Abstract
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.
Original language | English |
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Title of host publication | ISARC 2016 - 33rd International Symposium on Automation and Robotics in Construction |
Publisher | International Association for Automation and Robotics in Construction (IAARC) |
Pages | 218-225 |
Number of pages | 8 |
Publication status | Published - 2016 |
Externally published | Yes |
Event | International Symposium on Automation and Robotics in Construction 2016 - Auburn University, Auburn, United States of America Duration: 18 Jul 2016 → 21 Jul 2016 Conference number: 33rd http://eventsinamerica.com/events/isarc-2016-international-symposium-on-automation-robotics-in-construction/ev557f290c8a2e8 |
Conference
Conference | International Symposium on Automation and Robotics in Construction 2016 |
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Abbreviated title | ISARC 2016 |
Country/Territory | United States of America |
City | Auburn |
Period | 18/07/16 → 21/07/16 |
Internet address |
Keywords
- Laser scanning
- Machine learning
- Object classification
- Object recognition
- Scattered point clouds