Real-Time Three-Dimensional Occupancy Grid Modeling for the Detection and Tracking of Construction Resources

Awareness of the construction environment can be improved by automatic three-dimensional (3D) sensing and modeling of job sites in real time. Commercially available 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model dynamic objects in real time in an environment comprised of moving humans, equipment, and materials. Emerging prototype video range cameras offer an alternative by facilitating affordable, wide field of view, dynamic object tracking at frame rates better than 1 Hz (real time). This paper describes a methodology to model, detect, and track the position of static and moving objects in real time, based on data obtained from video range cameras. Experiments with this technology have produced results that indicate that video rate 3D data acquisition and analysis of construction environments can support effective modeling, detection, and tracking of project resources. This approach to job site awareness has inherent value and broad application. In combination with effective management practices and other sensing techniques, this technology has the potential to significantly improve safety on construction job sites.

[1]  Carlos H. Caldas,et al.  Experiments in real-time spatial data acquisition for obstacle detection , 2005 .

[2]  David L. Goetsch,et al.  Construction Safety, and Health , 2002 .

[3]  Burcu Akinci,et al.  Formalization and Automation of Time-Space Conflict Analysis , 2002 .

[4]  Kamel S. Saidi,et al.  Performance Evaluation of a High-Frame Rate 3D Range Sensor for Construction Applications , 2005 .

[5]  Frédéric Bosché,et al.  Rapid human-assisted, obstacle avoidance system using sparse range point clouds , 2004 .

[6]  Jimmie Hinze.  Use of Trench Boxes for Worker Protection , 2005 .

[7]  Burcu Akinci,et al.  Automating the task of tracking the delivery and receipt of fabricated pipe spools in industrial projects , 2006 .

[8]  Jimmie Hinze,et al.  Viability of Designing for Construction Worker Safety , 2005 .

[9]  William C. Stone,et al.  Performance Analysis of Next-Generation LADAR for Manufacturing , 2004 .

[10]  Ronie Navon,et al.  Automated project performance control of construction projects , 2005 .

[11]  Helmut Dr. Riedel,et al.  3D Vision Systems for Active Safety , 2002 .

[12]  R.J. Fontana,et al.  Recent system applications of short-pulse ultra-wideband (UWB) technology , 2004, IEEE Transactions on Microwave Theory and Techniques.

[13]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[14]  Hilmar Ingensand,et al.  Calibration and improvements of the high-resolution range-imaging camera SwissRanger , 2005 .

[15]  S. V. Sreenivasan,et al.  Rapid Human-Assisted Creation of Bounding Models for Obstacle Avoidance in Construction , 2004 .

[16]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[17]  Roland Siegwart,et al.  A state-of-the-art 3D sensor for robot navigation , 2004 .

[18]  Soon-Wook Kwon,et al.  Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds , 2004 .

[19]  John A. Gambatese,et al.  Tool to design for construction worker safety , 1997 .

[20]  Carlos H. Caldas,et al.  Framework for Real-Time Three-Dimensional Modeling of Infrastructure , 2005 .