  Simultaneous Localization & Mapping (SLAM) is the process of building a
mutual relationship between localization and mapping of the subject in its
surrounding environment. With the help of different sensors, various types of
SLAM systems have developed to deal with the problem of building the
relationship between localization and mapping. A limitation in the SLAM process
is the lack of consideration of dynamic objects in the mapping of the
environment. We propose the Dynamic Object Tracking SLAM (DyOb-SLAM), which is
a Visual SLAM system that can localize and map the surrounding dynamic objects
in the environment as well as track the dynamic objects in each frame. With the
help of a neural network and a dense optical flow algorithm, dynamic objects
and static objects in an environment can be differentiated. DyOb-SLAM creates
two separate maps for both static and dynamic contents. For the static
features, a sparse map is obtained. For the dynamic contents, a trajectory
global map is created as output. As a result, a frame to frame real-time based
dynamic object tracking system is obtained. With the pose calculation of the
dynamic objects and camera, DyOb-SLAM can estimate the speed of the dynamic
objects with time. The performance of DyOb-SLAM is observed by comparing it
with a similar Visual SLAM system, VDO-SLAM and the performance is measured by
calculating the camera and object pose errors as well as the object speed
error.
