Redzone Robotics, Inc.

Jason Mizgorski

Foster J Salotti

Justin Starr

Todd Kueny

Galin Konakchiev

Thorin Tobiassen

Nate Alford

Mark Jordan

Valerie Walch

Mohamed Morsy

Gabe Detter

Andrew Lawniczak

Galin Konalchiev

Jjianan Lin

Andrew Harmen

Matthew Crowell

Jack Buffington

Mohsen Pashna

Richard Rigby

One aspect provides a modular inspection robot for inspecting vertical shafts, chambers or tunnels. An embodiment provides related methods and products. One method includes: capturing, using a plurality of video cameras associated with an infrastructure inspection unit, two or more videos of infrastructure; accessing, using one or more processors, image metadata indicating a mesh of connected vertices based on the two or more videos; selecting, using the one or more processors, image data of frames of the two or more videos for inclusion in an output based on the mesh; and outputting, using the one or more processors, a photo-realistic image of the infrastructure comprising the image data selected. Other examples are described and claimed.

Photo-realistic infrastructure inspection

One aspect provides operating a mobile pipe inspection platform to obtain two or more types of sensor data for the interior of a pipe; analyzing, using a processor, the two or more types of sensor data using a trained model, where the trained model is trained using a dataset including training sensor data of pipe interiors; the analyzing including performing: identifying, using a processor, a pipe feature location using a first type of the two or more types of sensor data; and classifying, using a processor, an identified pipe feature using a second type of the two or more types of sensor data; and thereafter producing, using a processor, an output including an indication of the classified pipe feature. Other aspects are described and claimed.

Underground infrastructure feature identification using multi-sensor inspection data analysis

One aspect provides a method, including: obtaining sensor data from a ground penetrating radar (GPR) unit; analyzing, using a processor, the sensor data to detect a first object and a second object, the second object being associated with the first object based on location; identifying, with the processor, an underground pipe feature based on the analyzing; associating a position of the underground pipe feature with a location in a pipe network; selecting a subset of the pipe network including a pipe segment associated with the position of the underground pipe feature; and providing the subset of the pipe network as displayable data to a display device. Other aspects are described and claimed.

Underground infrastructure sensing using unmanned aerial vehicle (UAV)

One aspect provides a method, including: obtaining sensor data from an unmanned aerial vehicle (UAV); the sensor data comprising data obtained by one or more sensors of the UAV; analyzing, using a processor, the sensor data to detect underground water associated with a pipe; and identifying, with the processor, an underground feature based on the analyzing. Other aspects are described and claimed.

One aspect provides a method, including: displaying, at a display screen, an image of an interior of a pipe, the image being obtained using a pipe inspection robot; accessing, using a processor, calibration data associated with the image; receiving, via an input device, user input marking at least a portion of the image; determining, using a processor, quantitative pipe feature data for at least one feature of the pipe using the marking and the calibration data; and providing, based on the determining, data associated with the at least one feature. Other aspects are described and claimed.

Pipe image feature analysis using calibration data

One aspect provides a method, including: operating a mobile pipe inspection platform to obtain sensor data for the interior of a pipe; analyzing, using a processor, the sensor data using a trained model, where the trained model is trained using a dataset including sensor data of pipe interiors and one or more of: metadata identifying pipe feature locations contained within the sensor data of the dataset and metadata classifying pipe features contained within the sensor data of the dataset; performing one or more of: identifying, using a processor, a pipe feature location within the sensor data; and classifying, using a processor, a pipe feature of the sensor data; and thereafter producing, using a processor, an output including one or more of an indication of the identifying and an indication of the classifying. Other aspects are described and claimed.

Pipe feature identification using pipe inspection data analysis

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Gibsonia, PA, United States of America

Jason Mizgorski