A robotic vision system to measure tree traits

Abstract

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${}^3$.

9 pages, IEEE/RSJ IROS 2017 conference paper, added Erratum 11/1/2021

References in corpus (1)

• Solving the Robot-World Hand-Eye(s) Calibration Problem with Iterative Methods

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• Fast and robust curve skeletonization for real-world elongated objects
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• Approach for modeling single branches of meadow orchard trees with 3D point clouds