A survey of state-of-the-art on visual SLAM. (1st November 2022)

• Record Type: Journal Article
• Authors: Abaspur Kazerouni, Iman; Fitzgerald, Luke; Dooly, Gerard; Toal, Daniel
• Is Part Of: Expert systems with applications. Volume 205(2022)
• Publication Date: 2022-11-01
• Subjects: SLAM -- Feature matching -- Sensors -- Robot -- Deep learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117734
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A comprehensive review for mobile robots Visual Slam application. Including the state-of-the-art deep learning methods for Visual Slam. Simulation results for each section based on feature extraction techniques. Abstract: This paper is an overview to Visual Simultaneous Localization and Mapping (V-SLAM). We discuss the basic definitions in the SLAM and vision system fields and provide a review of the state-of-the-art methods utilized for mobile robot's vision and SLAM. This paper covers topics from the basic SLAM methods, vision sensors, machine vision algorithms for feature extraction and matching, Deep Learning (DL) methods and datasets for Visual Odometry (VO) and Loop Closure (LC) in V-SLAM applications. Several feature extraction and matching algorithms are simulated to show a better vision of feature-based techniques.