A method based on an adaptive radius cylinder model for detecting pole-like objects in mobile laser scanning data

This letter presents a method based on an adaptive radius cylinder model (MBARCM) for detecting pole-like objects in mobile laser scanning data. First, the unorganized point clouds are voxelized to compress and organize the original data. Second, the bottom-up tracing algorithm is implemented to detect potential vertical-object locations, and the neighbour segments are merged. Finally, the adaptive radius cylinder model is built based on the selected layers of the vertical segment, and the model is applied to the merged vertical segments to perform isolation analysis. The performance of the proposed method is validated on three test sites for two datasets. Using the proposed method, the completeness values ranged from 94.6% to 97.7% for the three sites tested, while the correctness values were from 79% to 100%.