The GREENBOT dataset: Multimodal mobile robotic dataset for a typical Mediterranean greenhouse

This paper introduces an innovative dataset specifically crafted for challenging agricultural settings (a greenhouse), where achieving precise localization is of paramount importance. The dataset was gathered using a mobile platform equipped with a set of sensors typically used in mobile robots, as it was moved through all the corridors of a typical Mediterranean greenhouse featuring tomato crop. This dataset presents a unique opportunity for constructing detailed 3D models of plants in such indoor-like space, with potential applications such as robotized spraying. For the first time to the best knowledge of authors, a dataset suitable to put at test Simultaneous Localization and Mapping (SLAM) methods is presented in a greenhouse environment, which poses unique challenges. The suitability of the dataset for such goal is assessed by presenting SLAM results with state-of-the-art algorithms. The dataset is available online in \url{https://arm.ual.es/arm-group/dataset-greenhouse-2024/}.

MultiVehicle Simulator (MVSim): lightweight dynamics simulator for multiagents and mobile robotics research

Development of applications related to closed-loop control requires either testing on the field or on a realistic simulator, with the latter being more convenient, inexpensive, safe, and leading to shorter development cycles. To address that need, the present work introduces MVSim, a simulator for multiple vehicles or robots capable of running dozens of agents in simple scenarios, or a handful of them in complex scenarios. MVSim employs realistic physics-grounded friction models for tire-ground interaction, and aims at accurate and GPU-accelerated simulation of most common modern sensors employed in mobile robotics and autonomous vehicle research, such as depth and RGB cameras, or 2D and 3D LiDAR scanners. All depth-related sensors are able to accurately measure distances to 3D models provided by the user to define custom world elements. Efficient simulation is achieved by means of focusing on ground vehicles, which allows the use of a simplified 2D physics engine for body collisions while solving wheel-ground interaction forces separately. The core parts of the system are written in C++ for maximum efficiency, while Python, ROS 1, and ROS 2 wrappers are also offered for easy integration into user systems. A custom publish/subscribe protocol based on ZeroMQ (ZMQ) is defined to allow for multiprocess applications to access or modify a running simulation. This simulator enables and makes easier to do research and development on vehicular dynamics, autonomous navigation algorithms, and simultaneous localization and mapping (SLAM) methods.