  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.
