General Electric Company

Transportation IP Holdings, LLC

Ge Global Sourcing LLC

Arpit Jain

Huan Tan

David Scott Diwinsky

Charles Burton Theurer

Balajee Kannan

Xiao Bian

Shiraj Sen

Tai-Peng Tian

Bernard Patrick Bewlay

Peter Henry Tu

Ser Nam Lim

Douglas Roy Forman

Ghulam Ali Baloch

Eric Michael Gros

Viktor Holovashchenko

Ming-Ching Chang

Junli Ping

John Michael Lizzi

Romano Patrick

Guiju Song

Shubao Liu

Wei Wang

Shuai Li

Pramod Sharma

Frederick Wilson Wheeler

Li Zhang

Omar Al Assad

Jilin Tu

Steven Robert Gray

Yonatan Gefen

Mohammed Yousefhussien

Longyin Wen

Swaminathan Sankaranarayanan

Sravanthi Bondugula

Achalesh Kumar

Marshall Gordon Jones

Todd William Danko

Guanghua Wang

James Vradenburg Miller

Walter Vincent Dixon, Iii

Mauricio Castillo-Effen

Shaopeng Liu

Feng Xue

Yakov Polishchuk

Judith Ann Guzzo

John Karigiannis

Alexander Kyle Duncan

Colin James Parris

Yang Zhao

Venkata Vijayaraghava Nalladega

Guangliang Zhao

Gyeong Woo Cheon

Stephane Harel

Steeves Bouchard

Weina Ge

Tapan Shah

Roberto Silveira Silva Filho

Ching-Ling Huang

Raju Venkataramana

Yen-Liang Lin

Alex Tepper

Shourya Sarcar

Jean-Philippe Choiniere

Habib Abi-Rached

Marc-Andre Marois

Kari Thompson

Methods, systems, and apparatus are described for maintaining the interior cavities of pipes. In one aspect, a motorized apparatus includes a main body having a length extending along a longitudinal axis of the main body, where the main body comprises a first end and a second end opposite the first end. The motorized apparatus further includes at least one drive assembly coupled to at least one of the first end and the second end of the main body, where the at least one drive assembly comprises driven members that engage with an inner surface of the pipe and move the motorized apparatus through an interior cavity of the pipe. The motorized apparatus further includes a maintenance head movably coupled to the main body that moves along the length of the main body and rotates about the longitudinal axis of the main body, where the maintenance head comprises at least one tool configured to perform an action on the inner surface of the pipe.

Motorized apparatus for pipe inspection and repair

A robotic system includes a controller configured to obtain image data from one or more optical sensors and to determine one or more of a location and/or pose of a vehicle component based on the image data. The controller also is configured to determine a model of an external environment of the robotic system based on the image data and to determine tasks to be performed by components of the robotic system to perform maintenance on the vehicle component. The controller also is configured to assign the tasks to the components of the robotic system and to communicate control signals to the components of the robotic system to autonomously control the robotic system to perform the maintenance on the vehicle component.

Integrated robotic system and method for autonomous vehicle maintenance

According to some embodiments, a system and method are provided comprising a vegetation module to receive image data from an image source; a memory for storing program instructions; a vegetation processor, coupled to the memory, and in communication with the vegetation module, and operative to execute program instructions to: receive image data; estimate a vegetation segmentation mask; generate at least one of a 3D point cloud and a 2.5D Digital Surface Model based on the received image data; estimate a relief surface using a digital terrain model; generate a vegetation masked digital surface model based on the digital terrain model, the vegetation segmentation mask and at least one of the 3D point cloud and the 2.5D DSM; generate a canopy height model based on the generated vegetation masked digital surface model; and generate at least one analysis with an analysis module, wherein the analysis module receives the generated canopy height model prior to execution of the analysis module, and wherein the analysis module uses the generated canopy height model in the generation of the at least one analysis. Numerous other aspects are provided.

System and method for vegetation modeling using satellite imagery and/or aerial imagery

A method of inspecting a component using an image retrieval module includes storing an inspection image file in a memory and identifying a region of interest in the inspection image file. The method further includes accessing a database storing image files and determining feature vectors associated with the image files. The method also includes determining a hash code for each image file based on the feature vectors and classifying a subset of image files as relevant based on the hash codes. The method further includes sorting the subset of image files based on the feature vectors and generating search results based on the sorted subset of image files. The image retrieval module includes a convolutional neural network configured to learn from the determination of the feature vectors and increase the accuracy of the image retrieval module in classifying the image files.

Inspection systems and methods including image retrieval module

According to some embodiments, a system and method are provided comprising a vegetation management module to receive image data from an image source; a memory for storing program instructions; a vegetation management processor, coupled to the memory, and in communication with the vegetation module, and operative to execute program instructions to: receive first image data and second image data for an area of interest; overlay the first image data over the second image data to generate an overlaid image; receive feeder attribute data for at least one feeder in the overlaid image; generate a risk score for the at least one feeder based in part on the received feeder attribute data; and generate a visualization based on the at least one feeder and the generated risk score. Numerous other aspects are provided.

Multi source geographic information system (GIS) web based data visualization and interaction for vegetation management

The present approach relates to an automatic and efficient motion plan for a drone to collect and save a qualified dataset that may be used to improve reconstruction of 3D models using the acquired data. The present architecture provides an automatic image processing context, eliminating low quality images and providing improved image data for point cloud generation and texture mapping.

Image and video capture architecture for three-dimensional reconstruction

The present approach relates to the use of a point cloud of an object to initialize or seed a space carving technique used to generate a 3D model of the object. In one implementation, feature matching is performed on 2D images, with matched features constituting the points of a point cloud model. The point cloud generated in this manner, is one input of a foreground/background segmentation algorithm, which generates a set of segmented 2D images used by a space carving routine to generate a 3D model of the object.

Three-dimensional modeling of an object

A method and system, the method including receiving semantic descriptions of features of an asset extracted from a first set of images; receiving a model of the asset, the model constructed based on a second set of a plurality images of the asset; receiving, based on an optical flow-based motion estimation, an indication of a motion for the features in the first set of images; determining a set of candidate regions of interest for the asset; determining a region of interest in the first set of images; iteratively determining a matching of features in the set of candidate regions of interest and the determined region of interest in the first set of images to generate a record of matches in features between two images in the first set of images; and displaying a visualization of the matches in features between two images in the first set of images.

Systems and methods of feature correspondence analysis

A system includes a borescope and at least one processor. The borescope includes a camera configured to acquire an acquisition series of frames of at least one target component. The at least one processor is operably coupled to the camera, and is configured to acquire the acquisition series of frames from the camera; determine a blurriness metric value for each of the frames; select frames that satisfy a threshold for the blurriness metric value to form an inspection series of frames; and perform an inspection analysis for the at least one target component using the inspection series of frames.

Systems and methods for improved component inspection

An asset inspection system includes a robot and a server. The robot collects inspection data corresponding to an asset. The server, includes a user interface, a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive the inspection data from the robot, display the inspection data via the user-interface, receive feedback on the inspection data via the user interface, generate a human-assisted inspection based on the received feedback, analyze the inspection data via a trained model, generate an automated inspection based on the analysis by the trained model, combine the automated inspection and the human-assisted inspection to generate an inspection report, and transmit the inspection report for review.

Systems and method for human-assisted robotic industrial inspection

An inspection system includes one or more imaging devices and one or more processors. The imaging devices generate a first set of images of a work piece at a first position relative to the work piece and a second set of images of the work piece at a second position relative to the work piece. At least some of the images in the first and second sets are acquired using different light settings. The processors analyze the first set of images to generate a first prediction image associated with the first position, and analyze the second set of images to generate a second prediction image associated with the second position. The first and second prediction images include respective candidate regions. The processors merge the first and second prediction images to detect at least one predicted defect in the work piece depicted in at least one of the candidate regions.

System and method for work piece inspection

Provided are systems and methods for monitoring an asset via an autonomous model-driven inspection. In an example, the method may include storing an inspection plan including a virtually created three-dimensional (3D) model of a travel path with respect to a virtual asset that is created in virtual space, converting the virtually created 3D model of the travel path about the virtual asset into a physical travel path about a physical asset corresponding to the virtual asset, autonomously controlling vertical and lateral movement of the unmanned robot in three dimensions with respect to the physical asset based on the physical travel path and capturing data at one or more regions of interest, and capturing data at one or more regions of interest, and storing information concerning the captured data about the asset.

Three-dimensional robotic inspection system

A system includes one or more processors configured to detect perimeter edges of one or more rotor blades of a turbine assembly as depicted in a series of image frames using boundary analysis performed on the image frames. The one or more processors are configured to identify a set of the image frames as key frames based on positional offsets between the perimeter edges that are detected in the image frames and a reference blade pose such that the key frames are able to be inspected for objects-of-interest without inspecting the image frames that are not the key frames.

System and method for image-based target object inspection

Growing community of inventors

Niskayuna, NY, United States of America

Arpit Jain