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Information technology asset type identification using a mobile vision-enabled robot

  • US 9,346,168 B2
  • Filed: 05/20/2014
  • Issued: 05/24/2016
  • Est. Priority Date: 05/20/2014
  • Status: Expired due to Fees
First Claim
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1. A method, in a data processing system comprising a processor and a memory, for classifying an obstacle as an asset type, comprising:

  • receiving, by the data processing system, a digital image of an obstacle from an image capture device of an automated robot;

    performing, by the data processing system, a classification operation on the digital image of the obstacle to identify a proposed asset type classification for the obstacle;

    determining, by the data processing system, a final asset type for the obstacle based on the proposed asset type classification for the obstacle; and

    updating, by the data processing system, a map data structure for a physical premises in which the obstacle is present based on the final asset type, wherein performing the classification operation further comprises;

    gathering additional sensor information from one or more other sensors provided on either the robot or in the physical premises;

    performing the classification operation based on a classification of characteristics of the obstacle obtained from analysis of the digital image and analysis of the additional sensor information; and

    utilizing a plurality of classification algorithms that each classify the obstacle with regard to one or more different characteristics of the obstacle as identified in at least one of the digital image or the additional sensor information, wherein performing the classification operation based on the classification of the one or more different characteristics of the obstacle obtained from analysis of the digital image and analysis of the additional sensor information comprises comparing conditions of the physical premises in proximity to the obstacle as identified by the additional sensor information to patterns of similar information acquired through training of the plurality of trained classification algorithms.

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