GAIA: Ground-Aerial maps Integration for increased Autonomy outdoors

多机器人地图正在成为一个总是更需要的应用程序，针对各种各样的问题，如现场检查，人类搜索和救援，果园监控，由于具有多个机器人代理合作完成相同的任务的灵活性。从这个角度来看，机器人需要相互通信，以共享有关其相对位置和周围环境的信息。综合这一信息可以获得对环境和使命进展状况的全面概览。共享信息的一个简单例子是由单个机器人在操作过程中构建的本地地图。在文献中，多个地图集成通常被视为地图合并问题，其中地图被表示为占用网格并通过寻找重叠部分缝合在一起，或者作为多机器人同时定位和映射（SLAM）问题，其中使用图论构建和连接各个机器人的姿态图。然而，这仍然是一个有趣的和开放的研究问题，尽管在过去的二十年里，机器人定位的努力。与单个机器人用例不同，多机器人地图集成需要更高级别的抽象来识别哪些元素在多个地图中是共同的，以便使集成高效和实时。特别是，在这个项目中，我们称之为“地面-空中地图集成以增加户外自主性”（GAIA），我们的目标是拥有一个由互补行为，运动和感知能力表征的异构机器人舰队的场景，这使得地图集成问题比同质舰队更难（例如，仅使用地面机器人）。为了解决这个问题，本项目的重点是利用人类对场景的理解，以集成多视角机器人地图，特别是，我们计划使用语义信息的机器人观察有一个更好的理解场景，以确定哪些实体存在于它，并利用这些信息，以便将多视角观察到一个单一的地图表示。更具体地说，在GAIA和本次电话会议的范围内，我们解决了农业领域的多机器人地图集成问题，其中机器人解决方案可以代表一种改变游戏规则的技术。在现场部署自主代理以协助（如果不是取代）人类工人进行监测和收获任务的可能性开启了一场专注于精准农业和可持续性的新革命。事实上，机器人配备了专用的软件和硬件，可以通过收集降雨量、土壤湿度和土壤成分的数据来帮助农民，从而帮助他们做出更多有针对性的干预措施。更具体地说，地面机器人提供了一个更近、更详细的农作物检查点，而无人机允许在更短的时间内观察更大的田地。UAV提供主动感测能力以按需完成和更新部分完整的地图，同时通过例如绘制人类工人或任何其他农业相关工具（例如，拖拉机或手推车）。UGV的路径规划器可以利用这些更新的信息，使地面平台的部署更有效，避免那些受阻的路径。

Multi-robot mapping is becoming an always more demanded application for targeting a large variety of problems, such as site inspection, human search and rescue, and orchard monitoring, due to the flexibility of having multiple robotic agents acting cooperatively for completing the same task. In this view, the robots need to communicate one with another to share information on their relative position and their surrounding. Integrating this information allows for obtaining a global overview of the environment and the progress status of the mission. One of the simpler examples of shared information is represented by the local maps built by individual robots during their operations. In the literature, multiple maps integration is usually cast as a map merging problem, in which maps are represented as occupancy grids and stitched together by looking for overlapping parts, or as a multi-robot Simultaneous Localisation and Mapping (SLAM) problem, where graphs of individual robots' pose are built and connected using graph theory. However, this is still an interesting and open research problem despite the effort put into robot localisation in the last twenty years. Differently from the single robot use case, multi-robot maps integration needs a higher level of abstraction to identify which elements are common across multiple maps so to make the integration efficient and real-time. In particular, within this project we call "Ground-Aerial maps Integration for increased Autonomy Outdoors" (GAIA), we target the scenario of having a fleet of heterogeneous robots characterised by complementary behaviours, movements and perception capabilities, making the maps integration problem even harder than homogeneous fleet (e.g., using only ground robots). To solve this problem, this project focuses on exploiting the human understanding of a scene so as to integrate multi-perspective robotic maps.In particular, we plan to use the semantic information in robotic observations to have a better understanding of the scene, to identify which entities are present in it, and to leverage such information so as to integrate multi-perspective observations into a single map representation. More specifically, within GAIA and the scope of this call, we tackle the problem of multi-robot maps integration in the agricultural domain, where robotics solutions can represent a game-changer technology. The possibility of deploying autonomous agents in the field to assist, if not replace, human workers in monitoring and harvesting tasks opens up a new revolution focused on precision agriculture and sustainability. Indeed, robots are equipped with dedicated software and hardware that can assist farmers by collecting data on rainfall, soil moisture and soil composition, so to help them make more target interventions. More specifically, the ground robot offers a closer and more detailed inspection point of view over the crops, while the UAV allows observing a larger field in a shorter time. The UAV offers active sensing capabilities to complete and update a partially complete map on demand, while improving its level of confidence (how much we trust the map) by, for example, mapping human workers or any other agriculture-related tools (e.g., tractors or trolleys) located in the fields. This updated information can be exploited by the UGV's path planner to make the ground platform's deployment more efficient, avoiding those obstructed paths.