Semantics-guided Skeletonization of Sweet Cherry Trees for Robotic Pruning

TL;DR

This paper presents a semantics-guided skeletonization algorithm for modeling sweet cherry trees from point clouds, improving pruning decisions in agricultural robotics by producing labeled tree structures.

Contribution

The paper introduces a novel skeletonization algorithm that incorporates semantic labels and priors, enhancing tree modeling accuracy for robotic pruning applications.

Findings

Median skeleton accuracy of 70% against human standards

Open-source point cloud data for 82 trees provided

Algorithm successfully distinguishes tree parts like trunk and branches

Abstract

Dormant pruning for fresh market fruit trees is a relatively unexplored application of agricultural robotics for which few end-to-end systems exist. One of the biggest challenges in creating an autonomous pruning system is the need to reconstruct a model of a tree which is accurate and informative enough to be useful for deciding where to cut. One useful structure for modeling a tree is a skeleton: a 1D, lightweight representation of the geometry and the topology of a tree. This skeletonization problem is an important one within the field of computer graphics, and a number of algorithms have been specifically developed for the task of modeling trees. These skeletonization algorithms have largely addressed the problem as a geometric one. In agricultural contexts, however, the parts of the tree have distinct labels, such as the trunk, supporting branches, etc. This labeled structure is important for understanding where to prune. We introduce an algorithm which produces such a labeled skeleton, using the topological and geometric priors associated with these labels to improve our skeletons. We test our skeletonization algorithm on point clouds from 29 upright fruiting offshoot (UFO) trees and demonstrate a median accuracy of 70% with respect to a human-evaluated gold standard. We also make point cloud scans of 82 UFO trees open-source to other researchers. Our work represents a significant first step towards a robust tree modeling framework which can be used in an autonomous pruning system.