ESTIMATE OF CONTAINER LOADING / UNLOADING TIME

Abstract

Method for estimating an estimated time to completion (ETC) of loading a container (102), wherein the method comprises: Capturing, via an image capture device, a three-dimensional image representing a three-dimensional formation, wherein the three-dimensional image has a plurality of points with three-dimensional point data; Determining an active loading time for the container (102) based at least partially on a first submultiple of the points; Determining a filling of the container (102) based at least partially on a second submultiple of the points; Estimating the estimated time to completion (ETC) by a controller based on the active loading time and filling level.

Description

BACKGROUND

[0001] Goods can be transported in many different ways and using many different methods. Especially in long-distance transport, containers are frequently used, which are loaded with goods and then transported to the desired destination by vehicles, trains, vessels, or aircraft. Although removable containers are not always used, short-distance transport of goods similarly utilizes vehicles such as vans / panel vans, which have attached containers for storing items and cargo.

[0002] In the past, most loading and unloading of goods was carried out with minimal use of computer systems. However, with the development of computing power, the availability of collected environmental data, and the increasing focus on efficiency, today's loading and unloading operations are monitored, supervised, and / or supported by computer platforms that can react to information in a given situation. One aspect of particular importance is the estimation of the time required to load and unload a container. Knowing the estimated completion time for loading or unloading a shipment allows for more efficient planning of resources such as personnel and loading equipment. It can also aid in the appropriate routing of containers to the corresponding loading bays, thereby improving bay utilization.Furthermore, supervisors can streamline loading and unloading operations by quickly identifying situations where the movement of cargo is slower than expected.

[0003] DE 11 2016 005 287 T5 describes a method and a device for receiving a depth frame from a depth sensor oriented towards an open end of a shipping container, wherein the depth frame comprises a plurality of grid elements, each with an associated depth value. One or more occlusions in the depth frame are identified, one or more occlusions in the depth frame are corrected using one or more temporally adjacent depth frames, and the corrected depth frame is output for completeness estimation.

[0004] US 2010 / 0073476A1 describes a method for detecting at least one object in a storage space. The method includes identifying at least one surface among the surfaces that bound the storage space and dividing each of the at least one surface into a plurality of sub-areas. The method further includes detecting an occupancy status of each sub-area, the occupancy status indicating the presence of the at least one object above each of the at least one surface, and deriving at least one of the following information: volume, location, and shape information of the at least one object, based on the occupancy status of the sub-areas.

[0005] The subsequently published document DE 11 2018 005 021 T5 describes systems and methods for maintaining a digital chain of custody for containers and the products they contain. A container can be verified, and the products to be loaded into it can be identified. Video data can be captured throughout the loading process and combined with product information to generate a digital container model. Furthermore, a monitoring device can be included inside the container to collect data during shipping. During shipping, the digital container model can be provided to and reviewed by the recipient. Upon arrival of the container, the recipient can also review the monitoring data from the monitoring device.Based on reviews of the digital container model and monitoring data, a digital chain of custody can be established and maintained for all or part of the products in the container from loading to arrival.

[0006] WO 2016 / 133608 A1 describes methods, devices, and systems for quantifying the space inside a container using a detachable scan sensor node. In general, the detachable scan sensor node identifies a container type, uses at least one depth sensor on the detachable scan sensor node to scan the space inside the container and generate scan data, wherein the detachable scan sensor node is temporarily mounted inside and above the space within the container, and wherein the depth sensor is oriented to scan the space inside the container from above and within the container. The detachable scan sensor node then uses the scan data to determine an unoccupied area of ​​the space inside the container.

[0007] DE 10 2013 215 532 A1 describes a system comprising a processor and an external fuel indicator. The processor is configured to direct the power supply to a digital display upon detecting that one or more activation indicators have been met. The processor is also configured to retrieve fuel-related measurements when a vehicle is refueled. Furthermore, the processor is configured to determine at least one fuel-related statistic in addition to the current fuel level. Finally, the processor is configured to output this fuel-related statistic to the external fuel indicator.

[0008] It is an object of the invention to provide improved systems and methods that can offer an estimated time to completion for the loading and / or unloading of containers such as shipping containers, trailers, vans, etc. This object is achieved by the subject matter of the independent claims. BRIEF DESCRIPTION OF THE DIFFERENT VIEWS OF THE DRAWINGS

[0009] The accompanying figures, in which identical reference numerals denote identical or functionally similar elements in the individual views, are incorporated into the disclosure together with the following detailed description and form an integral part of the disclosure and serve to further illustrate embodiments of concepts comprising the claimed invention described herein and to explain various principles and advantages of these embodiments. Fig. Figure 1 illustrates a loading device according to an embodiment of the present invention. Fig. Figure 2 shows the interior of the loading facility of Fig. 1. Fig. Figure 3 shows a container monitoring unit according to an embodiment of the present invention. Fig. Figure 4A shows a top view of the loading equipment of Fig. Figure 1 shows an example of the field of view of a container monitoring unit. Fig. 4B shows a side view of the loading equipment of Fig. Figure 1 shows an example of the field of view of a container monitoring unit. Fig. Figure 5 shows an exemplary schematic block diagram of a system used in the establishment of Fig. 1 implemented communication network. Fig. Figure 6 illustrates a flowchart representative of a method for estimating the estimated time to completion (ETC) of loading a container according to an embodiment of the present invention. Fig. Figure 7 shows an example of loading and unloading a container.

[0010] Experts will recognize that elements in the figures are shown for the sake of simplicity and clarity and are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to improve the understanding of embodiments of the present invention.

[0011] Where appropriate, the apparatus and process components have been represented by conventional symbols in the drawings, which show only those specific details relevant to understanding the embodiments disclosed herein, so as not to obscure the disclosure with details that are readily apparent to those skilled in the field who refer to the present description. DETAILED DESCRIPTION OF THE INVENTION

[0012] The term "container" as used herein refers to any container that can be transported by at least one vehicle, train, watercraft, or aircraft and is configured to hold transportable goods such as packaged and / or unpackaged items and / or other types of cargo. Accordingly, an example of a container includes an enclosed container permanently attached to a platform with wheels and a coupling for towing by a powered vehicle. An example of a container also includes an enclosed container that is detachably attached to a platform with wheels and a coupling for towing by a powered vehicle. An example of a container also includes a housing that is permanently attached to the frame of a powered vehicle, such as a van, box truck, etc.While the exemplary embodiment(s) described below appear to relate to one type of container, the scope of the invention is intended to extend to other types of containers as defined above.

[0013] In one embodiment, the present invention is a method for estimating the estimated time to completion (hereinafter abbreviated as ETC) of loading a container. The method comprises: acquiring, via an image acquisition device, a three-dimensional image representing a three-dimensional formation, wherein the three-dimensional image has a plurality of points with three-dimensional point data; determining an active loading time for the container, at least partially based on a first submultiple of the points; determining a filling level of the container, at least partially based on a second submultiple of the points; and estimating the ETC by a controller, based on the active loading time and the filling level.

[0014] In another embodiment, the present invention is a method for estimating an ETC for unloading a container. The method comprises: acquiring, via an image acquisition device, a three-dimensional image representing a three-dimensional formation, wherein the three-dimensional image has a plurality of points with three-dimensional point data; determining an active unloading time for the container, at least partially based on a first submultiple of the points; determining a filling level of the container, at least partially based on a second submultiple of the points; and estimating the ETC by a controller, based on the active unloading time and the filling level.

[0015] In a further embodiment, the present invention is a container monitoring unit (hereinafter referred to as CMU) for estimating an ETC for the loading of a container. The CMU comprises: a housing; an imaging assembly, which is at least partially located within the housing and is operable to acquire a three-dimensional image representing a three-dimensional formation, wherein the three-dimensional image has a plurality of points with three-dimensional point data; and a controller, which is communicatively connected to the imaging assembly. The controller is operable to: analyze a first subset of the points to determine an active loading time for the container; analyze a second subset of the points to determine a filling level of the container; and estimate the ETC based on the active loading time and the filling level.

[0016] With reference to the drawings, illustrated Fig. Figure 1 shows an exemplary environment in which embodiments of the present invention can be implemented. In this example, the environment is provided in the form of a loading ramp 100 (also referred to as a loading facility) where containers 102 are loaded with various goods and / or various goods are unloaded from the containers 102. The loading ramp 100 comprises a facility 104 with several loading bays 106.1 - 106.n opposite a loading area 108, where vehicles, such as semi-trailer trucks (not shown), deliver and pick up containers 102. For loading, each container 102 is moved back towards the facility 104 so that it is generally perpendicular to the wall with the loading bays 106 and aligned with one of the loading bays (in this case 106.3).As shown, each loading bay 106 has a bay door 110, which can be lowered to close the respective loading bay 106 or raised to open the respective loading bay, allowing access to the interior of the facility 104 through it. Additionally, each loading bay 106 is equipped with a container monitoring unit (CMU) 112. The CMU is mounted near the container loading area, preferably in the upper section of the loading bay 106 outside the door 110 opposite the loading area parking space 108, or inside / on the rear of a container 102 when it is docked at the respective loading bay. To protect the CMU from adverse weather conditions, it could be mounted under a bay canopy 114. Once docked, goods can be loaded into and unloaded from the container 102, with the CMU 112 maintaining a view of the rear / interior of the container.

[0017] Fig. Figure 2 is an exemplary perspective view of the loading facility 104 from Fig. Figure 1 shows the view from the inside, with containers 102 docked at loading bay 106.3 with the container door open and containers 116 docked at loading bay 163.2 with the container door 118 closed. The CMU 112 is used to determine the status of the container door, as described below.

[0018] In the version described here and as in Fig. As shown in Figure 3, the CMU 112 is a mountable device comprising a 3D depth camera 120 for capturing 3D (three-dimensional) images (e.g., 3D image data composed of a multitude of points with three-dimensional point data) and a 2D camera 122 for capturing 2D images (e.g., 2D image data). The 2D camera can be an RGB (red, green, blue) camera for capturing 2D images. The CMU 112 also includes one or more processors and one or more computer memories for storing image data and / or for executing applications / instructions that perform analyses or other functions as described herein. For example, the CMU 112 can include flash memory used for determining, storing, or otherwise processing the image data and / or rescan data. In addition, the CMU 112 can include a network interface to enable communication with other devices (such as Server 130).The network interface of the CMU 112 can include any suitable type of communication interface(s) (e.g., wired and / or wireless interfaces) configured to operate according to any suitable protocol(s). Various embodiments and as described in the... Fig. 1 and Fig. As shown in Figure 2, the CMU 112 is mounted via a bracket 124 and aligned towards the docked containers to capture 3D and / or 2D image data from their inside and outside.

[0019] To capture 3D image data, the 3D depth camera 120 includes an infrared (IR) projector and an associated IR camera. The IR projector projects a pattern of IR light or rays onto an object or surface, which may include surfaces of the container 102 (such as the door, walls, floor, etc.), objects inside the container (such as boxes, packages, temporary shipping equipment, etc.), and / or surfaces of the loading area 108 (such as the surface of the loading area where the containers are parked). The IR light or rays can be distributed by the IR projector on the object or surface in a pattern of spots or dots that can be detected or scanned by the IR camera. A depth sensing application, such as a depth sensing application running on one or more processors or memories of the CMU 112, can, based on the pattern of spots or dots, determine various depth values, e.g.,The depth values ​​of the interior of container 102 are determined. For example, objects with shallow depth (e.g., nearby boxes, packages, etc.) can be identified where the spots or dots are densely packed, and objects with greater depth (e.g., distant boxes, packages, etc.) where the dots are further apart. The various depth values ​​can be used by the depth sensing application and / or the CMU 112 to create a depth map. The depth map can represent a 3D image of the objects or surfaces detected or scanned by the 3D depth camera 120, or it can contain 3D image data from them.

[0020] Additionally, in one embodiment, the 2D camera 122 includes an RGB (red, green, blue)-based camera for capturing 2D images with RGB-based pixel data. In some embodiments, the 2D camera 122 captures 2D images and associated 2D image data at the same or a similar time as the 3D depth camera 120, so that the CMU 112 can have both 3D image datasets and 2D image data for a given surface, object, or scene available at the same or a similar time.

[0021] With reference to the Fig. 4A and Fig. 4B The CMU can be oriented so that its fields of view (FsOV) 126 for the 3D camera and the 2D camera are extended to capture a large portion of the container interior. Furthermore, both FsOVs can overlap considerably to capture data over essentially the same area. As a result, the CMU 112 can scan, probe, or otherwise acquire image data from walls, floor, ceiling, packaging, or other objects or surfaces within the container to determine the 3D and 2D image data. Similarly, in the absence of a container from the loading bay, the CMU can scan, probe, or otherwise acquire image data from the surface of the loading equipment area 108 to determine the 3D and 2D image data.The image data can be processed by one or more processors and / or memories of the CMU 112 (or, in some embodiments, one or more remote processors and / or memories of a server) to implement analyses, functions, such as graphical or imaging analyses, as described in one or more different flowcharts, block diagrams, procedures, functions or different embodiments herein.

[0022] In some embodiments, the CMU 112 processes the 3D and 2D image data for use by other devices (e.g., client device 128 (which may be a mobile device such as a tablet, smartphone, laptop, or other such mobile computing device) or server 130 (which may be one or more computers managing access to a central resource or service on a network)). Processing the image data can generate post-scan data, which may include metadata, simplified data, normalized data, result data, status data, or warning data derived from the originally scanned or captured image data. As described in Fig. Figure 5, which shows a block connection diagram between the CMU 112, the server 130 and the client device 128, shows that these devices can be connected via any suitable means of communication, including wired and / or wireless connection components that implement one or more communication protocol standards such as TCP / IP, WiFi (802.11b), Bluetooth, Ethernet or other suitable communication protocols or standards.

[0023] In some embodiments, the server 130 can be located in the same loading device 104. In other embodiments, the server 130 can be located at a remote location, e.g., on a cloud platform or another remote location. In still other embodiments, the server 130 can be composed of a combination of local and cloud-based computers.

[0024] Server 130 is configured to execute computer instructions to perform the operations associated with the systems and procedures described herein. Server 130 can implement enterprise service software, which may include, for example, RESTful (Representational State Transfer) API services, message queuing services, and event services. These can be provided by various platforms or specifications, such as the J2EE specification, implemented by one of the Oracle WebLogic Server platforms, the JBoss platform, or the IBM WebSphere platform, among others. Other technologies or platforms, such as Ruby on Rails, Microsoft .NET, or similar, can also be used.

[0025] To support reporting on the estimated time of completion (ETC) of loading and / or unloading a container, the above components can be used alone or in combination to capture and / or provide various measurements of the interior of the container docked at a loading bay and to use these measurements (i.e., data) to perform the necessary analyses.

[0026] With reference to Fig. Figure 6 contains a flowchart illustrating an exemplary procedure 200 for estimating an ETC for loading a container. In step 202, the procedure involves capturing a three-dimensional image representing a three-dimensional formation using an image acquisition device. The three-dimensional image contains a multitude of points with three-dimensional point data. The image acquisition device can be implemented using the CMU 112, which is configured for 3D image acquisition. It is preferable to orient the image acquisition device so that its 3D FOV (3D field of view) extends into the area of ​​the loading equipment location, specifically the area where a trailer (such as trailer 102) is to be positioned during loading and unloading operations.This configuration allows the image acquisition device to detect the presence or absence of various objects near its FOV (by capturing and analyzing 3D data) and to make various determinations based on this.

[0027] The next step in the procedure, step 204, involves determining the active load time for the container. This operation can be performed by the CMU itself, by the server 130, or a combination of both. In some implementations, the active load time can be determined by subtracting the idle load time from the total elapsed load time. For example, if the total elapsed load time is set to 5 hours and the idle load time to 1 hour, the active load time would be 4 hours.

[0028] In some embodiments, the total elapsed loading time is calculated from the moment a container door is opened until a later event. In some embodiments, this time can also be calculated from the moment the container is docked at a container loading platform or from the moment a first loader (human or non-human) enters the interior of the container. In some embodiments, idle loading time is generally considered to be the time during which no loading takes place. This can be monitored, for example, via motion detection, where, for instance, the cumulative time during which no movement occurs within the trailer is considered the idle loading time. In other cases, the analysis can be more selective in that only the movement (or lack thereof) of goods within the container is considered for calculating the idle loading time.In this case, it may be necessary to disregard some points of the 3D image and focus only on a specific subset representing the goods of interest. To ensure that the absence of movement contributes to the overall idle charging time, it might be preferable to consider the absence of movement over a test period of limited length. For example, if no movement occurs for a period of 1 minute, this period is added to the idle charging time.

[0029] Next, in step 206, procedure 200 involves determining the container's occupancy. This value can be expressed as a percentage and represents an estimate of the volume occupied by the goods in the container relative to the total volume. It can be calculated, for example, by obtaining a total container volume, determining the volume of each individual item within the container, adding the individual volumes together to obtain a combined occupied volume, and calculating what proportion of the total volume is occupied by the combined occupied volume.

[0030] In other implementations, the fill can be derived from the three-dimensional point data acquired within the three-dimensional image. In some implementations, for example, the fill can be based on depth measurements of at least some parts of the loading wall. The loading wall may be composed of surfaces that are substantially facing the image acquisition device and / or a container door near which the image acquisition device is mounted (i.e., within 0-45 degrees of the image acquisition device and / or a container door near which the image acquisition device is mounted) and are substantially orthogonal to the container floor.

[0031] Determining an essentially flat shape can be achieved through 3D image segmentation analysis. In some embodiments, Sample Consensus (SAC) segmentation analysis can be used to identify points in the 3D image data that correspond to different planes or surfaces. This can be applied to a variety of surfaces, including the interior and exterior surfaces of the trailer (e.g., interior walls, floor, ceiling, and exterior surfaces such as the outside of the door) and also surfaces of objects located within the trailer itself. SAC segmentation analysis determines or segments the different planes or surfaces of the environment in x, y, and z coordinate planes by identifying a correlation of common points along the x, y, and z planes oriented in the 3D image data.This method can be used to analyze a specific set of points within the 3D image to identify the presence of a plane corresponding to an essentially flat surface. Additionally, it can be determined whether the variance of the respective depth values ​​of the second subset of points lies within a predefined depth variance threshold, where the variance within the predefined depth variance threshold indicates that the three-dimensional formation is essentially flat.

[0032] With reference to Fig. Figure 7, which shows an exemplary image 300 of a container being loaded or unloaded, shows that in some cases, goods 302 stacked on the container floor are not considered part of the loading wall 304 for the purpose of calculating the fill level. In other cases, the stacked goods 302 are also considered part of the loading wall, and their depth is also taken into account when determining the total fill level of the container. To determine the fill level, in some embodiments it is possible to determine an average depth value of at least a portion of the loading wall, subtract the determined average depth value from the total depth of the container to obtain an estimated occupied depth, and divide the estimated occupied depth by the total depth. This gives the percentage of the total volume (expressed as a decimal) that is estimated to occupy space in the container at the time of the estimate. For example, if...Assuming the average depth of the container is set to 20 feet and the total depth to 80 feet, the above calculations yield a result of 0.75, meaning that approximately 75% of the container is occupied. Since this approach to occupancy detection may only consider some of the surfaces within the 3D image, the analysis can only be based on a subset of points within the 3D image.

[0033] Finally, in step 208 of procedure 200, the ETC is estimated based on the active loading time and the container's fill level. In an example, the ETC estimate is given by the following equation: ETC=tl(1f−1) where t l The active loading time is , and where f is the filling rate, expressed as a percentage in decimal form. If one considers the example values ​​of t lTaking f = 4 hours and f = 0.75 from the examples above, equation (1) yields the result ETC = 1.33, or 1 hour and 20 minutes, which means that this is approximately the time required to fill the container to 100%. It should be understood that the formula of equation (1), or the calculations that form the basis of equation (1), can be modified to provide an ETC for completion for any desired capacity threshold. That is, in some examples, there may be a reason to estimate the time to complete loading a container to 90% rather than 100% capacity. In one embodiment, this can be achieved by adjusting the filling calculation to be based not on the total depth of the container, but only on 90% of the total depth. In another embodiment, the equation can be modified as follows: ETC=tl(Df−1) where D is the percentage (expressed as a decimal) of the total depth of the container for which the load is to be estimated.

[0034] It should be noted that the operations described above are possible on each of the controllers of Fig. 5 can be executed. After acquiring 3D data, the CMU 112 can, for example, transfer all necessary data to the server 130, allowing the server's controller to perform all necessary operations. In another implementation, all steps can be executed on the CMU 112's controller itself. In yet another implementation, the operation can be distributed between the CMU 112 and the server 130. Once the ETC is defined, it can be executed by any of the components of the Fig. 5. It can be used. For example, it can be sent to client device 128 to display it to the customer. In another example, it can be added to the current time to obtain an estimated time for completing the trailer loading. In yet another example, server 130 can use the ECT for planning, dispatch, etc.

[0035] During the process of Fig. As described in section 6 regarding estimating the time to complete loading a container, a similar approach can be used to estimate the time to complete unloading a container. In this case, the estimate must determine an expected time it will take to remove the estimated occupied volume from the container. As an example, this can be determined by the following modification of equation (1): ETC=tl(1(1−f)−1)

[0036] The result provides an estimate of the time required to remove all items remaining in the container. Another way to view equation (3) is that it relies on a measurement for the container's emptiness (obtained by subtracting the fill level from 1). Since the fill level can be derived by determining the average depth of a loading wall and subtracting it from the total depth, the emptiness can be determined simply from the average depth of the loading wall without subtracting it from the total depth. In other embodiments, either the emptiness or the fill level can be measured from a point that can be considered the starting point, which is not necessarily an edge of a container. This can be particularly advantageous when unloading a container that was not fully loaded.For example, if, upon opening the door of an 80-foot container, it is found that the loading wall of a loaded container extends 20 feet into the container, and that this loading wall has moved a further 20 feet back (to a total of 40 feet) from the image capture device or the edge of the container after 1 hour, the emptiness can be determined by subtracting the 20 unused feet from both the average depth of a loading wall (40 feet) and the total depth (80 feet), and then dividing the adjusted average depth by the adjusted total depth, resulting in 0.33 or 33%. This value can then be used in a similar or identical manner to how emptiness is accounted for in the ETC determination procedures described above.

[0037] Specific embodiments have been described in the foregoing description. However, a person skilled in the art will recognize that various modifications and changes can be made without altering the scope of protection of the invention as defined in the claims below. Accordingly, the description and figures should be considered illustrative rather than limiting, and all such modifications are to be included within the scope of the present teachings. Furthermore, the described embodiments / examples / implementations should not be interpreted as mutually exclusive, but rather as potentially combinable, provided such combinations are in any way permissive.In other words, any feature disclosed in one of the embodiments / examples / implementations mentioned above may be included in any of the other embodiments / examples / implementations mentioned above. Furthermore, no steps of any method disclosed herein shall be understood to have a specific order unless it is expressly stated that no other order is possible or is required by the other steps of the respective method.

[0038] The benefits, advantages, solutions to problems, and all elements that may lead to the occurrence or enhancement of a benefit, advantage, or solution are not to be understood as critical, necessary, or essential features or elements in some or all of the claims. The invention is defined solely by the attached claims, including any amendments made during the pendency of this application and all equivalents of the granted claims.

[0039] Furthermore, in this document, relational terms such as first and second, upper and lower, and the like may be used merely to distinguish one entity or action from another, without necessarily requiring or implying any actual relationship or order of such an entity or action between such entities or actions. The expressions "includes," "comprising," "has," "have," "exhibits," "exhibiting," "contains," "containing," or any other variation thereof are intended to cover non-exclusive inclusion, such that a process, procedure, product, or device that includes, has, exhibits, or contains a list of elements may not only have those elements but may also have other elements not expressly listed or inherent in such process, procedure, product, or device. An element that "includes," "has," "exhibits," or "contains"The use of the term "a" does not, without further limitations, preclude the existence of additional identical elements in the process, method, product, or device that comprises, has, features, or contains the element. The terms "a" and "a" are defined as one or more unless expressly stated otherwise herein. The terms "essentially," "generally," "approximately," "about," or any other version thereof are defined in such a way as to be understood by a person skilled in the art in this field, and in one non-restrictive embodiment, the expression is defined as within 10%, in another embodiment as within 5%, in yet another embodiment as within 1%, and in yet another embodiment as within 0.5%. The term "coupled," as used herein, is defined as connected, but not necessarily directly and not necessarily mechanically.A device or structure that is “designed” in a certain way is at least also designed in that way, but may also be designed in ways that are not listed.

[0040] It is understood that some embodiments may include one or more generic or specialized processors (or “processing devices”) such as microprocessors, digital signal processors, custom processors, and field-programmable gate arrays (FPGAs), and uniquely stored program instructions (including both software and firmware) that control the one or more processors to implement, in conjunction with certain non-processor circuitry, some, most, or all of the functions of the method and / or device described herein. Alternatively, some or all of the functions may be implemented by a state machine that does not have any stored program instructions, or in one or more application-specific integrated circuits (ASICs) in which each function, or some combinations of certain functions, are implemented as user-defined logic.Of course, a combination of the two approaches can be used.

[0041] Furthermore, an embodiment may be implemented as a computer-readable storage medium on which computer-readable code is stored for programming a computer (which, for example, includes a processor) to execute a method as described and claimed herein. Examples of such computer-readable storage media include, but are not limited to, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (read-only memory), a PROM (programmable read-only memory), an EPROM (erasable programmable read-only memory), an EEPROM (electrically erasable programmable read-only memory).Furthermore, it is assumed that an average professional, regardless of possible significant effort and many design choices motivated, for example, by available time, current technology, and economic considerations, will be readily able to generate such software instructions, programs, and ICs with minimal experimentation if guided by the concepts and principles disclosed herein.

Claims

[1] A method for estimating an estimated time to completion (ETC) of loading a container (102), the method comprising: Acquiring, via an image acquisition device, a three-dimensional image representing a three-dimensional formation, the three-dimensional image comprising a plurality of points having three-dimensional point data; Determining an active loading time for the container (102) based at least in part on a first sub-plurality of the points; determining a fill of the container (102) based at least in part on a second sub-plurality of the points; Estimating the estimated time to completion (ETC) by a controller based on the active loading time and filling. [2] The method of claim 1, wherein the process of estimating the estimated time to completion (ETC) is defined by ETC=tl(1f−1) where t l the active loading time, and where f is the filling, expressed as a percentage in decimal form. [3] The method of claim 1, wherein the image capture device comprises a three-dimensional camera and wherein the plurality of points represents a point cloud. [4] The method of claim 1, wherein the act of determining the active loading time for the container (102) comprises subtracting an idle loading time of the container (102) from a total elapsed loading time of the container (102). [5] The method of claim 4, wherein the idle charging time is based on a period of time over which no change in the movement of objects within the container (102) is detected. [6] The method of claim 5, wherein the time period is comprised of a plurality of time samples over which no change in the movement of objects within the container (102) is detected. [7] The method of claim 1, wherein the process of determining the filling of the container (102) includes: detecting at least one shape having a substantially flat surface that is substantially orthogonal to a bottom of the container (102) and substantially facing the image capture device; Determining an average depth value of the entire at least one shape having the substantially flat surface; and Determining the filling based on the average depth value and a total depth of the container (102). [8] The method of claim 7, wherein the process of determining the filling includes: Subtracting the average depth value from the total depth value to obtain an estimated occupied depth; and where the estimated occupied depth is divided by the total depth. [9] A method for estimating an estimated time to completion (ETC) of unloading a container (102), the method comprising: Acquiring, via an image acquisition device, a three-dimensional image representing a three-dimensional formation, the three-dimensional image comprising a plurality of points having three-dimensional point data; determining an active unloading time for the container (102) based at least in part on a first sub-plurality of the points; determining a fill of the container (102) based at least in part on a second sub-plurality of the points; Estimating the estimated time to completion (ETC) by a controller based on the active unloading time and filling. [10] The method of claim 9, wherein the process of estimating the estimated time to completion (ETC) is defined by ETC=tl(1(1−f)−1) where t l is the active discharge time, and where f is the filling, expressed as a percentage in decimal form. [11] The method of claim 9, wherein the image capture device comprises a three-dimensional camera and wherein the plurality of points represents a point cloud. [12] The method of claim 9, wherein the act of determining the active unloading time for the container (102) comprises subtracting an idle loading time of the container (102) from a total elapsed unloading time of the container (102). [13] The method of claim 12, wherein the idle charging time is based on a period of time over which no change in the movement of objects within the container (102) is detected. [14] The method of claim 13, wherein the time period is composed of a plurality of time samples over which no change in the movement of objects within the container (102) is detected. [15] The method of claim 9, wherein the process of determining the filling of the container (102) includes: detecting at least one shape having a substantially flat surface that is substantially orthogonal to a bottom of the container (102) and substantially facing the image capture device; Determining an average depth value of the entire at least one shape having the substantially flat surface; and Determining the filling based on the average depth value and a total depth of the container (102). [16] The method of claim 15, wherein the process of determining the filling includes: Subtracting the average depth value from the total depth value to obtain an estimated occupied depth; and Divide the estimated occupied depth by the total depth. [17] Container Monitoring Unit (CMU) (112) for estimating an estimated time to completion (ETC) of a loading of a container (102), comprising: a housing; an imaging assembly located at least partially within the housing and operable to acquire a three-dimensional image representing a three-dimensional formation, the three-dimensional image comprising a plurality of points having three-dimensional point data; and a controller communicatively connected to the imaging assembly, the controller operable to: analyzing a first sub-plurality of the points to determine an active loading time for the container (102); analyzing a second sub-plurality of the points to determine a fill of the container (102); and; Estimate the estimated time to completion (ETC) based on the active loading time and fill. [18] Container Monitoring Unit (CMU) (112) according to claim 17, wherein the controller is operable to estimate the estimated time to completion (ETC) based on the active loading time and the filling by solving ETC=t1(1f−1) where t l the active loading time, and where f is the filling, expressed as a percentage in decimal form. [19] The container monitoring unit (CMU) of claim 17, wherein the controller is operable to analyze the first sub-plurality of points to determine the active loading time by subtracting an idle loading time of the container (102) from a total elapsed loading time of the container (102). [20] Container monitoring unit (CMU) (112) according to claim 17, wherein the controller can analyze the second sub-plurality of points to determine the filling of the container (102) by: detecting at least one shape having a substantially flat surface that is substantially orthogonal to a bottom of the container (102) and substantially facing the container monitoring unit (CMU) (112); Determining an average depth value of the entirety of the at least one shape having the substantially flat surface; and Determining the filling based on the average depth value and a total depth of the container (102).

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