University of Pennsylvania

Walgreen Company

Quidient, LLC

Kostas Daniilidis

Jonas Cleveland

Vijay Kumar

John M Bergstrom

Philip Dames

Cody Philips

Dinesh Thakur

Terry Kientz

David Scott Ackerson

Donald J Meagher

John K Leffingwell

A stored volumetric scene model of a real scene is generated from data defining digital images of a light field in a real scene containing different types of media. The digital images have been formed by a camera from opposingly directed poses and each digital image contains image data elements defined by stored data representing light field flux received by light sensing detectors in the camera. The digital images are processed by a scene reconstruction engine to form a digital volumetric scene model representing the real scene. The volumetric scene model (i) contains volumetric data elements defined by stored data representing one or more media characteristics and (ii) contains solid angle data elements defined by stored data representing the flux of the light field. Adjacent volumetric data elements form corridors, at least one of the volumetric data elements in at least one corridor represents media that is partially light transmissive. The constructed digital volumetric scene model data is stored in a digital data memory for subsequent uses and applications.

Quotidian scene reconstruction engine

A method for automatically mapping a store layout includes identifying a path for traversing a retail area and capturing images of the retail area at various points along the path. The images may be analyzed to identify visual characteristics which may be compared to a template of retail products in a template library. When an object depicted in the image matches with a retail product in the template library, the object may correspond to the retail product. Additionally, a retail department for the object may also be identified. The retail department may be compared to the retail product corresponding to the object, and when the retail product is not associated with the retail department, another retail product which is associated with the retail department may be identified as corresponding to the object. A map of the store layout may be generated based on the identified retail products.

Automatic mapping of store layout using soft object recognition

Autonomous vehicle for mapping store layout

The subject matter described herein includes methods for visual odometry using rigid structures identified by an antipodal transform. One exemplary method includes receiving a sequence of images captured by a camera. The method further includes identifying rigid structures in the images using an antipodal transform. The method further includes identifying correspondence between rigid structures in different image frames. The method further includes estimating motion of the camera based on motion of corresponding rigid structures among the different image frames.

Methods, systems, and computer readable media for visual odometry using rigid structures identified by antipodal transform

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Wynnewood, PA, United States of America

Kostas Daniilidis