The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics

We present a multi-modal dataset collected in a soybean crop field, comprising over two hours of recorded data from sensors such as stereo infrared camera, color camera, accelerometer, gyroscope, magnetometer, GNSS (Single Point Positioning, Real-Time Kinematic and Post-Processed Kinematic), and wheel odometry. This dataset captures key challenges inherent to robotics in agricultural environments, including variations in natural lighting, motion blur, rough terrain, and long, perceptually aliased sequences. By addressing these complexities, the dataset aims to support the development and benchmarking of advanced algorithms for localization, mapping, perception, and navigation in agricultural robotics. The platform and data collection system is designed to meet the key requirements for evaluating multi-modal SLAM systems, including hardware synchronization of sensors, 6-DOF ground truth and loops on long trajectories. We run multimodal state-of-the art SLAM methods on the dataset, showcasing the existing limitations in their application on agricultural settings. The dataset and utilities to work with it are released on https://cifasis.github.io/rosariov2/.

Reusability and Modifiability in Robotics Software (Extended Version)

We show the design of the software of the microcontroller unit of a weeding robot based on the Process Control architectural style and design patterns. The design consists of 133 modules resulting from using 8 design patterns for a total of 30 problems. As a result the design yields more reusable components and an easily modifiable and extensible program. Design documentation is also presented. Finally, the implementation (12 KLOC of C++ code) is empirically evaluated to prove that the design does not produce an inefficient implementation.