Methods for improving the accuracy of dimensioning-system measurements

• US 10,775,165 B2
• Filed: 10/02/2015
• Issued: 09/15/2020
• Est. Priority Date: 10/10/2014
• Status: Active Grant

• Current Assignee: Hand Held Products Incorporated (Honeywell International Inc.)
• Original Assignee: Hand Held Products Incorporated (Honeywell International Inc.)
• Inventors: McCloskey, Scott, Lloyd, Ryan Andrew, Li, Jingquan, Jovanovski, Brian L.
• Primary Examiner(s): Tran, Dzung
• Application Number:
  US14/873,613
• Publication Number:
  US 20160102975A1
• Time in Patent Office: 1,810 Days
• Field of Search: 702 97
• CPC Class Codes: G01B 11/00 Measuring arrangements characterised by the use of optical techniques
• CPC Class Codes: G01B 11/02 for measuring length, width or thickness G01B11/08 takes precedence
• CPC Class Codes: G01B 11/2504 Calibration devices
• CPC Class Codes: G01B 21/02 for measuring length, width, or thickness G01B21/10 takes precedence
• CPC Class Codes: G01B 21/045 Correction of measurements G01B9/02055 takes precedence

Abstract

Methods to improve the accuracy of non-contact measurements of an object'"'"'s dimensions using a dimensioning system are disclosed. The methods include a method for creating a mathematical model (i.e., error model) based on an observed correlation between errors in an estimated dimension and the characteristics of the measurement used to obtain the estimated dimension. These error models may be created for various dimensions and stored for future use. The methods also include a method for using the stored error models to reduce the error associated with a particular dimensioning-system measurement. Here an error model is used to create an estimated error. The estimated error is then removed from the estimate of the dimension to produce a final estimate of the dimension that is more accurate.

Claims

1. A method for removing errors from a dimensioning-system measurement, the method comprising:

gathering, using a dimensioning system, three-dimensional (3D) data corresponding to an object in an environment, comprising projecting a pattern onto the object, and capturing an image of the pattern on the object;

selecting a particular dimension of the object to be estimated;

obtaining values for one or more predictor variables, the predictor variables describing aspects of a dimensioning-system measurement of the particular dimension;

creating, using the 3D data gathered using the dimensioning system, an intermediate estimate of the particular dimension;

retrieving, by a processor, from a library of error models stored in memory, an error model corresponding to the particular dimension;

computing, by the processor, using the error model and the values for one or more predictor variables, an error estimate for the particular dimension; and

subtracting the error estimate from the intermediate estimate to obtain a final estimate of the particular dimension.

2. The method according to claim 1, wherein the aspects of the dimensioning-system measurement comprise intrinsic properties of the dimensioning system, the object, and/or the environment.

3. The method according to claim 2, wherein the intrinsic properties of the dimensioning system comprise the dimensioning-system'"'"'s acquisition parameters.

4. The method according to claim 2, wherein the intrinsic properties of the object comprise the object'"'"'s size, shape, and/or the object'"'"'s appearance.

5. The method according to claim 2, wherein the intrinsic properties of the environment comprise the light level of the environment.

6. The method according to claim 1, wherein the aspects of the dimensioning-system measurement comprise (i) a physical relationship between the dimensioning system and the object, (ii) a physical relationship between the dimensioning system and the environment, and/or (iii) a physical relationship between the object and the environment.

7. The method according to claim 1, wherein the error model comprises a linear equation relating the error estimate to the one or more predictor variables.

8. The method according to claim 1, wherein the error model comprises a non-linear equation relating the error estimate to the one or more predictor variables.

9. The method according to claim 1, wherein (i) the 3D data comprises a minimum-volume-bounding box (MVBB) and (ii) the particular dimension is the length, width, or height of the MVBB.

10. The method according to claim 9, wherein the steps of retrieving, computing, and subtracting are repeated to obtain final estimates for the length, width, and height of the minimum-volume-bounding box (MVBB).

11. The method according to claim 1, wherein the step of retrieving, comprises:

selecting a class of error models from the library of error models, wherein the class of error models corresponds to (i) a particular operating environment and/or (ii) a feature-set corresponding to the object, and retrieving, from the class of error models, an error model corresponding to the particular dimension.

12. A dimensioning system comprising:

a pattern projector configured to project a pattern onto an object in an environment;

a range camera configured to;

capture an image of the pattern on the object; and

gather three-dimensional (3D) data corresponding to the object;

a memory configured to store a library of error models; and

a processor configured to;

select a particular dimension of the object to be estimated;

obtain values for one or more predictor variables, the predictor variables describing aspects of a dimensioning system measurement of the particular dimension;

create, using the 3D data gathered by the range camera, an intermediate estimate of the particular dimension;

retrieve from the library of error models stored in the memory, an error model corresponding to the particular dimension;

compute using the error model and the values for one or more predictor variables, an error estimate for the particular dimension; and

subtract the error estimate from the intermediate estimate to obtain a final estimate of the particular dimension.

13. The dimensioning system of claim 12, wherein the aspects of the dimensioning-system measurement comprise intrinsic properties of the dimensioning system, the object, and/or the environment.

14. The dimensioning system of claim 13, wherein the intrinsic properties of the dimensioning system comprise the dimensioning-system'"'"'s acquisition parameters.

15. The dimensioning system of claim 13, wherein the intrinsic properties of the object comprise the object'"'"'s size, shape, and/or the object'"'"'s appearance.

16. The dimensioning system of claim 13, wherein the intrinsic properties of the environment comprise the light level of the environment.

17. The dimensioning system of claim 12, wherein the error model comprises a linear equation relating the error estimate to the one or more predictor variables.

18. The dimensioning system of claim 12, wherein the 3D data comprises a minimum-volume-bounding box (MVBB), and wherein the particular dimension is length, width, or height of the MVBB.

19. The dimensioning system of claim 18, wherein the processor is configured to repeat the steps of retrieving, computing, and subtracting to obtain final estimates for the length, width, and height of the MVBB.

20. The dimensioning system of claim 12, wherein the aspects of the dimensioning system measurement comprise:

a physical relationship between the dimensioning system and the object, a physical relationship between the dimensioning system and the environment, and/or a physical relationship between the object and the environment.