文献类型： 会议论文

第一作者： T. Czerniawski

作者： T. Czerniawski ¹ ; M. Nahangi ¹ ; S. Walbridge ¹ ; C. Haas ¹ ;

作者机构： 1.Ralph Haas Civil Infrastructure Sensing Laboratory, University of Waterloo

关键词： Fabrication and process control;Industrial fabrication;Pipe spool;3D laser scanning;3D point cloud;3D object recognition;Clutter removal

会议名称： International Symposium on Automation and Robotics in Construction and Mining

主办单位：

页码： 315-322

摘要： The industrial construction industry makes use of prefabrication, preassembly, modularization and off-site fabrication (PPMOF) for project execution because they offer a superior level of control as compared to on-site operations. This control is enabled by systematic and thorough performance feedback loops. Improvement of the feedback systems within these facilities will require a transition away from suboptimal manual data collection to more reliable automated data collection and processing. Laser scanners are an effective tool for automatically gathering dimensional data but extraction of useful information from point clouds remains a challenge. The speed of 3D object recognition methods depends on the size of the search space. Methods for reducing this search space are needed in order to improve the performance of 3D object recognition and subsequent information extraction. Large planar objects (e.g. floors and walls) constitute a large portion of the search space in fabrication facilities, yet are rarely the objects of interest for analysis. In this paper, an automated framework for detecting and removing large planes in point clouds is presented to speed up object recognition. The raw point cloud is first Guassian mapped to normal vector space by calculating normal vectors at each point. The Gaussian sphere is clustered using a density-based clustering algorithm and major parallel planes are segmented from the rest of the point cloud. The major planes are removed and the remaining objects in the scene continue on to 3D object recognition. Results show the algorithm for automatic plane removal can reduce the search space for object recognition by as much as 60% or 70%.

分类号： TP2-53