Convective Vertical Air Motion Estimation from Nadir Pointing Airborne Dopper Radar
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Gerald Heymsfield↗
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Estimating satellite pose and motion parameters using a novelty filter and neural net tracker
A system for determining the position, orientation and motion of a satellite with respect to a robotic spacecraft using video data is advanced. This system utilizes two levels of pose and motion estimation: an initial system which provides coarse estimates of pose and motion, and a second system which uses the coarse estimates and further processing to provide finer pose and motion estimates. The present paper emphasizes the initial coarse pose and motion estimation sybsystem. This subsystem utilizes novelty detection and filtering for locating novel parts and a neural net tracker to track these parts over time. Results of using this system on a sequence of images of a spin stabilized satellite are presented.
Lee, Andrew J.↗
Demonstration of precise estimation of polar motion parameters with the global positioning system: Initial results
Data from the Global Positioning System (GPS) were used to determine precise polar motion estimates. Conservatively calculated formal errors of the GPS least squares solution are approx. 10 cm. The GPS estimates agree with independently determined polar motion values from very long baseline interferometry (VLBI) at the 5 cm level. The data were obtained from a partial constellation of GPS satellites and from a sparse worldwide distribution of ground stations. The accuracy of the GPS estimates should continue to improve as more satellites and ground receivers become operational, and eventually a near real time GPS capability should be available. Because the GPS data are obtained and processed independently from the large radio antennas at the Deep Space Network (DSN), GPS estimation could provide very precise measurements of Earth orientation for calibration of deep space tracking data and could significantly relieve the ever growing burden on the DSN radio telescopes to provide Earth platform calibrations.
Lichten, S. M.↗
Path following using visual odometry for Mars Rover in high-slip environments.
An architecture for autonomous operation of Mars rovers in high slip environments has been designed, implemented, and tested. This architecture is composed of several key technologies that enable the rover to accurately follow a designated path, compensate for slippage, and reach intended goals independent of the terrain over which it is traversing (within the mechanical constraints of the mobility system). These technologies include: visual odometry, full vehicle kinematics, a Kalman filter, and a slop compensation/path follower. Visual odometry tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs using a maximum likelihood motion estimation algorithm. The full vehicle kinematics for a rocker-bogie suspension system estimates motion, with a no-slip assumption, by measuring wheel, rates, and rocker, bogie, and steering angles. The Kalman filter merges data from an Inertial Measurement Unit (IMU) and visual odometry. This merged estimate is then compared to the kinematic estimate to determining (tracking into account estimate uncertainties) if and how much slippage has occurred then a slip vector is calculated by differencing the current Kalman filter estimate from the kinematic estimate. This slip vector is then used, in conjunction with the inverse kinematics, to determine the necessary wheel velocities and steering angles to compensate for slip and follow the desired path.
Roumeliotis, Stergios I.↗
Path Following with Slip Compensation for a Mars Rover
A software system for autonomous operation of a Mars rover is composed of several key algorithms that enable the rover to accurately follow a designated path, compensate for slippage of its wheels on terrain, and reach intended goals. The techniques implemented by the algorithms are visual odometry, full vehicle kinematics, a Kalman filter, and path following with slip compensation. The visual-odometry algorithm tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs, by use of a maximum-likelihood motion-estimation algorithm. The full-vehicle kinematics algorithm estimates motion, with a no-slip assumption, from measured wheel rates, steering angles, and angles of rockers and bogies in the rover suspension system. The Kalman filter merges data from an inertial measurement unit (IMU) and the visual-odometry algorithm. The merged estimate is then compared to the kinematic estimate to determine whether and how much slippage has occurred. The kinematic estimate is used to complement the Kalman-filter estimate if no statistically significant slippage has occurred. If slippage has occurred, then a slip vector is calculated by subtracting the current Kalman filter estimate from the kinematic estimate. This slip vector is then used, in conjunction with the inverse kinematics, to determine the wheel velocities and steering angles needed to compensate for slip and follow the desired path.
Helmick, Daniel↗
Effect of Recent Revisions to the Geomagnetic Reversal Time Scale on Estimates of Current Plate Motions
Recent revisions to the geomagnetic time scale indicate that global plate motion model NUVEL-1 should be modified for comparison with rates of motion estimated from space geodetic measurements. The optimal recalibration, which is a compromise among slightly different calibrations appropriate for slow, medium, and fast spreading rates is to multiply NUVEL-1 angular velocities by a multiplicative constant, alpha, of 0.9562. We refer to this simply recalibrated plate motion model as NUVEL-1A, and give correspondingly revised tables of angular velocities and uncertainties. Prior work has shown that space geodetic rates are slower on average than those calculated from NUVEL-1 by 6±1%. This average discrepancy is reduced to less that 2% when space geodetic rates are instead compared to NUVEL-1A.
geodetic↗
Robot Acting on Moving Bodies (RAMBO): Interaction with tumbling objects
Interaction with tumbling objects will become more common as human activities in space expand. Attempting to interact with a large complex object translating and rotating in space, a human operator using only his visual and mental capacities may not be able to estimate the object motion, plan actions or control those actions. A robot system (RAMBO) equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a tumbling object, is being developed. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations rearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enhancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using dynamic interpolations between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors.
Davis, Larry S.↗
Feasibility of Measuring Mean Vertical Motion for Estimating Advection
Numerous recent studies calculate horizontal and vertical advection terms for budget studies of net ecosystem exchange of carbon. One potential uncertainty in such studies is the estimate of mean vertical motion. This work addresses the reliability of vertical advection estimates by contrasting the vertical motion obtained from the standard practise of measuring the vertical velocity and applying a tilt correction, to the vertical motion calculated from measurements of the horizontal divergence of the flow using a network of towers. Results are compared for three different tilt correction methods. Estimates of mean vertical motion are sensitive to the choice of tilt correction method. The short-term mean (10 to 60 minutes) vertical motion based on the horizontal divergence is more realistic compared to the estimates derived from the standard practise. The divergence shows long-term mean (days to months) sinking motion at the site, apparently due to the surface roughness change. Because all the tilt correction methods rely on the assumption that the long-term mean vertical motion is zero for a given wind direction, they fail to reproduce the vertical motion based on the divergence.
Vickers, Dean↗
Estimated Surface Motions of the Earth's Core
Estimated surface fluid motions of earth core, and rotational and irrotational flow data
EARTH MOTION↗
Autonomous Navigation Results from the Mars Exploration Rover (MER) Mission
In January, 2004, the Mars Exploration Rover (MER) mission landed two rovers, Spirit and Opportunity, on the surface of Mars. Several autonomous navigation capabilities were employed in space for the first time in this mission. ]n the Entry, Descent, and Landing (EDL) phase, both landers used a vision system called the, Descent Image Motion Estimation System (DIMES) to estimate horizontal velocity during the last 2000 meters (m) of descent, by tracking features on the ground with a downlooking camera, in order to control retro-rocket firing to reduce horizontal velocity before impact. During surface operations, the rovers navigate autonomously using stereo vision for local terrain mapping and a local, reactive planning algorithm called Grid-based Estimation of Surface Traversability Applied to Local Terrain (GESTALT) for obstacle avoidance. ]n areas of high slip, stereo vision-based visual odometry has been used to estimate rover motion, As of mid-June, Spirit had traversed 3405 m, of which 1253 m were done autonomously; Opportunity had traversed 1264 m, of which 224 m were autonomous. These results have contributed substantially to the success of the mission and paved the way for increased levels of autonomy in future missions.
autonomous↗
Self-motion magnitude estimation during linear oscillation - Changes with head orientation and following fatigue
Two types of experiments concerning the estimated magnitude of self-motion during exposure to linear oscillation on a parallel swing are described in this paper. Experiment I examined changes in magnitude estimation as a function of variation of the subject's head orientation, and Experiments II a, II b, and II c assessed changes in magnitude estimation performance following exposure to sustained, 'intense' linear oscillation (fatigue-inducting stimulation). The subjects' performance was summarized employing Stevens' power law R = k x S to the nth, where R is perceived self-motion magnitude, k is a constant, S is amplitude of linear oscillation, and n is an exponent). The results of Experiment I indicated that the exponents, n, for the magnitude estimation functions varied with head orientation and were greatest when the head was oriented 135 deg off the vertical. In Experiments II a-c, the magnitude estimation function exponents were increased following fatigue. Both types of experiments suggest ways in which the vestibular system's contribution to a spatial orientation perceptual system may vary. This variability may be a contributing factor to the development of pilot/astronaut disorientation and may also be implicated in the occurrence of motion sickness.
Parker, D. E.↗
Estimation of motion parameters for a rigid body from its orthogonal projection
An estimate is presented of the motion parameters, namely, linear and angular velocities of a rigid body rotating and translating in three-dimensional-space. It is assumed that the velocities are constant and that only the orthogonal projection of the motion is observable. In particular, if (x, y, z) is the Cartesian coordinate, it is assumed that the projection of the motion on the x-y plane is observed and the information along the z coordinate is lost.
Ganguly, S.↗
Estimated surface motions of the earth's core.
Frozen field estimation of surface flow of earth core and effects of secular change resulting from flow pattern nonuniformity
SECULAR PERTURBATION↗
Post-Flight Estimation of Motion of Space Structures: Part 1
A computer program estimates the relative positions and orientations of two space structures from data on the angular positions and distances of fiducial objects on one structure as measured by a target tracking electronic camera and laser range finders on another structure. The program is written specifically for determining the relative alignments of two antennas, connected by a long truss, deployed in outer space from a space shuttle. The program is based partly on transformations among the various coordinate systems involved in the measurements and on a nonlinear mathematical model of vibrations of the truss. The program implements a Kalman filter that blends the measurement data with data from the model. Using time series of measurement data from the tracking camera and range finders, the program generates time series of data on the relative position and orientation of the antennas. A similar program described in a prior NASA Tech Briefs article was used onboard for monitoring the structures during flight. The present program is more precise and designed for use on Earth in post-flight processing of the measurement data to enable correction, for antenna motions, of scientific data acquired by use of the antennas.
Brugarolas, Paul↗
A method to estimate the motion of unresolved VLBI components in extragalactic radio sources - The case of NRAO 140
A method is proposed for estimating the relative motion of two blended VLBI compact components from measurements of the motion of their brightness centroid. The method requires a priori information about the source components, such as flux densities and separate knowledge of the motion of one of the blended components. Observations of the superluminal quasar NRAO 140 are analyzed to test the technique. Superluminal motion is found for the blended component A2 at 6-cm wavelength with an apparent velocity of about 5c (H0 = 100 km/s/Mpc, q0 = 0.5, z = 1.258).
Charlot, P.↗
Estimating Conical Motion From Magnetometer Measurements
Method of digital processing of outputs of magnetometer mounted on scientific instrument or other assembly provides estimates of parameters of slow oscillatory motion of assembly at constant or nearly constant frequency, in which motion one axis of assembly describes elliptical cone about nominal fixed or nearly fixed axis. Conceived for use in estimating rotational-and-vibrational motion of small instrumented satellite and 20-km-long tether anchored at lower end to Space Shuttle. Applicable to almost any situation in which assembly moves conically about known axis while measuring known vector.
Polites, M. E.↗
Robot acting on moving bodies (RAMBO): Preliminary results
A robot system called RAMBO is being developed. It is equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a moving object. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations nearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enchancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows the use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using parametric cubic splines between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors.
Davis, Larry S.↗