Uploaded May 2023 | Updated September 2026, 2 weeks ago
In this video we present results from the recent field-testing campaign of the DigiForest project at Evo, Finland. The DigiForest project started in September 2022 and runs up to February 2026. It brings together diverse partners working on aerial robots, walking robots, autonomous lightweight harvesters, as well as forestry decision makers and commercial companies with the goal to create a full data pipeline for digitized forestry.
During this field campaign, our lab tested two distinct autonomy stacks onboard flying robots performing under-canopy navigation, exploration and mapping.
First, we tested our traditional autonomy pipeline involving onboard SLAM, volumetric mapping and path planning on such maps. Our focus was to test performance against different tree distributions.
Second, we tested two variations of a new learning-based navigation approach that calculates collision-free actions without assuming access to any form of consistent map or even position estimates. Instead, an appropriately trained neural network uses a partial estimate of the robot state (including roll and pitch angles, yaw rate and linear velocities), as well as the associated covariance and the immediate depth image from an RGBD sensor to derive an admissible motion primitive in velocity space that flies towards a desired direction in a collision-free manner.
These results are preliminary and one of the future goals will be to enable safe fast flight in perceptually-degraded dense forests involving complex distributions of thin hard-to-perceive branches.
In this video we present results from the recent field-testing campaign of the DigiForest project at Evo, Finland. The DigiForest project started in September 2022 and runs up to February 2026. It brings together diverse partners working on aerial robots, walking robots, autonomous lightweight harvesters, as well as forestry decision makers and commercial companies with the goal to create a full data pipeline for digitized forestry.
During this field campaign, our lab tested two distinct autonomy stacks onboard flying robots performing under-canopy navigation, exploration and mapping.
First, we tested our traditional autonomy pipeline involving onboard SLAM, volumetric mapping and path planning on such maps. Our focus was to test performance against different tree distributions.
Second, we tested two variations of a new learning-based navigation approach that calculates collision-free actions without assuming access to any form of consistent map or even position estimates. Instead, an appropriately trained neural network uses a partial estimate of the robot state (including roll and pitch angles, yaw rate and linear velocities), as well as the associated covariance and the immediate depth image from an RGBD sensor to derive an admissible motion primitive in velocity space that flies towards a desired direction in a collision-free manner.
These results are preliminary and one of the future goals will be to enable safe fast flight in perceptually-degraded dense forests involving complex distributions of thin hard-to-perceive branches.
