A survey of state-of-the-art on visual SLAM

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.

Automated summary

This paper provides an overview of Visual Simultaneous Localization and Mapping (V-SLAM), covering basic concepts, state-of-the-art methods in vision and SLAM, and the use of Deep Learning techniques and datasets for Visual Odometry and Loop Closure in V-SLAM applications.