Automation of Basic Forestry Operations

加拿大林产品工业可以从自动化方面的最新进展中大大受益，以减轻诸如生产力、劳动力短缺、安全和重复伤害等问题。在本提案中，我们的目标是通过利用和建立当前移动机器人和机器学习的最新技术，展示基本日志操作的自动化。为此，我们选择了林业经营的关键要素，即采伐和转运，因为它们最容易从这些研究领域的当前突破中迅速和现实地受益。我们的建议旨在1)利用车载传感器与激光雷达和惯性测量单元在线构建有用的3D地图；2)开发和测试许多算法，用于在模拟（收获）和模型（转发）中自动采伐和转发树木。我们的提案还将做出基础科学贡献，特别是在3D制图，3D感知和终身学习领域。事实上，森林环境带来了有趣的挑战，斜坡和山丘普遍存在，树叶遮挡明显，由于季节影响（树叶、雪），场景外观变化很大。该提案将有助于FORAC 4.0研究计划的努力，该研究计划是一个旨在提高加拿大森林工业竞争力的研究联盟。这项研究将与加拿大森林工业的领导人直接合作进行。我们的建议将提供一个独特的培训经验，以形成下一代机器人专家和数据科学家。

The Canadian forest products industry can greatly benefit from recent advances in automation to alleviate issues such as productivity, labour shortage, safety, and repetitive injuries. In this proposal, we aim at demonstrating the automation of basic logging operations, by leveraging and building upon the current state-of-the-art in mobile robotics and machine learning. To this effect, we have selected key elements in forestry operation, namely harvesting and forwarding as the most susceptible to benefit rapidly and realistically from the current breakthroughs in these research areas. Our proposal aim at 1) building useful 3D maps online from onboard vehicles sensors with a LiDAR and an inertial measurement unit and 2) develop and test a number of algorithms for autonomous tree harvesting and forwarding in simulation (harvesting) and with a mock-up (forwarding). Our proposal will also make fundamental scientific contributions, notably in the area of 3D mapping, 3D perception and life-long learning. Indeed, the forest environment poses interesting challenges, with the prevalence of slopes and hills, significant occlusion by foliage, and high changes of scene appearances due to seasonal effects (foliage, snow). This proposal will contribute to the effort of FORAC 4.0 research program, a research consortium aimed at improving the competitiveness of the Canadian forest industry. The research will be conducted in direct partnership with leaders of the Canadian forest industry. Our proposal will provide a unique training experience to form the next generation of roboticists and data scientists.