  Grid maps, especially occupancy grid maps, are ubiquitous in many mobile
robot applications. To simplify the process of learning the map, grid maps
subdivide the world into a grid of cells, whose occupancies are independently
estimated using only measurements in the perceptual field of the particular
cell. However, the world consists of objects that span multiple cells, which
means that measurements falling onto a cell provide evidence on the occupancy
of other cells belonging to the same object. This correlation is not captured
by current models. In this work, we present a way to generalize the update of
grid maps relaxing the assumption of independence by modeling the relationship
between the measurements and the occupancy of each cell as a set of latent
variables, and jointly estimating those variables and the posterior of the map.
Additionally, we propose a method to estimate the latent variables by
clustering based on semantic labels and an extension to the Normal
Distributions Transfer Occupancy Map (NDT-OM) to facilitate the proposed map
update method. We perform comprehensive experiments of map creation and
localization with real world data sets, and show that the proposed method
creates better maps in highly dynamic environments compared to state-of-the-art
methods. Finally, we demonstrate the ability of the proposed method to remove
occluded objects from the map in a lifelong map update scenario.
