A Lightweight and Drift-Free Fusion Strategy for Drone Autonomous and Safe Navigation
Self-localization and state estimation are crucial capabilities for agile drone autonomous navigation. This article presents a lightweight and drift-free vision-IMU-GNSS tightly coupled multisensor fusion (LDMF) strategy for drones’ autonomous and safe navigation. The drone is carried out with a fro...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/1/34 |
| _version_ | 1797443651340599296 |
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| author | Chi Zhang Zhong Yang Haoze Zhuo Luwei Liao Xin Yang Tang Zhu Guotao Li |
| author_facet | Chi Zhang Zhong Yang Haoze Zhuo Luwei Liao Xin Yang Tang Zhu Guotao Li |
| author_sort | Chi Zhang |
| collection | DOAJ |
| description | Self-localization and state estimation are crucial capabilities for agile drone autonomous navigation. This article presents a lightweight and drift-free vision-IMU-GNSS tightly coupled multisensor fusion (LDMF) strategy for drones’ autonomous and safe navigation. The drone is carried out with a front-facing camera to create visual geometric constraints and generate a 3D environmental map. Ulteriorly, a GNSS receiver with multiple constellations support is used to continuously provide pseudo-range, Doppler frequency shift and UTC time pulse signals to the drone navigation system. The proposed multisensor fusion strategy leverages the Kanade–Lucas algorithm to track multiple visual features in each input image. The local graph solution is bounded in a restricted sliding window, which can immensely predigest the computational complexity in factor graph optimization procedures. The drone navigation system can achieve camera-rate performance on a small companion computer. We thoroughly experimented with the LDMF system in both simulated and real-world environments, and the results demonstrate dramatic advantages over the state-of-the-art sensor fusion strategies. |
| first_indexed | 2024-03-09T13:00:08Z |
| format | Article |
| id | doaj.art-c5e411aee6c545108b675d2caf0719f6 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-09T13:00:08Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-c5e411aee6c545108b675d2caf0719f62023-11-30T21:55:22ZengMDPI AGDrones2504-446X2023-01-01713410.3390/drones7010034A Lightweight and Drift-Free Fusion Strategy for Drone Autonomous and Safe NavigationChi Zhang0Zhong Yang1Haoze Zhuo2Luwei Liao3Xin Yang4Tang Zhu5Guotao Li6College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSelf-localization and state estimation are crucial capabilities for agile drone autonomous navigation. This article presents a lightweight and drift-free vision-IMU-GNSS tightly coupled multisensor fusion (LDMF) strategy for drones’ autonomous and safe navigation. The drone is carried out with a front-facing camera to create visual geometric constraints and generate a 3D environmental map. Ulteriorly, a GNSS receiver with multiple constellations support is used to continuously provide pseudo-range, Doppler frequency shift and UTC time pulse signals to the drone navigation system. The proposed multisensor fusion strategy leverages the Kanade–Lucas algorithm to track multiple visual features in each input image. The local graph solution is bounded in a restricted sliding window, which can immensely predigest the computational complexity in factor graph optimization procedures. The drone navigation system can achieve camera-rate performance on a small companion computer. We thoroughly experimented with the LDMF system in both simulated and real-world environments, and the results demonstrate dramatic advantages over the state-of-the-art sensor fusion strategies.https://www.mdpi.com/2504-446X/7/1/34real-time autonomous navigationvision-IMU-GNSS state estimationsensor fusionroboticsintegrated navigationsimultaneous localization and mapping |
| spellingShingle | Chi Zhang Zhong Yang Haoze Zhuo Luwei Liao Xin Yang Tang Zhu Guotao Li A Lightweight and Drift-Free Fusion Strategy for Drone Autonomous and Safe Navigation Drones real-time autonomous navigation vision-IMU-GNSS state estimation sensor fusion robotics integrated navigation simultaneous localization and mapping |
| title | A Lightweight and Drift-Free Fusion Strategy for Drone Autonomous and Safe Navigation |
| title_full | A Lightweight and Drift-Free Fusion Strategy for Drone Autonomous and Safe Navigation |
| title_fullStr | A Lightweight and Drift-Free Fusion Strategy for Drone Autonomous and Safe Navigation |
| title_full_unstemmed | A Lightweight and Drift-Free Fusion Strategy for Drone Autonomous and Safe Navigation |
| title_short | A Lightweight and Drift-Free Fusion Strategy for Drone Autonomous and Safe Navigation |
| title_sort | lightweight and drift free fusion strategy for drone autonomous and safe navigation |
| topic | real-time autonomous navigation vision-IMU-GNSS state estimation sensor fusion robotics integrated navigation simultaneous localization and mapping |
| url | https://www.mdpi.com/2504-446X/7/1/34 |
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