Zebra Technologies Corporation

Symbol Technologies, LLC

Adithya H Krishnamurthy

Justin F Barish

Miroslav Trajkovic

Igor R Vinogradov

Allan Perry Herrod

Jay J Williams

Bassam S Arshad

Charles W Roark

Jyotsna Prasad

Bryn P Martino

An example method includes: during a container load process, controlling a sensor assembly to capture sensor data depicting a container interior; detecting, from the sensor data, items in the container interior; determining, based on the detected items, first and second load process metrics associated with first and second targets; generating first and second normalized metrics based on the first and second load process metrics, and the first and second targets; obtaining a first weighting factor associated with the first load process metric, and a second weighting factor associated with the second load process metric; combining the first normalized metric and the first weighting factor, with the second normalized metric and the second weighting factor, to generate an aggregated load process metric; and transmitting a control command according to the aggregated load process metric; and rendering, at an indicator device, a load process state indicator according to the control command.

Container load performance metric aggregation

An example server includes a memory configured to store sensor data from a plurality of sensors in a facility; a communications interface; and a processor interconnected with the memory and the communications interface, the processor configured to: in response to receiving, via the communications interface, an event indicator from a source sensor of a client device: identify a subset of the plurality of sensors based on the event indicator; retrieve and correlate the sensor data from the identified subset of the plurality of sensors in the facility; detect a candidate event associated with a user of the client device from the correlated sensor data; and when the candidate event is detected, send an event notification to the client device.

System and method for detecting and confirming fall events

Methods and systems for determining rotation and clipping parameters for images of unit load devices (ULDs) are disclosed herein. An example method includes capturing a set of image data featuring a ULD. The example method may further include locating a fiducial marker proximate to the ULD within the set of image data. The example method may further include cropping the set of image data, based upon the located fiducial marker, to generate a set of marker point data and a set of floor point data. The example method may further include rotating the set of image data based upon the set of marker point data and the set of floor point data, and clipping the rotated set of image data based upon the set of marker point data and the set of floor point data.

System and method for automatic container configuration using fiducial markers

An example trailer monitoring system includes a trailer monitoring unit (TMU) that has an image capture arrangement disposed within the TMU, the image capture arrangement to capture first image data, and an accelerometer carried by the TMU, the accelerometer to generate acceleration data of the TMU. The system also has one or more processors configured to access the acceleration data and configured to compare the acceleration data to a reference acceleration data range to determine if the acceleration data is within the reference acceleration range, in response to the acceleration data being outside the reference acceleration data range, the one or more processors are to record an impact event associated with the TMU being impacted, and in response to the acceleration data being outside the reference acceleration data range, the one or more processors are to generate a message associated with the impact event.

Methods and apparatus for recording impact events associated with trailer monitoring units

Methods for determining a unit load device (ULD) door status are disclosed herein. An example method includes capturing a set of image data featuring the ULD. The example method further includes segmenting the set of image data to identify a top portion of the ULD, and determining an amplitude of the top portion of the ULD. The example method further includes determining the ULD door status based on whether the amplitude of the top portion of the ULD exceeds an amplitude threshold.

Methods for unit load device (ULD) door tarp detection

Three-dimensional (3D) imaging systems and methods are described for implementing virtual grading of package walls in commercial trailer loading. The 3D imaging systems and methods comprise capturing, by a 3D-depth camera, 3D image data of a vehicle storage area. A package wall is determined, based on the set of 3D image data and by a 3D data analytics application (app) executing on one or more processors communicatively coupled to the 3D-depth camera, a package wall within the vehicle storage area. A wall grade is assigned to the package wall, where the package wall is defined within the set of 3D image data by a plurality of packages each having a similar depth dimension, and wherein the package wall has a wall height.

Three-dimensional (3D) imaging systems and methods for virtual grading of package walls in commercial trailer loading

Methods for determining a trailer status are disclosed herein. An example method includes capturing a three-dimensional image and a two-dimensional image. The three-dimensional image may comprise three-dimensional image data, and the two-dimensional image may comprise two-dimensional image data. The example method may further include determining a first trailer status based on the three-dimensional image data, and determining a second trailer status based on the two-dimensional image data. The example method may further include comparing the first trailer status to the second trailer status to determine a final trailer status.

Method for detecting trailer status using combined 3D algorithms and 2D machine learning models

Three-dimensional (3D) imaging systems and methods are disclosed for detecting and dimensioning a vehicle storage area. A 3D-depth camera captures 3D image data comprising one or more 3D image datasets of the vehicle storage area during corresponding one or more image capture iterations. A 3D data analytics application (app) executing on one or more processors communicatively coupled to the 3D-depth camera and for each one or more image capture iterations, updates a number of planar regions detected within the one or more 3D image datasets. The 3D data analytics app further assigns a vehicle storage area type to the vehicle storage area based on the number of planar regions detected by the 3D analytics app over the one or more image capture iterations.

Three-dimensional (3D) imaging systems and methods for detecting and dimensioning a vehicle storage area

Three-dimensional (3D) imaging systems and methods are disclosed for determining vehicle storage areas and vehicle door statuses. A 3D-depth camera captures 3D image data of one or more vehicle storage areas. A 3D data analytics application (app) analyzes a first 3D image dataset of the 3D image data to determine a first depth measurement corresponding to a first vehicle storage area. The 3D data analytics app further analyzes a second 3D image dataset of the 3D image data to determine a second depth measurement. The 3D data analytics app detects a depth-change event based on the second depth measurement differing from the first depth measurement by more than a predefined depth-change threshold value. The 3D analytics app assigns, based on the depth-change event, an open door status to a new second vehicle storage area and a closed door status to the previous first vehicle storage area.

Three-dimensional (3D) imaging systems and methods for determining vehicle storage areas and vehicle door statuses

Methods for detecting if a Time of Flight (ToF) sensor is looking into a container are disclosed herein. An example method includes capturing a three-dimensional image. The three-dimensional image may comprise three-dimensional point data having a plurality of points. The example method may further include analyzing the plurality of points to determine a plurality of image components. Each image component may be representative of the plurality of points. The example method may further include comparing each image component of the plurality of image components to a threshold value. Each image component may correspond to a respective threshold value. The example method may further include determining that a number of image components N of the plurality of image components satisfy the respective threshold values, and determining the presence or absence of the container by comparing the number of image components N to an agreement threshold X.

Methods for detecting if a time of flight (ToF) sensor is looking into a container

Embodiments of the present invention are generally directed to system and methods for estimating the time associated with completion of loading and/or unloading of a container. In an embodiment, the present invention is a method of estimating an estimated time to completion (ETC) of loading a container. The method includes: capturing, via an image capture apparatus, a three-dimensional image representative of a three-dimensional formation, the three-dimensional image having a plurality of points with three-dimensional point data; based at least in part on a first sub-plurality of the points, determining an active load time for the container; based at least in part on a second sub-plurality of the points, determining a fullness of the container; and estimating, by a controller, the ETC based on the active load time and on the fullness.

Container loading/unloading time estimation

A system and method for performing robust depth calculations with time of flight (ToF) sensors using multiple exposure times is disclosed. A three-dimensional (3D) depth sensor assembly captures a first array of n point values, where each point value of the first array has a respective first-array depth component and a respective first-array quality component. The 3D depth sensor assembly then captures a second array of n point values, where each point value of the second array has a respective second-array depth component and a respective second-array quality component. A processor then renders a 3D point cloud comprising a third array of n point values, where each point value of the third array has a respective third-array depth component. The respective third-array depth component for each point value of the third array is based on either the corresponding respective first-array depth component or the corresponding respective second-array depth component.

System and method for robust depth calculation with ToF sensors using multiple exposure times

A system and method for detecting a presence or absence of objects in a trailer are described. A 3D depth-camera is oriented to capture a 3D image. The 3D image includes a plurality of 3D point data defining a portion of a wall, floor, and top of a trailer. The plurality of 3D point data is then analyzed to determine a first, second, and third sub-plurality of points, associated with the portion of the wall, floor, and top, respectively. The first, second, and third sub-pluralities are then removed from the plurality of points to obtain a modified plurality of points, representing a modified 3D image. The modified 3D image is then segmented into a plurality of bins, and the plurality of bins are analyzed to determine one or more points-bin values. A communication is then provided based on whether any of the points-bin values exceeds a threshold value.

System and method for detecting a presence or absence of objects in a trailer

Three-dimensional (3D) depth imaging systems and methods are disclosed for use in commercial trailer loading. In various aspects, a 3D-depth camera is configured and oriented in a direction to capture 3D image data of a vehicle storage area. A depth-detection application (app) executing on one more processors determines, based on the 3D image data, a void data region, and a floor data region within the 3D image data. Based on the determination of the void data region and the floor data region, the depth-detection app generates an out-of-range indicator that indicates that a wall (e.g., a package wall) situated at a rear section of the vehicle storage area is not detected. The determination that a package wall is not detected causes a dashboard app to modify a graphical representation of the capacity of the vehicle storage area.

Systems and methods for detecting if package walls are beyond 3D depth camera range in commercial trailer loading

An example trailer monitoring system includes a trailer monitoring unit including an image capture arrangement disposed within the trailer monitoring unit to capture first image data. An accelerometer is carried by the trailer monitoring unit is to generate acceleration data of the trailer monitoring unit. The trailer monitoring system also includes one or more processors configured to access the acceleration data and configured to compare the acceleration data to a reference acceleration data range to determine if the acceleration data is within the reference acceleration range. In response to the acceleration data being outside the reference acceleration data range, the one or more processors are to increase a tally of impact events associated with the trailer monitoring unit being impacted. In response to the tally satisfying a threshold, the one or more processors are to generate an alert indicative of the trailer monitoring unit requiring maintenance.

Growing community of inventors

Hicksville, NY, United States of America

Adithya H Krishnamurthy