  Vision-based sensors have shown significant performance, accuracy, and
efficiency gain in Simultaneous Localization and Mapping (SLAM) systems in
recent years. In this regard, Visual Simultaneous Localization and Mapping
(VSLAM) methods refer to the SLAM approaches that employ cameras for pose
estimation and map generation. We can see many research works that demonstrated
VSLAMs can outperform traditional methods, which rely only on a particular
sensor, such as a Lidar, even with lower costs. VSLAM approaches utilize
different camera types (e.g., monocular, stereo, and RGB-D), have been tested
on various datasets (e.g., KITTI, TUM RGB-D, and EuRoC) and in dissimilar
environments (e.g., indoors and outdoors), and employ multiple algorithms and
methodologies to have a better understanding of the environment. The mentioned
variations have made this topic popular for researchers and resulted in a wide
range of VSLAMs methodologies. In this regard, the primary intent of this
survey is to present the recent advances in VSLAM systems, along with
discussing the existing challenges and trends. We have given an in-depth
literature survey of forty-five impactful papers published in the domain of
VSLAMs. We have classified these manuscripts by different characteristics,
including the novelty domain, objectives, employed algorithms, and semantic
level. We also discuss the current trends and future directions that may help
researchers investigate them.
