Pole-like object detection and classification from urban point clouds

This paper focuses on detecting and classifying pole-like objects from point clouds obtained in urban areas. To achieve our goal, we propose a system consisting of three stages: localization, segmentation and classification. The localization algorithm based on slicing, clustering, pole seed generation and bucket augmentation takes advantage of the unique characteristics of pole-like objects and avoids heavy computation on the feature of every point in traditional methods. Then, the bucket-shaped neighborhood of the segments is integrated and trimmed with region growing algorithms, reducing the noises within candidate´s neighborhood. Finally, we introduce a representation of six attributes based on the height and five point classes closely related to the pole categories and apply SVM to classify the candidate objects into 4 categories, including 3 pole categories light, utility pole and sign, and the non-pole category. The performance of our method is demonstrated through comparison with previous works on a large-scale urban dataset.