Publications / 2016 Proceedings of the 33rd ISARC, Auburn, USA

Automated Equipment Recognition and Classification from Scattered Point Clouds for Construction Management

Jing Dao Chen, Yihai Fang and Yong Kwon Cho
Pages 218-225 (2016 Proceedings of the 33rd ISARC, Auburn, USA, ISBN 978-1-5108-2992-3, ISSN 2413-5844)

Recognizing construction assets from as-is point cloud data of construction environment provides essential information for engineering and management applications including progress monitoring, safety management, supply-chain management, and quality control. This study proposes a fast and automated processing pipeline for construction target assets recognition from scattered as-is point clouds. The recognition tasks can be subdivided into object detection, which involves computing the bounding box around each construction equipment, and object classification, which involves labelling point cloud clusters from discrete equipment categories, such as backhoe loader, bulldozer, dump truck, excavator and front loader. The object detection step consists of point cloud down-sampling, segmentation and clustering. For the object classification step, machine learning methods were employed to determine class membership probability using features derived from the ESF (Ensemble of Shape Functions) descriptor. The classifiers were trained on synthetic point clouds generated from CAD (Computer-aided Design) models. The method was validated using laser scanned point clouds from an equipment yard. The test results demonstrate promising advancements towards semantic labelling and scene understanding of point cloud data.

Keywords: Object recognition, Object classification, Scattered point clouds, Laser scanning, Machine learning.