EU States are mandated by the 92/43/EEC Habitats Directive to generate recurring reports on the conservation status and functionality of habitats at the national level. This assessment is based on their floristic and, especially for forest habitats, structural characterization. Currently, habitat field monitoring efforts are carried out only by trained human operators. The H2020 Project “Natural Intelligence for Robotic Monitoring of Habitats – NI” aims to develop quadrupedal robots able to move autonomously in the unstructured environment of forest habitats. In this work, we tested the locomotion performance, efficiency and the accuracy of a robot performing structural habitat monitoring, comparing it with traditional field survey methods inside selected stands of beech forests (9110 Annex I Habitat). We used a quadrupedal robot equipped with a Mobile Laser Scanning system (MLS), implementing for the first time a structural monitoring of EU forest habitats with a Robotically-mounted Mobile Laser Scanning (RMLS) platform. Two different scanning trajectories were used to automatically map individual tree locations and extract tree Diameter at Breast Height (DBH) from point clouds. Results were compared with field human measurements in terms of accuracy and efficiency of the survey. The robot was able to successfully execute both scanning trajectories, for which we obtained a tree detection rate of 100 %. Circular scanning trajectory performed better in terms of battery consumption, Root Mean Square Error (RMSE) of the extracted DBH (2.43 cm or 10.73 %) and prediction power (R = 0.72, p < 0.001). The RMLS platform improved survey efficiency with 19.31 m/min or 1.77 trees/min in comparison with 3.45 m/min or 0.32 trees/min of traditional survey. Finally, a processing script was developed to allow the repeatability of RMLS surveys in similar habitat monitoring missions. In the future, a human-robotic monitoring framework might represent an accurate support for those repetitive and time-consuming activities in habitat monitoring, offering a valuable benefit for biodiversity conservation.

中文翻译：

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机器人的一小步，栖息地监测的一大步：利用机器人安装的移动激光扫描（RMLS）对欧盟森林栖息地进行结构调查

92/43/EEC 栖息地指令要求欧盟国家定期生成关于国家层面栖息地保护状况和功能的报告。该评估基于其植物区系，特别是森林栖息地的结构特征。目前，栖息地实地监测工作仅由经过培训的操作人员进行。 H2020项目“栖息地机器人监测的自然智能——NI”旨在开发能够在森林栖息地的非结构化环境中自主移动的四足机器人。在这项工作中，我们测试了执行结构栖息地监测的机器人的运动性能、效率和准确性，并将其与选定的山毛榉林（9110附件一栖息地）内的传统实地调查方法进行了比较。我们使用配备移动激光扫描系统（MLS）的四足机器人，首次通过机器人安装的移动激光扫描（RMLS）平台对欧盟森林栖息地进行结构监测。使用两种不同的扫描轨迹自动绘制单个树木位置并从点云中提取树木胸高直径 (DBH)。在调查的准确性和效率方面将结果与现场人类测量进行了比较。机器人能够成功执行两条扫描轨迹，我们获得了 100% 的树木检测率。圆形扫描轨迹在电池消耗、提取的 DBH 的均方根误差 (RMSE)（2.43 cm 或 10.73 %）和预测能力（R = 0.72，p < 0.001）方面表现更好。 RMLS平台提高了调查效率，与传统调查的3.45 m/min或0.32棵树/分钟相比，提高了19.31 m/min或1.77棵树/分钟。最后，开发了一个处理脚本，以便在类似的栖息地监测任务中实现 RMLS 调查的可重复性。未来，人机监测框架可能会为栖息地监测中那些重复且耗时的活动提供准确的支持，为生物多样性保护提供宝贵的好处。