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US Patent 10062010 System for building a map and subsequent localization

Patent 10062010 was granted and assigned to Intel on August, 2018 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Applicant
Intel
Intel
Current Assignee
Intel
Intel
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10062010
Date of Patent
August 28, 2018
Patent Application Number
14752093
Date Filed
June 26, 2015
Patent Citations Received
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US Patent 10699134 Method, apparatus, storage medium and device for modeling lane line identification, and method, apparatus, storage medium and device for identifying lane line
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US Patent 10782691 Deep learning and intelligent sensing system integration
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US Patent 10719759 System for building a map and subsequent localization
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US Patent 10936907 Training a deep learning system for maritime applications
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US Patent 10885666 Hybrid metric-topological camera-based localization
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US Patent 11514594 Composite imaging systems using a focal plane array with in-pixel analog storage elements
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US Patent 10299982 Systems and methods for blind and visually impaired person environment navigation assistance
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US Patent 10636198 System and method for monocular simultaneous localization and mapping
Patent Primary Examiner
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Ryan P Potts
Patent abstract

SLAM systems are provided that utilize an artificial neural network to both map environments and locate positions within the environments. In some example embodiments, a sensor arrangement is used to map an environment. The sensor arrangement acquires sensor data from the various sensors and associates the sensor data, or data derived from the sensor data, with spatial regions in the environment. The sensor data may include image data and inertial measurement data that effectively describes the visual appearance of a spatial region at a particular location and orientation. This diverse sensor data may be fused into camera poses. The map of the environment includes camera poses organized by spatial region within the environment. Further, in these examples, an artificial neural network is adapted to the features of the environment by a transfer learning process using image data associated with camera poses.

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