From grids to continuous occupancy maps through area kernels

In this work, we introduce a novel method for two-dimensional occupancy mapping using Gaussian processes. We address mapping as the task of classifying the robot's environment between free and occupied regions. The biggest challenge when using Gaussian processes for this task is the size of the input datasets. We tackle this problem by introducing a novel kernel, able to use as input data aggregated into two-dimensional cells. Using this kernel, we achieve comparable performance to previous Gaussian process occupancy mapping techniques in a fraction of the time taken by them. The approach can also be used to convert popular occupancy grids into continuous Gaussian process occupancy maps.