Sensing and perception research for space telerobotics at JPL

Gennery, Donald B.; Litwin, Todd; Wilcox, Brian; Bon, B.

doi:10.1109/robot.1987.1087998

Cited by 16 publications

(11 citation statements)

References 4 publications

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“…(Two other cameras, with 25 mm focal length, are mounted on an arm for closeup views and can be seen in figure 4, but they were not used in the examples presented here.) The cameras were calibrated as described by Gennery et al [1987]. The tracker program runs on a Digital Equipment Corporation MicroVAX II.…”

Section: Resultsmentioning

confidence: 99%

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Visual tracking of known three-dimensional objects

Gennery

1992

Int J Comput Vision

239

109

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A method is described of visually tracking a known three-dimensional object as it moves with six degrees of freedom. The method uses the predicted position of known features on the object to find the features in images from one or more cameras, measures the position of the features in the images, and uses these measurements to update the estimates of position, orientation, linear velocity, and angular velocity of the object model. The features usually used are brightness edges that correspond to markings or the edges of solid objects, although point features can be used. The solution for object position and orientation is a weighted least-squares adjustment that includes filtering over time, which reduces the effects of errors, allows extrapolation over times of missing data, and allows the use of stereo information from multiple-camera images that are not coincident in time. The filtering action is derived so as to be optimum if the acceleration is random. (Alternatively, random torque can be assumed for rotation.) The filter is equivalent to a Kalman filter, but for efficiency it is formulated differently in order to take advantage of the dimensionality of the observations and the state vector which occur in this problem. The method can track accurately with arbitrarily large angular velocities, as long as the angular acceleration (or torque) is small. Results are presented showing the successful tracking of partially obscured objects with clutter.

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Section: Resultsmentioning

confidence: 99%

“…where Cc, ca, %, and c v constitute the camera model as defined by Yakimovsky and Cunningham [1978], and are assumed to be known from a previous calibration, as described by Gennery et al [1987]. (This camera model includes the central projection and a general affine transformation in the image plane.)…”

Section: Projectionmentioning

confidence: 99%

Visual tracking of known three-dimensional objects

Gennery

1992

Int J Comput Vision

239

109

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“…(It often is called the CAHV model.) In 1986, a better method of calibrating that model was developed, using a rigorous least-squares adjustment (Gennery et al, 1987). In 1990, that camera model and the method for its calibration were extended to include radial lens distortion (Gennery, 1991).…”

Section: Introductionmentioning

confidence: 99%

“…It also shows how it reduces to the older version as a special case. A method of measuring the calibration data (finding the dots in images of a calibration fixture) was previously described (Gennery et al, 1987). (The names CAHV, CAHVOR, and CAHVORE were coined by Todd Litwin, and are derived from the names of the parameters that comprise the models, as shown in Sections 2.5, 2.6, and 3.3.…”

Section: Introductionmentioning

confidence: 99%

Generalized Camera Calibration Including Fish-Eye Lenses

Gennery

2006

Int J Comput Vision

124

100

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A method is described for accurately calibrating cameras including radial lens distortion, by using known points such as those measured from a calibration fixture. Both the intrinsic and extrinsic parameters are calibrated in a single least-squares adjustment, but provision is made for including old values of the intrinsic parameters in the adjustment. The distortion terms are relative to the optical axis, which is included in the model so that it does not have to be orthogonal to the image sensor plane. These distortion terms represent corrections to the basic lens model, which is a generalization that includes the perspective projection and the ideal fish-eye lens as special cases. The position of the entrance pupil point as a function of off-axis angle also is included in the model. (The complete camera model including all of these effects often is called CAHVORE.) A way of adding decentering distortion also is described. A priori standard deviations can be used to apply weight to given initial approximations (which can be zero) for the distortion terms, for the difference between the optical axis and the perpendicular to the sensor plane, and for the terms representing movement of the entrance pupil, so that the solution for these is well determined when there is insufficient information in the calibration data. For the other parameters, initial approximations needed for the nonlinear least-squares adjustment are obtained in a simple manner from the calibration data and other known information. (Weight can be given to these also, if desired.) Outliers among the calibration points that disagree excessively with the other data are removed by means of automatic editing based on analysis of the residuals. The use of the camera model also is described, including partial derivatives for propagating both from object space to image space and vice versa. These methods were used to calibrate the cameras on the Mars Exploration Rovers.

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“…Additionally, a nominal set of values for the camera intrinsic parameters (Trucco & Verri, 1998) is entered in the program. The JPL software incorporates an algorithm described by Gennery, Litwin, Wilcox, and Bon (1987), which detects the centers of the circles in the four images from each camera and correlates the results with the known locations of the centers. Intrinsic and extrinsic parameters of the camera are extracted and tested.…”

mentioning

confidence: 99%

Three-Dimensional Stereo Reconstruction and Sensor Registration With Application to the Development of a Multi-Sensor Database

Oberle¹,

Haas²

2002

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Sensing and perception research for space telerobotics at JPL

Cited by 16 publications

References 4 publications

Visual tracking of known three-dimensional objects

Visual tracking of known three-dimensional objects

Generalized Camera Calibration Including Fish-Eye Lenses

Three-Dimensional Stereo Reconstruction and Sensor Registration With Application to the Development of a Multi-Sensor Database

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