Kwon, S-W (2003) Human-assisted fitting and matching of objects to sparse point clouds for rapid workspace modeling in construction automation. PhD thesis, University of Texas at Austin, USA.
Abstract
Most current site modeling methods in the construction industry use large, expensive laser-scanning systems that produce dense range point clouds of a scene from different perspectives. While useful for many purposes, this approach is not feasible for real-time modeling which would enable automated obstacle avoidance and improved semi-automated equipment control. The dynamic nature of the construction environment requires that a real-time local area modeling system be capable of handling a rapidly changing and uncertain work environment. In practice, large, simple, and reasonably accurate object primitives are adequate feedback to an operator who is attempting to place target materials in the midst of obstacles with an occluded view. This dissertation presents human-assisted rapid environmental modeling methods for construction. These methods exploit the human operator's ability to quickly evaluate and associate objects in a scene and only require a limited number of scanned range data (sparse point clouds). These sparse clouds are then used to create geometric primitives for visualization and modeling purposes Five fitting and matching methods were developed that make use of sparse (fewer than fifty) point clouds per object: (1) workspace partitioning (planar least squares fit), (2) cuboids, (3) solid cylinders, (4) hollow cylinders and (5) spheres. Experiments have been conducted to determine how rapidly and accurately fitting and matching methods can model the objects in a scene, by comparing location, orientation, and size of objects between modeled and actual objects. Method development and revisions were also based on lab experiments. The experimental results indicated that these models can be created rapidly and with sufficient accuracy for automated obstacle avoidance and equipment control functions for safety applications.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Haas, C T and Liapi, K A |
| Uncontrolled Keywords: | accuracy; automation; equipment; feedback; safety; visualization |
| Index terms: | accuracy, experiment, point cloud, visualization, workspace, work environment, dissertation, automation, modelling, construction industry |
| Subjects: | automation and robotics, digital design, analytical methods, research dissemination and communication, industry analysis, professional development, design practice, management, data collection methods |
| Topics: | Organizational Design, Design Practice, Digital Applications, Stakeholder Management, Research Practice, Information Management, Engineering Principles |
| Descriptive scope: | 4 PCTE |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here