Zebra Technologies Corporation

Raghavendra Tenkasi Shankar

Patrick B Tilley

David S Koch

Seth David Silk

Sanjeewa Thimirachandra

Aleksandar Rajak

Ronald Zancola

Zhijia Yuan

Alexander Miller

Alan J Epshteyn

Peter Randall Daly

Kevin Kar Yin Chan

Eliran Noach

Scott R Murchison

Mohitosh Mondal

Ted D Trask

A method includes: capturing, via a first sensor, a three-dimensional image associated with a first orientation of a computing device; capturing, via a second sensor, a two-dimensional image associated with a second orientation of the computing device; determining a difference between the first orientation and the second orientation; comparing the difference with a threshold; and determining whether to deliver the three-dimensional image and the two-dimensional image to an image-processing module, based on the comparison of the difference with the threshold.

Motion-based frame synchronization

A computing device includes: a three-dimensional (3D) sensor configured to capture point cloud data from a field of view (FOV); an auxiliary sensor configured to capture reference depth measurements corresponding to a surface within the FOV; a controller connected with the 3D sensor and the auxiliary sensor, the controller configured to: detect a reference depth capture condition; when the reference depth capture condition satisfies a quality criterion, control the auxiliary sensor to capture a reference depth corresponding to the surface within the FOV; and initiate, based on the captured reference depth, generation of corrective data for use at the 3D sensor to capture the point cloud data.

Three-dimensional sensor acuity recovery assistance

A computing device includes: a tracking sensor including a camera; a controller connected with the tracking sensor, the controller configured to: control the tracking sensor to track successive poses of the computing device in a frame of reference; detecting a plurality of dimensioning events associated with an object; in response to detecting each of the dimensioning events, generate a respective position in the frame of reference based on a current one of the poses; generate, based on the positions, an object boundary in the frame of reference; and dimension the object based on the generated object boundary.

Method, system and apparatus for mobile dimensioning

A computing device for dimensioning an object includes: a dimensioning subsystem configured to execute a default dimensioning method and a backup dimensioning method; a memory storing quality evaluation rules; a processor connected with the dimensioning subsystem and the memory, the processor configured to: control the dimensioning subsystem to execute a default dimensioning method to obtain default dimensioning data; compare a quality metric for the default dimensioning data to a threshold condition defined in the quality evaluation rules; when the quality metric exceeds the threshold condition, compute dimensions of the object based on the default dimensioning data; and when the quality metric does not exceed the threshold condition, control the dimensioning subsystem to execute the backup dimensioning method to obtain backup dimensioning data and compute the dimensions of the object based on the backup dimensioning data.

System and method for dimensioning objects

A method for classifying depth scan data at a computing device includes: obtaining, at the computing device, a set of depth measurements and a graphical representation of the depth measurements; automatically selecting, at the computing device, a subset of the depth measurements indicating a region of interest; rendering, on a display of the computing device, an image including (i) the graphical representation of the depth measurements and (ii) a graphical indication of the region of interest overlaid on the graphical representation of the depth measurements; receiving, via an input device, a selection associated with the image; and generating a region of interest indicator based on the subset of the depth measurements and the selection.

Method and apparatus for classifying depth scans

A method of detecting sensor obstructions in a computing device includes: at an emitter, emitting a beam of light through a scan window toward a treaded surface; at an image sensor, for a sequence of positions of the computing device along the treaded surface: capturing a sequence of images corresponding to the sequence of positions, each image in the sequence having a first region and a second region; wherein the first regions depict a first subset of reflections of the beam of light originating from a first depth range; and wherein the second regions depict a second subset of the reflections originating from a second depth range; at a controller: receiving the sequence of images; determining, based on the second regions, whether an intensity of the second subset of the reflections exceeds a occlusion threshold; and when the determination is affirmative, generating an alert indicating obstruction of the scan window.

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Holbrook, NY, United States of America

Raghavendra Tenkasi Shankar