METHODS FOR IMPROVING THE ACCURACY OF DIMENSIONING-SYSTEM MEASUREMENTS

• US 20160102975A1
• Filed: 10/02/2015
• Published: 04/14/2016
• 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.
• Granted Patent: US 10,775,165 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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:

providing a dimensioning system;

gathering, using a dimensioning-system measurement, three-dimensional (3D) data corresponding to an object in an environment;

selecting a particular dimension to be estimated;

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

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

retrieving, from a library of error models, an error model corresponding to the particular dimension;

computing, 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.

20. The method according to claim 11, wherein the feature is the object'"'"'s length, width, or height.

12. A method for creating an error model for a feature measured by a dimensioning system, the method comprising:

providing a dimensioning system and a calibration object, the calibration object having a feature with a known size;

gathering, using the dimensioning system, measurements of the feature;

calculating errors for each measurement by comparing each measurement to the known size;

defining predictor variables, wherein the predictor variables describe aspects of the measurements;

deriving a mathematical model to relate the predictor variables to an estimated error, wherein (i) the mathematical model includes predictor coefficients and (ii) each predictor coefficient corresponds to a particular predictor variable;

fitting the mathematical model to the errors by adjusting the predictor coefficients; and

refining the mathematical model to create an error model for the feature.

13. The method according to claim 12, further comprising storing the error model in memory for future use.

14. The method according to claim 12, wherein the mathematical model is (i) a linear combination of predictor variables and predictor coefficients or (ii) a nonlinear equation using predictor variables.

15. The method according to claim 12, wherein the aspects of the measurements comprise intrinsic properties of the dimensioning system, the object, and/or the object'"'"'s environment.

16. The method according to claim 12, wherein the aspects of the measurements comprise (i) a physical relationship between the dimensioning system and the object, (ii) a physical relationship between the dimensioning system and the object'"'"'s environment, and/or (iii) a physical relationship between the object and the object'"'"'s environment.

17. The method according to claim 12, wherein refining the mathematical model comprises removing insignificant predictor variables and their corresponding predictor coefficients.

18. The method according to claim 12, wherein the fitting the mathematical model comprises a linear regression.

19. The method according to claim 18, wherein the refining the mathematical model comprises:

obtaining residuals by comparing the estimated error to the errors;

creating a histogram of the residuals; and

rejecting or accepting the mathematical model based on the normality of the histogram.