Abstract:
Forests present compounding challenges for mobile mapping systems. Dense canopy degrades GNSS, uneven terrain demands deployment across diverse platforms, and no single sensing platform can capture the full vertical structure of a forest — from the canopy above to the understory below. Yet precise, georeferenced maps of individual trees are exactly what ecologists and forest managers need to monitor invasive species, estimate biomass, and track ecosystem change at scale.

This talk presents MapForest, a modular field robotics system that turns multi-modal sensor data (LiDAR, IMU, GNSS, and RGB imagery) into georeferenced 3D reconstructions and GIS-ready outputs. A single compact payload deploys across five carriers (handheld, bicycle, ATV, and two UAVs) without hardware modification, enabling seamless data collection across heterogeneous terrain.

The core mapping pipeline extends a LiDAR-inertial SLAM framework with covariance-aware GNSS priors and a Huber robust loss, reducing trajectory error by 67% relative to a GNSS-blind baseline. To bridge the viewpoint gap between aerial and ground surveys, we develop two complementary aerial-terrestrial alignment methods: Tensor-MI, an analytical mutual-information approach operating on tree-likelihood fields, and CRAF, a learned registration model combining modality-specific encoders with a cross-attention transformer. As a concrete ecological application, MapForest localizes invasive Tree-of-Heaven from onboard imagery using a fine-tuned detector, projecting detections into the 3D map and exporting georeferenced GeoTIFF layers suitable for direct use in forestry workflows.

MapForest is evaluated across six field sites spanning approximately 30 km of multi-modal traversal data, demonstrating that robust, actionable forest inventory is achievable at a granularity unavailable to satellite or conventional aerial methods.

Thesis Committee:
Prof. Abhisesh Silwal (chair)
Prof. Michael Kaess
John Kim