Method and measuring assembly for determining the wheel or axle geometry of a vehicle

• US 8,836,764 B2
• Filed: 07/27/2009
• Issued: 09/16/2014
• Est. Priority Date: 09/17/2008
• Status: Active Grant

• Current Assignee: Beissbarth Automotive Testing Solutions GmbH
• Original Assignee: Robert Bosch GmbH
• Inventors: Gruetzmann, Andreas, Abraham, Steffen, Haja, Andreas, Knoll, Christian, Muhle, Daniel, Gebhard, Matthias
• Primary Examiner(s): Perungavoor, Sath V
• Assistant Examiner(s): Le, Peter D
• Application Number:
  US13/119,560
• Publication Number:
  US 20120026293A1
• Time in Patent Office: 1,877 Days
• US Class Current: 348/46
• CPC Class Codes: G01B 11/275 for testing wheel alignment
• CPC Class Codes: G01B 2210/20 Vehicle in a state of translatory motion
• CPC Class Codes: G01B 2210/26 Algorithms, instructions, databases, computerized methods and graphical user interfaces employed by a user in conjunction with the wheel aligner
• CPC Class Codes: G01B 2210/283 Beam projectors and related sensors

Abstract

In a method for determining a wheel or axle geometry of a vehicle, the following steps are provided: illuminating a wheel region with structured and with unstructured light during a motion of at least one wheel and/or of the vehicle; acquiring multiple images of the wheel region during the illumination, in order to create a three-dimensional surface model having surface parameters, a texture model having texture parameters, and a motion model having motion parameters of the sensed wheel region; calculating values for the surface parameters, the texture parameters, and the motion parameters using a variation computation as a function of the acquired images, in order to minimize a deviation of the three-dimensional surface model, texture model, and motion model from image data of the acquired images; and determining a rotation axis and/or a rotation center of the wheel as a function of the calculated values of the motion parameters.

Claims

1. A method for determining at least one of (a) a wheel and (b) an axle geometry of a vehicle, comprising:

illuminating a wheel region with structured and with unstructured light during a motion of at least one of (a) at least one wheel and (b) the vehicle;

acquiring multiple images of the wheel region during the illumination, in order to create a three-dimensional surface model having surface parameters, a texture model having texture parameters, and a motion model having motion parameters of the sensed wheel region;

calculating values for the surface parameters, the texture parameters, and the motion parameters using a variation computation as a function of the acquired images, in order to minimize a deviation of the three-dimensional surface model, texture model, and motion model from image data of the acquired images; and

determining at least one of (a) a rotation axis and (b) a rotation center of the wheel as a function of the calculated values of the motion parameters, wherein the variation computation includes;

minimizing a sum of a first, second, and third term, the first term encompassing a degree of deviation between acquired grayscale image data and a projection of texture parameters subjected to the motion model and mapped onto the three-dimensional surface model, the second term encompassing a degree of deviation between three-dimensional point clouds determined from the acquired images and three-dimensional surface parameters subjected to the motion model, and the third term encompassing a norm of the motion model that depends on a geometric transformation by the motion parameters and the kinematic parameters.

2. The method according to claim 1, further comprising:

illuminating the wheel region with structured and with unstructured light while at least one of (a) the vehicle and (b) wheel is stationary;

acquiring multiple images of the wheel region during illumination, in order to generate measurement image data of the wheel;

modifying the values of the motion parameters, with fixed values for the surface and texture parameters, using a variation computation, such that a deviation of the three-dimensional surface model, texture model, and motion model from the measurement image data becomes minimal; and

determining track and camber vectors, and normal and translation vectors, of the wheel as a function of the modified values of the motion parameters.

3. The method according to claim 1, wherein the images of the wheel region are acquired as at least one of (a) image data and (b) measurement image data by a stereo camera during the illumination with structured light, in order to determine three-dimensional point clouds by triangulation.

4. The method according to claim 1, wherein the images of the wheel region are acquired as at least one of (a) image data and (b) measurement image data during the illumination with unstructured light, in order to determine two-dimensional grayscale images.

5. The method according to claim 1, wherein the structured light encompasses at least one of (a) a regular light pattern, (b) a pattern of dots, and (c) ribbons of light.

6. The method according to claim 1, wherein a wheel rim runout of the wheel is ascertained from the rotation axis and the rotation center.

7. The method according to claim 1, wherein the first, second, and third term each having a weighting factor.

8. The method according to claim 1, wherein calculation of the values for the surface parameters, the texture parameters, and the motion parameters is accomplished in consideration of image data of multiple different wheels of a same axle of the vehicle.

9. The method according to claim 1, wherein a bodywork region adjacent to the wheel region is also sensed, and a segmentation is performed in terms of an affinity of an acquired image point with at least one of (a) the wheel and (b) the bodywork region.

10. The method according to claim 1, wherein a sum having a fourth term that encompasses a norm of a segmentation term that depends on texture parameters subjected to the motion model and mapped onto the three-dimensional surface model, and on the three-dimensional surface parameters subjected to the motion model.

11. The method according to claim 10, wherein a wheel housing upper edge is ascertained as a function of the segmentation in order to determine a ride height of the vehicle.

12. A measurement system, comprising:

an illumination device adapted to illuminate at least one wheel region of a vehicle with structured and with unstructured light;

at least one image acquisition device adapted to acquire at least one of (a) image data and (b) measurement image data of the wheel region; and

a control and evaluation device adapted to perform the method recited in claim 1.

13. A non-transitory computer-readable storage medium with an executable program stored thereon, wherein the program instructs a microprocessor to perform a method as recited in claim 1.