Ms. Leah Vaughan Profile

Leah Vaughan

at Mississippi State Univ

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Proceedings Article | 7 June 2024 Presentation + Paper

Accelerated Hough transform-based tree diameter extraction from LiDAR point cloud data

Bruce Hicks, Andrew Bonner, Leah Vaughan, David Saucier, Lalitha Dabbiru, John Ball, Ali Gurbuz

Proceedings Volume 13052, 1305206 (2024) https://doi.org/10.1117/12.3013117

KEYWORDS: LIDAR, Point clouds, Particle swarm optimization, Hough transforms, Sensors, Vegetation

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Detecting and localizing large obstacles, particularly trees in unpaved regions, holds significant importance in autonomy, navigation, and related fields. Equally crucial is the extraction of detailed physical information from sensor captures. Accurately estimating the physical parameters of trees, such as the tree diameter at breast height (DBH), is particularly valuable for commercial and research purposes, especially in forestry and ecological studies. This estimation also plays a pivotal role in navigational tasks within densely vegetated regions, where overcoming obstacles becomes essential to achieving objectives. Achieving the required accuracy often entails labor-intensive processes such as manual collection aiming, data segmentation, or shape-building through mapping. In this context, we propose an algorithm based on particle swarm optimization (PSO) assisted Hough Transformation (HT) for tree DBH estimation, utilizing solely the physical spatial information from an actively available LiDAR point cloud. As a point of comparison, a straightforward circular HT-based method is also implemented. Our proposed approach surpasses the base HT method, demonstrating superior performance with an average error of 5.60 cm and RMSE of 6.57 cm, all while maintaining low time costs. These results reveal promising implications for this research direction in real-world applications, particularly in push-through navigation scenarios.

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