Near real-time stereo vision system

The apparatus for a near real-time stereo vision system for use with a robotic vehicle is described. The system is comprised of two cameras mounted on three-axis rotation platforms, image-processing boards, a CPU, and specialized stereo vision algorithms. Bandpass-filtered image pyramids are computed, stereo matching is performed by least-squares correlation, and confidence ranges are estimated by means of Bayes' theorem. In particular, Laplacian image pyramids are built and disparity maps are produced from the 60 x 64 level of the pyramids at rates of up to 2 seconds per image pair. The first autonomous cross-country robotic traverses (of up to 100 meters) have been achieved using the stereo vision system of the present invention with all computing done onboard the vehicle. The overall approach disclosed herein provides a unifying paradigm for practical domain-independent stereo ranging

Uncertainty updating in the description of heterogeneous materials

At a macroscopic scale, the details of mechanical behaviour are often uncertain, due to incomplete knowledge of details at small scales; this is especially acute if the materials have to be described before actually having been manufactured, such as in the case of concrete. Here the uncertainties are described by probabilistic methods, using recent numerical techniques for random fields based on white noise analysis. Numerical procedures are then developed to change/update the description once the materials have been manufactured, in order to take into accountadditionalinformation, obtainedforexamplefrommeasurements. This results in an improved description of the uncertainties of the material behaviour

Quantum dynamics of two bosons in an anharmonic trap: Collective vs internal excitations

This work deals with the effects of an anharmonic trap on an interacting two-boson system in one dimension. Our primary focus is on the role of the induced coupling between the center of mass and the relative motion as both anharmonicity and the (repulsive) interaction strength are varied. The ground state reveals a strong localization in the relative coordinate, counteracting the tendency to fragment for stronger repulsion. To explore the quantum dynamics, we study the system's response upon (i) exciting the harmonic ground state by continuously switching on an additional anharmonicity, and (ii) displacing the center of mass, this way triggering collective oscillations. The interplay between collective and internal dynamics materializes in the collapse of oscillations, which are explained in terms of few-mode models.Comment: 8 pages, 7 figure

Water Detection Based on Sky Reflections

This software has been designed to detect water bodies that are out in the open on cross-country terrain at mid- to far-range (approximately 20 100 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). Non-traversable water bodies, such as large puddles, ponds, and lakes, are indirectly detected by detecting reflections of the sky below the horizon in color imagery. The appearance of water bodies in color imagery largely depends on the ratio of light reflected off the water surface to the light coming out of the water body. When a water body is far away, the angle of incidence is large, and the light reflected off the water surface dominates. We have exploited this behavior to detect water bodies out in the open at mid- to far-range. When a water body is detected at far range, a UGV s path planner can begin to look for alternate routes to the goal position sooner, rather than later. As a result, detecting water hazards at far range generally reduces the time required to reach a goal position during autonomous navigation. This software implements a new water detector based on sky reflections that geometrically locates the exact pixel in the sky that is reflecting on a candidate water pixel on the ground, and predicts if the ground pixel is water based on color similarity and local terrain feature

Multi-Sensor Mud Detection

Robust mud detection is a critical perception requirement for Unmanned Ground Vehicle (UGV) autonomous offroad navigation. A military UGV stuck in a mud body during a mission may have to be sacrificed or rescued, both of which are unattractive options. There are several characteristics of mud that may be detectable with appropriate UGV-mounted sensors. For example, mud only occurs on the ground surface, is cooler than surrounding dry soil during the daytime under nominal weather conditions, is generally darker than surrounding dry soil in visible imagery, and is highly polarized. However, none of these cues are definitive on their own. Dry soil also occurs on the ground surface, shadows, snow, ice, and water can also be cooler than surrounding dry soil, shadows are also darker than surrounding dry soil in visible imagery, and cars, water, and some vegetation are also highly polarized. Shadows, snow, ice, water, cars, and vegetation can all be disambiguated from mud by using a suite of sensors that span multiple bands in the electromagnetic spectrum. Because there are military operations when it is imperative for UGV's to operate without emitting strong, detectable electromagnetic signals, passive sensors are desirable. JPL has developed a daytime mud detection capability using multiple passive imaging sensors. Cues for mud from multiple passive imaging sensors are fused into a single mud detection image using a rule base, and the resultant mud detection is localized in a terrain map using range data generated from a stereo pair of color cameras

Using Thermal Radiation in Detection of Negative Obstacles

A method of automated detection of negative obstacles (potholes, ditches, and the like) ahead of ground vehicles at night involves processing of imagery from thermal-infrared cameras aimed at the terrain ahead of the vehicles. The method is being developed as part of an overall obstacle-avoidance scheme for autonomous and semi-autonomous offroad robotic vehicles. The method could also be applied to help human drivers of cars and trucks avoid negative obstacles -- a development that may entail only modest additional cost inasmuch as some commercially available passenger cars are already equipped with infrared cameras as aids for nighttime operation

Intraspeaker Comparisons of Acoustic and Articulatory Variability in American English /r/ Productions

The purpose of this report is to test the hypothesis that speakers utilize an acoustic, rather than articulatory, planning space for speech production. It has been well-documented that many speakers of American English use different tongue configurations to produce /r/ in different phonetic contexts. The acoustic planning hypothesis suggests that although the /r/ configuration varies widely in different contexts, the primary acoustic cue for /r/, a dip in the F3 trajectory, will be less variable due to tradeoffs in articulatory variability, or trading relations, that help maintain a relatively constant F3 trajectory across phonetic contexts. Acoustic data and EMMA articulatory data from seven speakers producing /r/ in different phonetic contexts were analyzed. Visual inspection of the EMMA data at the point of F3 minimum revealed that each speaker appeared to use at least two of three trading relation strategies that would be expected to reduce F3 variability. Articulatory covariance measures confirmed that all seven speakers utilized a trading relation between tongue back height and tongue back horizontal position, six speakers utilized a trading relation between tongue tip height and tongue back height, and the speaker who did not use this latter strategy instead utilized a trading relation between tongue tip height and tongue back horizontal position. Estimates of F3 variability with and without the articulatory covariances indicated that F3 would be much higher for all speakers if the articulatory covariances were not utilized. These conclusions were further supported by a comparison of measured F3 variability to F3 variabilities estimated from the pellet data with and without articulatory covariances. In all subjects, the actual F3 variance was significantly lower than the F3 variance estimated without articulatory covariances, further supporting the conclusion that the articulatory trading relations were being used to reduce F3 variability. Together, these results strongly suggest that the neural control mechanisms underlying speech production make elegant use of trading relations between articulators to maintain a relatively invariant acoustic trace for /r/ across phonetic contexts

Self-Supervised Learning of Terrain Traversability from Proprioceptive Sensors

Robust and reliable autonomous navigation in unstructured, off-road terrain is a critical element in making unmanned ground vehicles a reality. Existing approaches tend to rely on evaluating the traversability of terrain based on fixed parameters obtained via testing in specific environments. This results in a system that handles the terrain well that it trained in, but is unable to process terrain outside its test parameters. An adaptive system does not take the place of training, but supplements it. Whereas training imprints certain environments, an adaptive system would imprint terrain elements and the interactions amongst them, and allow the vehicle to build a map of local elements using proprioceptive sensors. Such sensors can include velocity, wheel slippage, bumper hits, and accelerometers. Data obtained by the sensors can be compared to observations from ranging sensors such as cameras and LADAR (laser detection and ranging) in order to adapt to any kind of terrain. In this way, it could sample its surroundings not only to create a map of clear space, but also of what kind of space it is and its composition. By having a set of building blocks consisting of terrain features, a vehicle can adapt to terrain that it has never seen before, and thus be robust to a changing environment. New observations could be added to its library, enabling it to infer terrain types that it wasn't trained on. This would be very useful in alien environments, where many of the physical features are known, but some are not. For example, a seemingly flat, hard plain could actually be soft sand, and the vehicle would sense the sand and avoid it automatically