  In the field of indoor robotics, accurately localizing and mapping in dynamic
environments using point clouds can be a challenging task due to the presence
of dynamic points. These dynamic points are often represented by people in
indoor environments, but in industrial settings with moving machinery, there
can be various types of dynamic points. This study introduces DynaHull, a novel
technique designed to enhance indoor mapping accuracy by effectively removing
dynamic points from point clouds. DynaHull works by leveraging the observation
that, over multiple scans, stationary points have a higher density compared to
dynamic ones. Furthermore, DynaHull addresses mapping challenges related to
unevenly distributed points by clustering the map into smaller sections. In
each section, the density factor of each point is determined by dividing the
number of neighbors by the volume these neighboring points occupy using a
convex hull method. The algorithm removes the dynamic points using an adaptive
threshold based on the point count of each cluster, thus reducing the false
positives. The performance of DynaHull was compared to state-of-the-art
techniques, such as ERASOR, Removert, OctoMap plus SOR, and Dynablox, by
comparing each method to the ground truth map created during a low activity
period in which only a few dynamic points were present. The results indicated
that DynaHull outperformed these techniques in various metrics, noticeably in
the Earth Mover's Distance, false negatives, and false positives.
