Methods, Systems and Apparatus for Segmenting and Dimensioning Objects

• US 20180053305A1
• Filed: 08/19/2016
• Published: 02/22/2018
• Est. Priority Date: 08/19/2016
• Status: Active Grant

• Current Assignee: Symbol Technologies, LLC (Zebra Technologies Corporation)
• Original Assignee: Symbol Technologies, LLC (Zebra Technologies Corporation)
• Inventors: Gu, Ye, Zhang, Yan, Fu, Bo, Williams, Jay J., O'Connell, Kevin J.
• Granted Patent: US 10,776,661 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/23213 with fixed number of clusters, e.g. K-means clustering
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 10/083 Shipping
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/174 involving the use of two or more images
• CPC Class Codes: G06T 7/55 from multiple images
• CPC Class Codes: G06T 7/60 Analysis of geometric attributes
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume
• CPC Class Codes: G06T 7/77 using statistical methods
• CPC Class Codes: G06T 7/90 Determination of colour characteristics
• CPC Class Codes: G06T 7/97 Determining parameters from multiple pictures depth or shape recovery from multiple images G06T7/55; stereo camera calibration G06T7/85
• CPC Class Codes: G06V 10/56 relating to colour
• CPC Class Codes: G06V 10/763 Non-hierarchical techniques, e.g. based on statistics of modelling distributions
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 2201/06 Recognition of objects for industrial automation

Abstract

Methods, systems, and apparatus for segmenting and dimensioning objects are disclosed. An example method disclosed herein includes determining a first sensor of a plurality of sensors toward which a vehicle is moving based on image data generating by the plurality of sensors; designating the first sensor as a reference sensor; combining the image data from the plurality of sensors to generate combined image data representative of the vehicle and an object carried by the vehicle, the combining based on reference sensor; generating a plurality of clusters based on the combined image data; and identifying a first one of the clusters nearest the reference sensor as the object.

Claims

1. A method comprising:

determining, using a logic circuit, a first sensor of a plurality of sensors toward which a vehicle is moving based on image data generating by the plurality of sensors, the image data representative of the vehicle and an object carried by the vehicle;

designating the first sensor as a reference sensor;

combining, using the logic circuit, the image data from the plurality of sensors to generate combined image data representative of the vehicle and the object carried by the vehicle, the combining based on the reference sensor;

generating, using the logic circuit, a plurality of clusters based on the combined image data; and

identifying, using the logic circuit, a first one of the clusters nearest the reference sensor as the object.

2. A method as defined in claim 1, further comprising segmenting the first one of the clusters from a second one of the clusters by removing the second one of the clusters from the combined image data.

3. A method as defined in claim 2, further comprising dimensioning the first one of the clusters.

4. A method as defined in claim 1, further comprising:

identifying a first structure of the vehicle nearer to the reference sensor than other structures of the vehicle; and

removing points in the combined image data corresponding to the first structure of the vehicle.

5. A method as defined in claim 4, wherein:

identifying the first structure of the vehicle comprises determining a color of a front assembly of the vehicle; and

removing the points in the combined image data corresponding to the first structure of the vehicle comprises determining that the points have the determined color of the front assembly of the vehicle.

6. A method as defined in claim 5, wherein:

the vehicle is a forklift; and

the front assembly of the forklift is a carrying portion of the forklift on which the object is carried.

7. A method as defined in claim 1, wherein combining the image data to generate the combined image data comprises transforming the image data into a coordinate system of the reference sensor.

8. A tangible machine-readable medium comprising instructions that, when executed, cause a machine to at least:

determine a first sensor of a plurality of sensors toward which a vehicle is moving based on image data generating by the plurality of sensors, the image data representative of the vehicle and an object carried by the vehicle;

designate the first sensor as a reference sensor;

combine the image data from the plurality of sensors to generate combined image data representative of the vehicle and the object carried by the vehicle, the combining based on the reference sensor;

generate a plurality of clusters based on the combined image data; and

identify a first one of the clusters nearest the reference sensor as the object.

9. A tangible machine-readable medium as defined in claim 8, wherein the instructions, when executed, cause the machine to segment the first one of the clusters from a second one of the clusters by removing the second one of the clusters from the combined image data.

10. A tangible machine-readable medium as defined in claim 9, wherein the instructions, when executed, cause the machine to dimension the first one of the clusters.

11. A tangible machine-readable medium as defined in claim 8, wherein the instructions, when executed, cause the machine to:

identify a first structure of the vehicle nearer to the reference sensor than other structures of the vehicle; and

remove points in the combined image data corresponding to the first structure of the vehicle.

12. A tangible machine-readable medium as defined in claim 11, wherein the instructions, when executed, cause the machine to:

identify the first structure of the vehicle by determining a color of a front assembly of the vehicle; and

remove the points in the combined image data corresponding to the first structure of the vehicle by determining that the points have the determined color of the front assembly of the vehicle.

13. A tangible machine-readable medium as defined in claim 12, wherein:

the vehicle is a forklift; and

the front assembly of the forklift is a carrying portion of the forklift on which the object is carried.

14. A tangible machine-readable medium as defined in claim 8, wherein the instructions, when executed, cause the machine to combine the image to generate the combined image data by transforming the image data into a coordinate system of the reference sensor.

15. An apparatus comprising:

a reference setter to;

determine a first sensor of a plurality of sensors toward which a vehicle is moving based on image data generating by the plurality of sensors, the image data representative of the vehicle and an object carried by the vehicle;

designate the first sensor as a reference sensor; and

combine the image data from the plurality of sensors to generate combined image data representative of the vehicle and the object carried by the vehicle, the combining based on the reference sensor; and

a freight analyzer to;

generate a plurality of clusters based on the combined image data; and

identify a first one of the clusters nearest the reference sensor as the object, wherein at least one of the reference setter or the freight analyzer is implemented via a logic circuit.

16. An apparatus as defined in claim 15, wherein the freight analyzer is to segment the first one of the clusters from a second one of the clusters by removing the second one of the clusters from the combined image data.

17. An apparatus as defined in claim 16, wherein the freight analyzer is to dimension the first one of the clusters.

18. An apparatus as defined in claim 15, wherein the freight analyzer is to:

identify a first portion of the vehicle nearest to the reference sensor; and

remove points in the combined image data corresponding to the first portion of the vehicle.

19. An apparatus as defined in claim 18, wherein the freight analyzer is to:

identify the first portion of the vehicle by determining a color of a front assembly of the vehicle; and

remove the points in the combined image data corresponding to the first portion of the vehicle by determining that the points have the determined color of the front assembly of the vehicle.

20. An apparatus as defined in claim 19, wherein:

the vehicle is a forklift; and

the front assembly of the forklift is a carrying portion of the forklift on which the object is carried.

21. (canceled)

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26. (canceled)