Feng, Chen (2015) Camera marker networks for pose estimation and scene understanding in construction automation and robotics. PhD thesis, University of Michigan, USA.
Abstract
The construction industry faces challenges that include high workplace injuries and fatalities, stagnant productivity, and skill shortage. Automation and Robotics in Construction (ARC) has been proposed in the literature as a potential solution that makes machinery easier to collaborate with, facilitates better decision-making, or enables autonomous behavior. However, there are two primary technical challenges in ARC: 1) unstructured and featureless environments; and 2) differences between the as-designed and the as-built. It is therefore impossible to directly replicate conventional automation methods adopted in industries such as manufacturing on construction sites. In particular, two fundamental problems, pose estimation and scene understanding, must be addressed to realize the full potential of ARC. This dissertation proposes a pose estimation and scene understanding framework that addresses the identified research gaps by exploiting cameras, markers, and planar structures to mitigate the identified technical challenges. A fast plane extraction algorithm is developed for efficient modeling and understanding of built environments. A marker registration algorithm is designed for robust, accurate, cost-efficient, and rapidly reconfigurable pose estimation in unstructured and featureless environments. Camera marker networks are then established for unified and systematic design, estimation, and uncertainty analysis in larger scale applications. The proposed algorithms' efficiency has been validated through comprehensive experiments. Specifically, the speed, accuracy and robustness of the fast plane extraction and the marker registration have been demonstrated to be superior to existing state-of-the-art algorithms. These algorithms have also been implemented in two groups of ARC applications to demonstrate the proposed framework's effectiveness, wherein the applications themselves have significant social and economic value. The first group is related to in-situ robotic machinery, including an autonomous manipulator for assembling digital architecture designs on construction sites to help improve productivity and quality; and an intelligent guidance and monitoring system for articulated machinery such as excavators to help improve safety. The second group emphasizes human-machine interaction to make ARC more effective, including a mobile Building Information Modeling and way-finding platform with discrete location recognition to increase indoor facility management efficiency; and a 3D scanning and modeling solution for rapid and cost-efficient dimension checking and concise as-built modeling.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Kamat, Vineet Rajendra; Prakash, Atul; Lee, Sanghyun and Menassa, Carol C |
| Uncontrolled Keywords: | construction automation and robotics; camera marker network; marker based pose estimation; fast plane extraction; in-situ robotic assembly; excavation monitoring |
| Index terms: | decision-making, excavation, state of the art, construction site, face, accuracy, experiment, skill shortage, injury, modelling, interaction, construction industry, dissertation, automation, dimension, fatalities, monitoring, in-situ, effectiveness, marker, built environment, robotics, productivity, estimation, efficiency, building information modelling, uncertainty analysis, platform |
| Subjects: | building construction, behavioral psychology, environmental hazards, digital design, automation and robotics, psychology, analytical methods, construction operations, research dissemination and communication, health conditions and diseases, decision analysis, information systems, industry analysis, infrastructure and transport systems, performance management, health risk and incident analysis, management, probability and distributions, professional development, work location, financial and cost management, health monitoring assessment and metrics, data collection methods, control systems |
| Topics: | Human Resources, Urban Studies, Digital Applications, Site Management, Organizational Design, Research Practice, Information Management, Business Strategy, Cost Management, Quality Management, Engineering Principles, Health and Safety, Sustainability, Risk Management |
| Descriptive scope: | 5 PCTEA |
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