Publications

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This Lab-Directed Research and Development (LDRD) sought to develop technology that enhances scenario construction speed, entity behavior robustness, and scalability in Live-Virtual-Constructive (LVC) simulation. We investigated issues in both simulation architecture and behavior modeling. We developed path-planning technology that improves the ability to express intent in the planning task while still permitting an efficient search algorithm. An LVC simulation demonstrated how this enables 'one-click' layout of squad tactical paths, as well as dynamic re-planning for simulated squads and for real and simulated mobile robots. We identified human response latencies that can be exploited in parallel/distributed architectures. We did an experimental study to determine where parallelization would be productive in Umbra-based force-on-force (FOF) simulations. We developed and implemented a data-driven simulation composition approach that solves entity class hierarchy issues and supports assurance of simulation fairness. Finally, we proposed a flexible framework to enable integration of multiple behavior modeling components that model working memory phenomena with different degrees of sophistication.

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This paper presents a 3D facial recognition algorithm based on the Hausdorff distance metric. The standard 3D formulation of the Hausdorff matching algorithm has been modified to operate on a 2D range image, enabling a reduction in computation from O(N2) to O(N) without large storage requirements. The Hausdorff distance is known for its robustness to data outliers and inconsistent data between two data sets, making it a suitable choice for dealing with the inherent problems in many 3D datasets due to sensor noise and object self-occlusion. For optimal performance, the algorithm assumes a good initial alignment between probe and template datasets. However, to minimize the error between two faces, the alignment can be iteratively refined. Results from the algorithm are presented using 3D face images from the Face Recognition Grand Challenge database version 1.0.

This paper presents a 3D facial recognition algorithm based on the Hausdorff distance metric. The standard 3D formulation of the Hausdorff matching algorithm has been modified to operate on a 2D range image, enabling a reduction in computation from O(N2) to O(N) without large storage requirements. The Hausdorff distance is known for its robustness to data outliers and inconsistent data between two data sets, making it a suitable choice for dealing with the inherent problems in many 3D datasets due to sensor noise and object self-occlusion. For optimal performance, the algorithm assumes a good initial alignment between probe and template datasets. However, to minimize the error between two faces, the alignment can be iteratively refined. Results from the algorithm are presented using 3D face images from the Face Recognition Grand Challenge database version 1.0.

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The need to register data is abundant in applications such as: world modeling, part inspection and manufacturing, object recognition, pose estimation, robotic navigation, and reverse engineering. Registration occurs by aligning the regions that are common to multiple images. The largest difficulty in performing this registration is dealing with outliers and local minima while remaining efficient. A commonly used technique, iterative closest point, is efficient but is unable to deal with outliers or avoid local minima. Another commonly used optimization algorithm, simulated annealing, is effective at dealing with local minima but is very slow. Therefore, the algorithm developed in this paper is a hybrid algorithm that combines the speed of iterative closest point with the robustness of simulated annealing. Additionally, a robust error function is incorporated to deal with outliers. This algorithm is incorporated into a complete modeling system that inputs two sets of range data, registers the sets, and outputs a composite model.

Traditionally law enforcement agencies have relied on basic measurement and imaging tools, such as tape measures and cameras, in recording a crime scene. A disadvantage of these methods is that they are slow and cumbersome. The development of a portable system that can rapidly record a crime scene with current camera imaging, 3D geometric surface maps, and contribute quantitative measurements such as accurate relative positioning of crime scene objects, would be an asset to law enforcement agents in collecting and recording significant forensic data. The purpose of this project is to develop a feasible prototype of a fast, accurate, 3D measurement and imaging system that would support law enforcement agents to quickly document and accurately record a crime scene.

This project visualizes characterization data in a 3D setting, in real time. Real time in this sense means collecting the data and presenting it before it delays the user, and processing faster than the acquisition systems so no bottlenecks occur. The goals have been to build a volumetric viewer to display 3D data, demonstrate projecting other data, such as images, onto the 3D data, and display both the 3D and projected images as fast as the data became available. The authors have examined several ways to display 3D surface data. The most effective was generating polygonal surface meshes. They have created surface maps form a continuous stream of 3D range data, fused image data onto the geometry, and displayed the data with a standard 3D rendering package. In parallel with this, they have developed a method to project real-time images onto the surface created. A key component is mapping the data on the correct surfaces, which requires a-priori positional information along with accurate calibration of the camera and lens system.

The ability to use an interactive world model, whether it is for robotics simulation or most other virtual graphical environments, relies on the users ability to create an accurate world model. Typically this is a tedious process, requiring many hours to create 3-D CAD models of the surfaces within a workspace. The goal of this ongoing project is to develop usable methods to rapidly build world models of real world workspaces. This brings structure to an unstructured environment and allows graphical based robotics control to be accomplished in a reasonable time frame when traditional CAD modelling is not enough. To accomplish this, 3D range sensors are deployed to capture surface data within the workspace. This data is then transformed into surface maps, or models. A 3D world model of the workspace is built quickly and accurately, without ever having to put people in the environment.