Deep Learning Techniques for Visual SLAM : a Survey

Visual Simultaneous Localization and Mapping (VSLAM) has attracted considerable attention in recent years. This task involves using visual sensors to localize a robot while simultaneously constructing an internal representation of its environment. Traditional VSLAM methods involve the laborious hand...

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Veröffentlicht in:IEEE access Jg. 11; S. 1
Hauptverfasser: Mokssit, Saad, Licea, Daniel Bonilla, Guermah, Bassma, Ghogho, Mounir
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Active learning
Deep learning
Estimation
Feature extraction
Joint learning
Learning systems
Location awareness
Robot sensing systems
Robots
Simultaneous localization and mapping
Survey
Taxonomies
Taxonomy
Visual odometry
Visual SLAM
Visual tasks
Visualization
ISSN:2169-3536, 2169-3536
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Zusammenfassung:Visual Simultaneous Localization and Mapping (VSLAM) has attracted considerable attention in recent years. This task involves using visual sensors to localize a robot while simultaneously constructing an internal representation of its environment. Traditional VSLAM methods involve the laborious hand-crafted design of visual features and complex geometric models. As a result, they are generally limited to simple environments with easily identifiable textures. Recent years, however, have witnessed the development of deep learning techniques for VSLAM. This is primarily due to their capability of modeling complex features of the environment in a completely data-driven manner. In this paper, we present a survey of relevant deep learning-based VSLAM methods and suggest a new taxonomy for the subject. We also discuss some of the current challenges and possible directions for this field of study.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3249661