  In this paper, we demonstrate our work on Gaussian Process Occupancy Mapping
(GPOM). We concentrate on the inefficiency of the frame computation of the
classical GPOM approaches. In robotics, most of the algorithms are required to
run in real time. However, the high cost of computation makes the classical
GPOM less useful. In this paper we dont try to optimize the Gaussian Process
itself, instead, we focus on the application. By analyzing the time cost of
each step of the algorithm, we find a way that to reduce the cost while
maintaining a good performance compared to the general GPOM framework. From our
experiments, we can find that our model enables GPOM to run online and achieve
a relatively better quality than the classical GPOM.
