Abstract

Achieving a robust long-term deployment with mobile robots in the agriculture domain is both a demanded and challenging task. The possibility to have autonomous platforms in the field performing repetitive tasks, such as monitoring or harvesting crops, collides with the difficulties posed by the always-changing appearance of the environment due to seasonality.
With this scope in mind, we report an ongoing effort in the long-term deployment of an autonomous mobile robot in a vineyard, with the main objective of acquiring what we called the Bacchus Long-Term (BLT) Dataset. This dataset consists of multiple sessions recorded in the same area of a vineyard but at different points in time, covering a total of 7 months to capture the whole canopy growth from March until September. The multimodal dataset recorded is acquired with the main focus put on pushing the development and evaluations of different mapping and localisation algorithms for long-term autonomous robots operation in the agricultural domain. Hence, besides the dataset, we also present an initial study in long-term localisation using four different sessions belonging to four different months with different plant stages. We identify that state-of-the-art localisation methods can only cope partially with the amount of change in the environment, making the proposed dataset suitable to establish a benchmark on which the robotics community can test its methods. On our side, we anticipate two solutions pointed at extracting stable temporal features for improving long-term 4d localisation results.
The BLT dataset is available at https://lncn.ac/lcas-blt}{lncn.ac/lcas-blt.