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.

中文翻译：

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调查：动态视觉同时定位和建图需要哪些特征？

近年来，动态环境中的同步定位和映射（动态 SLAM）引起了学术界和工业界的极大关注。这项技术的一些开创性工作扩大了机器人应用的潜力。与静态世界假设下的标准 SLAM 相比，动态 SLAM 将特征分为静态和动态类别，并适当地利用每种类型的特征。因此，动态 SLAM 可以为在复杂动态环境中运行的智能机器人提供更稳健的定位。此外，为了满足一些高级任务的需求，动态 SLAM 可以与多目标跟踪集成。本文从特征选择的角度对动态SLAM进行了综述。