Abstract: The method generating a point of sale data matrix from a purchase record, the point of sale data matrix including buying information of each item and purchase quantity information of each item, extracting purchase patterns from the point of sale data matrix using a matrix factorization method to provide a maximum score value, the maximum score value representing the purchase patterns, identifying related factors associated with each of the purchase patterns to provide a related factor model, and testing at least one simulated related factor in a simulated environment based on the related factor model to determine a difference in purchase patterns.

Abstract: Performing map construction under a crowded environment where there are a lot of people. It includes a successive image acquisition unit that obtains images that are taken while a robot is moving, a local feature quantity extraction unit that extracts a quantity at each feature point from the images, a feature quantity matching unit that performs matching among the quantities in the input images, where quantities are extracted by the extraction unit, an invariant feature quantity calculation unit that calculates an average of the matched quantities among a predetermined number of images by the matching unit as an invariant feature quantity, a distance information acquisition unit that calculates distance information corresponding to each invariant feature quantity based on a position of the robot at times when the images are obtained, and a map generation unit that generates a local metrical map as a hybrid map.

Abstract: Disclosed is a position estimation device including a feature extraction unit that extracts invariant features from an input image, a matching unit that obtains matching between an input image and a registered place by referring to a database containing each registered place and invariant features in association, a similarity calculation unit that calculates a similarity with inclusion of a registered place near a selected registered place when the matching is a threshold or more, and a position identification unit that identifies the input image as a registered place when the similarity is a threshold or more. The feature extraction unit extracts local features from each input image being sequential images taken sequentially, selects features matched between the sequential images as sequential features, and calculates invariant features based on the sequential features. The number of sequential images is variable depending on the number of matched features.

Abstract: Performing map construction under a crowded environment where there are a lot of people. It includes a successive image acquisition unit that obtains images that are taken while a robot is moving, a local feature quantity extraction unit that extracts a quantity at each feature point from the images, a feature quantity matching unit that performs matching among the quantities in the input images, where quantities are extracted by the extraction unit, an invariant feature quantity calculation unit that calculates an average of the matched quantities among a predetermined number of images by the matching unit as an invariant feature quantity, a distance information acquisition unit that calculates distance information corresponding to each invariant feature quantity based on a position of the robot at times when the images are obtained, and a map generation unit that generates a local metrical map as a hybrid map.

Abstract: Disclosed is a position estimation device including a feature extraction unit that extracts invariant features from an input image, a matching unit that obtains matching between an input image and a registered place by referring to a database containing each registered place and invariant features in association, a similarity calculation unit that calculates a similarity with inclusion of a registered place near a selected registered place when the matching is a threshold or more, and a position identification unit that identifies the input image as a registered place when the similarity is a threshold or more. The feature extraction unit extracts local features from each input image being sequential images taken sequentially, selects features matched between the sequential images as sequential features, and calculates invariant features based on the sequential features. The number of sequential images is variable depending on the number of matched features.