Abstract: Systems and methods for assessing trailer utilization are disclosed herein. The method generates a trailer interior map and captures an image of the trailer interior. The map includes first voxels associated with the trailer interior and the image includes a plurality of three-dimensional (3D) image data points. The method generates a 3D map of an object based on a set of 3D points indicative of respective 3D image data points corresponding to respective first voxels and determines whether the object is non-conforming. The method determines at least one of second voxels associated with unusable space proximate to a non-conforming object, third voxels associated with the non-conforming object, and fourth voxels associated with a conforming object. The method determines an occupied portion of the trailer based on the first voxels, third voxels, and fourth voxels and trailer utilization based on the occupied portion of the trailer, the first voxels, and the second voxels.

Abstract: Methods and systems for optimizing one or more decoder parameters of an indicia decoder are disclosed herein. An example method includes applying a decoder algorithm to a first image data set to detect and decode one or more indicia, wherein the decoder algorithm utilizes a first set of parameters and a second set of parameters. The example method includes determining a minimum value and a maximum value for each parameter of the first set of parameters, and adjusting a parameter of the second set of parameters from a first value to a second value. The example method includes applying the decoder algorithm to a second image data set to detect and decode one or more indicia, and setting the parameter to one of the first value or the second value during subsequent applications of the decoder algorithm.

Abstract: Systems and methods for encoding metadata in image data captured by an imaging device, such as a barcode device or machine vision device, are provided. An example method includes analyzing raw image data at a front-end applicant specific integrated circuit to determine image metadata for each of a plurality of different pixel groupings collectively forming the raw image data. A least significant bit process is then used to encode the metadata into the image data, in a manner visually hidden from a user. A host processor receives the encoded image data, decodes the image metadata and uses that to process the image data, for example, performing barcode decoding or machine vision processes.

Abstract: 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.

Abstract: Methods for assessing trailer utilization are disclosed herein. An example method includes capturing an image featuring a trailer, and segmenting the image into a plurality of regions. For each region the example method may include cropping the image to exclude data that exceeds a respective forward distance threshold, and iterating over each data point to determine whether or not a matching point is included. Responsive to whether or not a matching point included for a respective data point, the method may include adding the respective data point or the matching point to a respective region based on a position of the respective data point. Further, the method may include calculating a normalized height of the respective region based on whether or not a gap is present in the respective region; and creating a 3D model visualization of the trailer that depicts trailer utilization.

Abstract: Systems and methods for fast autotuning of industrial fixed vision cameras are disclosed herein. An example method includes modifying illumination settings of a camera until a brightness value for captured image data reaches a minimum value; modifying a focus level of the camera to determine a range of camera focus levels for successfully decoding barcodes; configuring the camera to operate using a midpoint focus value of the range; modifying illumination settings of the camera until the brightness associated with the captured image data reaches an optimal value; and modifying the focus level of the camera, within the range, to determine an optimal focus level at which sharpness for the captured image is optimized; and capturing, by the camera, operating at the determined optimal focus level and with illumination settings for the optimal threshold brightness value, image data associated with a new target object affixed with a new barcode.

Abstract: Systems and methods for adaptively determining a region of interest (ROI) are disclosed herein. An example device includes an imaging assembly and a controller. The imaging assembly captures image data comprising pixel data from a plurality of pixels. The controller calculates a contrast value for each pixel of the plurality of pixels, generates a histogram of contrast values, calculates an area under the curve of the histogram, and determines a contrast value threshold to delineate between high-contrast value pixels and low-contrast value pixels. The controller also identifies a ROI within the image data by locating a region within the image data that (i) satisfies a pre-determined size threshold and (ii) contains a largest number of high-contrast value pixels relative to all other regions that satisfy the pre-determined size threshold, and adjusts imaging parameters of the imaging assembly based on the ROI to capture at least one subsequent image.

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: Methods for assessing trailer utilization are disclosed herein. An example method includes capturing an image featuring a trailer, and segmenting the image into a plurality of regions. For each region the example method may include cropping the image to exclude data that exceeds a respective forward distance threshold, and iterating over each data point to determine whether or not a matching point is included. Responsive to whether or not a matching point included for a respective data point, the method may include adding the respective data point or the matching point to a respective region based on a position of the respective data point. Further, the method may include calculating a normalized height of the respective region based on whether or not a gap is present in the respective region; and creating a 3D model visualization of the trailer that depicts trailer utilization.

Abstract: 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.

Abstract: A method includes: generating a three-dimensional grid of cells representing respective portions of an interior of a container, each cell having a status indicator defining an occupancy state of the corresponding portion of the container interior; during loading of the container: maintaining, for each of the cells, a current status indicator, controlling a depth sensor to capture a sequence of point clouds, each point cloud depicting the container interior, in response to each point cloud capture in the sequence, generating updated status indicators for the cells, based on (i) the point cloud, and on (ii) the current status indicators, replacing the current status indicators with the updated status indicators, measuring a current fullness of the container based on the current status indicators, and transmitting the current fullness to a computing device for at least one of display of the current fullness, or alert generation associated with the current fullness.

Abstract: Systems and methods for fast autotuning of industrial fixed vision cameras are disclosed herein. An example method includes modifying illumination settings of a camera until a brightness value for captured image data reaches a minimum value; modifying a focus level of the camera to determine a range of camera focus levels for successfully decoding barcodes; configuring the camera to operate using a midpoint focus value of the range; modifying illumination settings of the camera until the brightness associated with the captured image data reaches an optimal value; and modifying the focus level of the camera, within the range, to determine an optimal focus level at which sharpness for the captured image is optimized; and capturing, by the camera, operating at the determined optimal focus level and with illumination settings for the optimal threshold brightness value, image data associated with a new target object affixed with a new barcode.

Abstract: Systems and methods for adaptively determining a region of interest (ROI) are disclosed herein. An example device includes an imaging assembly and a controller. The imaging assembly captures image data comprising pixel data from a plurality of pixels. The controller calculates a contrast value for each pixel of the plurality of pixels, generates a histogram of contrast values, calculates an area under the curve of the histogram, and determines a contrast value threshold to delineate between high-contrast value pixels and low-contrast value pixels. The controller also identifies a ROI within the image data by locating a region within the image data that (i) satisfies a pre-determined size threshold and (ii) contains a largest number of high-contrast value pixels relative to all other regions that satisfy the pre-determined size threshold, and adjusts imaging parameters of the imaging assembly based on the ROI to capture at least one subsequent image.

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 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: 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: Systems and methods for fast autotuning of industrial fixed vision cameras are disclosed herein. An example method includes modifying illumination settings of a camera until a brightness value for captured image data reaches a minimum value; modifying a focus level of the camera to determine a range of camera focus levels for successfully decoding barcodes; configuring the camera to operate using a midpoint focus value of the range; modifying illumination settings of the camera until the brightness associated with the captured image data reaches an optimal value; and modifying the focus level of the camera, within the range, to determine an optimal focus level at which sharpness for the captured image is optimized; and capturing, by the camera, operating at the determined optimal focus level and with illumination settings for the optimal threshold brightness value, image data associated with a new target object affixed with a new barcode.

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: 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 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.