Abstract:
The United States Green Industry faces a persistent labor shortage that motivates the adoption of agricultural automation.  However, existing systems are not designed for the unstructured, densely planted environment of a tree nursery.

This thesis presents a robotic system intended to alleviate this shortage while remaining usable by non-technical farmers, built around a map-based representation of the nursery environment. A custom robotic platform, the mini-Amiga, and an accompanying LiDAR-camera-IMU sensor rig were developed to satisfy the maneuverability and payload requirements of tight nursery inter-row spacing. Point cloud maps constructed with this platform, using the GLIM LiDAR-inertial SLAM framework augmented with a custom GNSS georeferencing extension, were processed with a new constrained Gaussian Mixture Model algorithm to segment individual trees without requiring trunk visibility or large annotated training datasets.

The resulting per-tree map was further augmented with photographic colorization and encoded as a hierarchically organized Universal Scene Description (USD) scene, supporting non-destructive, multi-mode visualization and per-tree metadata storage intended for intuitive interaction by non-technical operators, and was used to derive a Nav2-compatible occupancy grid and row-traversal paths intended for autonomous task execution. These results demonstrate that individual nursery trees can be accurately and efficiently segmented from point cloud data, and that the resulting map can be represented in a form suited to both non-technical human interaction and autonomous navigation, providing a practical foundation for future work integrating localization and autonomous task execution to fully realize the labor-saving potential of this system.

Committee:
George Kantor (advisor)
Michael Kaess
Easton Potokar