Auris Health, Inc.

General Electric Company

Rensselaer Polytechnic Institute

Auris Surgical Robotics, Inc.

Charles Vernon Stewart

David S Mintz

Prasanth Jeevan

Gehua Yang

Atiyeh Ghoreyshi

Yiliang Xu

Matthew Joseph Leotta

Van-Duc Nguyen

Victor Nzomigni

Joseph Leagrand Mundy

Donald Wagner Hamilton

Roderic Greene Collins

Chia-Ling Tsai

Badri Roysam

Nathalie Claire Poirier

Lila Abdessemed

Kishore Bubna

Methods and apparatuses provide improved navigation through tubular networks such as lung airways by providing improved estimation of location and orientation information of a medical instrument (e.g., an endoscope) within the tubular network. Various input data such as image data, EM data, and robot data are used by different algorithms to estimate the state of the medical instrument, and the state information is used to locate a specific site within a tubular network and/or to determine navigation information for what positions/orientations the medical instrument should travel through to arrive at the specific site. Probability distributions together with confidence values are generated corresponding to different algorithms are used to determine the medical instrument's estimated state.

Navigation of tubular networks

An iterative method and associated algorithm for performing image registration to map features in a first image to corresponding features in a second image in accordance with a transformation model. Upon convergence of a parameter vector associated with the model, a current bootstrap region includes and exceeds an initial bootstrap region that is initially established. During the iterations, the parameter vector is estimated by minimizing an objective function with respect to the parameter vector. Each iteration generates a next bootstrap region that minimally includes the current bootstrap region and may exceed the current bootstrap region. The model may change in successive iterations.

Dual bootstrap iterative closest point method and algorithm for image registration

The present invention is directed to an imaging apparatus for examining an object having smooth surfaces to determine shape deformations in the surface of object. The imaging apparatus includes an imaging device for obtaining a scanned image of the object to be examined. A reference image of the object is stored in a memory. An image register is coupled to the imaging device and to the memory containing the reference image of the object. The image register stores patch information corresponding to both the reference image and the scanned image. A transformation estimator is coupled to the image register, and provides a transform for registering the scanned image to the reference image. A deformation estimator is coupled to the transformation estimator and to the image register. The deformation estimator is configured to utilize the transform and the patch information to determine shape deformations of the object.

Method and apparatus for finding shape deformations in objects having smooth surfaces

A machine vision system includes an apparatus for registering an input image of an object, such as an aircraft engine blade, to a reference image comprising ideal specifications for the object in order to detect flaws in the object. The system includes one or more imaging devices for obtaining the input image representing the object. The system further includes a processor for registering the input image to the reference image. The processor includes a patch determining device for identifying low curvature portions of the reference image off-line. A transformation estimator matches the low curvature portions of the reference image to corresponding low curvature portions of the object, and provides a transformation matrix which maps points on the reference image to corresponding points on the input image for precise and fast registration.

Method and apparatus for image registration

A method for calibrating a non-contact range sensor comprises the steps of employing the range sensor to obtain a range image of an object to be inspected. The range image is registered with a reference image of the object thereby providing registered data. The reference image may be derived through computer assisted drawing (CAD) data. Normal deviations between the registered data and the reference image are computed. Noise is filtered from the normal deviations. A plurality of bias vectors and a covariance matrix are estimated based on the normal deviations, stored in memory as a lookup table comprising geometric correction factors for the sensor. Images of the object subsequently obtained by the sensor are compensated in accordance with the lookup table.

Method and apparatus for calibrating a non-contact range sensor

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Clifton Park, NY, United States of America

Charles Vernon Stewart