Qualcomm Incorporated

Dheeraj Ahuja

Bolan Jiang

Charles Wheeler Sweet, Iii

Prasun Choudhury

Ashwin Deepak Swaminathan

Serafin Diaz Spindola

Christopher Brunner

Murali Chari

Arvind Ramanandan

Yanghai Tsin

Seyed Hesameddin Najafi Shoushtari

Paulo Ricardo Dos Santos Mendonca

Kiyoung Kim

Murali Ramaswamy Chari

Iii Charles Wheeler Sweet

A first map comprising local features and 3D locations of the local features is generated, the local features comprising visible features in a current image and a corresponding set of covisible features. A second map comprising prior features and 3D locations of the prior features may be determined, where each prior feature: was first imaged at a time prior to the first imaging of any of the local features, and lies within a threshold distance of at least one local feature. A first subset comprising previously imaged local features in the first map and a corresponding second subset of the prior features in the second map is determined by comparing the first and second maps, where each local feature in the first subset corresponds to a distinct prior feature in the second subset. A transformation mapping a subset of local features to a subset of prior features is determined.

Geometric matching in visual navigation systems

A multi-user augmented reality (AR) system operates without a previously acquired common reference by generating a reference image on the fly. The reference image is produced by capturing at least two images of a planar object and using the images to determine a pose (position and orientation) of a first mobile platform with respect to the planar object. Based on the orientation of the mobile platform, an image of the planar object, which may be one of the initial images or a subsequently captured image, is warped to produce the reference image of a front view of the planar object. The reference image may be produced by the mobile platform or by, e.g., a server. Other mobile platforms may determine their pose with respect to the planar object using the reference image to perform a multi-user augmented reality application.

Online reference generation and tracking for multi-user augmented reality

Disclosed embodiments pertain to apparatus, systems, and methods for robust feature based tracking. In some embodiments, a score may be computed for a camera captured current image comprising a target object. The score may be based on one or more metrics determined from a comparison of features in the current image and a prior image captured by the camera. The comparison may be based on an estimated camera pose for the current image. In some embodiments, one of a point based, an edge based, or a combined point and edge based feature correspondence method may be selected based on a comparison of the score with a point threshold and/or a line threshold, the point and line thresholds being obtained from a model of the target. The camera pose may be refined by establishing feature correspondences using the selected method between the current image and a model image.

Robust tracking using point and line features

A homography between two captured images of a planar object is decomposed into at least one possible solution, and typically at least two ambiguous solutions. The ambiguity between the two solutions is removed, or a single solution validated, using measurements from orientation sensors. The measurements from orientation sensors may be used by comparing at least one of the yaw, pitch, and/or roll angles derived from a relative rotation matrix for the one or more solutions to a corresponding at least one of the yaw, pitch, and/or roll angles derived from the measurements from the orientation sensors.

Resolving homography decomposition ambiguity based on orientation sensors

A reference patch of an unknown environment is generated on the fly for positioning and tracking. The reference patch is generated using a captured image of a planar object with two perpendicular sets of parallel lines. The planar object is detected in the image and axes of the world coordinate system are defined using the vanishing points for the two sets of parallel lines. The camera rotation is recovered based on the defined axes, and the reference patch of at least a portion of the image of the planar object is generated using the recovered camera rotation. The reference patch can then be used for vision based detection and tracking. The planar object may be detected in the image as sets of parallel lines or as a rectangle.

Online reference patch generation and pose estimation for augmented reality

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San Diego, CA, United States of America

Dheeraj Ahuja