Survey and Research Challenges in Monocular Visual Odometry
Abstract
This study discusses traditional techniques and deep learning-based methodologies for monocular visual odometry. The paper presents an overview of state-of-the-art methods that could be useful for displacement measurement applications. From the review of the state of art, it has been seen in particular that new deep learning-based methods may reduce dependencies on the equipment in the system, with respect to traditional methods.
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- Authors
- Neyestani, A., Picariello, F., Basiri, A., Daponte, P., & Vito, L. D.
- Affiliations
- University of Sannio (IT)
- Venue
- 2023 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv)
- Publisher
- IEEE
- Published
- 2023-05-29
- Pages
- 107-112
- OpenAlex
- W4383097518
- Cited by
- 17
- References
- 39
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 6 · 2023: 2
- FWCI
- 9.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Workshop on Metrology for Living Environment (MetroLivEnv) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- monocular visual odometry, displacement measurement
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