Abstract: Methods and systems for assessing a unit load device (ULD) package density are disclosed herein. An example method includes capturing a primary and secondary image featuring the ULD. The method further includes segmenting the primary image and the secondary image into a plurality of subsections and determining a primary largest depth value and secondary largest depth value for each subsection. The method further includes determining a depth change by comparing the primary largest depth value for each subsection to the secondary largest depth value for each subsection. The method further includes clustering subsections in the secondary image including a respective secondary largest depth value that is substantially similar to a subsection corresponding to a depth change to create a plurality of clusters. The method further includes calculating a wasted volume, and calculating a ULD package density by subtracting the wasted volume from a filled volume of the ULD.

Abstract: Methods for determining a unit load device (ULD) container type are disclosed herein. An example method includes capturing a set of image data featuring the ULD and aligning the set of image data with a template. The method further includes converting the set of image data and the template to down-sampled grids including a plurality of rows and columns. The method further includes removing portions of the image data grid that do not exceed a density threshold. The method further includes identifying a ULD border and a template border by extracting leftmost, rightmost, and topmost grid values from the respective grids. The method further includes calculating a match score corresponding to the template by determining a shortest respective distance between grid values in the ULD border and the template border, and determining ULD container type corresponding to the ULD based on the match score.

Abstract: Methods and systems for assessing a unit load device (ULD) package density are disclosed herein. An example method includes capturing a primary and secondary image featuring the ULD. The method further includes segmenting the primary image and the secondary image into a plurality of subsections and determining a primary largest depth value and secondary largest depth value for each subsection. The method further includes determining a depth change by comparing the primary largest depth value for each subsection to the secondary largest depth value for each subsection. The method further includes clustering subsections in the secondary image including a respective secondary largest depth value that is substantially similar to a subsection corresponding to a depth change to create a plurality of clusters. The method further includes calculating a wasted volume, and calculating a ULD package density by subtracting the wasted volume from a filled volume of the ULD.

Abstract: Methods for determining a unit load device (ULD) container type are disclosed herein. An example method includes capturing a set of image data featuring the ULD and aligning the set of image data with a template. The method further includes converting the set of image data and the template to down-sampled grids including a plurality of rows and columns. The method further includes removing portions of the image data grid that do not exceed a density threshold. The method further includes identifying a ULD border and a template border by extracting leftmost, rightmost, and topmost grid values from the respective grids. The method further includes calculating a match score corresponding to the template by determining a shortest respective distance between grid values in the ULD border and the template border, and determining ULD container type corresponding to the ULD based on the match score.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods for unit loading device (ULD) localization are disclosed herein. An example method includes capturing a set of image data featuring the ULD. The example method further includes cropping the set of image data to generate a cropped image. The cropped image features a portion of the ULD. The example method further includes determining one or more candidate edges of the portion of the ULD within the cropped image. The example method further includes identifying one or more edges of the portion of the ULD from the one or more candidate edges, wherein each of the one or more edges represents a boundary of the portion of the ULD.

Abstract: 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.

Abstract: Three-dimensional (3D) depth imaging systems and methods are disclosed for automatically determining shipping container fullness based on imaging templates. A 3D-depth camera captures 3D image data of a shipping container located in a predefined search space during a shipping container loading session. A container fullness application (app) receives the 3D image data, and determines therefrom a 3D container point cloud representative of a shipping container. An imaging template that defines a 3D template point cloud corresponding to a shipping container type of the shipping container is loaded into memory. A fullness value of the shipping container is determined based on a 3D mapping that is generated from alignment of a 3D container front portion of the 3D container point cloud with a 3D template front portion of the 3D template point cloud.

Abstract: Three-dimensional (3D) depth imaging systems and methods are disclosed for automatically determining shipping container fullness based on imaging templates. A 3D-depth camera captures 3D image data of a shipping container located in a predefined search space during a shipping container loading session. A container fullness application (app) receives the 3D image data, and determines therefrom a 3D container point cloud representative of a shipping container. An imaging template that defines a 3D template point cloud corresponding to a shipping container type of the shipping container is loaded into memory. A fullness value of the shipping container is determined based on a 3D mapping that is generated from alignment of a 3D container front portion of the 3D container point cloud with a 3D template front portion of the 3D template point cloud.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the present invention generally relate to the field of space dimensioning. In an embodiment, the present invention is a method of dimensioning a container bound by at least a first wall and a second wall opposite the first wall, the space being 3D and definable via height, width, and depth coordinates. The method includes: obtaining, a 3D image of at least a portion of the space; analyzing the image to determine a first and second equations defining first plane and second planes corresponding to the first and second walls; solving the first equation for a first coordinate value; solving the second equation for a second coordinate value, the first coordinate value and the second coordinate value being one of a width coordinate or a height coordinate; and computing a first distance based at least in part on the first coordinate value and the second coordinate value.

Abstract: 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.

Abstract: Embodiments of the present invention are generally directed to estimating capacity usage of a container. In an embodiment, the present invention is a method of estimating a fullness of a container. The method includes: mounting an image capture apparatus proximate a container-loading area, the image capture apparatus operable to capture three-dimensional images; capturing, via the 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 including depth data; generating a histogram of the depth data from the three-dimensional image; and estimating the fullness of the container based at least in part on the histogram.