Deep Learning Techniques for Visual SLAM : a Survey

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Mokssit, Saad, Licea, Daniel Bonilla, Guermah, Bassma, Ghogho, Mounir
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/ACCESS.2023.3249661

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.