Articulated arm coordinate measurement machine that uses a 2D camera to determine 3D coordinates of smoothly continuous edge features

• US 9,879,976 B2
• Filed: 04/07/2017
• Issued: 01/30/2018
• Est. Priority Date: 01/20/2010
• Status: Active Grant

• Current Assignee: Faro Technologies Incorporated
• Original Assignee: Faro Technologies Incorporated
• Inventors: Bridges, Robert E., Yadav, Joydeep
• Primary Examiner(s): FULTON, CHRISTOPHER W
• Application Number:
  US15/481,673
• Publication Number:
  US 20170211927A1
• Time in Patent Office: 298 Days
• Field of Search: 33503
• CPC Class Codes: G01B 11/005 coordinate measuring machines
• CPC Class Codes: G01B 11/2509 Color coding
• CPC Class Codes: G01B 11/2513 with several lines being projected in more than one direction, e.g. grids, patterns
• CPC Class Codes: G01B 21/047 Accessories, e.g. for positioning, for tool-setting, for measuring probes
• CPC Class Codes: G01B 2210/58 Wireless transmission of information between a sensor or probe and a control or evaluation unit
• CPC Class Codes: G01B 5/008 using coordinate measuring machines
• CPC Class Codes: G01B 5/012 Contact-making feeler heads therefor
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/401 characterised by control arrangements for measuring, e.g. calibration and initialisation, measuring workpiece for machining purposes G05B19/19 takes precedence
• CPC Class Codes: G05B 2219/37193 Multicoordinate measuring system, machine, cmm
• CPC Class Codes: G05B 2219/40233 Portable robot
• CPC Class Codes: G05B 2219/45061 Measuring robot
• CPC Class Codes: G06T 7/13 Edge detection
• CPC Class Codes: H04N 13/204 using stereoscopic image cameras stereoscopic photography G03B35/00
• CPC Class Codes: H04N 13/221 using the relative movement between cameras and objects
• CPC Class Codes: H04N 23/50 Constructional details
• CPC Class Codes: H04N 23/51 Housings

Abstract

A portable articulated arm coordinate measuring machine (AACMM) having an integrated camera captures 2D images of an object at three or more different poses. A processor determines 3D coordinates of a smoothly continuous edge point of the object based at least in part on the captured 2D images and pose data provided by the AACMM.

Claims

1. A method of determining three-dimensional (3D) coordinates of an edge point of an object, comprising:

providing an articulated arm coordinate measuring machine (AACMM) that includes a base, a manually positionable arm portion having an opposed first end and second end, the arm portion being rotationally coupled to the base, the arm portion including a plurality of connected arm segments, each arm segment including at least one position transducer for producing a position signal, a first camera assembly coupled to the first end, an electronic circuit that receives the position signal from each of the at least one position transducer and provides data corresponding to a pose of the first camera assembly;

providing a processor;

in a first instance;

capturing with the first camera assembly in a first pose a first image of the object;

obtaining from the electronic circuit first data corresponding to the first pose;

in a second instance;

capturing with the first camera assembly in a second pose a second image of the object;

obtaining from the electronic circuit second data corresponding to the second pose;

in a third instance;

capturing with the first camera assembly in a third pose a third image of the object;

obtaining from the electronic circuit third data corresponding to the third pose;

determining with the processor the 3D coordinates of a first edge point, the first edge point being smoothly continuous within an interval of edge points, the 3D coordinates of the first edge point determined based at least in part on the first data, the second data, the third data, the first image, the second image, and the third image; and

storing the determine 3D coordinates of the first edge point.

2. The method of claim 1 wherein the first edge point is identified by the processor as an edge point based at least in part on pixel light levels in the first image.

3. The method of claim 2 wherein the first edge point is determined by an identification method selected from the group consisting of:

subpixel-edge-location-based-on-partial-area-effect technique, moment-based technique, least-squared-error-based technique, and interpolation technique.

4. The method of claim 2 wherein the 3D coordinates of the first edge point are further determined based on a smoothing of a first edge portion that contains the first edge point, the smoothing filter selected from the group that filters the first edge portion to more closely match a shape selected from the group consisting of:

a straight line, a circular curve, and a polynomial curve.

5. The method of claim 2 further including determining a position of the first edge point in each of the first image, the second image, and the third image based at least in part on epipolar geometry relating the first pose, the second pose, and the third pose of the first camera assembly.

6. The method of claim 5 further including checking whether the first edge point in the first image, the first edge point in the second image, and the first edge point in the third image, as determined based at least in part on epipolar geometry, are self-consistently determined to be edge points when evaluated based on pixel light levels in the first image, the second image, and the third image, respectively.

7. The method of claim 6 wherein, in response to a determined lack of self-consistency in the 3D coordinates of the first edge point, an action is selected from the group consisting of:

eliminating the first edge point from a list of determined edge points, filtering or smoothing the first edge to obtain consistency in the first edge point, and performing an optimization of the pose of the object in 3D space to obtain self-consistency in the first edge point.

8. The method of claim 2 further including adjusting with the processor the pose of a first object in 3D space to match edges in the first image, the second image, and the third image, wherein at least one of the matched edges includes the first edge point.

9. A method of determining three-dimensional (3D) coordinates of an edge point of an object, comprising:

providing an articulated arm coordinate measuring machine (AACMM) that includes a base, a manually positionable arm portion having an opposed first end and second end, the arm portion being rotationally coupled to the base, the arm portion including a plurality of connected arm segments, each arm segment including at least one position transducer for producing a position signal, a first camera assembly and a second camera assembly coupled to the first end, an electronic circuit that receives the position signal from each of the at least one position transducer and provides data corresponding to a pose of the first camera assembly and the second camera assembly;

providing a processor;

in a first instance;

capturing with the first camera assembly in a first pose a first image of the object;

obtaining from the electronic circuit first data corresponding to the first pose;

capturing with the second camera assembly in a second pose a second image of the object;

obtaining from the electronic circuit second data corresponding to the second pose;

in a second instance;

capturing with the first camera assembly in a third pose a third image of the object;

obtaining from the electronic circuit third data corresponding to the third pose;

determining with the processor the 3D coordinates of a first edge point, the first edge point being smoothly continuous within an interval of edge points, the 3D coordinates of the first edge point determined based at least in part on the first data, the second data, the third data, the first image, the second image, and the third image; and

storing the determine 3D coordinates of the first edge point.

10. The method of claim 9 wherein the first edge point is identified by the processor as an edge point based at least in part on pixel light levels in the first image.

11. The method of claim 10 wherein the first edge point is determined by an identification method selected from the group consisting of:

subpixel-edge-location-based-on-partial-area-effect technique, moment-based technique, least-squared-error-based technique, and interpolation technique.

12. The method of claim 10 further including determining a position of the first edge point in each of the first image, the second image, and the third image based at least in part on epipolar geometry relating the first pose, the second pose, and the third pose of the first camera assembly.

13. The method of claim 12 further including determining a position of the first edge point in each of the first image, the second image, and the third image based at least in part on epipolar geometry relating the first pose, the second pose, and the third pose of the first camera assembly.

14. The method of claim 13 further including checking whether the first edge point in the first image, the first edge point in the second image, and the first edge point in the third image, as determined based at least in part on epipolar geometry, are self-consistently determined to be edge points when evaluated based on pixel light levels in the first image, the second image, and the third image, respectively.

15. The method of claim 14 wherein, in response to a determined lack of self-consistency in the 3D coordinates of the first edge point, an action is selected from the group consisting of:

eliminating the first edge point from a list of determined edge points, filtering or smoothing the first edge to obtain consistency in the first edge point, and performing an optimization of the pose of the object in 3D space to obtain self-consistency in the first edge point.

16. The method of claim 10 further including adjusting with the processor the pose of a first object in 3D space to match edges in the first image, the second image, and the third image, wherein at least one of the matched edges includes the first edge point.