Simultaneous Localization and Mapping (SLAM) algorithms are frequently deployed to support a wide range of robotics applications, such as autonomous navigation in unknown environments, and scene mapping in virtual reality. Many of these applications require autonomous agents to perform SLAM in highly dynamic scenes. To this end, this tutorial extends a recently introduced, unifying optimization-based SLAM backend framework to environments with moving objects and features. Using this framework, we consider a rapprochement of recent advances in dynamic SLAM. Moreover, we present dynamic EKF SLAM: a novel, filtering-based dynamic SLAM algorithm generated from our framework, and prove that it is mathematically equivalent to a direct extension of the classical EKF SLAM algorithm to the dynamic environment setting. Empirical results with simulated data indicate that dynamic EKF SLAM can achieve high localization and mobile object pose estimation accuracy, as well as high map precision, with high efficiency.

翻译：同时定位与地图构建（SLAM）算法广泛应用于支持各类机器人应用，例如未知环境中的自主导航以及虚拟现实中的场景建图。其中许多应用要求自主智能体在高度动态的场景中执行SLAM。为此，本教程将近期提出的统一化优化型SLAM后端框架扩展至包含运动物体与特征的环境。借助该框架，我们整合了动态SLAM领域的最新进展。此外，本文提出动态EKF SLAM：一种基于该框架生成的新型滤波型动态SLAM算法，并证明其在数学上等价于经典EKF SLAM算法向动态环境设置的直接扩展。基于仿真数据的实验结果表明，动态EKF SLAM能够以高效率实现高精度的定位、移动物体位姿估计以及高精度的地图构建。