  The exploration of unknown environments using robots is a task that
integrates different areas such as location, mapping, and planning. For
mapping, there are numerous methods to represent the environments through which
a robot can travel, in two and three dimensions. The probabilistic occupation
grid, Octomap, and STVL can be mentioned among the most important in recent
years. Nowadays, RGB-D cameras are widely used to generate a detailed
representation of the environment. RGB-D camera measurements present a large
volume of data, which must be reduced in order to be used in platforms with
limited computing resources. This work presents an implementation of the point
cloud decimation method capable of being executed on platforms with unified
memory. It consists of reducing the point cloud iteratively using a subdivision
of space. Results were obtained for different sizes of grids, platforms, and
scenarios, both real and simulated. The results indicate that in embedded
systems it is convenient to have architectures that share memory between CPU
and GPU to optimize data block communication processes.
