Information technology asset type identification using a mobile vision-enabled robot
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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Abstract
Mechanisms are provided for classifying an obstacle as an asset type. The mechanisms receive a digital image of an obstacle from an image capture device of an automated robot. The mechanisms perform a classification operation on the digital image of the obstacle to identify a proposed asset type classification for the obstacle. The mechanisms determine a final asset type for the obstacle based on the proposed asset type classification for the obstacle. The mechanisms update a map data structure for a physical premises in which the obstacle is present based on the final asset type.
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Citations
14 Claims
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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:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:
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receive a digital image of an obstacle from an image capture device of an automated robot; perform a classification operation on the digital image of the obstacle to identify a proposed asset type classification for the obstacle; determine a final asset type for the obstacle based on the proposed asset type classification for the obstacle; and update a map data structure for a physical premises in which the obstacle is present based on the final asset type, wherein the computer readable program further causes the computing device to perform the classification operation at least by; 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 the computer readable program further causes the computing device to perform the classification operation based on the classification of characteristics of the obstacle obtained from analysis of the digital image and analysis of the additional sensor information at least by comparing conditions of the physical premises in close 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. - View Dependent Claims (9, 10, 11, 12, 13)
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14. An apparatus comprising:
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a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to; receive a digital image of an obstacle from an image capture device of an automated robot; perform a classification operation on the digital image of the obstacle to identify a proposed asset type classification for the obstacle; determine a final asset type for the obstacle based on the proposed asset type classification for the obstacle; and update a map data structure for a physical premises in which the obstacle is present based on the final asset type, wherein the instructions further cause the computing processor to perform the classification operation at least by; 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 the instructions further cause the processor to perform the classification operation based on the classification of characteristics of the obstacle obtained from analysis of the digital image and analysis of the additional sensor information at least by comparing conditions of the physical premises in close 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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Specification