020
Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Robust unmanned aerial vehicles tracking amid electronic interference utilizing auxiliary particle filtering.PloS one · 2025Article
- Inspection of Floating Offshore Wind Turbines Using Multi-Rotor Unmanned Aerial Vehicles: Literature Review and Trends.Sensors (Basel, Switzerland) · 2024Review
- The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.Sensors (Basel, Switzerland) · 2021Review
- Kalman Filter-Based RAIM for Reliable Aircraft Positioning with GPS and NavIC Constellations.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
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