초록 ▼

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. M

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.

대표청구항 ▼

1. An automated object dimensioning system for measuring the dimensions of a scanned object transported along a conveyor belt structure, comprising:a system housing having a light transmission aperture, supportable above a conveyor belt structure; an optical bench, disposed within said system housin

1. An automated object dimensioning system for measuring the dimensions of a scanned object transported along a conveyor belt structure, comprising:a system housing having a light transmission aperture, supportable above a conveyor belt structure; an optical bench, disposed within said system housing; a laser beam production module disposed within said system housing, for generating an amplitude modulated laser beam; a scanning mechanism, mounted on said optical bench, for projecting and said amplitude modulated laser beam through said light transmission aperture, and repeatedly scanning said amplitude modulated laser beam across the width of said conveyor belt structure; a light collecting mechanism, mounted within said system housing, for collecting laser light reflected off said scanned object and focusing said reflected laser light; a photodetector, mounted on said optical bench, for detecting said focused laser light and producing an electrical signal corresponding thereto; signal processing circuitry, 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, 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 automatic object dimensioning system; a preprocessing data buffer, disposed within said system housing, for storing rows of raw range data produced by said signal processing circuitry; and a programmed digital image processor, disposed within said system housing, for receiving rows of raw range data from said preprocessing data buffer, and processing data, including raw range data buffered in said preprocessing data buffer, wherein said programmed digital image processor includes a first data processing stage for automatically processing said rows of raw range data in said preprocessing data buffer so as to produce a range data map representative of the space above said conveyor belt structure, and 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 objects being transported along said conveyor belt structure; 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 objects being transported along said conveyor belt structure; 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 along said conveyor belt structure, said candidate corner points being represented by a third set of indices (m,n) and indicative of the corners of a particular object being transported along said conveyor belt structure; 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 a 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 a regular-shaped polygonal object which most likely corresponds to the particular object being transported along said conveyor belt structure; a sixth data processing stage for automatically processing said fourth set of indices (m,n) associated with the corner points of a regular-shaped polygonal object so as to compute dimension-related parameters of the object represented by said image contours traced within said buffered range data map. 2. The automated object dimensioning system of claim 1, 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 a 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 system housing. 3. The automated object dimensioning system of claim 2, 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. 4. The automated object dimensioning system of claim 1, wherein said first data processing stage employs window-type convolution kernals that smooth and edge-detect the raw range data and thus improve its quality for subsequent dimension data extraction operations.5. The automated object dimensioning system of claim 1, wherein said first data processing stage includes means for subtracting detected background information (including noise) from the continuously updated range data map as to accommodate for changing background lighting conditions.6. The automated object dimensioning system of claim 1, wherein said scanning mechanism comprises a polygonal-type laser scanning mechanism for scanning said amplitude modulated laser beam across the width of said conveyor belt structure.7. The automated object dimensioning system of claim 1, wherein said scanning mechanism comprises a holographic-type laser scanning mechanism for scanning said amplitude modulated laser beam across the width of said conveyor belt structure.8. The automated object dimensioning system of claim 1, wherein said photodetector comprises an avalanche-type photodetector mounted on said optical bench.9. The automated object dimensioning system of claim 1, wherein said regular-shaped polygonal object is a package.10. An automated method of measuring the dimensions of a scanned object transported along a conveyor belt structure, said method comprising the steps of:(a) generating an amplitude modulated laser beam from a system housing of an automatic object dimensioning system disposed above said conveyor belt structure; (b) automatically repeatedly scanning said amplitude modulated laser beam across the width of said conveyor belt structure; (c) automatically collecting laser light reflected off said scanned object and focusing said reflected laser light; (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, measured with respect to a polar-type coordinate system symbolically-embedded within said automatic object dimensioning system; (f) automatically providing a preprocessing data buffer for storing generated rows of raw range data; (g) automatically receiving, in said preprocessing data buffer, rows of said raw range data, and processing data, including raw range data; (h) automatically processing said rows of raw range data in said preprocessing data buffer so as to produce a range data map representative of the space above said conveyor belt structure, and (i) automatically tracing contours within said buffered range data map, said traced contours being represented by a first set of indices (m,n) indicative of objects being transported along said conveyor belt structure; (j) 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 objects being transported along said conveyor belt structure; (k) 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 along said conveyor belt structure, said candidate corner points being represented by a third set of indices (m,n) and indicative of the corners of a particular object being transported along said conveyor belt structure; (l) 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 a 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 a regular-shaped polygonal object which most likely corresponds to the particular object being transported along said conveyor belt structure; and (m) automatically processing said fourth set of indices (m,n) associated with the corner points of a regular-shaped polygonal object so as to compute dimension-related parameters of the object represented by said image contours traced within said buffered range data map. 11. The method of claim 10, wherein step (m) further comprises substeps(m)(1) automatically processing said fourth set of indices (m,n) associated with the corner points of a 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 (m)(2) 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 system housing. 12. The method of claim 10, which further comprises after step (l)(m)(3) 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 traced in said buffered range data map, referenced relative to said global Cartesian-type coordinate reference system. 13. The method of claim 10, wherein step (h) comprisesemploying window-type convolution kernals that smooth and edge-detect the raw range data and thus improve its quality for subsequent dimension data extraction operations. 14. The method of claim 10, wherein step (h) comprisessubtracting detected background information (including noise) from the continuously updated range data map as to accommodate for changing background lighting conditions. 15. The method of claim 10, wherein step (h) comprisesscanning said amplitude modulated laser beam across the width of said conveyor belt structure. 16. The method of claim 10, wherein step (b) comprises using a holographic-type laser scanning mechanism for scanning said amplitude modulated laser beam across the width of said conveyor belt structure.17. The method of claim 10, wherein step (d) comprises using an avalanche-type photodetector.