AUTOMATED METHOD OF AND SYSTEM FOR DIMENSIONING OBJECTS TRANSPORTED THROUGH A WORK ENVIRONMENT USING CONTOUR TRACING, VERTICE DETECTION, CORNER POINT DETECTION, AND CORNER POINT REDUCTION METHODS ON TWO-DIMENSIONAL RANGE DATA MAPS CAPTURED BY AN AMPLITUDE MODULATED LASER SCANNING BEAM

• US 6,705,526 B1
• Filed: 09/21/2000
• Issued: 03/16/2004
• Est. Priority Date: 12/18/1995
• Status: Expired due to Fees

• Current Assignee: Metrologic Instruments Incorporated (Honeywell International Inc.)
• Original Assignee: Metrologic Instruments Incorporated (Honeywell International Inc.)
• Inventors: Zhu, Xiaoxun, Good, Timothy A., Au, Ka Man, Blake, Robert E., Dickson, LeRoy, Rockstein, George B., Scott, Ian, Schnee, Michael, Groot, John, Wilz, David M. Sr., Germaine, Gennady, Knowles, Carl Harry
• Primary Examiner(s): LE, THIEN MINH
• Application Number:
  US09/667,190
• Time in Patent Office: 1,272 Days
• Field of Search: 235/462.01, 235/462.02, 235/462-3-4, 235/472.01, 235/472.02, 235/472.03, 235/454, 235/494, 235/375, 235/470
• US Class Current: 235/472.01
• CPC Class Codes: B82Y 15/00 Nanotechnology for interacting, sensing or actuating, e.g. quantum dots as markers in protein assays or molecular motors
• CPC Class Codes: G02B 26/106 having diffraction gratings as scanning elements, e.g. holographic scanners holographic optical elements G02B5/32, holography G03H
• CPC Class Codes: G06K 17/0022 arrangements or provisions for transferring data to distant stations, e.g. from a sensing device "transfer between computer elements G06F13/00 "; data-transmission H04L
• CPC Class Codes: G06K 2207/1013 Multi-focal
• CPC Class Codes: G06K 7/10 by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
• CPC Class Codes: G06K 7/10564 Light sources
• CPC Class Codes: G06K 7/10584 Source control
• CPC Class Codes: G06K 7/10594 Beam path
• CPC Class Codes: G06K 7/10603 Basic scanning using moving elements
• CPC Class Codes: G06K 7/10663 using hologram
• CPC Class Codes: G06K 7/10673 Parallel lines
• CPC Class Codes: G06K 7/10693 for omnidirectional scanning
• CPC Class Codes: G06K 7/10702 Particularities of propagating elements, e.g. lenses, mirrors G06K7/10831 takes precedence
• CPC Class Codes: G06K 7/10792 Special measures in relation to the object to be scanned
• CPC Class Codes: G06K 7/10801 Multidistance reading
• CPC Class Codes: G06K 7/10811 Focalisation
• CPC Class Codes: G06K 7/10851 Circuits for pulse shaping, amplifying, eliminating noise signals, checking the function of the sensing device basic electronic circuitry H03
• CPC Class Codes: G06K 7/10861 sensing of data fields affixed to objects or articles, e.g. coded labels postal sorting B07C3/14, conveying articles B65G47/48
• CPC Class Codes: G06K 7/10871 randomly oriented data-fields, code-marks therefore, e.g. concentric circles-code
• CPC Class Codes: G06K 7/10881 constructional details of hand-held scanners
• CPC Class Codes: G06K 7/10891 : the scanner to be worn on a finger or on a wrist
• CPC Class Codes: G06K 7/109 : adaptations to make the hand-held scanner useable as a fixed scanner
• CPC Class Codes: G06K 7/14 : using light without selection of wavelength, e.g. sensing reflected white light G06K7/10831 - G06K7/1097 take precedence
• CPC Class Codes: G07G 1/0054 : with control of supplementary check-parameters, e.g. weight or number of articles
• CPC Class Codes: H01L 21/28575 : on semiconductor bodies comprising AIIIBV compounds
• CPC Class Codes: H01L 29/452 : on AIII-BV compounds

Abstract

A fully automated package identification and measuring system, in which an omni-directional holographic scanning tunnel is used to read bar codes on packages entering the tunnel, while a package dimensioning subsystem is used to capture information about the package prior to entry into the tunnel. Mathematical models are created on a real-time basis for the geometry of the package and the position of the laser scanning beam used to read the bar code symbol thereon. The mathematical models are analyzed to determine if collected and queued package identification data is spatially and/or temporally correlated with package measurement data using vector-based ray-tracing methods, homogeneous transformations, and object-oriented decision logic so as to enable simultaneous tracking of multiple packages being transported through the scanning tunnel.

Claims

1. An automated object dimensioning system for dimensioning an object, comprising:

a system housing having a light transmission aperture, supportable within a work environment;

a laser beam production module disposed within said system housing, for generating an amplitude modulated laser beam;

a scanning mechanism, mounted within said system housing, for projecting said amplitude modulated laser beam through said light transmission aperture, and repeatedly scanning said amplitude modulated laser beam across a laser scanning field and an object being transported through said laser scanning field within said work environment;

a light collection mechanism, mounted within said system housing, for collecting laser light reflected off said scanned object and focusing said reflected laser light;

a photodetector, mounted within said system housing, for detecting said focused laser light and producing an electrical signal corresponding thereto;

a signal processor, disposed within said system housing, for processing said produced electrical signal and, during each scan of said amplitude modulated laser beam across said scanned object within said laser scanning field, generating a row of raw digital range data representative of the distance from said scanning mechanism to sampled points along said scanned object;

a preprocessing data buffer, disposed within said system housing, for buffering rows of raw digital range data produced by said signal processor; and

a programmed digital image processor, disposed within said system housing, for (1) receiving rows of raw digital range data from said preprocessing data buffer, (2) automatically processing said raw digital range data to produce a range data map representative of the object transported through and scanned within said laser scanning field, (3) buffering said range data map, (4) tracing contours within said buffered range data map, (5) detecting, from said traced contours, vertices associated with polygonal-shaped objects extracted from said range data map and corresponding to the scanned object, (6) detecting, from said vertices, a set of candidate corner points associated with the corners of the scanned object, (7) reducing said set of candidate corner points down to a set of corner points most likely to correspond to the corner points of a regular-shaped polygonal object corresponding to the scanned object, and (8) using said reduced set of corner points to compute dimension-related parameters of the scanned object.

2. The automated object dimensioning system of claim 1, wherein said distance is measured with respect to a polar-type coordinate system symbolically-embedded within said automated object dimensioning system.

3. The automated object dimensioning system of claim 1, wherein said programmed digital image processor comprises:

a first data processing stage for automatically processing said rows of raw digital range data in said preprocessing data buffer so as to produce said range data map representative of the scanned object;

a second data processing stage for automatically tracing contours within said buffered range data map, said traced contours being represented by a first set of indices (m,n) indicative of the scanned object;

a third data processing stage for automatically processing said first set of indices (m,n) associated with said traced contours so as to detect vertices associated with polygonal-shaped objects extracted from said range data map, said detected vertices being represented by a second set of indices (m,n) and indicative of polygonal-shaped objects corresponding to the scanned object;

a fourth data processing stage for automatically processing said second set of indices (m,n) associated with said detected vertices so as to detect a set of candidate corner points associated with the corners of a particular object being transported through said work environment, said candidate corner points being represented by a third set of indices, (m,n) and indicative of the corners of the scanned object;

a fifth data processing stage for automatically processing said third set of indices (m,n) associated with detected corner point candidates so as to reduce said set of candidate corner points down to a set of corner points most likely to correspond to the corner points of said regular-shaped polygonal object, said most likely set of candidate corner points being represented by a fourth set of indices (m,n) and indicative of the corners of said regular-shaped polygonal object which most likely corresponds to the scanned object; and

a sixth data processing stage for automatically processing said fourth set of indices (m,n) associated with the corner points of said regular-shaped polygonal object so as to compute said dimension-related parameters of the scanned object.

4. The automated object dimensioning system of claim 3, wherein said sixth data processing stage further comprises:

a first data processing substage for automatically processing said fourth set of indices (m,n) associated with the corner points of said regular-shaped polygonal object so as to compute the surface area of the object represented by said traced contours within said buffered range data map; and

a second data processing substage for automatically processing said fourth set of indices (m,n) associated with the corner points of said regular-shaped polygonal object so as to compute the average height of the object represented by said traced contours in said buffered range data map, referenced relative to a global Cartesian-type coordinate reference system symbolically embedded within said work environment.

5. The automated object dimensioning system of claim 4, wherein said sixth data processing stage further comprises:

a third data processing substage for automatically processing said fourth set of indices (m,n) associated with the corner points of said regular-shaped polygonal object so as to compute the x, y, z coordinates corresponding to the corners of the object represented by said traced contours in said buffered range data map, referenced relative to said global Cartesian-type coordinate reference system.

6. The automated object dimensioning system of claim 3, wherein said first data processing stage employs window-type convolution kernels that smooth and edge-detect said raw digital range data and thus improve its quality for subsequent dimension data extraction operations.

7. The automated object dimensioning system of claim 3, wherein said first data processing stage includes means for subtracting detected background information, including noise, from the continuously updated range data map so as to accommodate for changing background lighting conditions.

8. The automated object dimensioning system of claim 3, wherein said scanning mechanism comprises a polygonal-type laser scanning mechanism for scanning said amplitude modulated laser beam across said laser scanning field.

9. The automated object dimensioning system of claim 3, wherein said scanning mechanism comprises a holographic-type laser scanning mechanism for scanning said amplitude modulated laser beam across said laser scanning field.

10. The automated object dimensioning system of claim 3, wherein said photodetector comprises an avalanche-type photodetector.

11. The automated object dimensioning system of claim 1, wherein said object is a package.

12. The automated system of claim 1, wherein said dimension-related parameters include the height, width, and length of said scanned object.

19. The method of claim 12, wherein said dimension-related parameters include the height, width, and length of said scanned object.

13. An automated method of dimensioning an object, said method comprising the steps of:

(a) automatically generating an amplitude modulated laser beam from a system housing from which a laser scanning field extends within a work environment;

(b) automatically repeatedly scanning said amplitude modulated laser beam across said laser scanning field and an object being transported through said laser scanning field;

(c) automatically collecting laser light reflected off said scanned object and focusing said reflected laser light within said system housing;

(d) automatically detecting said focused laser light and producing an electrical signal corresponding thereto;

(e) automatically processing said produced electrical signal and, during each scan of said amplitude modulated laser beam across said scanned object, generating a row of raw digital range data representative of the distance from said scanning element to sampled points along said scanned object;

(f) automatically storing rows of raw digital range data in a preprocessing data buffer;

(g) automatically receiving rows of raw digital range data from said preprocessing data buffer;

(h) automatically processing said raw digital range data to produce a range data map representative of the scanned object;

(i) automatically tracing contours within said buffered range data map;

(j) automatically detecting, from said traced contours, vertices associated with polygonal-shaped objects extracted from said range data map and corresponding to the scanned object;

(k) automatically detecting, from said vertices, a set of candidate corner points associated with the corners of the scanned object;

(l) automatically reducing said set of candidate corner points down to a set of corner points most likely to correspond to the corner points of a regular-shaped polygonal object which corresponds to the scanned object; and

(m) using said reduced set of corner points;

to automatically compute dimension-related parameters of the scanned object.

14. The method of claim 13, wherein step (e) comprises automatically generating a row of raw digital range data representative of the distance from said scanning element to sampled points along said scanned object, measured with respect to a polar-type coordinate system symbolically-embedded within said system housing.

15. The method of claim 13, wherein step (f) further comprises

employing window-type convolution kernels that smooth and edge-detect said raw digital range data and thus improve its quality for subsequent dimension data extraction operations.

16. The method of claim 13, wherein step (f) further comprises

subtracting detected background information, including noise, from the continuously updated range data map as to accommodate for changing background lighting conditions.

17. The method of claim 13, wherein step (b) comprises using a holographic-type laser scanning mechanism for scanning said amplitude modulated laser beam across said laser scanning field.

18. The method of claim 13, wherein step (d) comprises using an avalanche-type photodetector.