Скачать или смотреть From AI to 3D mapping: leveraging scientific breakthroughs to automate forestry operations

Forestry plays a significant role in many economies, yet productivity gains of human operators have more or less plateaued in the last decade. Starting five years ago, we have been exploring how currently-existing breakthroughs in AI (Deep Learning) and mobile robotics (3D mapping) could be used to alleviate the workload of forestry operators, and thereby improve productivity. At the same time, forests environment provides challenges to many algorithms and techniques, and consequently offer the opportunity to extend the scientific and technical state-of-the-art.

In this presentation, we will start by going over some of our results. In particular, we will discuss the performance of using bark images for two tasks: tree species identification, and fingerprinting for tree re-identification. We will then discuss ongoing efforts on large-scale 3D mapping of forests with mobile robots, and how well we can estimate the key statistic of Diameter at Breast Height (DBH) from reconstructed point clouds. We will conclude by presenting our new project related to the automation of tree felling and forwarding.

Presented remotely at IROS 2020 Workshop on Perception, Planning and Mobility in Forestry Robotics (WPPMFR 2020).