  This paper presents an automated method for creating spatial maps of soil
condition with an outdoor mobile robot. Effective soil mapping on farms can
enhance yields, reduce inputs and help protect the environment. Traditionally,
data are collected manually at an arbitrary set of locations, then soil maps
are constructed offline using Kriging, a form of Gaussian process regression.
This process is laborious and costly, limiting the quality and resolution of
the resulting information. Instead, we propose to use an outdoor mobile robot
for automatic collection of soil condition data, building soil maps online and
also adapting the robot's exploration strategy on-the-fly based on the current
quality of the map. We show how using Kriging variance as a reward function for
robotic exploration allows for both more efficient data collection and better
soil models. This work presents the theoretical foundations for our proposal
and an experimental comparison of exploration strategies using soil compaction
data from a field generated with a mobile robot.
