Markerless aerial-terrestrial co-registration of forest point clouds using a deformable pose graph

Conference item

For biodiversity and forestry applications, endusers desire maps of forests that are fully detailed—from the forest floor to the canopy. Terrestrial laser scanning and aerial laser scanning are accurate and increasingly mature methods for scanning the forest. However, individually they are not able to estimate attributes such as tree height, trunk diameter and canopy density due to the inherent differences in their field-of-view and mapping processes. In this work, we present a pipeline that can automatically generate a single joint terrestrial and aerial forest reconstruction. The novelty of the approach is a marker-free registration pipeline, which estimates a set of relative transformation constraints between the aerial cloud and terrestrial sub-clouds without requiring any co-registration reflective markers to be physically placed in the scene. Our method then uses these constraints in a pose graph formulation, which enables us to finely align the respective clouds while respecting spatial constraints introduced by the terrestrial SLAM scanning process. We demonstrate that our approach can produce a fine-grained and complete reconstruction of large-scale natural environments, enabling multi-platform data capture for forestry applications without requiring external infrastructure.

Authors: Casseau, B; Chebrolu, N; Mattamala Aravena, M; Freissmuth, L; Fallon, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Pages: 39-46
• Publication date: 2024-12-25
• Acceptance date: 2024-08-01
• Event title: International Conference on Intelligent Robots and Systems (IROS 2024)
• Event location: Abu Dhabi, UAE
• Event website: https://iros2024-abudhabi.org/
• Event start date: 2024-10-14
• Event end date: 2024-10-18
• DOI: 10.1109/IROS58592.2024.10802448
• EISSN: 2153-0866
• ISSN: 2153-0858
• Language: English
• Keywords: lasers; biodiversity; environmental management; pipelines; point cloud compression; accuracy; simultaneous localization and mapping; forests; floors; intelligent robots