Mitchell M. Rohde

A Real-Time, Interactive Simulation Environment for Unmanned Ground Vehicles: The Autonomous Navigation Virtual Environment Laboratory (ANVEL)

Modeling and simulation tools have become an integral part of modern engineering processes. In particular, accurate and efficient simulation tools are critical for the design, development, and testing of autonomous unmanned ground vehicles (UGVs). However, because of the complexity of the problem, many UGV simulators are computationally intensive, require expensive hardware to run, and are often not interactive or real-time. Those simulation environments that do provide users with some degree of interactivity and real-time or faster performance gain these features at the sacrifice of simulation fidelity, and these products often provide inadequate results. A new simulation environment for UGV design and development, called the Autonomous Navigation Virtual Environment Laboratory (ANVEL), has been created to address the need for a real-time, interactive, physics-based simulation environment for UGVs. ANVEL is able to meet this need by fusing readily available, off-the-shelf video game technology with high-fidelity, physics-based models. This paper presents the methodology used in developing ANVEL, an example use of ANVEL for development and testing of an autonomous UGV, and plans for the future development.

AN INTERACTIVE, PHYSICS-BASED UNMANNED GROUND VEHICLE SIMULATOR LEVERAGING OPEN SOURCE GAMING TECHNOLOGY: PROGRESS IN THE DEVELOPMENT AND APPLICATION OF THE VIRTUAL AUTONOMOUS NAVIGATION ENVIRONMENT (VANE) DESKTOP

It is widely recognized that simulation is pivotal to vehicle development, whether manned or unmanned. There are few dedicated choices, however, for those wishing to perform realistic, end-to-end simulations of unmanned ground vehicles (UGVs). The Virtual Autonomous Navigation Environment (VANE), under development by US Army Engineer Research and Development Center (ERDC), provides such capabilities but utilizes a High Performance Computing (HPC) Computational Testbed (CTB) and is not intended for on-line, real-time performance. A product of the VANE HPC research is a real-time desktop simulation application under development by the authors that provides a portal into the HPC environment as well as interaction with wider-scope semi-automated force simulations (e.g. OneSAF). This VANE desktop application, dubbed the Autonomous Navigation Virtual Environment Laboratory (ANVEL), enables analysis and testing of autonomous vehicle dynamics and terrain/obstacle interaction in real-time with the capability to interact within the HPC constructive geo-environmental CTB for high fidelity sensor evaluations. ANVEL leverages rigorous physics-based vehicle and vehicle-terrain interaction models in conjunction with high-quality, multimedia visualization techniques to form an intuitive, accurate engineering tool. The system provides an adaptable and customizable simulation platform that allows developers a controlled, repeatable testbed for advanced simulations. ANVEL leverages several key technologies not common to traditional engineering simulators, including techniques from the commercial video-game industry. These enable ANVEL to run on inexpensive commercial, off-the-shelf (COTS) hardware. In this paper, the authors describe key aspects of ANVEL and its development, as well as several initial applications of the system.

PointCom: semi-autonomous UGV control with intuitive interface

Unmanned ground vehicles (UGVs) will play an important role in the nations next-generation ground force. Advances in sensing, control, and computing have enabled a new generation of technologies that bridge the gap between manual UGV teleoperation and full autonomy. In this paper, we present current research on a unique command and control system for UGVs named PointCom (Point-and-Go Command). PointCom is a semi-autonomous command system for one or multiple UGVs. The system, when complete, will be easy to operate and will enable significant reduction in operator workload by utilizing an intuitive image-based control framework for UGV navigation and allowing a single operator to command multiple UGVs. The project leverages new image processing algorithms for monocular visual servoing and odometry to yield a unique, high-performance fused navigation system. Human Computer Interface (HCI) techniques from the entertainment software industry are being used to develop video-game style interfaces that require little training and build upon the navigation capabilities. By combining an advanced navigation system with an intuitive interface, a semi-autonomous control and navigation system is being created that is robust, user friendly, and less burdensome than many current generation systems. mand).

Fusion of visual odometry and inertial data for enhanced real-time egomotion estimation

This paper presents a real-time motion estimation module for ground vehicles based on the fusion of monocular visual odometry and low-cost inertial measurement unit data. The system features a novel algorithmic scheme enabling accurate and robust scale estimation and odometry at high speeds. Results of multiple performance characterization experiments (on rough terrain at speeds up to 20 mph and smooth roadways at speeds of up to 75 mph) are presented. The prototype system demonstrates high levels of precision (relative distance error less than 1%, and less than 0.5% on paved roads, yaw drift rate ~2 degrees per km) in multiple configurations, including various optics and vehicles. Performance limitations, including those specific to monocular vision, are analyzed and directions for further improvements are outlined.

Upper‐Bounding the Incidence Rate of Associations Between Camouflage Uniforms and Surveillance Images

Abstract:  Camouflage garments can be associated with surveillance images of a crime scene even in the absence of unique wear marks or very high‐quality images. However, the probability of an accidental association, or incidence rate, is significant. The present work describes and validates a method for estimating the incidence rate based on a statistical model of the garment manufacturing process. The model was developed primarily for use with the current U.S. Army Combat Uniform (ACU), but can be applied to any camouflage garment. Eight garment manufacturers were studied, and all sources of variation in the manufacturing process were characterized. The marking and spreading procedures were found to be dominant and consistent sources of variation. However, some sources of variation, in particular those because of human operators, were not consistent enough to accurately characterize. Sources of variation that could not be well‐characterized were ignored in the statistical model, yielding a worst‐case estimate that is an upper‐bound to the true incidence rate. The model was evaluated for a variety of cases. Depending on the quality of the surveillance image, the manufacturing parameters, and the local population, incidence rates range from about 3% to negligibly small. The model was validated by returning to one manufacturer, and sampling a large number of completed garments and estimating empirical match probabilities. The empirical probabilities validated the estimates of the worst‐case incidence rate and also demonstrated that typical incidence rates are significantly lower.

Establishing Likelihood Ratios for Patterned Garment Comparisons from Seam Measurement Data

It is often challenging to ascribe an objective measure of confidence for identifications based on surveillance imagery from a crime scene. The present work seeks to address this deficiency in the case of garment comparison evidence by developing a quantitative method for establishing a conservative lower bound on the likelihood ratio (LR) for identifications involving patterned garments. The method is based on statistical analysis of pattern offset measurements taken from a sample of garments of the same type (manufacturer, style, and size) as the seized evidence. The developed analysis framework was demonstrated on different types of garments over a range of modeled surveillance imaging scenarios with variable image quality; the lower bounds on the LRs ranged from approximately 10–1 to over 400–1. The statistical model was tested and validated through a large‐scale empirical study involving both simulated and human observer‐performed garment comparisons.

ESARR: enhanced situational awareness via road sign recognition

The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). Sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.

Toward the rapid development of face and speech biometric methods

Biometrics has become an increasingly important part of the overall set of tools for securing a wide range of facilities, areas, information, and environments. At the core of any biometric verification/identification technique lies the development of the algorithm itself. Much research has been performed in this area to varying degrees of success, and it is well recognized within the biometrics community that substantial room for improvement exists. The focus of this paper is to describe ongoing biometrics algorithm development efforts by the authors. An overview of the data collection, algorithm development, and testing efforts is described. The focus of the research is to develop core algorithmic concepts that serve as the basis for robust techniques in both the face and speech modalities. A broad overview of the methodology is provided with some sample results. The end goal is to have a robust, modular set of tools which can balance complexity and need for accuracy and robustness for a wide variety of applications.

Application of Advanced Signal Processing Methods to Automotive Systems Testing

During the past twenty years there have been rapid developments in the creation and application of mathematical computer-based capabilities and tools (e.g., FEA) to simulate and synthesize vehicle systems. This has led to the concept of virtual product development. In parallel with the development of these tools, an equally sophisticated set of tools have been developed in the area of advanced signal processing. These tools, based upon mathematical and statistical modeling techniques, enable the extraction of useful information from data and have application throughout the entire vehicle creation process. Moreover, signal processing bridges the gap between the “virtual” and the “real” worlds — an extremely important concept that is changing the entire nature of what is thought of as “testing.” This paper discusses the use of advanced signal processing methods in vehicle creation with particular emphasis on its use in vehicle systems testing. Modern Time Frequency Analysis (TFA), a technique that was specifically designed to study transient signals and was in part pioneered by one of the authors (WJW), is highlighted. TFA expresses a signal jointly in time and frequency at very high resolution and as such can often provide profound insights. Applications of TFA to vehicle systems testing are presented related to Noise, Vibration, and Harshness (NVH) that enable sound quality analyses. For example, using TFA predictive models of consumer preferences for transient sounds that are useful to the automotive engineer in testing and modifying new vehicle subsystem designs are discussed. Other applications that are discussed deal with brake pedal feel, and characterizing vehicle crash signals. In the latter case TFA has resulted in some unique insights that were not provided by conventional statistical and mathematical analyses.Copyright