  Accurate soil mapping is critical for a highly-automated agricultural vehicle
to successfully accomplish important tasks including seeding, ploughing,
fertilising and controlled traffic, with limited human supervision, ensuring at
the same time high safety standards. In this research, a multi-sensor ground
mapping and characterisation approach is proposed, whereby data coming from
heterogeneous but complementary sensors, mounted on-board an unmanned rover,
are combined to generate a multi-layer map of the environment and specifically
of the supporting ground. The sensor suite comprises both exteroceptive and
proprioceptive devices. Exteroceptive sensors include a stereo camera, a
visible and near infrared camera and a thermal imager. Proprioceptive data
consist of the vertical acceleration of the vehicle sprung mass as acquired by
an inertial measurement unit. The paper details the steps for the integration
of the different sensor data into a unique multi-layer map and discusses a set
of exteroceptive and proprioceptive features for soil characterisation and
change detection. Experimental results obtained with an all-terrain vehicle
operating on different ground surfaces are presented. It is shown that the
proposed technologies could be potentially used to develop all-terrain
self-driving systems in agriculture. In addition, multi-modal soil maps could
be useful to feed farm management systems that would present to the user
various soil layers incorporating colour, geometric, spectral and mechanical
properties.
