The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jan. 07, 2014
Filed:
Sep. 20, 2010
Patrick Robertson, Ammerland, DE;
Michael Angermann, Graefelfing, DE;
Bernhard Krach, Hilpoltstein, DE;
Patrick Robertson, Ammerland, DE;
Michael Angermann, Graefelfing, DE;
Bernhard Krach, Hilpoltstein, DE;
Deutsches Zentrum für Luft—und Raumfahrt e.V., Cologne, DE;
Abstract
The invention relates to a method for creating a map relating to location-related data on the likelihood of the future movement of a person in a spatial environment, such as a building, forest, tunnel system or public place, and in particular a shopping center, airport or train station. In the method according to the invention, at least one person is supplied with one or more sensors (e.g. inertial sensors, rotation rate sensors, optical sensors) for odometrical measurement (odometry) (e.g. attached to a shoe), wherein the odometry has errors due to inherent measurement inaccuracies of the sensor(s) (e.g. angle deviations, length deviations). The at least one person moves by foot through the spatial environment. Information regarding the pedestrian step lengths and/or pedestrian step direction and/or orientation of the sensor or the person (called odometry information Z) is determined from the measurement signals of the sensor(s). Based on said odometry information, a map is created using a Bayesian estimator (e.g. particle filter, Rao-Blackwellized particle filter, Kalman filter, Mone-Cario hypothesis filter or combined forms of such filters), the state-variable space thereof (e.g. hypothetical space of the particle of a particle filter) comprising both the current pedestrian step (pedestrian step length and optionally movement direction change as well) of the person U, odometry errors E (e.g. drift parameters with respect to the determined pedestrian step lengths and/or pedestrian step direction and/or orientation of the sensor or the person) and also the location-related data on the probabilities of the future movement of the person.