In recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted
significant attention from both academia and industry. Some pioneering work on this technique has expanded the
potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM
divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic
SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments.
Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object
tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of
the advantages and disadvantages of different visual features is provided in this article.