Enhancing Visual–Inertial Odometry Robustness and Accuracy in Challenging Environments
Visual–Inertial Odometry (VIO) algorithms are widely adopted for autonomous drone navigation in GNSS-denied environments. However, conventional monocular and stereo VIO setups often lack robustness under challenging environmental conditions or during aggressive maneuvers, due to the sensitivity of v...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Robotics |
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| Online Access: | https://www.mdpi.com/2218-6581/14/6/71 |
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| author | Alessandro Minervini Adrian Carrio Giorgio Guglieri |
| author_facet | Alessandro Minervini Adrian Carrio Giorgio Guglieri |
| author_sort | Alessandro Minervini |
| collection | DOAJ |
| description | Visual–Inertial Odometry (VIO) algorithms are widely adopted for autonomous drone navigation in GNSS-denied environments. However, conventional monocular and stereo VIO setups often lack robustness under challenging environmental conditions or during aggressive maneuvers, due to the sensitivity of visual information to lighting, texture, and motion blur. In this work, we enhance an existing open-source VIO algorithm to improve both the robustness and accuracy of the pose estimation. First, we integrate an IMU-based motion prediction module to improve feature tracking across frames, particularly during high-speed movements. Second, we extend the algorithm to support a multi-camera setup, which significantly improves tracking performance in low-texture environments. Finally, to reduce the computational complexity, we introduce an adaptive feature selection strategy that dynamically adjusts the detection thresholds according to the number of detected features. Experimental results validate the proposed approaches, demonstrating notable improvements in both accuracy and robustness across a range of challenging scenarios. |
| format | Article |
| id | doaj-art-9bb325d53b3348a387abe30b39ede5cb |
| institution | OA Journals |
| issn | 2218-6581 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj-art-9bb325d53b3348a387abe30b39ede5cb2025-08-20T02:21:57ZengMDPI AGRobotics2218-65812025-05-011467110.3390/robotics14060071Enhancing Visual–Inertial Odometry Robustness and Accuracy in Challenging EnvironmentsAlessandro Minervini0Adrian Carrio1Giorgio Guglieri2Departement of Aerospace and Mechanical Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 1029 Torino, TO, ItalyDronomy, Paseo de la Castellana 40, 8th Floor, 28046 Madrid, SpainDepartement of Aerospace and Mechanical Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 1029 Torino, TO, ItalyVisual–Inertial Odometry (VIO) algorithms are widely adopted for autonomous drone navigation in GNSS-denied environments. However, conventional monocular and stereo VIO setups often lack robustness under challenging environmental conditions or during aggressive maneuvers, due to the sensitivity of visual information to lighting, texture, and motion blur. In this work, we enhance an existing open-source VIO algorithm to improve both the robustness and accuracy of the pose estimation. First, we integrate an IMU-based motion prediction module to improve feature tracking across frames, particularly during high-speed movements. Second, we extend the algorithm to support a multi-camera setup, which significantly improves tracking performance in low-texture environments. Finally, to reduce the computational complexity, we introduce an adaptive feature selection strategy that dynamically adjusts the detection thresholds according to the number of detected features. Experimental results validate the proposed approaches, demonstrating notable improvements in both accuracy and robustness across a range of challenging scenarios.https://www.mdpi.com/2218-6581/14/6/71VIOmulti-cameralocalizationGNSS-denieddronesrobotics |
| spellingShingle | Alessandro Minervini Adrian Carrio Giorgio Guglieri Enhancing Visual–Inertial Odometry Robustness and Accuracy in Challenging Environments Robotics VIO multi-camera localization GNSS-denied drones robotics |
| title | Enhancing Visual–Inertial Odometry Robustness and Accuracy in Challenging Environments |
| title_full | Enhancing Visual–Inertial Odometry Robustness and Accuracy in Challenging Environments |
| title_fullStr | Enhancing Visual–Inertial Odometry Robustness and Accuracy in Challenging Environments |
| title_full_unstemmed | Enhancing Visual–Inertial Odometry Robustness and Accuracy in Challenging Environments |
| title_short | Enhancing Visual–Inertial Odometry Robustness and Accuracy in Challenging Environments |
| title_sort | enhancing visual inertial odometry robustness and accuracy in challenging environments |
| topic | VIO multi-camera localization GNSS-denied drones robotics |
| url | https://www.mdpi.com/2218-6581/14/6/71 |
| work_keys_str_mv | AT alessandrominervini enhancingvisualinertialodometryrobustnessandaccuracyinchallengingenvironments AT adriancarrio enhancingvisualinertialodometryrobustnessandaccuracyinchallengingenvironments AT giorgioguglieri enhancingvisualinertialodometryrobustnessandaccuracyinchallengingenvironments |