Method and systems for characterizing geometric parameters of an external surface of a tire or a track

Abstract

A system and a method for characterizing geometric parameters of an external surface of a tire or a track are provided. The method includes accessing a three- dimensional (3D) point cloud comprising a plurality of data points representative of the external surface, fitting a reference surface to a subset of data points in the 3D point cloud, the subset of data points being representative of one of a lowermost or an uppermost portion of the external surface of the tire or track, measuring distances between the 3D point cloud and the reference surface, and analyzing the measured distances to characterize the geometric parameters of the external surface.

Description

[0001] METHOD AND SYSTEMS FOR CHARACTERIZING GEOMETRIC PARAMETERS OF AN EXTERNAL SURFACE OF A TIRE OR A TRACK

[0002] CROSS-REFERENCE TO RELATED APPLICATION

[0003]

[0001] The present application claims the benefit of, and priority to, US Provisional Patent Application no. 63 / 702,400, filed October 2, 2024, and entitled “METHOD AND SYSTEMS FOR CHARACTERIZING GEOMETRIC PARAMETERS OF AN EXTERNAL SURFACE OF A TIRE OR A TRACK”, the entirety of which is incorporated herein by reference.

[0004] TECHNICAL FIELD

[0005]

[0002] The technical field generally relates to tire or track inspection, and more particularly to methods and systems for characterizing geometric parameters of an external surface of a tire or a track.

[0006] BACKGROUND

[0007]

[0002] Certain vehicles, including industrial vehicles, military vehicles, agricultural vehicles, construction vehicles, snowmobiles, and all-terrain vehicles (ATVs), for example, may be equipped with tire or track systems to enhance their traction or floatation on soft, slippery and / or irregular grounds (e.g., mud, sand, snow, etc.) on which they operate. Usage and environmental factors may contribute to different wear patterns of these tire or track systems. For example, deformation, sliding, and ground conditions can cause uneven wear on treads and / or “chunking” thereof. Further factors causing uneven wear include high vehicle loads, and geometric adjustments to tire suspension. Uneven wear can be difficult to characterize. As an example, FIG. 19A shows a tire having heel / toe wear, and FIG 19B shows a track having a cupping wear. Uneven wear in all directions can occur, making cross-sectional analysis methods ineffective. The heavy loads of trucks and the geometric adjustments of car tire suspensions also generate different wear

[0008] 021908-0149 patterns that are poorly characterized by simply measuring wear with a depth gauge.

[0009]

[0003] There is therefore a need for an inspection system and method that is suitable for characterizing geometric parameters of tires or track with uneven wear.

[0010] SUMMARY

[0011]

[0005] In a first aspect, the present technology provides a computer-implemented method for characterizing geometric parameters of an external surface of a tire or a track. The method includes accessing a three-dimensional (3D) point cloud comprising a plurality of data points representative of the external surface, fitting a reference surface to a first subset of data points in the 3D point cloud, the first subset of data points being representative of one of a lowermost or an uppermost portion of the external surface of the tire or track, measuring distances between the 3D point cloud and the reference surface and analyzing the measured distances to characterize the geometric parameters of the external surface.

[0012]

[0006] In a second aspect, the present technology provides a system for characterizing geometric parameters of an external surface of a tire or a track. The system includes a controller and a memory storing a plurality of executable instructions which, when executed by the controller, cause the system to access a three-dimensional (3D) point cloud comprising a plurality of data points representative of the external surface, fit a reference surface to a first subset of data points in the 3D point cloud, the first subset of data points being representative of one of a lowermost or an uppermost portion of the external surface of the tire or track, measure distances between the 3D point cloud and the reference surface and analyze the measured distances to characterize the geometric parameters of the external surface.

[0013] 021908-0149 BRIEF DESCRIPTION OF THE DRAWINGS

[0014]

[0007] These and other features, aspects and advantages of the present technology will be better understood with regard to the following description, appended claims and accompanying drawings where:

[0015]

[0008] FIG. 1 is a flowchart showing operations of a method for characterizing geometric parameters of an external surface of a tire or a track in accordance with an implementation of the present technology.

[0016]

[0009] FIG. 2 is an exemplary three-dimensional (3D) point cloud of a tire under inspection.

[0017]

[0010] FIG. 3 is a representation of a reference surface fit to the 3D point cloud of FIG. 2 in accordance with an implementation of the present technology.

[0018]

[0011] FIG. 4A is a representation of an initial reference surface fit to all data points in the point cloud of FIG. 2, in accordance with an implementation of the present technology. FIG. 4B is a histogram representing a distribution of the data points according to their measured distances with respected to the initial reference surface.

[0019]

[0012] FIG. 5A is a representation of the initial reference surface fit to all data points in the point cloud of FIG. 2, with an initial subset of data points corresponding to the carcass being selected. FIG. 5B is a histogram representing a distribution of the data points according to their measured distances with respected to the initial reference surface.

[0020]

[0013] FIG. 6A is a representation of a 3D point cloud corresponding to the initial subset of data points. FIG. 6B is a representation of a subsequent iteration of the reference surface that is fit to the initial subset of data points, in accordance with an implementation of the present technology.

[0021]

[0014] FIG. 7A is a representation of 3D point cloud corresponding to a final reduced subset of carcass data points, and a final refined surface fit thereto in

[0022] 021908-0149 accordance with an implementation of the present technology. FIG. 7B is a histogram representing a distribution of the final reduced subset of carcass data points according to their measured distances with respected to the final refined surface.

[0023]

[0015] FIG. 8 is a representation of an augmented 3D point cloud in which each data point is color-coded with its measured distance from reference surface.

[0024]

[0016] FIG. 9A is a representation of another augmented 3D point cloud in which each data point is color-coded with its measured distance from reference surface. FIG. 9B is a histogram illustrating a distribution of distance measurements of the augmented 3D point cloud of FIG. 9A.

[0025]

[0017] FIG. 10 is a schematic illustrating a subprocess for determining whether a data point corresponds to a side surface, in accordance with an implementation of the present technology.

[0026]

[0018] FIG. 11 A is a representation of the augmented 3D point cloud of FIG. 9A with data point corresponds to side surfaces removed. FIG. 11 B is a histogram illustrating a distribution of distance measurements of the augmented 3D point cloud of FIG. 11A.

[0027]

[0019] FIG. 12 is a schematic representation of an electronic device configured for executing a method for characterizing geometric parameters of an external surface of a tire or a track, in accordance with an implementation of the present technology.

[0028]

[0020] FIG. 13A is another exemplary 3D point cloud of a tire under inspection. FIG. 13B is a representation of a reference surface fit to data points representative of tread top surfaces of the 3D point cloud of FIG. 13B, in accordance with an implementation of the present technology.

[0029]

[0021] FIG. 14A is a representation of an augmented 3D point cloud generated from the point cloud and reference surface of FIGS. 13A and 13B, in which each

[0030] 021908-0149 data point is color-coded with its measured distance from reference surface. FIG. 14B is a histogram illustrating a distribution of distance measurements of the augmented 3D point cloud of FIG. 14A.

[0031]

[0022] FIGS. 15 to 17 illustrate iterations of fitting a reference surface to data points representative of tread top surfaces.

[0032]

[0023] FIG. 18 includes a representation of 3D point cloud corresponding to a final reduced subset of tread top surface data points, a final refined surface fit thereto in accordance with an implementation of the present technology, and a histogram representing a distribution of the final reduced subset of data points according to their measured distances with respected to the final refined surface.

[0033]

[0024] FIG. 19A is an image of a tire having a heel / toe wear, and FIG 19B is an image of a tire having a cupping wear.

[0034]

[0025] FIG. 20A is a representation of a 3D point cloud, and FIG. 20B is a representation of the 3D point cloud of FIG. 20A with a reference surface and an additional surface fit thereon.

[0035]

[0026] FIG. 21 A is a representation of a structure of interest in a 3D point cloud, and FIG. 21 B is a representation of segments extracted from the reference surface and the additional surface corresponding to the structure of interest.

[0036]

[0027] FIG. 22A is a legend of color-codes for indicating tread wear according to a manufacturer recommendation, and FIG. 22B is a 3D point cloud with data points color-coded according to the legend of FIG. 22B.

[0037] DETAILED DESCRIPTION

[0038]

[0028] In the following description, the same numerical references refer to similar elements. Furthermore, for the sake of simplicity and clarity, namely so as to not unduly burden the figures with several references numbers, not all figures contain references to all the components and features, and references to some components and features may be found in only one figure, and components and

[0039] 021908-0149 features of the present disclosure which are illustrated in other figures can be easily inferred therefrom. The implementations, geometrical configurations, materials mentioned and / or dimensions shown in the figures are optional, and are given for exemplification purposes only.

[0040]

[0029] Moreover, it will be appreciated that positional descriptions such as "above", "below", "forward", "rearward", "left", "right" and the like should, unless otherwise indicated, be taken in the context of the figures only and should not be considered limiting.

[0041]

[0030] To provide a more concise description, some of the quantitative expressions given herein may be qualified with the term "about". It is understood that whether the term "about" is used explicitly or not, every quantity given herein is meant to refer to an actual given value, and it is also meant to refer to the approximation to such given value that would reasonably be inferred based on the ordinary skill in the art, including approximations due to the experimental and / or measurement conditions for such given value.

[0042]

[0031] In the following description, an implementation is an example or implementation. The various appearances of "one implementation", "an implementation" or "some implementations" do not necessarily all refer to the same implementations. Although various features may be described in the context of a single implementation, the features may also be provided separately or in any suitable combination. Conversely, although the invention may be described herein in the context of separate implementations for clarity, it may also be implemented in a single implementation. Reference in the specification to "some implementations", "an implementation", "one implementation" or "other implementations" means that a particular feature, structure, or characteristic described in connection with the implementations is included in at least some implementations, but not necessarily all implementations.

[0043]

[0032] It is to be understood that the phraseology and terminology employed herein is not to be construed as limiting and are for descriptive purpose only. The

[0044] 021908-0149 principles and uses of the teachings of the present disclosure may be better understood with reference to the accompanying description, figures and examples. It is to be understood that the details set forth herein do not construe a limitation to an application of the disclosure.

[0045]

[0033] Furthermore, it is to be understood that the disclosure can be carried out or practiced in various ways and that the disclosure can be implemented in implementations other than the ones outlined in the description above. It is to be understood that the terms "including", "comprising", and grammatical variants thereof do not preclude the addition of one or more components, features, steps, or integers or groups thereof and that the terms are to be construed as specifying components, features, steps or integers. If the specification or claims refer to "an additional" element, that does not preclude there being more than one of the additional element. It is to be understood that where the claims or specification refer to "a" or "an" element, such reference does not mean that there is only one of that element. It is to be understood that where the specification states that a component, feature, structure, or characteristic "may", "might", "can" or "could" be included, that particular component, feature, structure, or characteristic is not required to be included.

[0046]

[0034] The descriptions, examples, methods and materials presented in the claims and the specification are not to be construed as limiting but rather as illustrative only. Meanings of technical and scientific terms used herein are to be commonly understood as by one of ordinary skill in the art to which the invention belongs, unless otherwise defined. It will be appreciated that the methods described herein may be performed in the described order, or in any suitable order.

[0047]

[0035] It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present. Other words used

[0048] 021908-0149 to describe the relationship between elements should be interpreted in a like fashion (e.g., "between" versus "directly between," "adjacent" versus "directly adjacent," etc.).

[0049]

[0036] The functions of the various elements shown in the figures, including any functional element labeled as a "processor", may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. In some implementations of the present technology, the processor may be a general purpose processor, such as a central processing unit (CPU) or a processor dedicated to a specific purpose, such as a digital signal processor (DSP). Moreover, explicit use of the term a "processor" should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read-only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and / or custom, may also be included.

[0050]

[0037] Software modules, or simply modules which are implied to be software, may be represented herein as any combination of flowchart elements or other elements indicating performance of process operations and / or textual description. Such modules may be executed by hardware that is expressly or implicitly shown. Moreover, it should be understood that module may include for example, but without being limitative, computer program logic, computer program instructions, software, stack, firmware, hardware circuitry or a combination thereof which provides the required capabilities.

[0051]

[0038] Software modules, or simply modules or units which are implied to be software, may be represented herein as any combination of flowchart elements or other elements indicating performance of process operations and / or textual

[0052] 021908-0149 description. Such modules may be executed by hardware that is expressly or implicitly shown, the hardware being adapted to (made to, designed to, or configured to) execute the modules. Moreover, it should be understood that module may include for example, but without being limitative, computer program logic, computer program instructions, software, stack, firmware, hardware circuitry or a combination thereof which provides the required capabilities.

[0053]

[0039] With these fundamentals in place, we will now consider some examples to illustrate various implementations of aspects of the present technology.

[0054]

[0040] The technology presented herein relates to characterizing geometric parameters of an external surface of a tire or track. As can be appreciated, the external surface of a tire or track may define grooves and treads. In the context of the present disclosure, a groove refers to a recessed or indented channel or area formed on the surface of the tire or track. These channels may, for example, be arranged along the circumference or across the surface of the tire or track, designed to facilitate the evacuation of water, snow, or other debris from the tire surface. The groove depth may vary depending on its placement on the tire surface, and its walls may form specific angles with the tire surface, such as perpendicular, inclined, or curved surfaces. Also in the context of the present disclosure, a tread refers to a raised portion of the tire surface, e.g., located between adjacent grooves, that typically comes into direct contact with the road during operation. For example, a tread may be made up of raised segments between the grooves, providing the necessary friction and grip for vehicle stability. The pattern of the tread is often designed to optimize performance characteristics such as traction, durability, noise reduction, and resistance to wear.

[0055]

[0041] In the described implementations, the geometric parameters that are characterized correspond to tread height, thus allowing qualifying and / or quantifying wear of the tire or track. It is appreciated, however, that in other implementations, the techniques described herein can be used to characterize other geometric parameters, such as groove depth, track shape, etc., to

[0056] 021908-0149 qual ify / quantify wear of the tire or track, and / or to qualify / quantify other parameters of the tire or track.

[0057]

[0042] Broadly described, in accordance with a possible implementation, the technology involves accessing a 3D point cloud representing an external surface of a tire or track and analyzing the 3D point cloud data to measure tread height. The 3D point cloud can be generated using any suitable optical scanning device, such as a specialized laser scanner, or more generic sensors such as photographic sensors and / or depth sensors of smartphone devices. Postprocessing of the 3D point cloud can be carried out to remove noise, extract objects of interest, and align the point cloud in a 3D coordinate system (e.g., X-Y-Z axes). The tread height can be measured from the 3D point cloud by fitting a reference surface representative of a carcass of the tire or track, and measuring distances between each data point in the 3D point cloud and the reference surface. Statistical analysis can be carried out on the measured distances of data points for the entire tire or track and / or regions of interest thereof to characterize tread height distribution and identify abnormal wear or flat spotting, for example.

[0058]

[0043] In more detail now, and with reference to FIG. 1 , a method 100 for characterizing geometric parameters of an external surface of a tire or a track is shown according to an exemplary implementation of the present technology. As can be appreciated, the method 100, its operations and / or subprocesses may be performed by a processor of a computer system. In some embodiments, a computer-readable storage medium, such as a non-transitory mass storage device, can be provided with instructions stored thereon which, when executed by a processor, cause the processor to carry out the method 100, its operations and / or subprocesses. Although a particular sequence of operations will be described, it is appreciated that some operations or portions of operations may be omitted or reordered.

[0059]

[0044] The method 100 includes a first operation 110 of accessing a three- dimensional (3D) point cloud comprising a plurality of data points representative of

[0060] 021908-0149 the external surface of a tire or track. In some embodiments, the 3D point cloud can be accessed by reading a file or data structure comprising the data points from persistent storage or other memory. In some embodiments, the 3D point cloud can be accessed from local memory of the computer system performing the method. In other embodiments. The 3D point cloud can be accessed remotely, for example from cloud storage. In some implementations, the method 100 can include preliminary operations for generating the 3D point cloud, for example based on images captured by an imaging or scanning device. For example, the 3D point cloud may be generated using different techniques such as laser scan, photogrammetry, true-depth, time-of-flight, or any other suitable technique.

[0061]

[0045] Subsequent operations of the method 100 will be described in connection with an exemplary 3D point cloud 200 as shown in FIG. 2. The illustrated point cloud 200 is representative of an external surface of a tire. Although a point cloud corresponding to a tire is shown, it should be understood that this is for illustrative purposes only, and that the described method 100 can also apply to a track.

[0062]

[0046] The 3D point cloud 200 includes a plurality of data points 201 representing an outer shape or a median shape of the external surface of the tire. Each data point 201 is defined by 3D coordinates with respect to a coordinate system (e.g., X, Y and Z coordinates). The 3D coordinates of a data point may represent an absolute position of the point in the coordinate system and / or a position relative to another data point.

[0063]

[0047] As depicted on FIG. 2, the 3D point cloud 200 defines different tire surface structures including groove areas 210, tread areas 220, and / or carcass areas 230 corresponding to grooves, treads, and / or the carcass of the external surface of the tire. It should be understood that the tire to be inspected may have a different shape, may have a different tread pattern, and / or may comprise different structures. The configuration of the illustrated point cloud should therefore not be taken as limitative.

[0064] 021908-0149

[0048] In the present implementation, post-processing of the 3D point cloud has been carried out to remove noise, extract objects of interest, and align the point cloud in the 3D coordinate system. The noise was removed by applying a denoising routine to the 3D point cloud 200 to remove outlier data points. The denoising routine may include denoising techniques such as, without limitation, marching cubes, Statistical Outlier Removal, Radius Outlier Removal, etc. Additionally or alternatively, the 3D point cloud may be denoised by, without limitation, color based denoising in various color spaces (RGB, Hue-Saturation- Lightness (HSL), Hue-Saturation-Value (HSV), etc.). In the present embodiment, the objects of interest are the surface of the tire and its corresponding structures, such as the treads and grooves. The objects of interest were extracted by removing undesirable structures, for example by discarding data points representative of side surfaces of the tire and / or data points corresponding to the environment.

[0065]

[0049] A subsequent operation 120 of the method 100 includes generating, relative to the 3D point cloud, a reference surface that corresponds to a carcass of the tire such as the data points representative of the grooves, the lowest points of the grooves, or “tread bases”. The reference surface can be generated by fitting a suitable shape in the 3D point cloud, and more specifically by fitting the shape to a subset of data points in the 3D point cloud that is representative of the carcass. An exemplary 3D reference surface 300 is illustrated in FIG. 3. As can be appreciated, the reference surface 300 may be generated to represent a median shape of the external surface of the tire and / or approximates an outer shape of the external surface. As can be further appreciated, the reference surface can be generated to represent different external surfaces of the tire depending on the measurements to be taken in order to characterize different geometric parameters. Moreover, in some implementations, more than one reference surface can be generated to allow taking different measurements. As such, broadly speaking, it can be said that the operation 120 includes fitting one or more reference surfaces to one or more subsets of data points in the 3D point cloud, the one or more subsets of data points being representative of one or more relevant surfaces of reference for a given measurement. In some implementations, the relevant surface

[0066] 021908-0149 of reference can correspond to a lowermost (e.g., carcass or tread bases) or an uppermost (e.g., tread tops) portion of the external surface of the tire.

[0067]

[0050] The configuration of the surface that is fit to the data points can be selected according to the shape of the object under inspection. For example, a parametric surface with sufficient compliance can be selected to accommodate to the shape of the object under inspection. In the present embodiment, a polynomial surface of order n=4 is selected to accommodate the toroidal shape of the tire. It is appreciated that other surface configurations can be selected for different types of objects. For example, in embodiments where a flat track is being inspected, a polynomial surface having a lower order, such as n=2, can be selected. Although a polynomial surface is provided as an example, it is appreciated that other parametric surfaces of any order can be provided (e.g. superellipsoid surfaces). Any suitable best-fit algorithm can be applied to fit the surface to the data points. For example, a shape and / or a position of the reference surface 300 can be adjusted, such as by iteratively adjusting coefficients of the polynomial surface, to fit the reference surface 300 as closely and as accurately as possible to the relevant data points of the 3D point cloud 200.

[0068]

[0051] In some implementations, the reference surface can be fit to the integrality of 3D point cloud, such that the surface can be fit over a maximum scanned portion of the tire to reduce distortions. In some implementation, a region-of-interest (ROI) in the 3D point cloud can be identified (e.g., corresponding to a segment along the surface of the tire), and the reference surface can be fit to the data points located within the ROI.

[0069]

[0052] In some implementations, the reference surface is iteratively adjusted to fit as closely and as accurately as possible to the carcass (e.g. data points representative of the grooves, or “tread bases”) of the tire. Initially, the surface can be roughly fit to all data points in the 3D point cloud, and an initial subset of data points representative of the tread bases can be selected (e.g., by discriminating between tread data point and carcass data points based on their position relative

[0070] 021908-0149 to the roughly fit surface). Next, a plurality of iterations can be carried out to refine the fit of the surface to the carcass. At each iteration, the subset of data points representative of the carcass can be reduced by excluding outlier data points, and the reference surface can be re-fit to the reduced subset of data points. Iterations can be repeated until a precision target or other stopping criterion, such as a predefined maximum number of iterations, is reached.

[0071]

[0053] In more detail now, an exemplary initial reference surface 300’ roughly fit to all data points in the 3D point cloud is shown in FIG. 4A. For each data point in the 3D point cloud, a shortest distance (e.g., shortest orthogonal distance) between the data point and the initial reference surface 300’ can be calculated (e.g., using any suitable cloud-to-cloud or cloud-to-model distance calculation algorithm). As can be appreciated, a first subset of data points 201 a will be located below the surface 300’, whereas a second subset of data points 201 b will be located above the surface 300’. As illustrated in the histogram of FIG. 4B, the data points 201 a below the surface 300’ may have a negative signed distance relative to the surface 300’, and the data points 201 b above the surface 300’ may have a positive signed distance. In the present implementation, and as illustrated in FIGS. 5A and 5B, an initial subset of data points representative of the carcass 230’ is selected as corresponding to the subset of data points 201 a below the surface 300’ and having a negative signed distance relative to the surface 300’. It is appreciated that in other implementations, the initial subset of data points 230’ can be selected in a different manner. For example, the initial subset of data points 230’ can be selected as corresponding to the data points 201a below the surface 300’, and data points 201 b that are within a predetermined distance above the surface 300’.

[0072]

[0054] As shown in FIGS. 6A and 6B, once the initial subset of data points 230’ has been selected, the surface can be re-fit to the subset of data points 230’ to generate a subsequent iteration of surface 300” that is a closer fit to the carcass. Further iterations can be carried out to reduce the subset of data point 230’ by excluding outliers and to generate further, more refined iterations of the surface. For example, once the new iteration of surface 300” is generated, the subset of

[0073] 021908-0149 data points 230’ will comprise a new first subset of data points 201a’ below the surface 300” and a new second subset of data points 201 b’ above the surface 300”. The same will be true for each subsequent subset of data points to which a new iteration of surface is re-fit. At each iteration in the present implementation, the subset of data points 230’ is reduced by trimming (i.e. excluding from the subset of data points 230’) data points 201 b’ above the surface 300”. As can be appreciated, the data points 201 b’ can be trimmed in different ways. In some configurations, all the data points 201b’ above the surface 300” can be removed / excluded from the subset of data points 230’. In some configurations, the trimming of data points 201 b’ above the surface 300” can be sized to avoid removing a large number of data points belonging to the carcass that may result in a less accurate fit. For example, a predetermined maximum number of data points to be trimmed at each iteration can be defined (such as a maximum of 15% of all data points), and the trimming can be sized such that only the furthest data points 201 b’ above the surface 300” are removed / excluded up to the predetermined maximum. As another example, a distribution of the subset of data points 203’ can be calculated according to their distance, and the trimming can be sized according to statistical measures of the distribution, such as by trimming all data points 201 b’ above the surface 300” having a distance that is greater than one standard deviation.

[0074]

[0055] Once the subset of data points 230’ is reduced, a subsequent iteration of the surface can be re-fit to the reduced set of data points. This process can be repeated for a plurality n iterations, until a final reference surface 300nis obtained by fitting to a final reduced subset of carcass data points 203n, as illustrated in FIG. 7A. As can be appreciated, the number n of iterations performed can vary depending on how many iterations are required to achieve a desired precision target of the final reference surface 300n. As an example, a plurality n of iterations can be performed until one or more stopping criteria are reached. In some implementations, the stopping criteria can comprise a predefined maximum number of iterations. In some implementations, the stopping criteria can be based on one or more statistical measures of the distribution of distances of the carcass

[0075] 021908-0149 data point 203nto the final reference surface 300n. For example, the stopping criteria can comprise the distribution of absolute distances of the carcass data point 203nhaving an average, standard deviation, variance, and / or maximum that is below a predetermined threshold. In the present embodiment, and as illustrated in FIG. 7B, the stopping criteria were reached after four iterations, and the final reference surface 300nwas generated by a best fit on the lowest 6.5% of the data points which were all located within 0.01 distance units from the final reference surface 300n.

[0076]

[0056] Referring back to FIG. 1 , once the final reference surface 300nis fit, a subsequent operation 130 of method 100 can include measuring distances between the 3D point cloud 200 and the final reference surface 300n. In the present implementation, the distances between the 3D point cloud and the final reference surface are measured by measuring distances between all the data points of the 3D point cloud 200 and the final reference surface 300n. The distances between the data points and the final reference surface 300ncan be measured in the same way as described above, for example by computing the shortest orthogonal distance between each data point and the final reference surface 300nusing any suitable cloud-to-cloud or cloud-to-model distance calculation algorithm.

[0077]

[0057] In a subsequent operation 140, the measured distances can be analyzed to characterize the geometric parameters of the external surface of the tire. More specifically, statistical analysis can be carried out on the measured distances to examine distributions of the distances and extract statistical parameters such as maximum, minimum, average, median, range, etc. of distances of population of data points and / or subpopulations thereof to determine whether such statistical parameters are in a nominal range or are indicative of potential anomalies in the geometry of the tire (e.g. a geometry of a tread thereof).

[0078]

[0058] As can be appreciated, the measured distances can be analyzed using different techniques. In some implementations, the measured distances can be analyzed in conjunction with other 3D point cloud data. For example, in the present

[0079] 021908-0149 implementation, the measured distances are new scalar values that are used to augment the 3D point cloud 200. More specifically, the 3D point cloud 200 can be augmented by associating each of the data points in the 3D point cloud with its corresponding measured distance to the final reference surface 300n. In this fashion, an augmented 3D point cloud is generated in which each data point comprises 3D coordinates (e.g., X, Y and Z coordinates), plus a scalar value D that corresponds to the distance between the data point and the reference surface 300n. An exemplary augmented 3D point cloud 200’ is illustrated in FIG. 8 in which each data point is color-coded with its corresponding scalar value D. Analyzing the measured distance can therefore involve analyzing the augmented 3D point cloud 200’.

[0080]

[0059] In some configurations, the entirety of the augmented 3D point cloud can be analyzed to characterize the tire as a whole. In some configurations, the 3D point cloud can be subdivided into distinct zones or regions of interest (ROIs) for analysis. For example, in the implementation illustrated in FIG. 8, three distinct zones are defined: a first zone 250a corresponds to an outer edge of the tire, a second zone 250b corresponds to a center of the tire, and a third zone 250c corresponds to the inner edge of the tire. Analysis of the 3D point cloud data points can be carried out separately in each zone individually, thus allowing characterizing the geometric parameters (such as wear) in each zone separately. In some implementations, the boundaries of the zones or ROIs can be defined to exclude certain data points from the analysis, such as data points from irrelevant portions of edges (e.g., outermost edges or sidewalls of the tires), or sprocket holes of construction tracks. It is appreciated that the 3D point cloud can be subdivided in other ways for the purposes of analysis. For example, in some implementations, data points may be clustered into one or more clusters, with each cluster corresponding to a given tread. The clusters can be analyzed individually, for example, to characterize individual treads separately. As another example, an analysis of the 3D point cloud data can be carried out to detect grooves, and ROIs can be defined in zones around the identified grooves.

[0081] 021908-0149

[0060] With reference to FIGS. 9A and 9B, analysis of an augmented 3D point cloud 200’ to characterize tread height is shown according to an exemplary implementation. In the illustrated implementation, the distribution 900 of distance measurements (i.e. , scalar value D) is established by grouping or binning the data points of the augmented 3D point cloud 200’ into discrete intervals or bins (e.g., to generate a histogram as illustrated in FIG. 9B). The binned data (and / or the histogram) is then analyzed to calculate the maximum tread height, minimum tread height, average tread height, standard deviation of tread height, and / or other statistical parameters. Such statistical parameters can be indicative of the level of wear of the tire treads. For example, problematic tread wear can be identified if the calculated maximum tread height, minimum tread height, and / or median tread height are below a predetermined threshold. Nominal tread wear can be identified if the calculated statistical parameters are within predetermined acceptable ranges.

[0082]

[0061] The statistical parameters representative of tread height can be calculated using any suitable technique. In the present implementation, the maximum tread height can be determined according to the distance measurement corresponding to a predetermined rank 901 of the distribution 900, such as the 95thpercentile or the 98thpercentile. The minimum tread height can be estimated by calculating a slope of the data points and identifying a distance measurement 905 corresponding to a maximum slope. The median tread height can be calculated from a subpopulation of data points between the minimum 905 and maximum 901 and / or by identifying one or more peaks between the minimum 905 and maximum 901. In some implementations, a horizontal caliper may be determined and set to identify data points or bins of the histogram with low value counts, that may be considered as outliers of the augmented 3D point cloud (e.g. noise, data points belonging to a background of the object, etc.) that can be disregarded from the analysis. For example, a horizontal caliper may be set at an average height of the bins of the histogram, or at half-maximum value of the highest bin of the histogram, among others.

[0083] 021908-0149

[0062] In some implementations, analysis of the augmented 3D point cloud 200’ can be improved by excluding therefrom data points corresponding to side surfaces of treads. As can be appreciated, data points corresponding to tread sides can act as white noise between distinct distributions of data points corresponding to tread surfaces and to the tire carcass. Removing the tread side data points can make it easier to isolate the distributions of data points corresponding to tread surfaces and to the tire carcass, respectively, and calculate statistical parameters therefrom. Identifying and removing data points corresponding to side surfaces of treads can be carried out in different manners. For example, in some implementations, data points corresponding to side surfaces of treads can first be identified based on the local normals of data points in relation to the final reference surface 300n. Such identified data points can subsequently be removed from the 3D point cloud 200’ and / or excluded from subsequent analysis.

[0084]

[0063] In more detail now, and with reference to FIG. 10, an exemplary subprocess for determining whether a data point 605 corresponds to a side surface of a tread is shown according to an exemplary implementation. It should be noted that FIG. 10 appears to be a 2D curve merely for clarity and simplicity purposes and that the reasoning and description related to FIG. 10 also applies in a 3D environment. It can be said that FIG. 10 illustrate a cross section of the 3D point cloud 200’.

[0085]

[0064] While a single data point 605 will be described, it is appreciated that the subprocess can be repeated for all data points in the point cloud 200’ and / or for all data points within a predetermined range of distances. In this implementation, a local normal 620 of the data point 605 and a corresponding normal 610 of the reference surface 300nare calculated and compared. As can be appreciated, the normals 610 and 620 can be calculated in different manners. For example, a surface model 601 can be fit to the 3D point cloud 200, and the local normal 620 of the data point 605 can be calculated relative to the surface model 601 . In some implementations, the surface model 601 can be fit to the entire 3D point cloud 200, whereas in other implementations, the surface model 601 can be a local surface

[0086] 021908-0149 model that is fit to a region of the 3D point cloud 200 that extends a predetermined distance around the data point 605. The normal of their reference surface 610 can be calculated at a position corresponding to the data point 605, for example by drawing a line that extends between the data pint 605 and intersects the reference surface 300nat a right angle.

[0087]

[0065] Once the normals 610 and 620 are calculated, they can be compared to determine whether the data point 605 is determined to correspond to a tread side. For example, the data point 605 can be determined as corresponding to a tread side if the normals 610 and 620 differ by a predetermined threshold, such as by more than 30°, or more than 45°, for example. In the present implementation, an angle between the normals a is calculated. In response to the angle a being higher than an angle threshold (e.g. 30°), the data point 605 can be identified as belonging to tread side surfaces, and removed from the point cloud 200’ and / or excluded from subsequent analysis. In response to the angle a being lower than the angle threshold, the data point 605 can be identified as not belonging to tread side surfaces (e.g., the data point corresponds to the tire carcass or tread top surfaces), and can be maintained in the point cloud 200’ and / or included in subsequent analysis.

[0088]

[0066] FIG. 11 A illustrates a 3D point cloud 200” for which data points belong to tread side surfaces have been removed, and FIG. 11 B illustrates the distribution of distance measurements 900’ of the data points thereof. As can be appreciated, the removal of tread side data points can result in a first distribution 900’a of data points corresponding to the tire carcass, and a second distribution 900’b of data points corresponding to the tire treads. The distributions 900’a, 900’b can be analyzed separately to facilitate and / or increase the accuracy of characterizing different geometric parameters. For example, in the present implementation, the maximum tread height can be determined according to the distance measurement corresponding to a predetermined rank 901 of the second distribution 900’b, such as the 95thpercentile or the 98thpercentile. The minimum tread height can be determined according to the distance measurement corresponding to a

[0089] 021908-0149 predetermined rank 907 of the second distribution 900’b, such as the 2ndpercentile or the 5thpercentile.

[0090]

[0067] In alternative implementation, the present technology may be used in a similar manner to determine depth of grooves instead of height of tread of an external surface of a tire. As a person skilled in the art may appreciate, the depth of grooves can be measured relative to a reference surface representative of the top of treads instead of a reference surface representative of the carcass and / or tread bases. An illustrative 3D point cloud 1300 of an external surface of a tire comprising grooves is shown on FIG. 13A. As the reader of the present disclosure may readily understand, the geometric parameters of the 3D point cloud 1300 can be characterized by analyzing depth of grooves, in a similar manner as the 3D point cloud 200 described above was characterized through analysis of the height of treads.

[0091]

[0066] In this case, a reference surface 1310 may be fit to data points representative of tread top surfaces of the tire as shown in FIG. 13B. FIG. 14A shows the augmented 3D point cloud 1320, where each data point has been augmented by associating its relative distance to the reference surface 1310. FIG. 14B is a histogram of the data points of the augmented 3D point cloud 1320.

[0092]

[0067] In this implementation, the reference surface 1310 may be iteratively adjusted in a similar manner as the reference surface 300. At each iteration, the reference surface 1310 can be re-fit to the subset of data points to generate a subsequent iteration of surface that is a closer fit to the tread top surface. Further iterations can be carried out to reduce the subset of data point by excluding outliers and to generate further, more refined iterations of surface. Each of FIGs. 15 to 17 shows an iteration of the adjustment of the reference surface 1310. For example, once a new iteration of the reference surface is generated, the subset of data points will include a new first subset of data points below the newly generated reference surface and a new second subset of data points above the newly generated reference surface. The same will be true for each subsequent subset of

[0093] 021908-0149 data points to which a new iteration of surface is re-fit. At each iteration in the present implementation, the subset of data points is reduced by trimming (i.e. excluding from the subset of data points) data points below the newly generated reference surface. As can be appreciated, the data points can be trimmed in different ways. In some configurations, all the data points below the newly generated reference surface can be removed / excluded from the subset of data points.

[0094]

[0068] As can be appreciated, the number n of iterations performed can vary depending on how many iterations are required to achieve a desired precision target of the final surface 1310nshown on FIG. 18. As an example, a plurality n of iterations can be performed until one or more stopping criteria are reached. Once the final reference surface 1310nis fit, distances between each data point of the 3D point cloud 1300 and the final reference surface 1310nmay be measured. The distances between the data points and the final reference surface 1310ncan be measured in the same way as described above, for example by computing the shortest orthogonal distance between each data point and the final reference surface 1310nusing any suitable cloud-to-cloud or cloud-to-model distance calculation algorithm. The calculated distances can be associated with each data point in order to produce the augmented point cloud 1320.

[0095]

[0069] As can be appreciated, the augmented point cloud 1320 can be analyzed to characterize groove depth in a similar manner as point cloud 200’ was analyzed to characterize tread height as described above, e.g., by carrying statistical analysis to examine distributions of the data points in the augmented point cloud 1320 and extract statistical parameters such as maximum, minimum, average, median, range, etc. For example, a distribution of distance measurements can be established by grouping or binning the data points of the augmented 3D point cloud 1320 into discrete intervals or bins (e.g., to generate a histogram as illustrated in FIG. 14B). The binned data (and / or the histogram) is then analyzed to calculate the maximum groove depth, minimum groove depth, average groove depth, standard deviation of groove depth, and / or other statistical parameters. In some

[0096] 021908-0149 implementations, a horizontal caliper may be determined and set to identify data points or bins of the histogram with low value counts, that may be considered as outliers of the augmented 3D point cloud (e.g. noise, data points belonging to a background of the object, etc.) that can be disregarded from the analysis. For example, a horizontal caliper may be set at an average height of the bins of the histogram, or at half-maximum value of the highest bin of the histogram, among others. With reference to FIG. 14B, a horizontal outlier caliper 1405 is determined based on an average height of a portion of the histogram (e.g. a first half of the bins corresponding to low distances). A vertical threshold 1410 may further be determined as a first crossing of the bins with the horizontal outlier caliper. In this example, data points of bins corresponding to distance values below the vertical threshold 1410 are identified as outliers or noise. As can be appreciated, a plurality of zones of the histogram may be defined, such that a corresponding outlier caliper may be defined for each zone as needed for different measurements.

[0097]

[0070] Such statistical parameters can be indicative of the level of wear of the tire and / or the tire treads. For example, problematic tread wear can be identified if the calculated maximum groove depth, minimum groove depth, and / or median groove depth are below a predetermined threshold. Nominal tread wear can be identified if the calculated statistical parameters are within predetermined acceptable ranges. In some implementations, the analysis can be improved by excluding from the point cloud 1320 data points corresponding to side surfaces of treads as described above. In some implementations, the 3D point cloud can be subdivided into distinct zones or ROIs for analysis, e.g. to analyze individual grooves, or specific clusters of grooves at different areas on the tire, and / or to analyze grooves corresponding to different segments of the tire, such as the inner side, center, and outer side. In such embodiments, separate histograms can be generated for each distinct zone or ROI, and such histograms can be analyzed individually to characterize groove depth or other geometric parameters in each zone or ROI separately. As can be appreciated, outlier calipers can be determined and / or other statistical techniques can be applied to each histogram as needed.

[0098] 021908-0149

[0071] Although in the implementations discussed above the distances between the 3D point cloud and the final reference surface are measured by measuring distances between all the data points of the 3D point cloud and the final reference surface, it is appreciated that other configurations are possible. For example, in some implementations, the distances can be measured by fitting an additional surface to the 3D point cloud and measuring distances between said additional surface and the final reference surface.

[0099]

[0072] With reference to FIGS. 20A and 20B, a final reference surface 300nand an additional surface 400 fit to an exemplary point cloud 1400 are shown. The additional surface 400 may be generated and fit as described hereinabove with respect to the reference surface. For example, a similar iterative process may be used to perform a fitting of the additional surface 400. Moreover, preprocessing of the 3D point cloud 1400 can be carried out using techniques similar to those as described above prior to fitting the additional surface 400, e.g. to denoise the 3D point cloud and / or to remove data points corresponding to side surfaces of a tread.

[0100]

[0073] In this illustrative example, the final reference surface 300nis fit to a subset of data points representative of the lowermost portion of the external surface of the tire, and the additional surface 400 is fit to a subset of data points representative of the uppermost portion of the external surface of the tire. In implementations where the final reference surface 300nis fit to a subset of data points representative of the lowermost portion of the external surface of the tire, the additional surface 400 can be fit to a subset of data points representative of the lowermost portion of the external surface of the tire. In other words, where the reference surface is fit to a first subset of data points representative of one of a lowermost or an uppermost portion of an external surface of a tire or track, the additional surface can be fit to a second subset of data point representative of the other one of the lowermost or the uppermost portion of the external surface of the tire or track. It is appreciated that other configurations are possible. For example, the reference surface and the additional surface can be fit relative to any two

[0101] 021908-0149 structures in the tire or track between which it is desired to measure distances to characterize the geometric parameters of said tire or track.

[0102]

[0074] Once the additional surface 400 is fit, distances between the additional surface 400 and the final reference surface 300ncan be measured using any suitable technique, for example using a cloud-to-cloud or cloud-to-model distance calculation algorithm. In some configurations, the additional surface 400 can be converted to a point cloud, e.g., by generating data points uniformly distributed along the additional surface 400, and distances can be measured between the generated data points and the final reference surface 300n. Statistical analyses can then be carried out as described above to establish and analyze the distribution of the measured distances.

[0103]

[0075] In some implementations, one or more structures of interest may be identified in the 3D point cloud, and the distances between the reference surface and the additional surface can be measured with respect to said one or more structures of interest. For example, as illustrated in FIGS. 21 A and 21 B, a structure of interest in the 3D point cloud 1400 can correspond to a groove 450 extending along the external surface of the tire. The groove 450 or other structure of interest may be identified using a shape-recognition algorithm. For example, the shaperecognition algorithm may be an artificial intelligence algorithm pre-trained to identify grooves and / or other structures of interest, such as one or more treads, within the 3D point cloud 1400. Upon identifying a structure of interest such as the groove 450, a segment 300a of the reference surface 300 and a segment 400a of the additional surface 400 corresponding to the structure of interest can be extracted. Distances between the extracted segments 300a and 400a can then be measured and analyzed as described above to characterize geometric parameters specific to the structure of interest.

[0104]

[0076] As can be appreciated, the present technology may be applied to any external surface to identify and characterize geometric parameters, such as tread

[0105] 021908-0149 geometry and / or wear, and does not need to rely on further appreciation of crosssection specifics (e.g. feathering). Rather, the present technology involves analyzing 3D data, such as the 3D reference surface that fits a carcass of the tire or track, and relative distance of the data points of the 3D point cloud to said 3D reference surface, to extract simple statistical parameters. The technology allows for conducting more generic analyses that can be applied to any tire or track, and involves simplified and more efficient processing operations that does not require hyper-specialized or powerful processing hardware. For example, the technology can be implemented in a mobile application.

[0106]

[0077] With reference now to FIG. 12, a system for characterizing geometric parameters of an external surface of a tire or a track is shown in accordance with an exemplary implementation of the present technology. In the present implementation, the system is a mobile device 500 such as a smartphone or tablet that includes hardware components capable of carrying out the methods and operations described above. The system is self-contained in that it is a single device 500 that incorporates hardware that is capable of carrying out all the methods and operations described above locally. It is appreciated, however, that in other implementations, some of the methods and / or operations can be offloaded from the device, and for example be carried out remotely, such as on a server, a cloud-computing architecture, or any other suitable separate device.

[0107]

[0078] In the present implementation, the device 500 includes a processor 510 for receiving captured images of an object to be inspected, such as a tire or a track, generating a 3D point cloud representation of the object to be inspected, and analyzing the 3D point cloud to characterize geometric parameters thereof. The processor 510 may include a general-purpose processor, such as a central processing unit (CPU) or a processor dedicated to a specific purpose, such as a digital signal processor (DSP). In some implementations, the processor 510 may also rely on an accelerator dedicated to certain given tasks, such as executing specific operations as described above. In some implementations, the processor 510 or the accelerator may be implemented as one or more field programmable

[0108] 021908-0149 gate arrays (FPGAs). Moreover, explicit use of the term "processor", should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, application-specific integrated circuit (ASIC), read-only memory (ROM) for storing software, RAM, and non-volatile storage. Other hardware, conventional and / or custom, may also be included.

[0109]

[0079] The processor 510 is operatively connected to memory 530 and to input / output interfaces 520. The memory 530 includes persistent storage for storing parameters 534, such as statistical parameters and / or geometric parameters as described above. The memory 530 may comprise a non-transitory computer-readable medium for storing instructions 532 that are executable by the processor 510 to allow the device 500 to perform the methods and / or operations disclosed above.

[0110]

[0080] The input / output interfaces 520 may provide networking capabilities such as wired or wireless access. As an example, the input / output interface 520 may comprise a networking interface such as, but not limited to, one or more network ports, one or more network sockets, one or more network interface controllers and the like. Multiple examples of how the networking interface may be implemented will become apparent to the person skilled in the art of the present technology. For example, but without being limitative, the networking interface may implement specific physical layer and data link layer standard such as Ethernet, Fibre Channel, Wi-Fi or Token Ring. The specific physical layer and the data link layer may provide a base for a full network protocol stack, allowing communication among small groups of computers on the same local area network (LAN) and large- scale network communications through routable protocols, such as Internet Protocol (IP).

[0111]

[0081] The input / output interfaces 520 may include a screen or display capable of rendering color images, including 3D images. In some implementations, the display may be used to display live images captured by the optical scanner 140, 3D point clouds, Augmented Reality (AR) images, Graphical User Interfaces

[0112] 021908-0149 (GUIs), program output, etc. For example, the display may be implemented using a Liquid Crystal Display (LCD) display or a Light Emitting Diode (LED) display, such as an Organic LED (OLED) display. In other implementations, the display may be remotely communicatively connected to the device 500 via a wired or a wireless connection (not shown), so that outputs of the processor 510 may be displayed at a location different from the location of the device 500. In this situation, the display may be operationally coupled to, but housed separately from, other functional units and systems in the device 500.

[0113]

[0082] In some implementation, the input / output interfaces 520 can be configured to display a visual representation of the geometric parameters characterized according to the methods described above. For example, as shown in FIGS. 22A and 22B, the input / output interfaces 520 can be configured to display a 3D point cloud 1500 whereby data point corresponding to tread tops are color-coded to indicate their level of wear. In the illustrated implementation, the data points corresponding to tread tops are colored either green, yellow, orange, or red, depending on the thickness of the tread at the location of the data point (i.e., depending on the measured distance of each data point). More specifically, the thicknesses are binned into discrete color values based on manufacturer recommendations, whereby green indicates a good thickness of 6mm+, yellow indicates an acceptable thickness between 4mm-5mm, orange indicates potentially problematic thicknesses around 3mm that must be inspected monthly, and red indicates problematic thicknesses of 2mm or less that won’t last long or are approaching the legal limit. As can be appreciated, different color scales can be adopted depending on different manufacturer recommendations. In some implementations, a continuous color scale can be adopted instead of binning each data point into one of a predetermined set of color values. Moreover, it is appreciated that data points corresponding to different structures in the 3D point cloud can be color coded and / or otherwise displayed to visualize the characterization of other geometric parameters of a tire or tread.

[0114] 021908-0149

[0083] The device 500 may also include an optical scanner 540. For example, the optical scanner may be configured to capture Red-Green-Blue (RGB) images. The optical scanner 540 may comprise image sensors such as, but not limited to, Charge-Coupled Device (CCD) or Complementary Metal Oxide Semiconductor (CMOS) sensors and / or digital cameras. The optical scanner 540 may convert an optical image into an electronic or digital image and may send captured images to the processor 510. In the same or other implementations, the optical scanner 540 may be a single-lens camera providing RGB pictures. In some implementations, the optical scanner 540 includes depth sensors to acquire RGB-Depth (RGBD) pictures. Broadly speaking, any device suitable for generating a 3D point cloud may be used as the optical scanner 540 including but not limited to depth sensors, 3D scanners, laser scanners, or any other suitable devices.

[0115]

[0084] In some implementations, the device 500 may include an Inertial Sensing Unit (ISU) configured to be used in part by the processor 510 to determine a position of the optical scanner 540 and / or the device 500. Therefore, the processor 510 may determine a set of coordinates describing the location of the optical scanner 540, and thereby the location of the device 500, in a coordinate system based on the output of the ISU. Generation of the coordinate system is described hereinafter. The ISU may comprise 3-axis accelerometer(s), 3-axis gyroscope(s), and / or magnetometer(s) and may provide velocity, orientation, and / or other position related information to the processor 510. The ISU may output measured information in synchronization with the capture of each image by the optical scanner 540. The ISU may be used to determine the set of coordinates describing the location of the device 500 for each captured image of a series of images. Therefore, each image may be associated with a set of coordinates of the device 500 corresponding to a location of the device 500 when the corresponding image was captured. Furthermore, information provided by the ISU may be used to determine a coordinate system and / or a scale corresponding of the object to be characterized. Other approaches may be used to determine said scale, for instance by including a reference object whose size is known in the captured images, near the object to be characterized.

[0116] 021908-0149

[0085] Several alternative implementations and examples have been described and illustrated herein. The implementations described above are intended to be exemplary only. It will be appreciated that the methods described herein can be performed in the described order, or in any suitable order. A person with common technical knowledge would appreciate the features of the individual implementations, and the possible combinations and variations of the components. A person of common technical knowledge would further appreciate that any of the implementations could be provided in any combination with the other implementations disclosed herein. It is understood that the product can have other specific forms without departing from the central characteristics thereof. The present examples and implementations, therefore, are to be considered in all respects as illustrative and not restrictive, and the product is not to be limited to the details given herein. Accordingly, while the specific implementations have been illustrated and described, numerous modifications come to mind. The scope of the product is therefore intended to be limited solely by the scope of the appended claims.

[0117]

[0086] The methods and systems are described herein in connection with vehicle tires and tracks. However, it is appreciated that such methods and systems can be applied to various other objects or items, such as rail tracks, body parts, welded surfaces, and / or any other object or item. The use of the systems and / or methods as described herein on anything other than vehicle tires and vehicle tracks is disclaimed.

[0118] 021908-0149

Claims

CLAIMS1. A computer-implemented method for characterizing geometric parameters of an external surface of a tire or a track, the method comprising: accessing a three-dimensional (3D) point cloud comprising a plurality of data points representative of the external surface; fitting a reference surface to a first subset of data points in the 3D point cloud, the first subset of data points being representative of one of a lowermost or an uppermost portion of the external surface of the tire or track; measuring distances between the 3D point cloud and the reference surface; and analyzing the measured distances to characterize the geometric parameters of the external surface.

2. The computer-implemented method of claim 1 , wherein measuring distances between the 3D point cloud and the reference surface comprises measuring distances between each of the plurality of data points in the 3D point cloud and the reference surface.

3. The computer-implemented method of claim 2, wherein the external surface comprises grooves and treads, and wherein analyzing the measured distances comprises identifying data points in the 3D point cloud belonging to either tread tops or groove bases, and analyzing measured distances associated with the identified data points to determine the geometrical characteristics of the external surface.

4. The computer-implemented method of claim 3, wherein analyzing the measured distances comprises, for each data point: calculating a local normal of the data point;021908-0149calculating a normal of the reference surface at a position of the data point; and excluding from the analysis the measured distance associated with the data point, based on an angle between the local normal and the normal of the reference surface.

5. The computer-implemented method of claim 4, further comprising: identifying a data point as belonging to tread side surfaces when an angle between the local normal of the datapoint and the normal of the reference surface at the position of the data point is above a predetermined angle threshold; and excluding from the analysis measured distances associated with data points belonging to tread side surfaces.

6. The computer-implemented method of any one of claims 2 to 5, wherein analyzing the measured distances comprises calculating a maximum, a minimum, a standard deviation and / or an average tread height or groove depth from a distribution of the measured distances.

7. The computer-implemented method of any one of claims 2 to 6, wherein analyzing the measured distances comprises subdividing the 3D point cloud into a plurality of regions of interest (ROIs) and analyzing measured distances in each ROI separately to characterize geometrical parameters of distinct portions of the external surface of the tire or track.

8. The computer-implemented method of claim 1 , wherein measuring distances between the 3D point cloud and the reference surface comprises: fitting an additional surface to a second subset of data point in the 3D point cloud, the second subset of data points being representative of the other one of the lowermost or the uppermost portion of the external surface of the tire or track; and021908-0149measuring distances between the additional surface and the reference surface.

9. The computer-implemented method of claim 8, wherein measuring distances between the additional surface and the reference surface comprises: identifying a structure of interest in the 3D point cloud; extracting a segment of the additional surface and a segment of the reference surface corresponding to the structure of interest; and measuring distances between the extracted segment of the additional surface and the extracted segment of the reference surface.

10. The computer-implemented method of claim 9, wherein the structure of interest corresponds to at least one tread or at least one groove extending along the external surface of the tire or track.

11. The computer-implemented method of any one of claims 1 to 10, further comprising, prior to measuring the distances between the 3D point cloud and the reference surface, iteratively adjusting the reference surface until a stopping criterion is reached, an iteration comprising: determining current relative distances between the data points of a current version of the 3D point cloud corresponding to the iteration and the reference surface; removing outlier data points from the current version of the 3D point cloud based on the current relative distances, thereby forming an adjusted 3D point cloud, a given data point being identified as an outlier data point in response to a distance between the given data point and the reference surface being higher than a distance threshold; and re-fitting the reference surface to the adjusted 3D point cloud.021908-014912. The computer-implemented method of any one of claims 1 to 11 , further comprising, prior to fitting the reference surface, applying a denoising routine to the 3D point cloud.

13. The computer-implemented method of any one of claims 1 to 12, further comprising, prior to fitting the reference surface, discarding data points representative of side surfaces of the tire or the track from the 3D point cloud.

14. A system for characterizing geometric parameters of an external surface of a tire or a track, the system comprising a controller and a memory storing a plurality of executable instructions which, when executed by the controller, cause the system to: access a three-dimensional (3D) point cloud comprising a plurality of data points representative of the external surface; fit a reference surface to a first subset of data points in the 3D point cloud, the first subset of data points being representative of one of a lowermost or an uppermost portion of the external surface of the tire or track; measure distances between the 3D point cloud and the reference surface; and analyze the measured distances to characterize the geometric parameters of the external surface.

15. The system of claim 14, wherein the system is configured to measure distances between the 3D point cloud and the reference surface by measuring distances between each of the plurality of data points in the 3D point cloud and the reference surface.

16. The system of claim 15, wherein the external surface comprises grooves and treads, and wherein the system analyzes the measured distances by identifying data points in the 3D point cloud belonging to either tread tops or groove bases, and analyzing measured distances associated with the021908-0149identified data points to determine the geometrical characteristics of the external surface.

17. The system of claim 16, wherein the system analyzes the measured distances by, for each data point: calculating a local normal of the data point; calculating a normal of the reference surface at a position of the data point; and excluding from the analysis the measured distance associated with the data point, based on an angle between the local normal and the normal of the reference surface.

18. The system of claim 17, further configured to: identify a data point as belonging to tread side surfaces when an angle between the local normal of the datapoint and the normal of the reference surface at the position of the data point is above a predetermined angle threshold; and exclude from the analysis measured distances associated with data points belonging to tread side surfaces.

19. The system of any one of claims 15 to 18, wherein the system analyzes the measured distances by calculating a maximum, a minimum, a standard deviation and / or an average tread height or groove depth from a distribution of the measured distances.

20. The system of any one of claims 15 to 19, wherein the system analyzes the measured distances by subdividing the 3D point cloud into a plurality of regions of interest (ROIs) and analyzing measured distances in each ROI separately to characterize geometrical parameters of distinct portions of the external surface of the tire or track.021908-014921 . The system of claim 14, wherein the system measures distances between the 3D point cloud and the reference surface by: fitting an additional surface to a second subset of data point in the 3D point cloud, the second subset of data points being representative of the other one of the lowermost or the uppermost portion of the external surface of the tire or track; and measuring distances between the additional surface and the reference surface.

22. The system of claim 21 , wherein the system measures distances between the additional surface and the reference surface by: identifying a structure of interest in the 3D point cloud; extracting a segment of the additional surface and a segment of the reference surface corresponding to the structure of interest; and measuring distances between the extracted segment of the additional surface and the extracted segment of the reference surface.

23. The system of claim 22, wherein the structure of interest corresponds to at least one tread or at least one groove extending along the external surface of the tire or track.

24. The system of any one of claims 14 to 23, further configured to, prior to measuring the distances between the 3D point cloud and the reference surface, iteratively adjusting the reference surface until a stopping criterion is reached, an iteration comprising: determining current relative distances between the data points of a current version of the 3D point cloud corresponding to the iteration and the reference surface;021908-0149removing outlier data points from the current version of the 3D point cloud based on the current relative distances, thereby forming an adjusted 3D point cloud, a given data point being identified as an outlier data point in response to a distance between the given data point and the reference surface being higher than a distance threshold; and re-fitting the reference surface to the adjusted 3D point cloud.

25. The system of any one of claims 14 to 24, further configured to, prior to fitting the reference surface, applying a denoising routine to the 3D point cloud.

26. The system of any one of claims 14 to 25, further configured to, prior to fitting the reference surface, discarding data points representative of side surfaces of the tire or the track from the 3D point cloud.021908-0149

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