Abstract:
A three-dimensional machine vision scanner head for obtaining raw scan data from a target object and an integrated scan information extraction module that performs data reduction and passes to a controller selected summary target object scan information that is significant for automated control decisions in an industrial process. The scanner head contains a laser light emitter, reflected laser light detector and a communication interface for transmitting the target object scan information from the information extraction module to the controller.

Description:
FIELD OF INVENTION 
       [0001]    This invention relates to the general field of devices that remotely measure the dimensions of objects, and more specifically to three-dimensional (3D) machine vision scanners with integral data reduction or computation methods that permit a direct interface with common industrial controllers. 
       BACKGROUND OF THE INVENTION 
       [0002]    Machine vision (MV) is a branch of engineering that uses computer vision in the context of manufacturing. “MV processes are targeted at recognizing the actual objects in an image and assigning properties to those objects—understanding what they mean.” (Fred Hapgood, Factories of the Future, Essential Technology, Dec. 15, 2006) 
         [0003]    “A 3D scanner is a device that analyzes a real-world object or environment to collect data on its shape and possibly its appearance. The collected data can then be used to construct digital, three dimensional models. The purpose of a 3D scanner is usually to create a point cloud of geometric samples of the surface of the subject. These points can then be used to extrapolate the shape of the subject.” [3D scanner, Wikipedia] 
         [0004]    The use of 3D scanners as machine vision for industrial manufacturing create a fundamental challenge when scanners generate increasingly larger amounts of scan data because that data must necessarily be reduced to fit into an industrial controller in a timely fashion or the process breaks down. As Moore&#39;s Law anticipates ever finer grained point clouds, the primary issue becomes effective real-time data management. If one uses a 3D scanner to create information about objects that allow industrial equipment to operate on said objects quickly and accurately, the data flow must be limited to only that which is needed to perform said task. 
         [0005]    Currently XYZ data clouds of half a million points per second are sent to a PC interface which must analyze and process the data into information that an industrial controller can utilize. Employing multiple PCs require programming and engineering expertise to abstract the relevant information from a point cloud or a series of 2D slices in quantities small enough that a simple industrial controller can utilize them effectively. Unfortunately that processing is often too slow to be acted upon in time by the controller, a delay which is often costly, wasteful, and sometimes dangerous in an industrial manufacturing or processing environment. 
         [0006]    Prior art scan data pre-processing techniques can be found in fields such as digital camera imaging systems (U.S. Pat. No. 7,791,671), POS scanners (U.S. Pat. No. 6,085,576), and defect detection systems (U.S. Pat. No. 7,783,103), but all require additional processing by a central unit external from the scanning device. A small step closer is the employment of a field-bus environment (U.S. Pat. No. 7,793,017) where data from multiple sensors is converted to a common addressable protocol network, but this does not effectively address the required analysis of 3D scanner data for near-realtime controller utilization. A triangulation scanning platform (U.S. Pat. No. 7,812,970) used for inspecting parts generates datasets that are processed by linear encoder electronics in order to control the rate of linear movement of the object being scanned, but do not feed near-real-time scan data to an industrial controller. 
         [0007]    Another concern is that a majority of 3D scanning systems employ 2D area image capture methods which stitch together 2D snapshots to form a 3D wire-frame model. This is not true 3D scanning and requires many problematic and inefficient solutions that are difficult to implement. 
         [0008]    Off the shelf, stand alone scanner units with protocol integrated data load management techniques applied to 3D machine vision scanning have not been found in the prior art and are needed to simplify and optimize industrial processing and manufacturing in many fields. 
       SUMMARY OF THE INVENTION 
       [0009]    A 3D machine vision scanner is traditionally designed to extract all relevant process data from each object scan and then send it directly to industrial process &amp; manufacturing controllers. 3D scanners employed for industrial processes (MV) can generate a set of 2D slices which can be ‘stacked together’ to produce a 3D representation. The novel device generates a 3D model from 2D slices that have been reduced by customizable information extraction tools &amp; methods so that the volume of scan data sent to a controller is more manageable and can be used more quickly. By this means more raw data can be processed or summarized onboard the 3D scanner unit and then be sent directly to an industrial controller for process control, effectively in real time. 
         [0010]    Directly interfacing a 3D scanner with an industrial controller and providing it thereby with extracted information that is significant for the controller&#39;s decision-making—rather than voluminous raw scan data—eliminates the need for a middleman processor to receive and process a large data cloud, while it also gives the process engineer much more direct control over the scanning output parameters without dependence on the scanner manufacturer to reconfigure the device for every new scan. A 3D machine vision scanner system embodying the present invention summarizes large amounts of data very quickly in a format industrial controllers can utilize so they can control, or make decisions based on, the item or items being scanned. 
         [0011]    A 3D machine vision scanner system can be utilized to improve many industrial and manufacturing processes. These include, but are not limited to scanning logs for trimming or cutting in a wood processing plant; detecting weld seam defects made by a robotic welder; accurately measuring the low point of a very large irregular surface for trimming; automatically culling fruit (or any object) by size or shape; measuring frozen pizza to ensure it will fit its box; tracking edges of rewinding spools to prevent wandering and tangling; accurately measuring object parameters to prevent accumulated errors when stacked; detecting imperfections in extrusions or pipes; accurately estimating volume of loose objects such as frozen foods for optimal refrigeration capacity, or woodchips/cereals to derive moisture content, etc. At present all of these processes require human counting, expert programming skills, database management, and data processing and are often expensive, labor and time consuming, and not always accurate or automatic. 
         [0012]    The present invention provides a three-dimensional machine vision system having a scanner head comprising a camera and a computer that functions as an information extraction module that performs data reduction and passes summary data to facilitate a direct significant information interface with common industrial controllers. By directly delivering key summaries of data from the scanner to the controller, the process engineer regains control of the scanning parameters as well as the decision processing. Scanner output and implementation is compatible with common industrial communication protocols used by process engineers in many fields. Raw 3D geometric measurements in a Cartesian coordinate system can be re-mapped into machine coordinates for industrial applications. Extracted information 3D machine vision scanning provides simpler, faster and more cost effective manufacturing and processing. 
         [0013]    Essentially, the invention provides a 3D machine vision scanning system having: 
         [0000]    1. a scanner head for obtaining raw scan data from a target object,
 
2. an information extraction module that processes and reduces the raw scan data into target object information that is significant for automated control decisions in an industrial process, and
 
3. a communication interface for transmitting the target object scan information to a controller.
 
         [0014]    The scanner head traditionally contains a laser light emitter and a reflected laser light detector. A scanner head embodying the present invention would also contain the information extraction module and the communication interface. The information extraction module has a set of embedded mathematical functions to extract key target object information from scan data, in order to reduce data transmission, system stalling and complexity of subsequent processing and decision analysis in an industrial control system. 
         [0015]    In a preferred embodiment: 
         [0000]    a) the computation method to be used by the information extraction module is selectable by the controller, choosing from a set of key scan information extraction tools embedded in data processing computer hardware that is integrated along with a laser projector, an imaging reflected laser sensor and into a sealed scanner head;
 
b) the target object scan information is derived only from scan data of a region of interest selected by the controller within a larger zone capable of being scanned by the scanner head;
 
c) the key scan information extraction tools include a multiplicity of predefined, controller-selectable regions of interest;
 
d) an information extraction tool is applied to scan data from a controller-selectable range of number of scan profiles, and resulting scan information is transmitted to the controller, before the information extraction tool is applied to a subsequent number of scan profiles selected;
 
e) the scanner head extracts key scan information from raw profile (X-Y) scan data and passes to the controller only the scan information that the controller needs to perform its functions.
 
f) the key target object scan information is formatted within the scanner head into an open standard communication protocol;
 
g) the scanner head summarize large amounts of target object scan data rapidly and passes on via a communication interface to an industrial controller a vastly smaller data set of summary target object scan information in a format industrial controllers can utilize to make industrial process control decisions.
 
         [0016]    The scanner head would be installed in an industrial setting such as a packaging or assembly conveyor line, in which application decision processing about target objects scanned by the scanner is done by a controller. 
         [0017]    The scanner head can be combined with multiple like scanners connected to a communication multiplexer encoder that includes time division synchronization so each scanner can be phase locked. This provides that one scanner head can fire its laser and obtain a scan profile without interference while the others in the array of multiple scanners are off and waiting their turn to scan sequentially. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0018]      FIG. 1   a  shows 3D scanners connected to an encoder/multiplexer and PC Interface which process scan data for an industrial controller. 
           [0019]      FIG. 1   b  shows the much simpler external elements of a 3D Machine Vision Scanning Information Extraction System. 
           [0020]      FIG. 2   a  shows the active side view of a 3D scanner housing. 
           [0021]      FIG. 2   b  shows a diagram of how a 3D scanner creates X-Y profiles. 
           [0022]      FIG. 2   c  shows an isometric interior view of the scanner operation as it scans a section of board with a distinctive profile. 
           [0023]      FIG. 2   d  shows an isometric view of the operational scan zone of a 3D scanner and a sample scan of an object by means of a fan of laser light emitted from the scanner 
           [0024]      FIG. 2   e  shows an isometric inside view of the operational scan zone of a 3D scanner and a sample scan of an object by means of a fan of laser light emitted from the scanner 
           [0025]      FIG. 3   a  shows a photograph of an orange being scanned 
           [0026]      FIG. 3   b  shows an isometric point cloud of the scan of the orange. 
           [0027]      FIG. 3   c  shows a side view of the point cloud of the orange. 
           [0028]      FIG. 4   a  shows a side view of the point cloud with profile extrema. 
           [0029]      FIG. 4   b  shows a side view of the profile extrema of the orange. 
           [0030]      FIG. 5   a  shows a side view of the profile and cloud extrema. 
           [0031]      FIG. 5   b  shows a top view of the profile and cloud extrema. 
           [0032]      FIG. 6   a  shows a photograph of a pizza being scanned 
           [0033]      FIG. 6   b  shows an isometric view of the scan of a pizza including its point cloud with profile extrema. 
           [0034]      FIG. 6   c  shows a top view of the scan of a pizza including its profile and cloud extrema. 
           [0035]      FIG. 7  shows an Extrema Derivation Chart 
           [0036]      FIG. 8   a  shows a dented section of corrugated pipe being scanned. 
           [0037]      FIG. 8   b  shows a graph of the moment when the scanner IET detects the dent as a divergence from the pipe&#39;s nominal profile. 
           [0038]      FIG. 9   a  shows a photograph of a pile of woodchips being scanned 
           [0039]      FIG. 9   b  shows an isometric view of the 3D scan of the woodchips. 
           [0040]      FIG. 10   a  shows a side view of the 3D scan of the woodchips. 
           [0041]      FIG. 10   b  shows a chart illustrating the area summing of a single profile of the woodchip scan within a selected region of interest. 
           [0042]      FIG. 11   a  shows a Venn diagram illustrating how the information extraction module with a set of information extraction tools (IET) enables 3D Machine Vision Scanning Information Extraction. 
           [0043]      FIG. 11   b  shows elements integrated into a 3D Machine Vision Scanning Information Extraction System. 
       
    
    
     DETAILED DESCRIPTION 
       [0044]    The 3D Machine Vision Scanning Information Extraction System will now be described by reference to figures and critical terminology will be discussed. 
         [0045]      FIG. 1   a  shows a number of scanners  12  sending scan data from each scanner output  24  to a multiplexer/encoder  26 , then by means of an ethernet industrial protocol (EtherNet/IP™)  28  connection to a workstation/PC Interface  30 , which analyzes and processes the data and converts it into a Common Industrial Protocol (CIP™)—CIP and EtherNet/IP are trademarks of ODVA, which is an international association comprising members from the world&#39;s leading automation companies. Collectively, ODVA and its members support network technologies based on the Common Industrial Protocol (CIP). These currently include DeviceNet, EtherNet/IP, CompoNet, and ControlNet, along with the major extensions to CIP CIP Safety and CIP Motion. All these trademarks are of ODVA, which manages the development of these open technologies, and assists manufacturers and users of CIP Networks through its activities in standards development, certification, vendor education and industry awareness. The CIP  32  formatted information is transmitted to an industrial controller  34  (Prior Art).  FIG. 1   b  shows the two external elements of a 3D Machine Vision Scanning Information Extraction System  10 , namely a scanner  12  sending summarized CIP  32  data from its output  24  via EtherNet/IP  28  directly to the controller  34 . (Internal data processing elements will be discussed below.) 
         [0046]      FIG. 2   a  shows the active side view of a 3D scanner housing unit  12  with a laser projector  14  emitting coherent light through its window  18 , a camera  16  viewing through its window  20 , an indicator panel  22  and the scanner output  24  connector. 
         [0047]      FIG. 2   b  shows a diagram of a scanner  12  operating a laser projector  14  which sends a beam  41  through its window  18  onto an object (not shown) at a point  48  labeled A. The laser beam  41  on the object (between points A &amp; B) is imaged by a sensor  38  at A′ by means of a return path  44  through the field of view of the camera lens  36 . As the laser projector  14  reaches point B on the object its position has correspondingly changed on the sensor  38  to B′. Since the baseline  40  is known, and the laser corner is a right angle, the angle of the camera corner can be determined from the location of the laser dot in the camera&#39;s field of view as detected by the sensor  38 . To speed up the acquisition process, the laser projector  14  actually emits a sheet of laser light, hereafter known as a laser fan  42  in order to derive an X-Y profile  50  of the item being scanned. 
         [0048]      FIG. 2   c  shows an isometric interior view illustrating the scanner  12  operation as it emits a laser fan  42  over an object  46 , here a section of board with a distinctive profile  50 , and then images it along the return path  44  through the lens  36  onto the imaging sensor  38 . The actual image of the profile  50  created by the laser fan  42  as shown on the surface of the sensor  38  is merely representative of the scanning operation in order to illustrate the principles involved. The orientation and size of the image of the profile  50  received by the sensor  38  depends on the characteristics of the lens  36  and imaging distance. 
         [0049]      FIG. 2   d  shows the operational scan zone  88  of a scanner  12  emitting laser fan  42  from laser window  18 . The profile  50  of an object  46  (an orange) placed within the scan zone  88  will be painted by the laser fan  42  and be imaged along the return path  44  through the camera window  20 . The laser emitter does not pivot—rather, the laser light emitted is refracted into a planar fan, the reflection of which off the target object is detected by a camera The profile  50  is the set of detected laser intersection points upon the surface of the target object, and is a subset of the actual surface section atomic anatomy of the target object. 
         [0050]      FIG. 2   e  shows the inside view of  FIG. 2   d  wherein the profile  50  painted by the laser fan  42  on the object  46  is now visible as it is seen through the camera window  20  via the return path  44 . 
         [0051]      FIG. 3   a  shows an isometric photograph of an orange (object  46 ) being scanned by a laser beam  42  and highlighting the orange&#39;s profile  50 .  FIG. 3   b  shows an isometric view of the point cloud  52  of a section of the orange  46 , comprised of successive profiles  50  of individual points  48 .  FIG. 3   c  shows a side view of the point cloud  52  of a section of the orange  46 , comprised of successive profiles  50  of individual points  48 .  FIGS. 3   b  &amp;  3   c  illustrate raw 3D scan data comprised of successive X, Y profile scans incremented along the Z Axis. 
         [0052]      FIG. 4   a  shows a side view of the point cloud  52  of a section of the orange  46  wherein profile extrema  54  of selected points  48  for each profile  50  are highlighted with small thin circles.  FIG. 4   b  shows a side view of only the profile extrema  54  of the same section of the scanned orange  46 . 
         [0053]      FIG. 5   a  shows a side view of the profile extrema  54  of the section of the orange  46  scanned and selected cloud extrema  68  marked to denote their axis, namely X min  56  &amp; X max  58  by squares, Y min  60  &amp; Y max  62  by circles, and Z min  64  &amp; Z max  66  by triangles.  FIG. 5   b  shows a top view of the profile extrema  54  of the section of the orange  46  scanned and selected cloud extrema  68  as above. Also shown by broken lines in  FIG. 5   b  is a single profile  50  with its extrema  54  as illustrated in  FIG. 5   a  above. 
         [0054]      FIG. 6   a  shows an isometric photograph of an object  46  (pizza) being scanned by a laser beam  42  and highlighting its profile  50 .  FIG. 6   b  shows an isometric view of the point cloud  52  of a pizza  46  collated from single profile  50  scans and highlighting profile extrema  54 .  FIG. 6   c  shows a top view of the scan of a pizza  46  showing its profile extrema  54  and highlighting selected cloud extrema  68  as shown in  FIGS. 5   a/b . Also shown by broken lines is a single profile  50  with its extrema  54 . 
         [0055]      FIG. 7  shows an Extrema Derivation Chart employing the same extrema labeling legend as in cloud extrema  68 , namely X min  56  &amp; X max  58  show the extremes along the X axis, and Y min  60  &amp; Y max  62  show the extremes in the Y direction. 
         [0056]      FIG. 8   a  shows a dented section of corrugated pipe (object  46 ) being scanned by a laser beam  42  and forming its profile  50  as it crosses the dent  72 .  FIG. 8   b  shows a graph highlighting the moment when the scanner&#39;s internal information extraction module&#39;s calculations detect the dent  72  as a divergence  76  from the pipe&#39;s  46  nominal profile  74 . 
         [0057]      FIG. 9   a  shows an isometric photograph of a pile of loose woodchips (object  46 ) being scanned by a laser beam  42  and creating a profile  50 .  FIG. 9   b  shows an isometric view of the 3D point cloud  52  accumulated from the profile scans  50  of the woodchips  46 . Also shown is a software selectable region of interest (ROI) the horizontal rectangle ROI  78 . The controller by selecting an ROI thereby tells the scanner  12  to extract information, for transmission to the controller, only from scan data that is within the selected ROI. 
         [0058]      FIG. 10   a  shows a side view of the 3D point cloud  52  accumulated from the profile scans  50  of the woodchips  46 , and the horizontal rectangle ROI  78  in side view.  FIG. 10   b  shows a chart illustrating the profile area  80  summing of a single profile  50  of the woodchip  46  scan within a selected vertical ROI  82 , that rises from the horizontal rectangle ROI  78 . It is convenient to define rectangles as regions of interest in a Cartesian plane, but an ROI could be defined as any shape, such as a circle or elipse, in a plane, or a even a sphere or other 3D ROI within the scan zone. 
         [0059]      FIG. 11   a  shows a Venn diagram illustrating the core integration of the Profile extraction  84  and Decision Processing  86  aspects of 3D Machine Vision Scanning Information Extraction  10 . Profile extraction  84  of unmanageable raw scan data (point A) by means of information extraction module  70  (in which a set of information extraction tools (IET) is listed) is able to send a manageable amount of data (point B) in a CIP  32  compatible format within an EtherNet/IP  28  communication infrastructure to the controller  34 .  FIG. 11   b  shows an overview of some of the elements that are integrated into a 3D Machine Vision Scanning Information Extraction System  10 , including camera  16  &amp; sensor  38 , information extraction module  70  with the media above representing its set of embedded information extraction tools, workstation/PC interface  30 , decision processing  86  and laser projector  14 . 
         [0060]    The scanner  12  unit shown in  FIG. 2   a  is a fully sealed, industrial grade package that houses the laser projector  14  imaging system (camera  16 , sensor  38 ) and scan data processing electronics. The scanner  12  scans by having a laser emit coherent light that is refracted into a planar fan. The laser light fan reflects off a profile on the target, that is, off one slice of the surface of an object  46  at a time, the process being incrementally advanced along the Z axis for successive slices. Z coordinates are embedded in the scanner output  24 . Multiplexer/Encoder  26  card enables communication from scanners to the processor including timing synchronization so each scanner can be phase locked (preventing overlapping lasers), and allows several scanners to be multiplexed. TCP/IP used with CIP  32  (Common Industrial Protocol) is designated EtherNet/IP  28 . A point  48  is one laser projector  14  dot imaged by the sensor  38  and designated by a coordinate in the X, Y plane. (see  FIG. 2   b , A&amp;B) A profile  50  is a series of imaged points  48  in the X, Y plane, comprising a figurative imaging slice of the scanned object. (see  FIG. 3   c ) A cloud  52  (from point cloud) is a series of profiles  50  along the Z axis that comprises the entire 3D scan of that portion of the object  46  visible to the sensor  38  (within the ROI  82  &amp; above the horizontal rectangle ROI  78 .) 
         [0061]    The preferred embodiment of the 3D Machine Vision Scanning Information Extraction  10  will now be discussed. The novelty and advantage of the disclosed scanning system depends on the integration of three related aspects of its design, namely its 3D scanning process, information extraction tools, and decision processing application. Each aspect will be discussed separately and then as an integrated system. 
       3D Machine Vision Scanning: 
       [0062]    The 3D scanning process employed by the present invention is not the kind where a 2D image (X-Y plane intensity map) or “picture” of an object is captured and then stitched together with other images to form a “3D map” of an object. This method is not true 3D scanning, and has many drawbacks such as being limited to an “in focus plane” and requiring adequate external illumination to be able to scan accurately. Also an area camera (2d image processor) requires many kinds of information to perform optimally such as target distance, focal length, camera pixels, lighting variations, registration marks for orientation of objects, pixel mapping to infer geometric shapes, brightest/darkest spot metering, area calculation, and edge detection for different planes. Also, each vendor has specialized proprietary solutions that require engineering and optical expertise to process. Custom 3D design from 2D area camera input is expensive and requires much re-engineering and cross discipline expertise to implement. Some technicians try to use 2D area cameras to solve 3D problems, but the resulting systems are typically complex, finicky, error-prone, and operator-dependent, and are typically capable of performing simple 3D tasks such as finding the position of an object or bar code, rather than difficult 3D tasks such as mapping shape or extremes of points of shape. Ultimately, “2D” versions of “3D” derived from 2D are not a true form of 3D, too many inferences are required for useful output, and there is no connection to 3D coordinate systems for mapping onto other systems. 
         [0063]    The 3D scanning process employed by the present invention uses the method of laser triangulation to image the intersection of an object  46  and the reference laser beam  42  to generate X-Y profiles (or slices) that are then combined incrementally along the Z-axis into a 3D point cloud representation (XYZ). 3D laser triangulation works as follows: (see  FIG. 2   b ) A projected reference beam  42  hits a target (A,B), which is imaged on a sensor  38 , and distance to target can be computed by triangulation. Multiple simultaneous readings can deliver an X,Y profile  50  ( FIGS. 2   c ,  3   a ) and multiple profiles  50  can be combined to generate a “point” cloud  52 . ( FIG. 3   b ) 
         [0064]    The point cloud generated in  FIG. 3   b  is only one part of the entire object  46  (orange) being scanned. The scanner currently outputs up to 660 data-points/sec×200 scans/sec totaling 0.5M points/sec sent to a processor. To process this amount of data quickly requires a parallel PC stack with cooling &amp; large speedy computing power. (See  FIG. 1   a ) The PC interface is then employed in converting the scanner output into information that allows the controller to operate industrial machinery. In order for this step to work, the PC interface must give the controller only what information it needs to perform its functions, and in a timely fashion. 
         [0065]    A controller cannot process the point cloud, but it can perform limited operations depending on its onboard processing power and buffering capabilities. The controller is normally the interface between the wholesale data cloud and the retail operation and management of industrial machinery. Controllers permit many forms and formats of digital/analog input/output and can do some rudimentary calculation on input data. The controller must be able to perform its calculations and provide meaningful output within a loop that typically varies between 10 ms and 100 ms, so that the machinery can operate optimally. The point is that there is a short, finite period of time during which a controller must be presented with appropriate shape data and react to it. For example, if a pizza on a conveyor belt is detected as being too misshapen to be stacked properly in a freezer, a go or no-go decision among many must be made in time to allow an operator, whether human or automated mechanical, to take appropriate action. If a controller is presented with a massive data cloud from multiple scanner outputs and is stalled for example by taking a mere 100 ms to process the data in one of the above-noted loops in order to derive some actionable output—then the surrounding industrial process fails. 
         [0066]    In an industrial production environment, a scanner data to controller interface based system has an inherent bottleneck that can slow slowing the entire process to a halt. Meaningful extraction of key information from each scan profile is necessary for efficient controller operation, and is made possible by scan data pre-processing tools (IET) incorporated into the 3D scanner unit, and described next. 
       Profile Extraction: 
       [0067]    Extracting key information from profile (X-Y) scan data is the overall purpose of the information extraction tools (IET) embedded in the improved 3D machine vision scanner. IET software extracts selected information from each X-Y profile as required by the industrial process performed, and then transmits only this data in CIP format to the controller. IET allows direct interface with the controller, eliminating costly, time consuming and expertise-driven PC interface analysis &amp; processing. IET performs generic functions that condense or summarize data, yet are also configurable to each specific task. Information extraction tools include, but are not limited to the following methods: Extrema Derivation, Profile Tracking/Matching, Area Summing, Down-Sampling, and Multi-Region Scanning, and will now be described. 
       Extrema Derivation: 
       [0068]    Extrema are derived from 2D profile scans in order to assemble a manageable 3D dataset for rapid and accurate controller output. Of the 660 points available from each X-Y profile multiplied by a typical 200 scans generated every second, four key data points are selected: (X min, Y) (X max, Y) (X, Y min) (X, Y max). (see  FIG. 7 ) As demonstrated in  FIG. 4   a  the circled points are the extrema for each profile scan. The fourth point is not shown, but it is available as there is a coincidence of max and min at one point. In  FIG. 4   b , one can see that the data load on the controller now is much less than before. As is illustrated in  FIGS. 5   a  &amp;  5   b , one can extract cloud extrema from the profile extrema, but this is done by the controller, with industrial environment parameters such as Over/Under Height, Over/Under Width, sorting by size etc., are the only information that is required because the extracted data is optimal for efficient controller operation. Examples of the steps of extrema derivation are shown in  FIGS. 3   a  to  5   b  for a spherical orange, and  FIGS. 6   a  to  6   c  for a frozen pizza.  FIG. 7  shows graphically how extrema are derived from a profile scan. 
       Profile Tracking/Matching: 
       [0069]    Another method of profile data extraction employs detecting the difference from selected or nominal profile.  FIG. 8   a  shows a section of a corrugated pipe which has a dent. As the laser passes over the dent the profile detected shows a divergence from the nominal profile. This is illustrated graphically in  FIG. 8   b  which represents the onboard processing done to detect the dent. One may wish to detect divergence from within some range of tolerance for the existing profile, but the actual dimensions do not matter, or one may wish to detect whether the scanned profile matches a specific profile template. This method of data extraction can be utilized for any regular longitudinal shape such as plastic extrusions or rolled metal pipes 
       Area Summing: 
       [0070]    This method employs taking multiple cross sections (profiles) of a mass of aggregate elements such as woodchips, cereal, flour, ores, etc. As can be seen in  FIGS. 9   a  to  10   b , profiles are derived and then areas summed and added within the controller rather than the scan head, to generate a total estimated volume. The invention by providing key information from the scan head rather than massive scan point data to the controller allows the calculation by the controller of additional information that would be normally very difficult to obtain. An example would be automatically deriving moisture content when one knows how much an aggregate with variable water content weighs and its volume is calculated in real-time by the controller attached to the invention. Water content-critical applications such as baking preparation, cement-making, or freezing of baked goods for storage in a limited volume of freezer space require the operator to know how much water to add to his mix and the system enables the correct adding because the timely scan information provided by the present system allows the controller to tells the operator how much moisture is already in the mixture. 
       Down-Sampling: 
       [0071]    This data extraction method employs reducing the amount of output sent to the controller by reducing the number of points released from any profile sample. For example, a profile scan of 660 points can be reduced to 16 points transmitted to the controller. 
       Multi-Region Scanning: 
       [0072]    This method is employed when there are a discrete number of objects placed in specific known regions of a scan zone. For example, when scanning a conveyor belt of cookies, 3-5 cookies are measured at a time for diameter or height or shape. Extrema may be generated for each cookie and if any are defective they are removed. 
       Other Methods: 
       [0073]    Any methods that allow one to reduce the data from an X-Y profile may be employed if they are required to operate a controller. For example, in “web control” applications, such as the winding of fabric or carpet, edge tracking is necessary, but the full scan data of a large spool of material is unnecessary—only information from scanning the position of the edge of potentially wayward rolling material would be required to detect “spilling” beyond a range of rolling edge position tolerance The sooner a variance from the intended path is detected, the easier it would be to correct, so the edge of a carpet that is being rolled, for example, would be scanned and monitored not just at the spool itself but also along an extent of carpet edge that is yet to reach the spool. The ongoing edge position information would be fed to a process controller which could then take electronic steps to cause mechanical correction of the rolling process. 
         [0074]    The system can supply and apply IETs to data from a single profile or from a pre-determined fixed range or number of scans in the Z axis, or alternatively from a variable range of profiles in the Z axis. For example, it could be decided (by the controller) that the lowest point from 5,000 scans should be passed to the controller. The range can be selectable by the controller, or could be varied automatically based on scan information previously received from target objects in the scan zone. For example, the width of pizzas moving on a conveyor could be crucial to decisions about sorting. The efficient way to extract and pass the relevant information from the scan data would be to have the information extraction module in the scan head pass on only each pizza width, which can be determined only after assessing multiple profiles for each pizza. The range of such multiple profiles to be used to determine pizza width could be selected by working downward from the entirety of scan profiles of the first few pizzas in a batch to a mid-pizza range of profiles that invariably contained the widest part of the pizza. An apt information extraction tool selected by the controller is thus applied to scan data from a controller-selectable range of number of scan profiles. Resulting scan information is transmitted to the controller, before the information extraction tool is applied to the raw data of a subsequent range of scan profiles. 
       Decision Processing: 
       [0075]    Prior art solutions employing PC interfaces provided a workstation to select parameters for analysis and processing of raw scan data. 3D Machine Vision Scanning Information Extraction scanning eliminates the middleman, in that due to a significantly reduced data transfer, extraction parameters can be selected within the controller&#39;s application solutions. Selection and optimization of IETs is done via existing development tools for controller. (industrial application development environment JADE) Add-on profiles have been developed for the 3D Machine Vision Scanning Information Extraction System so that IETs can be selected within existing JADE tools. (Extrema, scan rate, selection parameters, etc.) 
       Connections: 
       [0076]    These can include an Interface with a TCP/IP stack or EtherNet/IP. Either can pass information to a controller. 
       Controllers: 
       [0077]    In the field of automated industrial control and in this Specification and the appended Claims, “controller” means a device that can be programmed to control industrial processes. Examples would be: a mainframe computer, a personal computer (PC), a Programmable Logic Controller (PLC), or a Programmable Automation Controller (PAC). 
         [0078]    A logical alternate embodiment of the 3D Machine Vision Scanning Information Extraction System is to apply IETs to data along the Z-axis, one scan profile at a time, or to a range of profiles if it is a range that would contain the desired scan information to be extracted from the data. Other embodiments are not ruled out or similar methods leading to the same result. 
         [0079]    Other advantages of using the 3D Machine Vision Scanning Information Extraction System over other methods or devices will now be described. 
         [0080]    An Integrated 3D scanner is a standard off-the-shelf component and may be used in this invention to provide the raw scan data. The. IETs functions to generate the key target object scan information in a standard output format to the controller so that it can digest the information and act quickly. The Integrated 3D scanner provides self-contained, integrated, non-contact, true 3D machine vision scanning Integrated illumination, imaging and processing. 
         [0081]    An advantage of using controllers such as PLCs and PACs is that they are industry standard to operate machinery and do not require highly customized programming. An advantage of allowing scan parameters to be selected with industry standard controller development tools is that alterations do not require a programmer, only someone familiar with the JADE controller development environment. 
         [0082]    IET within CIP removes complexity of 3D scanning &amp; control. IET&#39;s are generic and can be used for multiple industry applications because application decision processing is done by the programmable automation controller (PAC) or programmable logic controller (PLC). The application solution key information extraction from scan data is done in the scanner head but the kind of key information is selected with controller development application. Handing the information off via EtherNet/IP within CIP is a prime example for the invention, but the system would work with any open standard communication protocol. 
         [0083]    The IET process can extend beyond summaries of data points. For example, a scanner head is often required to be mounted in an industrial setting such that the scan head&#39;s X-Y-Z coordinates are not coincident with its industrial environment&#39;s X-Y-Z coordinates. For example, the scan head might be mounted to a pole adjacent to a conveyor belt, or if the scan head of the present invention is not aligned with and perpendicular to a selected region of interest in the scan zone. Besides the data reduction to key scan information, the computational electronics of the scanner head can perform transformational calculations to simplify matters for a common industrial controller. The information extraction module would thus perform orientation adjustment calculations on X and Y data points and pass orientation adjusted target object information to the controller. The orientation adjustment calculations could be rotation or translation calculations, or both, depending on the location orientation of the scan head&#39;s own coordinates with respect to the real world industrial environment (setting) coordinates in which the scan head is mounted and used. 
         [0084]    The system is resilient enough to be configured to scan anything available without requiring excessive programming knowledge or processing power. Anyone who understands the controller application environment can control the scanning process efficiently; they do not need to know what is going on inside because pre-processing (IET) permits a simpler smaller manageable dataset. 
         [0085]    The system of the present invention can be implemented with multiple scan heads mounted in different orientations that are synchronized in order to provide information from geographically opposed regions of interest on a target object. For example, IET regarding the shape of a log in a saw mill may require four scanners mounted on four corners of a frame through which the log is passed longitudinally. 
         [0086]    The foregoing description of the preferred apparatus and method of operation should be considered as illustrative only, and not limiting. Other data extraction techniques and other devices may be employed towards similar ends. Various changes and modifications will occur to those skilled in the art, without departing from the true scope of the invention as defined in the above disclosure, and the following general claims.