Degraded rangelands undergo continual shifts in the appearance and distribution of plant life. The nature of these changes however is subtle: between seasons seedlings sprout up and some flourish while others perish, meanwhile, over multiple seasons they experience fluctuating precipitation volumes and can be grazed by livestock. The nature of these conditioning variables makes it difficult for ecologists to quantify the efficacy of intervention techniques under study. To support these observation and intervention tasks, we develop RestoreBot: a mobile robotic platform designed for gathering data in degraded rangelands for the purpose of data collection and intervention in order to support revegetation. Over the course of multiple deployments, we outline the opportunities and challenges of autonomous data collection for revegetation and the importance of further effort in this area. Specifically, we identify that localization, mapping, data association, and terrain assessment remain open problems for deployment, but that recent advances in computer vision, sensing, and autonomy offer promising prospects for autonomous revegetation.

翻译：退化牧场中的植被外观和分布会持续发生变化，然而这些变化的性质较为微妙：不同季节间，幼苗萌发，部分繁茂生长而另一些则枯萎死亡。同时，在多个季节周期中，植被还会经历降水量的波动并可能被牲畜啃食。这些调节变量的特性使得生态学家难以量化所研究干预技术的有效性。为支撑这些观测与干预任务，我们开发了RestoreBot：一款专为在退化牧场中采集数据而设计的移动机器人平台，旨在支持数据收集与干预作业以促进植被恢复。通过多次实地部署，我们梳理了自主数据采集在植被恢复中的应用机遇与挑战，以及该领域进一步研究的必要性。具体而言，我们指出定位、建图、数据关联和地形评估仍是部署中的未解难题，但计算机视觉、感知与自主技术的最新进展为自主化植被恢复提供了广阔前景。