Most Simultaneous localisation and mapping (SLAM) systems have traditionally assumed a static world, which does not align with real-world scenarios. To enable robots to safely navigate and plan in dynamic environments, it is essential to employ representations capable of handling moving objects. Dynamic SLAM is an emerging field in SLAM research as it improves the overall system accuracy while providing additional estimation of object motions. State-of-the-art literature informs two main formulations for Dynamic SLAM, representing dynamic object points in either the world or object coordinate frame. While expressing object points in a local reference frame may seem intuitive, it may not necessarily lead to the most accurate and robust solutions. This paper conducts and presents a thorough analysis of various Dynamic SLAM formulations, identifying the best approach to address the problem. To this end, we introduce a front-end agnostic framework using GTSAM that can be used to evaluate various Dynamic SLAM formulations.

翻译：大多数同时定位与地图构建（SLAM）系统传统上假设世界是静态的，但这与真实世界场景不符。为使机器人在动态环境中安全导航和规划，必须采用能够处理移动物体的表征方法。动态SLAM是SLAM研究中的一个新兴领域，它通过提供对物体运动的额外估计来提升系统整体精度。最新文献提出了两种主要的动态SLAM形式，分别在世界坐标系或物体坐标系中表示动态物体点。尽管在局部参考系中表示物体点可能直观，但这未必能带来最准确和鲁棒的解决方案。本文对多种动态SLAM公式进行了深入分析，确定了解决该问题的最佳方法。为此，我们引入了一个基于GTSAM的前端无关框架，可用于评估各种动态SLAM公式。