The stochastic error characteristics of the Kinect sensing device are presented for each axis direction. Depth (z) directional error is measured using a flat surface, and horizontal (x) and vertical (y) errors are measured using a novel 3D checkerboard. Results show that the stochastic nature of the Kinect measurement error is affected mostly by the depth at which the object being sensed is located, though radial factors must be considered, as well. Measurement and statistics-based models are presented for the stochastic error in each axis direction, which are based on the location and depth value of empirical data measured for each pixel across the entire field of view. The resulting models are compared against existing Kinect error models, and through these comparisons, the proposed model is shown to be a more sophisticated and precise characterization of the Kinect error distributions.
With the emergence of the Microsoft Kinect sensor, many developer communities and research groups have found countless uses and have already published a wide variety of papers that utilize the raw depth images for their specific goals. New methods and applications that use the device generally require an appropriately large ensemble of data sets with accompanying ground truth for testing purposes, as well as accurate models that account for the various systematic and stochastic contributors to Kinect errors. Current error models, however, overlook the intermediate infrared (IR) images that directly contribute to noisy depth estimates. We, therefore, propose a high fidelity Kinect IR and depth image predictor and simulator that models the physics of the transmitter/receiver system, unique IR dot pattern, disparity/depth processing technology, and random intensity speckle and IR noise in the detectors. The model accounts for important characteristics of Kinect's stereo triangulation system, including depth shadowing, IR dot splitting, spreading, and occlusions, correlation-based disparity estimation between windows of measured and reference IR images, and subpixel refinement. Results show that the simulator accurately produces axial depth error from imaged flat surfaces with various tilt angles, as well as the bias and standard lateral error of an object's horizontal and vertical edge.
With the rate of technological change growing rapidly and technological systems becoming increasingly complex, engineers capable of designing adaptable systems from both a systems level and a component level are needed for the U.S. to remain competitive. Most engineering schools fail to meet the growing need for engineers skilled in multiscale design: they educate engineers to handle systems issues or component issues, but not both. Furthermore, engineering education focuses on designing static, "point" solutions, not agile solutions that can adapt to change. Specifically, this project proposes the development of Technology Leaders, a transportable interdisciplinary program that will prepare engineers and technicians to lead teams in the designing and building of multiscale agile systems. Building on prior work at the University of ___A___, ___B___ Community College, and the Learning Factory at Penn State, the Technology Leaders program will integrate three elements: a new interdisciplinary, design-focused undergraduate curriculum; the hands-on Technology Leaders Program Lab (TLP Lab); and applied summer experiences. Grounded in constructivist learning theory, the interdisciplinary curriculum will focus on design throughout the undergraduate experience by incorporating multiple interconnected real-world problems into the courses. The curriculum will be developed for both four-year university and two-year community college students. As part of the curriculum, Technology Leaders students from all years will participate together in a learning community focused on developing leadership skills, fostering a sense of belonging, and providing space for reflection and student-led curriculum design. The TLP Lab will consist of easily reconfigurable multiscale hardware (e.g., servers, motes), software (e.g., service-oriented-architecture based products, peer-to-peer networks), multiple networks (e.g., Internet, 802.11, Zigbee), and test and evaluation tools (e.g., Network Sim, emulation tools) at multiple facilities including the University of ___A___, ___B___ Community College, and industrial partners. All students will complete summer industrial internships or research experiences before graduation with Technology Leaders industrial and research partners. The Technology Leaders Program is being implemented over the course of four years beginning in Fall 2008, with our first students graduating in Spring 2012. The first year focus is on developing the TLP Lab and integrating it into first-year courses as a means of marketing the program. To this end, the lab was built, one section of the first-year introduction to engineering design course has been completely redesigned to incorporate the TLP Lab, the Technology Leaders Community is meeting monthly, and the lab will be utilized in an introductory electrical engineering survey course. In addition to an overview of the entire Technology Leaders program, results from these initial activities are presented in the paper.
The Adaptive Multi-scale Prognostics and Health Management (AM-PHM) is a methodology designed to enable PHM in smart manufacturing systems. In application, PHM information is not yet fully utilized in higher-level decisionmaking in manufacturing systems. AM-PHM leverages and integrates lower-level PHM information such as from a machine or component with hierarchical relationships across the component, machine, work cell and assembly line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are then made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. To overcome the issue of exponential explosion of complexity associated with describing a large manufacturing system, the AM-PHM methodology takes a hierarchical Markov Decision Process (MDP) approach into describing the system and solving for an optimized policy. A description of the AM-PHM methodology is followed by a simulated industry-inspired example to demonstrate the effectiveness of AM-PHM.
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