Bacchus Long‐Term (BLT) data set: Acquisition of the agricultural multimodal BLT data set with automated robot deployment

• Published in: Journal of field robotics Vol. 41; no. 7; pp. 2280 - 2298
• Main Authors: Polvara, Riccardo, Molina, Sergi, Hroob, Ibrahim, Papadimitriou, Alexios, Tsiolis, Konstantinos, Giakoumis, Dimitrios, Likothanassis, Spiridon, Tzovaras, Dimitrios, Cielniak, Grzegorz, Hanheide, Marc
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.10.2024
• Subjects: agriculture; Algorithms; autonomous navigation; Data acquisition; Datasets; field robotics; Harvesting; Localization; long‐term; mobile robots; robot localization; Robotics; Robots; Vineyards; Wineries & vineyards
• ISSN: 1556-4959; 1556-4967
• DOI: 10.1002/rob.22228

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) data set. This data set 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 data set recorded is acquired with the main focus put on pushing the development and evaluations of different mapping and localization algorithms for long‐term autonomous robots operation in the agricultural domain. Hence, besides the data set, we also present an initial study in long‐term localization using four different sessions belonging to four different months with different plant stages. We identify that state‐of‐the‐art localization methods can only cope partially with the amount of change in the environment, making the proposed data set 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 localization results. The BLT data set is available at https://lncn.ac/lcas-blt.