Michael Magee

Progress toward the development of lifeform detection algorithms for the deep phreatic thermal explorer (DEPTHX)

Based on observations of seemingly hostile aqueous environments on earth, it is possible for lifeforms not only to evolve but to thrive in conditions that, by human standards, are extreme. Such lifeforms, typically termed extremophiles can, for example, live in the vicinity of deep water volcanic vents that are spewing superheated water laden with sulfur compounds at intense pressures. Since similar conditions may exist on Jupiters moon Europa, there is widespread interest in developing an autonomous search-for-life capability that could be deployed in aqueous, extraterrestrial environments. As one step toward this goal, the DEep Phreatic THermal eXplorer (DEPTHX) is a NASA Astrobiology Science and Technology for Exploring Planets (ASTEP) project to design, develop and field-test a robotic vehicle to explore such environments. The principal astrobiological science objective of DEPTHX is to develop an advanced methodology and protocol for the discrimination of microbial life in a sub-aqueous environment. Implementation requires the design, development, and demonstration of a fully autonomous architecture for intelligent biological sample detection and collection, whereby the robotic device will be capable of performing the following functions: 1. Deep hydrothermal springs will be mapped with great accuracy in three dimensions. 2. Data will be acquired from a hierarchical suite of on-board microbial life detection and sensors and processors and will be analyzed to determine whether life is present. 3. Specimens will be aseptically collected and returned for subsequent ex-situ laboratory analysis preserved under ambient conditions. The paper describes current progress toward these objectives, with an emphasis on the analysis of data acquired from the life sensors for the purpose of detecting lifeforms.

3D precision surface measurement by dynamic structured light

This paper describes a 3-D imaging technique developed as an internal research project at Southwest Research Institute. The technique is based on an extension of structured light methods in which a projected pattern of parallel lines is rotated over the surface to be measured. A sequence of images is captured and the surface elevation at any location can then be determined from measurements of the temporal pattern, at any point, without considering any other points on the surface. The paper describes techniques for system calibration and surface measurement based on the method of projected quadric shells. Algorithms were developed for image and signal analysis and computer programs were written to calibrate the system and to calculate 3-D coordinates of points on a measured surface. A prototype of the Dynamic Structured Light (DSL) 3-D imaging system was assembled and typical parts were measured. The design procedure was verified and used to implement several different configurations with different measurement volumes and measurement accuracy. A small-parts measurement accuracy of 32 micrometers (.0012”) RMS was verified by measuring the surface of a precision-machined plane. Large aircraft control surfaces were measured with a prototype setup that provided .02” depth resolution over a 4’ by 8’ field of view. Measurement times are typically less than three minutes for 300,000 points. A patent application has been filed.

System for the generation of digital terrain elevation data (DTED) from compressed arc digitized raster graphics (CADRG) images

There are many applications for which there is a strong need to analyze digital terrain elevation data (DTED). However, the availability of DTED is limited. For some areas of the world, DTED is not available at all, and for other areas it may be only partially available. This introduces significant problems if analysis is required for areas of constrained DTED availability. On the other hand, compressed arc digitized raster graphics (CADRG) maps provide a guaranteed source of digital data that contains a limited amount of elevation information, namely elevations that occur along the contours in the maps. Unfortunately, CADRG maps typically contain a significant amount of additional visual information that obscures features of interest, making difficult the automated locations of contours and the subsequent transformation into DTED.

Adaptive image processing and sensing technologies for detecting anomalous conditions near critical transportation infrastructure assets

This work presents a methodology for detecting anomalous conditions on transportation infrastructure assets and generating alarms to draw the attention of personnel monitoring them via various sensing technologies. The methodology developed, which consists of a four part process, is capable of detecting conditions that vary substantially from those that are normally observed. This four part process consists of the following steps: (1) an intensity characteristic model of the transportation asset is learned during a training phase. (2) During the foreground object segmentation phase, objects are segmented based on pixel characteristics that vary substantially from those embodied in the intensity characteristic model. (3) Segmented objects that match certain morphological, topological, and/or geometric constraints are flagged as being candidates for further (temporal) processing. (4) Segmented objects with unanticipated temporal persistence or geometric characteristics are then identified and brought to the attention of a human operator.

Evaluation of ballistic events using a Moiré fringe image processing methodology

This paper describes a Moire fringe based image analysis system that was developed to determine the out-of-plane deformation of target plates impacted by high velocity projectiles. Due to the highly dynamic conditions that occur as the results of these impacts, such data are very difficult to acquire and analyze. Nevertheless, they are essential for the evaluation of armor materials designed to minimize behind- armor debris, and to study the fracture effects of very strong yet brittle advanced armor materials. Additionally, the data sought is required for fundamental verification of computational material models meant to simulate failure in ballistic experiments. The major goal for developing such a system was therefore to image and analyze the three- dimensional high-speed deformation, fracturing, and propagation of fractures that leads to the onset of fragmentation of targets during impact. The specific image processing methodologies discussed include noise reduction, automated and assisted Moire fringe finding, and the remapping of two-dimensional fringe patterns into three-dimensionally distorted surfaces. Results of applying the image processing system are also provided and methods for increasing system robustness in the presence of higher levels of noise are also discussed.

Sensor-fusion-based approach for feature extraction in intelligent transportation system applications

The ability to extract useful and robust features from sensor data of vehicles in moving traffic is highly dependent on a number of factors. For imaging sensors that produce a two- dimensional representation of an observed scene such as a visible light camera, the principal factors influencing the quality of the acquired data include the ambient lighting and weather conditions as well the physical characteristics of the vehicles whose images are captured. Considerable variability in the ambient lighting conditions in combination with material characteristics may cause radically different appearances for various surfaces of a vehicle when viewed in the visible wavelengths. Infrared sensors, on the other hand, produce images that are far less sensitive to variations in ambient lighting conditions, but may not provide sufficient information that can be used to discriminate among vehicles. Combining information from these sensors provides the basis for exploiting the relative strengths of each sensor domain while attenuating the weaknesses that exist in single systems. This paper presents a basic framework for combining information from multiple sensor systems by describing methodologies for geometrically transforming between image spaces and extracting features using a multi-dimensional approach that exploits information gathered at different wavelengths. The potential use of point sensors (such as acoustic and microwave detectors) in combination with imaging sensors is also discussed.

RELATIVE SPATIAL POSE ESTIMATION FOR AUTONOMOUS GRASPING

A technique for finding the relative spatial pose between a robotic end effector and a target object to be grasped without a priori knowledge of the spatial relationship between the camera and the robot is presented. The transformation between the coordinate system of the camera and the coordinate system of the robot is computed dynamically using knowledge about the location of the end effector relative to both the camera and the robot. A previously developed computer vision tech- nique is used to determine the pose of the end effector relative to the camera. The robot geometry and data from the robot controller is used to determine the pose of the end effector relative to the robot. The spatial transformation between the robot end effector and the target object is computed with respect to the robots coordinate system. The algorithm was demonstrated using a five-degree-of-freedom robot and an RGB camera system. The camera can be dynamically positioned without con- cern for an assumed spatial relationship between the camera and robot, enabling optimization of the view of the object and the end effector. Further, the iterative nature of the grasping algorithm reduces the effects of camera calibration errors.