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Copyright by Jochen Teizer 2006 Copyright by Jochen Teizer 2006 The Dissertation Committee for Jochen Teizer certifies that this is the approved version of the following dissertation: REAL-TIME SPATIAL MODELING TO DETECT AND TRACK RESOURCES ON CONSTRUCTION SITES Committee: Carl T. Haas, Co-Supervisor Carlos H. Caldas, Co-Supervisor James T. O’Connor Katherine A. Liapi J. K. Aggarwal REAL-TIME SPATIAL MODELING TO DETECT AND TRACK RESOURCES ON CONSTRUCTION SITES by Jochen Teizer, Diplom-Ingenieur Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy The University of Texas at Austin August, 2006 Dedication An meine Mama und meinen Papa, Marlitt und Franz Teizer Acknowledgements This dissertation completes another segment of my life that was truly very enjoyable and on the other hand, not very often to recognize, very hard to accomplish. During my time as a Ph.D. student I have learned that excellence in research comes from intelligent leadership. Leadership means to pursue innovative goals, to teach, to support, to motivate, and to learn from others. Rapid progress is successful when work is reflected quickly, critically, and in a fair manner. People have spent time with me in recent years and they are true leaders. They are listed in a chronological way: Critical with me from the beginning but always strongly supporting my decisions have been my mom and dad, Marlitt und Franz Teizer. Next significant impact is the unity among us brothers, Winfried, Klaus, Peter, and me as an unbeatable stronghold. In the meanwhile their wives and kids gave me a lot of confidence too. Through the support of Dr. Gerhard W. Schmidt, Prof. Fritz Gehbauer, and Prof. Kunibert Lennerts, all at the University of Karlsruhe (TH) in Germany, I was awarded my German degree as a Diplom-Ingenieur. They also offered me the first insights into the academic life. As a visiting scholar with Prof. David N. Ford at Texas A&M University, I was allowed to make my first experiences with the educational system in the United States of America. This experience contributed to my decision to pursue my Ph.D. At the University of Texas at Austin my Ph.D. supervisors, Prof. Carl T. Haas and Prof. Carlos v H. Caldas, strongly supported me in decision making, education, research, and research travel. Their dedication to me has been greatly appreciated. I have to be very thankful to a lot of other people, since they had significant contributions or inspirations in my life. They are: Prof. Katherine A. Liapi, Frédéric Bosché, Jongchul Song, Dr. Richard Gebken II, Giovanni Migliaccio, Alan Lytle, Dr. Kamel Saidi, Dr. Wai Kiong Oswald Chong, Dr. Rayson Chou, Dr. Changwan Kim, Dr. Issam Srour, David Grau Torrent, Dr. Lilin Liang, Dr. Soon-Wook Kwon, Dr. Haitham Logman, Prof. Richard Tucker, Ruwini Weerasooriya, Shan-Pin Yeh, Raquel Esquatel, Jakub Felkl, Cecile Rey, Leif and Ingrid Ristroph, Hilda Ramos, Esther Maier, Mikko Börkircher, Jochen Gierich, Jens Harder, Ruth Jirauscheck, Bernhard Wunsch, and Jochen Merkenschlager. To all of you above thank you very much. I also like to thank the National Science Foundation under grant CMS #0409326, the National Institute of Standards and Technology under solicitation number SB1341-04-Q-0898, the Construction Industry Institute, and the “Shirley and Richard Tucker Endowed Scholarship” for their indirect and direct financial support. Since so many challenges and expectations remain in this world to be solved, each of us has the responsibility to work hard to solve some of these goals. A special thank you goes to my brothers Winfried, Klaus and Peter who were always available to reflect and support my career path. Thanks to y’all! August 11, 2006 vi REAL-TIME SPATIAL MODELING TO DETECT AND TRACK RESOURCES ON CONSTRUCTION SITES Publication No._____________ Jochen Teizer, Ph.D. The University of Texas at Austin, 2006 Supervisors: Carl T. Haas and Carlos H. Caldas For more than 10 years the U.S. construction industry has experienced over 1,000 fatalities annually. Many fatalities may have been prevented had the individuals and equipment involved been more aware of and alert to the physical state of the environment around them. Awareness may be improved by automatic 3D (three-dimensional) sensing and modeling of the job site environment in real-time. Existing 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model in real-time dynamic objects in an environment comprised of moving humans, equipment, and materials. Emerging prototype 3D video range cameras offer another alternative by facilitating affordable, wide field of view, automated static and dynamic object detection and tracking at frame rates better than 1Hz (real-time). This dissertation presents an imperical work and methodology to rapidly create a spatial model of construction sites and in particular to detect, model, and track the vii position, dimension, direction, and velocity of static and moving project resources in real- time, based on range data obtained from a three-dimensional video range camera in a static or moving position. Existing construction site 3D modeling approaches based on optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling approaches (dense, sparse, etc.) that offered potential solutions for this research are reviewed. The choice of an emerging sensing tool and preliminary experiments with this prototype sensing technology are discussed. These findings led to the development of a range data processing algorithm based on three-dimensional occupancy grids which is demonstrated in detail. Testing and validation of the proposed algorithms have been conducted to quantify the performance of sensor and algorithm through extensive experimentation involving static and moving objects. Experiments in indoor laboratory and outdoor construction environments have been conducted with construction resources such as humans, equipment, materials, or structures to verify the accuracy of the occupancy grid modeling approach. Results show that modeling objects and measuring their position, dimension, direction, and speed had an accuracy level compatible to the requirements of active safety features for construction. Results demonstrate that video rate 3D data acquisition and analysis of construction environments can support effective detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated three-dimensional models for improved visualization, communications, and process control has inherent value, broad application, and potential impact, e.g. as-built vs. as- planned comparison, condition assessment, maintenance, operations, and construction activities control. In combination with effective management practices, this sensing approach has the potential to assist equipment operators to avoid incidents that result in reduce human injury, death, or collateral damage on construction sites. viii Table of Contents Chapter 1: Introduction...........................................................................................1 1.1 Background..............................................................................................1 1.2 Motivation and Challenges ......................................................................3 1.2.1 Need for Safety in Construction ..................................................4 1.2.2 Need for Automated Assistance...................................................5 1.2.3 Need for Advanced Data Processing Methods ............................6 1.3 Research Hypothesis................................................................................8 1.4 Research Objectives and Scope ...............................................................8 1.5 Research Methodology ..........................................................................10 1.6 Structure of Dissertation ........................................................................11 Chapter 2: Literature Review................................................................................12 2.1 Potential Range Data Acquisition Technologies and Methods..............12 2.2 Optical and Infrared Range Sensing ......................................................14 2.2.1 The Physics behind Light Waves and Photons..........................15 2.3 Vision Based Ranging Technologies and Methods ...............................21 2.3.1 Triangulation, Photogrammetry, and Stereo Vision ..................23 2.3.2 Interferometry ............................................................................24 2.3.3 Time-of-Flight (TOF) ................................................................24 2.4 State-of-the-Art of Optical Range Sensing Using TOF.........................29 2.4.1 Contactless Distance Measurement Approaches .......................30 2.4.2 LADAR, LIDAR, and 3D Laser Scanner Approach .................31 2.4.3 Time Constraint and Clean Data................................................35 2.4.4 Sparse Point Cloud Approach....................................................37 2.5 Review of Range Data Processing Methods..........................................40 2.5.1 Model-Based and Data-Driven Approaches ..............................41 2.5.2 Occupancy Grids........................................................................41 2.5.3 Data Clustering ..........................................................................42 2.5.4 Image and
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