QuadNet: A Hybrid Framework for Quadrotor Dead Reckoning
Quadrotor usage is continuously increasing for both civilian and military applications such as surveillance, mapping, and deliveries. Commonly, quadrotors use an inertial navigation system combined with a global navigation satellite systems receiver for outdoor applications and a camera for indoor/o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/4/1426 |
| _version_ | 1797476735436980224 |
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| author | Artur Shurin Itzik Klein |
| author_facet | Artur Shurin Itzik Klein |
| author_sort | Artur Shurin |
| collection | DOAJ |
| description | Quadrotor usage is continuously increasing for both civilian and military applications such as surveillance, mapping, and deliveries. Commonly, quadrotors use an inertial navigation system combined with a global navigation satellite systems receiver for outdoor applications and a camera for indoor/outdoor applications. For various reasons, such as lighting conditions or satellite signal blocking, the quadrotor’s navigation solution depends only on the inertial navigation system solution. As a consequence, the navigation solution drifts in time due to errors and noises in the inertial sensor measurements. To handle such situations and bind the solution drift, the quadrotor dead reckoning (QDR) approach utilizes pedestrian dead reckoning principles. To that end, instead of flying the quadrotor in a straight line trajectory, it is flown in a periodic motion, in the vertical plane, to enable peak-to-peak (two local maximum points within the cycle) distance estimation. Although QDR manages to improve the pure inertial navigation solution, it has several shortcomings as it requires calibration before usage, provides only peak-to-peak distance, and does not provide the altitude of the quadrotor. To circumvent these issues, we propose QuadNet, a hybrid framework for quadrotor dead reckoning to estimate the quadrotor’s three-dimensional position vector at any user-defined time rate. As a hybrid approach, QuadNet uses both neural networks and model-based equations during its operation. QuadNet requires only the inertial sensor readings to provide the position vector. Experimental results with DJI’s Matrice 300 quadrotor are provided to show the benefits of using the proposed approach. |
| first_indexed | 2024-03-09T21:06:53Z |
| format | Article |
| id | doaj.art-22811cdf05254dcea8a5d6d23cbde406 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:06:53Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-22811cdf05254dcea8a5d6d23cbde4062023-11-23T21:59:20ZengMDPI AGSensors1424-82202022-02-01224142610.3390/s22041426QuadNet: A Hybrid Framework for Quadrotor Dead ReckoningArtur Shurin0Itzik Klein1The Hatter Department of Marine Technologies, University of Haifa, Haifa 3498838, IsraelThe Hatter Department of Marine Technologies, University of Haifa, Haifa 3498838, IsraelQuadrotor usage is continuously increasing for both civilian and military applications such as surveillance, mapping, and deliveries. Commonly, quadrotors use an inertial navigation system combined with a global navigation satellite systems receiver for outdoor applications and a camera for indoor/outdoor applications. For various reasons, such as lighting conditions or satellite signal blocking, the quadrotor’s navigation solution depends only on the inertial navigation system solution. As a consequence, the navigation solution drifts in time due to errors and noises in the inertial sensor measurements. To handle such situations and bind the solution drift, the quadrotor dead reckoning (QDR) approach utilizes pedestrian dead reckoning principles. To that end, instead of flying the quadrotor in a straight line trajectory, it is flown in a periodic motion, in the vertical plane, to enable peak-to-peak (two local maximum points within the cycle) distance estimation. Although QDR manages to improve the pure inertial navigation solution, it has several shortcomings as it requires calibration before usage, provides only peak-to-peak distance, and does not provide the altitude of the quadrotor. To circumvent these issues, we propose QuadNet, a hybrid framework for quadrotor dead reckoning to estimate the quadrotor’s three-dimensional position vector at any user-defined time rate. As a hybrid approach, QuadNet uses both neural networks and model-based equations during its operation. QuadNet requires only the inertial sensor readings to provide the position vector. Experimental results with DJI’s Matrice 300 quadrotor are provided to show the benefits of using the proposed approach.https://www.mdpi.com/1424-8220/22/4/1426dronesdeep learninginertial measurement unitindoor navigationquadrotor dead reckoning |
| spellingShingle | Artur Shurin Itzik Klein QuadNet: A Hybrid Framework for Quadrotor Dead Reckoning Sensors drones deep learning inertial measurement unit indoor navigation quadrotor dead reckoning |
| title | QuadNet: A Hybrid Framework for Quadrotor Dead Reckoning |
| title_full | QuadNet: A Hybrid Framework for Quadrotor Dead Reckoning |
| title_fullStr | QuadNet: A Hybrid Framework for Quadrotor Dead Reckoning |
| title_full_unstemmed | QuadNet: A Hybrid Framework for Quadrotor Dead Reckoning |
| title_short | QuadNet: A Hybrid Framework for Quadrotor Dead Reckoning |
| title_sort | quadnet a hybrid framework for quadrotor dead reckoning |
| topic | drones deep learning inertial measurement unit indoor navigation quadrotor dead reckoning |
| url | https://www.mdpi.com/1424-8220/22/4/1426 |
| work_keys_str_mv | AT arturshurin quadnetahybridframeworkforquadrotordeadreckoning AT itzikklein quadnetahybridframeworkforquadrotordeadreckoning |