Multi-sensor fusion for robust indoor localization of industrial UAVs using particle filter

Robotic platforms including Unmanned Aerial Vehicles (UAVs) require an accurate and reliable source of position information, especially in indoor environments where GNSS cannot be used. This is typically accomplished by using multiple independent position sensors. This paper presents a UAV position...

Full description

Saved in:
Bibliographic Details
Published in:Journal of Electrical Engineering 2024-08, Vol.75 (4), p.304-316
Main Authors: Mráz, Eduard, Trizuljak, Adam, Rajchl, Matej, Sedláček, Martin, Štec, Filip, Stanko, Jaromír, Rodina, Jozef
Format: Article
Language:English
Subjects:
Accuracy
Airborne/spaceborne computers
Algorithms
Cameras
Electrical engineering
Indoor environments
Kalman filters
Localization
Multisensor fusion
particle filter
Position errors
Position sensing
Robotics
Robots
Robustness
sensor fusion
Sensors
UAS
UAV
Unmanned aerial vehicles
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Robotic platforms including Unmanned Aerial Vehicles (UAVs) require an accurate and reliable source of position information, especially in indoor environments where GNSS cannot be used. This is typically accomplished by using multiple independent position sensors. This paper presents a UAV position estimation mechanism based on a particle filter, that combines information from visual odometry cameras and visual detection of fiducial markers. The article proposes very compact, lightweight and robust method for indoor localization, that can run with high frequency on the UAV’s onboard computer. The filter is implemented such that it can seamlessly handle sensor failures and disconnections. Moreover, the filter can be extended to include inputs from additional sensors. The implemented approach is validated on data from real-life UAV test flights, where average position error under 0.4 m was achieved.
ISSN:1339-309X
1335-3632
1339-309X
DOI:10.2478/jee-2024-0037