DigiForests: A Longitudinal LiDAR Dataset for Forestry Robotics

“DigiForests” is a real-world, longitudinal dataset for forestry robotics that enables the development and comparison of approaches for various relevant applications, ranging from semantic interpretation to estimating traits relevant to forestry management. The dataset consists of multiple recordings of the same plots in a forest in Switzerland during three different growth periods. We recorded the data with a mobile 3D LiDAR scanning setup. Additionally, we provide semantic annotations of trees, shrubs, and ground, instance-level annotations of trees, as well as more fine-grained annotations of tree stems and crowns. Furthermore, we provide reference field measurements of traits relevant to forestry management for a subset of the trees.

For further information and downloading the data, please visit our dataset webpage.

Oxford Forest Place Recognition Dataset

A second supplementary dataset focusing on the problem of LiDAR-based Place Recognition has also been released to connect with the following publication:

“Evaluation and Deployment of LiDAR-based Place Recognition in Dense Forests”
Presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 Authors: Haedam Oh, Nived Chebrolu, Matias Mattamala, Leonard Freißmuth and Maurice Fallon

The dataset can be downloaded from it own webpage.

GrandTour Dataset

The GrandTour project created an extremely rigorous dataset for legged robot navigation, perception and state estimation with the ANYmal robot. It includes high-precision ground-truth trajectories from satellite-based RTK-GNSS and a Leica Geosystems total station. The following publication describes the dataset in more detail:

“*GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation **”
*Under review for publication, 2024 Authors: Jonas Frey, Turcan Tuna, Frank Fu, Katharine Patterson, Tianao Xu, Maurice Fallon, Cesar Cadena, Marco Hutter

The dataset can be downloaded from its webpage. DigiForest partners ETH Zurich (leads), University of Oxford and Leica were involved in this effort.