The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics

World population will grow by a third by 2050, directly impacting global food demand (Fukase and Martin (2020)). In this context, the agricultural industry should increase its production to satisfy such demand. The use of autonomous robots to carry out agricultural tasks such as seeding, harvesting, weed remotion, pest control among others is an attractive solution since it can improve the production time in a sustainable manner reducing the environmental impact and pollution. However, the implementation of autonomous robots in the agricultural field is a challenging work due the rough terrain, natural light variations, perceptual aliasing, areas with GNSS-denied signal, and the long-term robot operation required to carry out the desired applications. All these challenges cause robot localization methods to fail or perform poorly, making them impractical for real agricultural tasks, as evidenced in Cremona et al. (2022, 2023); Soncini et al. (2024); Cox et al. (2023); Bai et al. (2023); Ait et al. (2023). In the last decade, there has been a growing trend towards the creation and public availability of agricultural datasets, enabling researchers to test new techniques and develop more sophisticated algorithms to address these challenges, such as Pire et al. (2019); Kragh et al. (2017); Tanco et al. (2024). However, none of them are properly curated for evaluating multi-modal SLAM algorithms. Effective multi-modal SLAM evaluation imposes specific requirements such as hardware-synchronized sensors, 6-DOF ground-truth, and trajectories with loops to effectively test loop closure algorithms. In this work, we present a multi-modal dataset recorded by the weed removing robot developed at CIFASIS (CONICET - UNR) in a soybean agricultural field.