SBIR Phase I: Robotic Forest Inventory and Mapping

这个小企业创新（SBIR）第一阶段项目的更广泛影响/商业潜力是使林业、环境可持续性和自然保护目的的监测系统现代化。通过更快、更便宜、更准确的库存系统，全球林业产业的商业潜力估计为3250亿美元，可释放34亿美元的额外价值。自1998年以来，气温升高导致害虫流行爆发，摧毁了美国超过1.2亿英亩的林地。此外，这些温度和枯死的树木加剧了野火，每年造成的经济损失估计为3500亿美元。这些问题的社会影响是普遍的，因为烟雾可以飘散数千英里，对人类健康产生不利影响，并污染环境。解决这些问题的一个基本要求是自动化森林监测系统，因为目前的系统仍然严重依赖人工测量。该项目将通过为茂密的自然森林开发自主和大规模语义测绘机器人系统来解决这些更广泛的高影响问题，从而加强科学和技术的理解。此外，该项目将通过培训熟练操作员开发、部署和控制森林中的机器人团队，为农村社区提供高科技就业机会。这个小企业创新（SBIR）第一阶段项目将开发第一个商业上可行的自动化木材巡航，从树冠层以下估算森林体积。林业仍然依赖于人工测量，因为由于基本的技术挑战，目前还不存在远距离自动测量树冠下树木大小的技术。该项目将克服两个限制挑战：1)强大的自主性挑战：没有人能够在真正的3D，非结构化，gps拒绝的环境中实现强大的自主性，这些环境无法进行手动控制（远程操作）；2)大规模语义映射挑战：没有人尝试过在所提出的规模和精度上进行语义映射，因为大多数演示都是针对一些对象实例化的，并且不需要精确的测量。为了应对这些挑战，该项目预计将取得三个技术成果：1)实时树木检测，在具有挑战性的条件下稳健地检测树木；2)连续时间语义同步定位和映射，以精确地模拟远距离树木；3)基于深度模型预测控制的快速在线运动规划，实现无人机在杂乱森林环境下的鲁棒导航。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation (SBIR) Phase I project is modernizing monitoring systems for the forestry industry, environmental sustainability, and nature conservation purposes. The estimated commercial potential on the $325 B global forestry industry is an additional $3.4 B of unlocked value through faster, cheaper, and more accurate inventory systems. Warmer temperatures have contributed to an explosion in pest epidemics that have destroyed over 120 million acres of timberland in the US since 1998. In addition, these temperatures and dead trees have exacerbated wildland fire, with annual economic damage estimated to be $350 B. The societal impact of these problems is pervasive, as smoke plumes can drift for thousands of miles and adversely affect human health and environmental pollution. One fundamental requirement to address these problems is an automated forest monitoring system, as current systems still heavily rely on manual measurements. This project will enhance scientific and technological understanding by developing autonomous and large-scale semantic mapping robotic systems for dense, natural forests to tackle these broader high-impact problems. In addition, this project will provide high-tech career opportunities in rural communities by training skilled operators to develop, deploy, and control the robot teams in forests. This Small Business Innovation (SBIR) Phase I project will develop the first commercially viable automated timber cruise to estimate forest volume from below the canopy level. The forestry industry still relies on manual measurements because, due to fundamental technical challenges, the technology to autonomously measure tree sizes under the canopy over long distances does not exist. This project will overcome two limiting challenges: 1) Robust Autonomy Challenge: No one has achieved robust autonomy in truly 3D, unstructured, GPS-denied environments where manual control (teleoperation) is not feasible; and 2) Large-Scale Semantic Mapping Challenge: No one has attempted semantic mapping at the scale and accuracy proposed, as most demonstrations have been for a few object instantiations and without the need for precise measurement. To tackle these challenges, the project anticipates three technical results: 1) Real-time tree detection to robustly detect trees in challenging conditions; 2) Continuous-Time Semantic Simultaneous Localization and Mapping to precisely model trees over vast distances; and 3) Fast Online Motion Planning with Deep Model Predictive Control to robustly navigate unmanned aerial vehicles in cluttered forest environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.