WO2006133982A1 - Verfahren zum bestimmen der rad- und/oder achsgeometrie von kraftfahrzeugen - Google Patents
Verfahren zum bestimmen der rad- und/oder achsgeometrie von kraftfahrzeugen Download PDFInfo
- Publication number
- WO2006133982A1 WO2006133982A1 PCT/EP2006/061521 EP2006061521W WO2006133982A1 WO 2006133982 A1 WO2006133982 A1 WO 2006133982A1 EP 2006061521 W EP2006061521 W EP 2006061521W WO 2006133982 A1 WO2006133982 A1 WO 2006133982A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- wheel
- features
- image
- point cloud
- determination
- Prior art date
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/26—Measuring arrangements characterised by the use of optical techniques for measuring angles or tapers; for testing the alignment of axes
- G01B11/275—Measuring arrangements characterised by the use of optical techniques for measuring angles or tapers; for testing the alignment of axes for testing wheel alignment
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B2210/00—Aspects not specifically covered by any group under G01B, e.g. of wheel alignment, caliper-like sensors
- G01B2210/10—Wheel alignment
- G01B2210/14—One or more cameras or other optical devices capable of acquiring a two-dimensional image
- G01B2210/146—Two or more cameras imaging the same area
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B2210/00—Aspects not specifically covered by any group under G01B, e.g. of wheel alignment, caliper-like sensors
- G01B2210/10—Wheel alignment
- G01B2210/20—Vehicle in a state of translatory motion
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B2210/00—Aspects not specifically covered by any group under G01B, e.g. of wheel alignment, caliper-like sensors
- G01B2210/10—Wheel alignment
- G01B2210/28—Beam projector and related sensors, camera, inclinometer or other active sensing or projecting device
- G01B2210/283—Beam projectors and related sensors
- G01B2210/286—Projecting a light pattern on the wheel or vehicle body
Definitions
- the invention relates to a method for determining the wheel and / or axle geometry of motor vehicles by means of an optical measuring device, in which - optionally taking into account reference features and body features - with an image pickup device comprising at least two Jardinaufiiahme wornen for generating digital images, a mutual Assignment of at least two recording locations and referencing made to the measuring room and from different perspectives an object section is detected with the wheel to be measured and the position of wheel features in the three-dimensional measuring space is evaluated in the measurement.
- the invention has for its object to provide a method of the type mentioned above, with the exact minimum adjustment effort and without additional adaptation effort for measurement marks accurate measurement results of the wheel and / or axle geometry are obtained.
- the wheel and / or axle geometry can thus be determined with the evaluation device, for example as specified in greater detail in the publications cited in the introduction.
- the Radmerkmal can exist either from a single 3D surface point or an averaging of several locally related 3D surface points or by a compensating function over several locally related 3D surface points obtained function parameters (eg maximum value or tendon, etc.) or from the surface model of locally related 3D surface points obtained function parameters.
- the wheel characteristics result from an analysis of 3D surface points in relation to a localized reference point, as a section line through a reference point or as a local area around a reference point.
- the reference points are used in the evaluation.
- the reference points can be specified with regard to the respective measurement requirement and evaluation characteristic for the wheel features (point, 2D function, 3D function / surface) in terms of number and position optimized, for example, depending on the vehicle or wheel type.
- the information from the 3D point cloud is used directly for extracting the wheel features, or an interface model first derives a surface model from the 3D point cloud and then from the surface model in a subsequent second step the wheel features are extracted.
- the surface model may e.g. be performed as a contour line model or as a regular grid or as a triangular mesh.
- further advantageous measures consist in that the at least one body feature used to determine the driving axis from the movement of the vehicle relative to the image recording devices and also the Wheel features of the wheel to be measured from the 3D surface structure of the captured image extracts are extracted.
- the determination of the wheel and / or axle geometry in the passage of the vehicle takes place on the image recording devices, wherein the movement of the vehicle is determined relative to the image recording devices and to the reference features on the basis of at least one body feature, and in that at least one wheel feature of the wheel to be measured is extracted from the surface structure of the detected object detail at several or all image pickup times in the pass-by.
- the wheel to be measured is checked for a rim impact in the evaluation device on the basis of the "rim impact" of at least three wheel features, of which at least one is significant, and this is optionally taken into account in the further evaluation.
- the extraction of these significant wheel features is carried out using the method already described for extracting wheel marks. From the significant wheel feature (e.g., valve), the angular position of the at least two other wheel features can be determined at each time of the measurement.
- the angular position refers strictly to the reference point of the relevant wheel feature.
- the process of referencing may take place prior to the actual measurement on the vehicle and also during the measurement, whereby due to the optical coverage by the vehicle may result in certain restrictions on visibility of the reference features. Despite the limited visibility during the measurement on the vehicle can be performed with the then still visible reference features a control of referencing during the measurement process.
- Another possibility for carrying out the method and obtaining features under difficult lighting or lighting conditions is that for obtaining the wheel features and / or the at least one body feature and / or the reference features pattern on the wheel , the body and / or projected into the measuring room by means of at least one projector.
- a simplified evaluation with a reduced amount of data is achieved in that a reduction of the data considered in the evaluation is performed by the pixels of non-interest, recorded image areas either before the determination of the 3D point cloud from the 2D pixels or after the determination of the 3D Point cloud can be excluded from the SD pixels.
- Fig. 1 shows an arrangement for determining the wheel and axle geometry from a view in
- FIG. 2 shows an arrangement according to FIG. 1 in a lateral view
- 3 shows a larger section with an arrangement according to FIGS. 1 and 2 in plan view
- FIG. 4 is a schematic representation in the partial image a) the determination of a pattern matrix A in a first digital image and in the partial image b) their positioning in a second digital image at different positions (1, 1) or (n, m),
- FIG. 1 shows an arrangement for determining the wheel and / or axle geometry of motor vehicles from a view in the vehicle longitudinal direction with a measuring head 1 taken up by way of example on a stand, for example comprising two image recording devices 2 in the form of cameras (see also FIG ) and an evaluation device 3.
- the field of view of the cameras is directed to the vehicle wheel 5 at least partially comprehensive object detail 6, wherein the fields of view of both cameras at least largely overlap.
- the coupled with a steering axis 9 vehicle wheel 5 is present on a arranged on the measuring station rotary plate 7.
- Fig. 2 shows the corresponding arrangement in a side view. As is apparent in particular from FIG. visually, the object section 6 is taken by the two cameras 2 from different perspectives.
- the mutual association of the recording locations of the image recording devices or cameras 2 with respect to position and direction (also called orientation) be known.
- the mutual association can be determined, for example, at the factory or at the test station (measuring station) by detecting a reference feature arrangement.
- Another object of the reference feature array is referencing, i. the establishment of the clear reference of the measuring device to the test station, e.g. clearly indicate the uprising or driving plane of the vehicle and / or a plane perpendicular to it and in this regard to determine the wheel and / or axle geometry, as also explained in the documents mentioned above.
- the mutual association of the recording locations of the image recording devices 2 and the referencing of the measuring device to the test station can be carried out independently of the actual vehicle measurement, that is, also be carried out in advance. If a reference feature arrangement has been specially installed for referencing on the test site or in the measuring room, this can be removed from the test site after the mutual assignment of the recording locations and referencing. This favors the detection of the wheel and axle geometry, because during the actual measurement no occlusion of the vehicle can occur and the measuring device can be optimized with regard to object detail, depth of focus and magnification. In addition, the acquisition and evaluation limited in this phase of the measurement only on the characteristics of the vehicle.
- the attachment and removal of a specially installed reference feature arrangement can be partially or even completely omitted by the surface geometry of the measuring system. recorded with the Schmaufiiahme wornen 2 Schmaufiiahmeanowski and advantageously determined by the method of image correlation, the surface geometry and obtained therefrom characteristic reference features in order to perform the assignment and / or referencing of the measuring device.
- a pattern matrix A is defined in the execution of the image correlation in the first two-dimensional digital image (partial image a)) and a (generally larger) search matrix B is defined in the second two-dimensional digital image (partial image b)).
- the correlation coefficients (r) are determined for all possible positions (n, m) of the pattern matrix within the search matrix.
- the maximum value of the correlation coefficient gives the most probable position of the pattern matrix A at the relevant location of the search matrix B.
- a 3D surface point is determined by methods known per se in photogrammetry. The sum of all SD surface points obtained in this way is called a 3D point cloud.
- a three-dimensional surface model with a predetermined or specifiable raster b can be derived, in addition to the possibilities already mentioned by way of example, as shown by way of example in FIG. 5, where x, y, z denote the spatial coordinates.
- a 2D compensation function is entered therein as a bold contour line KL, and a reference point BP for a wheel feature is also indicated.
- FIGS. 6A to 6D illustrate the process of image extraction up to the determination of the wheel position angles in the form of a flow chart, wherein various embodiments of the process are shown in FIGS. 6B to 6D. It can clearly be seen that the method of image correlation with regard to the evaluation method represents a significant difference to the methods of obtaining contour lines or edges which are also known in image processing on the basis of sudden gray value transitions in individually recorded two-dimensional images.
- the extraction of the relevant features does not take place in the two-dimensional image (as described for example in EP 0 895 056) but in the three-dimensional object space. This results u.a. significant advantages in terms of less interference from changing lighting or lighting conditions and the achievable accuracy under normal workshop conditions.
- the first and second two-dimensional digital image are recorded with the first and second camera in a first step Sil or S 12, respectively.
- the image correlation is then carried out and then, in a step S3, the 3D point cloud is determined.
- the evaluation device 3 queries whether an SD surface model is to be created. If the surface model is to be created, the 3D surface model is determined in steps S52 and S53 and the feature extraction is performed from the 3D surface model in the 3D space. If the query in step S4 indicates that no surface model is to be created, the feature extraction from the 3D point cloud in the SD object space is performed in a step S51. After the feature extraction, after the step S53 or S51, in a further step S6, the determination of the wheel plane and from this in a step S7 the determination of the wheel position angle, i. the angle between the respective wheel plane and the vehicle reference planes.
- a determination of the significant wheel characteristics and a definition of the characteristic of the wheel features (point, 2D Function, 3D function / surface).
- the significant radar times represents a clearly recognizable feature, such as the valve V, based on which then the remaining wheel features are clearly defined in their position.
- the characteristic of the wheel features can be suitably defined for optimal detection and evaluation.
- step S33 is provided, in which a query is made as to whether all 3D pixels are to be evaluated. If all the 3D pixels are to be evaluated, it goes to step S32, in which the characteristic of the wheel features is defined in accordance with Fig. 6B. If not all 3D pixels are to be evaluated, a further query is made in a step S34 as to whether the evaluation is to be limited radially. If a radial limitation is to be made, the number of 3D pixels is radially limited and thus reduced. Subsequently, the process goes to step S32.
- the number and location of the reference points BP is defined in a step S36 and then the number of 3D pixels reduced in a step S37 with local limitation around the reference points BP and then to step S32, followed by the further sequence shown in FIG. 6B or 6A expires.
- the radial limitation for reducing the number of 3D pixels is just one possibility, others would be e.g. a sector-by-sector boundary (see Figures 7C and 7D) or said local boundary around reference points.
- preprocessing with limiting the number of pixels may already be performed on the basis of the 2D images.
- preprocessing with limiting the number of pixels may already be performed on the basis of the 2D images.
- step S13 a determination of the significant wheel characteristics in the 2D image is made
- step S14 it is queried whether an evaluation of all 2D Pixels should be made. If this is the case, the process goes to step S2 of FIG. 6B, after which the further sequence of steps corresponding to FIG. 6B is carried out. If not all 2D pixels are to be evaluated, a query is made in a further step S 5 whether a radial limitation of the evaluation is to take place.
- FIGS. 7A to 7F show the collection of wheel features and the determination of the wheel plane E and of the wheel rotation axis D perpendicular thereto on the basis of a pictorial reproduction.
- a 3D point cloud of the wheel 5 is obtained, wherein the valve V can also be seen.
- the wheel features can then be extracted from the 3D point cloud, the valve V representing a significant, clearly recognizable wheel feature.
- the regions of the 3D point cloud to be evaluated are bounded radially or sectorally, with the middle rim region in FIG. 7C and individual sectors in FIG.
- FIGS. 7E and 7F show the determination of the normal vector of the wheel 5 and the direction of the axis of rotation D of the wheel and of the wheel plane E perpendicular thereto in three-dimensional space.
- wheel features of the outer contour of the sidewall of the tire are used here by way of example.
- the use of other acquired wheel features is also possible, including the combination of several different wheel features. While in Fig. 7E the obtained representation of the wheel 5 is shown obliquely to the plane, is shown in Fig. 7, the representation of the wheel rotated so that the axis of rotation D in the display plane and thus the wheel plane E is perpendicular to the plane of representation.
- the reference planes are determined from the detected reference features for referencing the image acquisition systems.
- self-checking of the referencing of the measuring system takes place based on reference features not covered by the vehicle.
- the wheel plane E is calculated from the recorded wheel characteristic len and the determined Radouswinkeln determined from the relation of the wheel plane E to the reference planes.
- the wheel plane E is determined from the sum of the at least one detected wheel feature and the at least one body feature from a plurality of images acquired during the passage.
- the resolution of the cameras 2 is insufficient to ensure a high measuring accuracy, the resolution can be increased by incorporating suitable methods of image processing known per se for evaluation in the sub-pixel range.
- the three-dimensional surface structure of the wheel 5 can be used, which is pronounced on the basis of characteristic components of the wheel such as tires, rim, hubcap or valve.
- the detection of the surface geometry is performed using an image processing method with image correlation, wherein an SD point cloud is determined from the object detail taken with a minimum of two cameras from different positions, which has a three-dimensional surface of the object structures present in the object detail with a multiplicity of 3D images. Spatial representation of pixels.
- a 3D surface model can be described using various methods, such as a contour line model, triangular meshing, or a regular mesh grid.
- characteristic body and wheel characteristics and also the reference features can be obtained.
- the symmetry of a wheel 5 and the rotation of each wheel point about the axis of rotation of the wheel facilitate a determination of the relevant wheel plane E and / or the wheel rotation axis D, which are required to mood track and camber.
- Significant surface features on the wheel such as valve, hole pattern, labeling, but also other features such as dirt and damage can be detected. This also enables the detection and consideration of a possible rim impact.
- the movement of the body relative to the measuring device and to the reference features is additionally determined.
- the determination of the 3D Point cloud or the 3D surface model on the surrounding the wheel body (wheel arch) extended. This corresponds to only a small change in the procedure, since usually the entire object section is detected three-dimensionally at least at the beginning of the measurement, because due to the different vehicle types and rim sizes, the wheel 5 to be measured has a not known from the outset position in the object space, but off the 3D structure of the entire recorded scene must be extracted.
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- Length Measuring Devices By Optical Means (AREA)
Abstract
Description
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Priority Applications (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2008505889A JP4617377B2 (ja) | 2005-04-15 | 2006-04-11 | 光学測定装置を用いた車両の車輪位置角度を求める方法 |
US11/909,386 US8254666B2 (en) | 2005-04-15 | 2006-04-11 | Method for the determination of the wheel geometry and/or axle geometry of motor vehicles |
DE502006001766T DE502006001766D1 (de) | 2005-04-15 | 2006-04-11 | Verfahren zum bestimmen der rad- und/oder achsgeometrie von kraftfahrzeugen |
EP06792451A EP1875164B1 (de) | 2005-04-15 | 2006-04-11 | Verfahren zum bestimmen der rad- und/oder achsgeometrie von kraftfahrzeugen |
CN2006800125890A CN101160505B (zh) | 2005-04-15 | 2006-04-11 | 用于确定机动车的车轮和/或轴几何特征的方法 |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102005017624A DE102005017624A1 (de) | 2005-04-15 | 2005-04-15 | Verfahren zum Bestimmen der Rad- und/oder Achsgeometrie von Kraftfahrzeugen |
DE102005017624.0 | 2005-04-15 |
Publications (1)
Publication Number | Publication Date |
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WO2006133982A1 true WO2006133982A1 (de) | 2006-12-21 |
Family
ID=36782271
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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PCT/EP2006/061521 WO2006133982A1 (de) | 2005-04-15 | 2006-04-11 | Verfahren zum bestimmen der rad- und/oder achsgeometrie von kraftfahrzeugen |
Country Status (6)
Country | Link |
---|---|
US (1) | US8254666B2 (de) |
EP (1) | EP1875164B1 (de) |
JP (1) | JP4617377B2 (de) |
CN (1) | CN101160505B (de) |
DE (2) | DE102005017624A1 (de) |
WO (1) | WO2006133982A1 (de) |
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- 2006-04-11 JP JP2008505889A patent/JP4617377B2/ja active Active
- 2006-04-11 CN CN2006800125890A patent/CN101160505B/zh active Active
- 2006-04-11 US US11/909,386 patent/US8254666B2/en active Active
- 2006-04-11 EP EP06792451A patent/EP1875164B1/de active Active
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Also Published As
Publication number | Publication date |
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CN101160505A (zh) | 2008-04-09 |
US20080319706A1 (en) | 2008-12-25 |
JP4617377B2 (ja) | 2011-01-26 |
CN101160505B (zh) | 2010-11-10 |
DE102005017624A1 (de) | 2006-10-19 |
DE502006001766D1 (de) | 2008-11-20 |
US8254666B2 (en) | 2012-08-28 |
JP2008536134A (ja) | 2008-09-04 |
EP1875164A1 (de) | 2008-01-09 |
EP1875164B1 (de) | 2008-10-08 |
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