  Most of the existing robotic exploration schemes use occupancy grid
representations and geometric targets known as frontiers. The occupancy grid
representation relies on the assumption of independence between grid cells and
ignores structural correlations present in the environment. We develop a
Gaussian Processes (GPs) occupancy mapping technique that is computationally
tractable for online map building due to its incremental formulation and
provides a continuous model of uncertainty over the map spatial coordinates.
The standard way to represent geometric frontiers extracted from occupancy maps
is to assign binary values to each grid cell. We extend this notion to novel
probabilistic frontier maps computed efficiently using the gradient of the GP
occupancy map. We also propose a mutual information-based greedy exploration
technique built on that representation that takes into account all possible
future observations. A major advantage of high-dimensional map inference is the
fact that such techniques require fewer observations, leading to a faster map
entropy reduction during exploration for map building scenarios. Evaluations
using the publicly available datasets show the effectiveness of the proposed
framework for robotic mapping and exploration tasks.
