  Long-term autonomy is one of the most demanded capabilities looked into a
robot. The possibility to perform the same task over and over on a long
temporal horizon, offering a high standard of reproducibility and robustness,
is appealing. Long-term autonomy can play a crucial role in the adoption of
robotics systems for precision agriculture, for example in assisting humans in
monitoring and harvesting crops in a large orchard. With this scope in mind, we
report an ongoing effort in the long-term deployment of an autonomous mobile
robot in a vineyard for data collection across multiple months. The main aim is
to collect data from the same area at different points in time so to be able to
analyse the impact of the environmental changes in the mapping and localisation
tasks. In this work, we present a map-based localisation study taking 4 data
sessions. We identify expected failures when the pre-built map visually differs
from the environment's current appearance and we anticipate LTS-Net, a solution
pointed at extracting stable temporal features for improving long-term 4D
localisation results.
