  The autonomous measurement of tree traits, such as branching structure,
branch diameters, branch lengths, and branch angles, is required for tasks such
as robotic pruning of trees as well as structural phenotyping. We propose a
robotic vision system called the Robotic System for Tree Shape Estimation
(RoTSE) to determine tree traits in field settings. The process is composed of
the following stages: image acquisition with a mobile robot unit, segmentation,
reconstruction, curve skeletonization, conversion to a graph representation,
and then computation of traits. Quantitative and qualitative results on apple
trees are shown in terms of accuracy, computation time, and robustness.
Compared to ground truth measurements, the RoTSE produced the following
estimates: branch diameter (root mean-squared error $2.97$ mm), branch length
(root mean-squared error $136.92$ mm), and branch angle (mean-squared error
$31.07$ degrees). The average run time was $8.47$ minutes when the voxel
resolution was $3$ mm$^3$.
