Collection and Evaluation of a Long-Term 4D Agri-Robotic Dataset
This work addresses the problem of robust long-term autonomy for agricultural robots, which is incremental as it builds on existing localization methods with a new dataset and feature extraction approach.
The authors tackled the challenge of long-term robot localization in changing agricultural environments by collecting a multi-month dataset from a vineyard and analyzing map-based localization across four sessions, identifying failures due to visual differences and proposing LTS-Net to extract stable temporal features for improvement.
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.