Abstract: A method and apparatus for determining position and orientation enabling navigation of an object using image data from at least a first and a second 2D image from at least one camera mounted on said object. The method comprises the steps of: correcting images from one or several cameras and from at least a first and a second 2D image for their respective radial distortion and other measurable effects which result in poor image precision; matching 2D image items in and between at least a first and second 2D image; calculating a fundamental matrix by using correlated image points from at least a first and a second 2D image; calculating and extracting estimated first rotation and translation values from the fundamental matrix using single value decomposition (SVD) based on information from at least a first and a second 2D image; iterating more accurate final rotation and translation values by using the LevenBerg-Marquard algorithm and determining the position and orientation of said object.

Abstract: A method and apparatus for determining a position and attitude of at least one camera by calculating and extracting estimated rotation and translation values from an estimated fundamental matrix based on information from at least a first and second 2D image. Variable substitution is utilized to strengthen derivatives and provide a more rapid convergence. A solution is provided for solving position and orientation from correlated point features in images using a method that solves for both rotation and translation simultaneously.

Abstract: A method and apparatus for determining position and orientation enabling navigation of an object using image data from at least a first and a second 2D image from at least one camera mounted on said object. The method comprises the steps of: correcting images from one or several cameras and from at least a first and a second 2D image for their respective radial distortion and other measurable effects which result in poor image precision; matching 2D image items in and between at least a first and second 2D image; calculating a fundamental matrix by using correlated image points from at least a first and a second 2D image; calculating and extracting estimated first rotation and translation values from the fundamental matrix using single value decomposition (SVD) based on information from at least a first and a second 2D image; iterating more accurate final rotation and translation values by using the LevenBerg-Marquard algorithm and determining the position and orientation of said object.

Abstract: A method and apparatus for determining a position and attitude of at least one camera by calculating and extracting estimated rotation and translation values from an estimated fundamental matrix based on information from at least a first and second 2D image. Variable substitution is utilized to strengthen derivatives and provide a more rapid convergence. A solution is provided for solving position and orientation from correlated point features in images using a method that solves for both rotation and translation simultaneously.

Abstract: A method and apparatus for generating and optimizing a fundamental matrix for a first 2D image and a second 2D image to obtain the relative geometrical information between said two 2D images for points in the two 2D images that correspond to a mutual 3D point. According to the method, the geometrical projection errors in the correspondence points are used to select correct and accurate inliers. This method and apparatus provides a more accurate and precise fundamental matrix than conventional methods.

Abstract: The present invention relates to automatic modeling of a physical scene. At least two images (I1, I2) of the scene are received, which are taken from different angles and/or positions. A matching module (130) matches image objects in the first image (I1) against image objects in the second image (I2), by first loading pixel values for at least one first portion of the first image (I1) into an artificial neural network (133). Then, the artificial neural network (133) scans the second image (I2) in search of pixels representing a respective second portion corresponding to each of the at least one first portion; determines a position of the respective second portion upon fulfillment of a match criterion; and produces a representative matching result (M12). Based on the matching result (M12), a first calculation module (140) calculates a fundamental matrix (F12), which defines a relationship between the first and second images (I1, I2).