Method, device and equipment for estimating the pose of cylindrical targets based on narrowband filtering

Abstract

The present application relates to a method, device and computer equipment for estimating the pose of a quasi-cylindrical target based on narrowband filtering. The method comprises: pre-marking two non-coplanar circular rings with a reflective coating on the quasi-cylindrical target by design, obtaining a narrowband filtered image of the quasi-cylindrical target by using a pre-processing camera equipped with an imaging light source of a specific wavelength and a narrowband filter of the same wavelength, performing ellipse detection on the quasi-cylindrical target based on the narrowband filtered image by using a fast ellipse detection algorithm based on an arc segment adjacency matrix to obtain two sets of ellipse information, and solving the pose information of the quasi-cylindrical target using an EPnP algorithm based on the two sets of ellipse information. The present invention uses narrowband filtering to allow only light of the corresponding wavelength to be used for imaging, effectively reducing interference from complex backgrounds in complex scenes, and combined with the fast ellipse detection algorithm based on the arc segment adjacency matrix, ellipse detection can be achieved even when the target is obscured, with higher detection accuracy and stronger robustness.

Description

Technical Field

[0001] The present application relates to the field of pose estimation, and in particular to a method, apparatus, and computer equipment for estimating the pose of a cylindrical target based on narrowband filtering. Background Art

[0002] Currently, real-time detection of circular objects in images has broad applications in intelligent transportation systems, intelligent surveillance systems, military target detection, and instrument positioning in medical navigation surgery. Target location is determined by detecting the shape, and important information such as the size and position of the object is further understood by processing it. However, since circles lack straight lines and feature points are not unique, utilizing the feature information of ellipses for detection is an urgent challenge.

[0003] Three methods for measuring the pose of spatial circles and rings based on multi-camera stereo vision systems can be categorized. Methods based on algebraic projective geometry derive the pose of a ring through geometric constraints between projections and surfaces. However, this method can result in low measurement accuracy when the target's outline is partially obscured. Methods that combine prior information from computer-aided design three-dimensional models with binocular stereo vision systems can accurately extract the edges of rings, reduce edge extraction errors, and achieve more accurate measurement results. However, in practical applications, these methods are easily affected by environmental factors and exhibit poor performance. Methods based on multi-camera stereo vision determine the three-dimensional coordinates of the edge points of a spatial circle and use this three-dimensional coordinate information to fit a spatial circle to determine the pose of the spatial ring. However, when the contour of the spatial circle is partially obscured, due to the inevitable error in camera extrinsic calibration, the error in the depth direction of the reconstructed edge points increases rapidly with the matching error, significantly impacting the algorithm's measurement accuracy.

[0004] Therefore, the existing technology has the problem of poor effect. Summary of the Invention

[0005] Based on this, it is necessary to provide a cylindrical target pose estimation method, device, computer equipment and storage medium based on narrowband filtering that can realize ellipse detection when the target is partially occluded or the background is complex, in order to address the above technical problems.

[0006] A method for estimating the pose of a cylindrical target based on narrowband filtering, the method comprising:

[0007] A narrowband filtered image of a cylindrical target to be detected is acquired through a pre-processing camera; the pre-processing camera is pre-installed with an imaging light source of a specific wavelength and a narrowband filter of the same wavelength; the cylindrical target includes two pre-marked non-coplanar rings with a reflective coating; the wavelengths of the imaging light source and the narrowband filter are determined by the material properties of the reflective coating;

[0008] According to the narrowband filtered image, ellipse detection is performed on the cylindrical target using a fast ellipse detection algorithm based on an arc segment adjacency matrix to obtain two sets of ellipse information;

[0009] According to the two sets of ellipse information, the position information of the cylindrical target is obtained by solving the EPnP algorithm.

[0010] In one embodiment, the method further includes: obtaining predicted three-dimensional skeleton model information of the cylindrical target;

[0011] Reprojecting the three-dimensional skeleton model of the cylindrical target onto the narrowband filtered image according to the pose information of the cylindrical target and the three-dimensional skeleton model information;

[0012] The accuracy of the obtained pose information is determined based on the reprojection results.

[0013] In one embodiment, the method further includes: extracting edge lines from the narrowband filtered image, dividing the edge lines into elliptical arcs, and constructing a directed arc segment adjacency matrix based on the elliptical arcs;

[0014] By bidirectionally traversing the arc segment adjacency matrix, a candidate arc segment combination and a cumulative matrix based on a cumulative factor are obtained;

[0015] Performing a secondary eigendecomposition on the cumulative matrix using the Jacobi algorithm to fit a candidate ellipse;

[0016] The candidate ellipse matrix is ​​verified by calculating the verification score, false ellipses are eliminated, and the ellipse detection result and the ellipse information corresponding to the two rings are obtained.

[0017] In one embodiment, the ellipse information further includes: the ellipse information is the coordinate information of the four endpoints of the major and minor axes of the ellipse and the coordinate information of the center point of the ellipse.

[0018] In one embodiment, the method further includes: obtaining coordinate information of four endpoints of the major and minor axes of two sets of ellipses and coordinate information of the center point of the ellipse to determine coordinate information of the control point;

[0019] Obtaining three-dimensional skeleton model information of the cylindrical target;

[0020] Establishing a 2D-3D correspondence relationship of the cylindrical target elliptical features according to the control point coordinate information and the three-dimensional skeleton model information;

[0021] The EPnP algorithm is used to solve the 2D-3D correspondence relationship of the elliptical features of the cylindrical target to obtain the pose information of the cylindrical target.

[0022] In one embodiment, the imaging light source and the narrowband filter have a wavelength of 850 nm.

[0023] A cylindrical target pose estimation device based on narrowband filtering, the device comprising:

[0024] A narrowband filtering module is configured to acquire a narrowband filtered image of a cylindrical target to be detected through a pre-processing camera; the pre-processing camera is pre-installed with an imaging light source of a specific wavelength and a narrowband filter of the same wavelength; the cylindrical target includes two pre-marked, non-coplanar rings with a reflective coating; the wavelengths of the imaging light source and the narrowband filter are determined by the material properties of the reflective coating;

[0025] an ellipse detection module, configured to perform ellipse detection on the cylindrical target using a fast ellipse detection algorithm based on an arc segment adjacency matrix according to the narrowband filtered image, thereby obtaining two sets of ellipse information;

[0026] The pose estimation module is used to obtain the pose information of the cylindrical target by solving the two sets of ellipse information through the EPnP algorithm.

[0027] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0028] A narrowband filtered image of a cylindrical target to be detected is acquired through a pre-processing camera; the pre-processing camera is pre-installed with an imaging light source of a specific wavelength and a narrowband filter of the same wavelength; the cylindrical target includes two pre-marked non-coplanar rings with a reflective coating; the wavelengths of the imaging light source and the narrowband filter are determined by the material properties of the reflective coating;

[0029] According to the narrowband filtered image, ellipse detection is performed on the cylindrical target using a fast ellipse detection algorithm based on an arc segment adjacency matrix to obtain two sets of ellipse information;

[0030] According to the two sets of ellipse information, the position information of the cylindrical target is obtained by solving the EPnP algorithm.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0032] A narrowband filtered image of a cylindrical target to be detected is acquired through a pre-processing camera; the pre-processing camera is pre-installed with an imaging light source of a specific wavelength and a narrowband filter of the same wavelength; the cylindrical target includes two pre-marked non-coplanar rings with a reflective coating; the wavelengths of the imaging light source and the narrowband filter are determined by the material properties of the reflective coating;

[0033] According to the narrowband filtered image, ellipse detection is performed on the cylindrical target using a fast ellipse detection algorithm based on an arc segment adjacency matrix to obtain two sets of ellipse information;

[0034] According to the two sets of ellipse information, the position information of the cylindrical target is obtained by solving the EPnP algorithm.

[0035] The above-mentioned method, device, computer equipment and storage medium for estimating the pose of a quasi-cylindrical target based on narrowband filtering are designed to pre-mark two non-coplanar circular rings with reflective coatings on the quasi-cylindrical target. A narrowband filtered image of the quasi-cylindrical target is obtained by using a pre-processing camera equipped with an imaging light source of a specific wavelength and a narrowband filter of the same wavelength. Based on the narrowband filtered image, ellipse detection is performed on the quasi-cylindrical target using a fast ellipse detection algorithm based on an arc segment adjacency matrix to obtain two sets of ellipse information. Based on the two sets of ellipse information, the pose information of the quasi-cylindrical target is obtained by solving the EPnP algorithm. The present invention uses narrowband filtering to ensure that only light of the corresponding wavelength can be used for imaging, which can effectively reduce interference from complex backgrounds in complex scenes. Combined with the fast ellipse detection algorithm based on the arc segment adjacency matrix, ellipse detection can also be achieved when the target is obscured, with higher detection accuracy and stronger robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 1 is a flow chart of a method for estimating the pose of a cylindrical target based on narrowband filtering in one embodiment;

[0037] Figure 2 A schematic diagram of two non-coplanar circular rings with reflective coatings in a cylindrical target model according to an embodiment;

[0038] Figure 3 This is the image processing effect after the pre-processing camera performs narrow-band filtering in one embodiment;

[0039] Figure 4 Schematic diagram of pose estimation in one embodiment;

[0040] Figure 5 A schematic diagram of a three-dimensional skeleton of a cylindrical-like target in one embodiment;

[0041] Figure 6 A schematic diagram of a reprojection result of projecting a three-dimensional skeleton model onto an image in one embodiment;

[0042] Figure 7 1 is a structural block diagram of a cylindrical target pose estimation device based on narrowband filtering in one embodiment;

[0043] Figure 8 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0045] The method provided by the present invention is applied to a quasi-cylindrical target including a pre-marked non-coplanar ring with a reflective coating. The quasi-cylindrical target is a symmetrical target with elliptical properties such as a cylinder, a cone, a frustum, and corresponding combinations.

[0046] In one embodiment, Figure 1 As shown, a method for estimating the pose of a cylindrical target based on narrowband filtering is provided, comprising the following steps:

[0047] Step 102: Obtain a narrowband filtered image of the cylindrical target to be detected through a preprocessing camera.

[0048] In this embodiment, the cylindrical target includes two pre-marked non-coplanar rings with reflective coatings. The two rings have different depths and sizes. On the one hand, they are used for target detection, and on the other hand, they are used for subsequent pose estimation. A single ellipse cannot determine the orientation of the target. The two non-coplanar rings can determine the orientation of the cylindrical target. Therefore, the two rings can provide sufficient information. The pre-processing camera is pre-installed with an imaging light source of a specific wavelength and a narrow-band filter of the same wavelength. The wavelengths of the imaging light source and the narrow-band filter are determined by the material properties of the reflective coating. In this way, only light of the corresponding wavelength can be used for imaging, effectively reducing the interference of complex backgrounds in complex scenes, greatly improving the imaging effect of the mark, and achieving higher detection accuracy and stronger robustness.

[0049] In a specific embodiment, the cylindrical target is a stepped target, such as an industrial part, and its detection can be realized by a robotic arm to grasp the target, thereby realizing automatic sorting of the target. The reflective coating used is a bright chemical fiber material (reflective sticker) or a reflective paint based on acrylic resin, and the corresponding imaging light source and narrow band filter have a wavelength of 850nm. Figure 2 The model diagram of the cylindrical target is shown in Figure 1. The dotted line represents the non-coplanar ring with reflective coating, such as Figure 3 This is the narrow-band filtered image processed by the pre-processing camera. It can be seen that this step achieves the ideal imaging effect. Two clear rings appear on the image, providing conditions for subsequent ellipse detection.

[0050] Step 104 : performing ellipse detection on the cylindrical target using a fast ellipse detection algorithm based on an arc segment adjacency matrix according to the narrowband filtered image, and obtaining two sets of ellipse information.

[0051] After preprocessing the image, the present invention uses a fast ellipse detection method AAMED based on arc adjacency matrix to perform ellipse detection on the target.

[0052] First, the extracted edge lines are segmented into elliptical arcs, and then a directed arc segment adjacency matrix (AAM) is constructed. Each element of the matrix represents three adjacency states, and curvature constraints and area constraints are used to make the AAM sparse. Secondly, by bidirectionally traversing the AAM, all arc segment combinations that may be true ellipse candidates are obtained, and the cumulative matrix (CM) based on the cumulative factor (CF) is calculated. CM is related to the arc or arc combination and can be calculated by adding or subtracting CF. The Jacobi method is used to perform a secondary eigendecomposition on CM to effectively fit the candidate ellipse. Finally, in order to effectively eliminate false ellipses, a comprehensive formula is given to calculate the verification score, which is mainly affected by constraints such as adaptive shape, tangent similarity, and distribution compensation.

[0053] In combination with step 102 and step 104, ellipse detection can also be implemented when the target is blocked, which serves as the basis for the next step of pose estimation.

[0054] Step 106: Based on the two sets of ellipse information, the EPnP algorithm is used to obtain the position information of the cylindrical target.

[0055] The ellipse equation of the target model can be obtained by detecting the ellipse using the arc segment adjacency matrix. The major and minor axes of the ellipse can be calculated using the ellipse equation, and the pixel coordinates of the major and minor axis endpoints and the ellipse center point on the image can be obtained.

[0056] By detecting the two ellipses of the target, the coordinates of 10 control points can be provided. Since the 3D model of the target is known, the 2D-3D correspondence of the target ellipse features can be established, such as Figure 4 shown.

[0057] The camera's internal parameters can be solved by querying official parameters or Zhang's calibration method, and the 2D-3D correspondence has been established. The pose relationship between the target and the camera can be solved by the EPnP algorithm.

[0058] The EPnP principle is to use the known three-dimensional space point coordinates, select four control points through the principal component analysis (PCA) method to establish a new control point coordinate system, and express the three-dimensional space point coordinates in the form of four control points, as shown in the following formula.

[0059]

[0060] Where j represents the control point number, i represents the feature point number, P wrepresents the feature points of the target in the world coordinate system, α represents the weight of each feature point corresponding to the control point, P c Represents the three-dimensional feature points of the target in the camera coordinate system, as shown in the following formula, where R0 and T0 are the initial values ​​of the pose relationship to be determined.

[0061]

[0062] By using principal component analysis (PCA), the matrix eigenvector can be solved to obtain the three-dimensional feature points P of the target in the camera coordinate system. c At this point, you can create a P w and P c The 3D-3D correspondence is obtained and solved by iterative closest point (ICP). According to the ICP solution steps, the centroid of the two sets of 3D feature points in the two coordinate systems and the centroid coordinates are calculated as follows:

[0063]

[0064] in, and M w They are the centroid coordinates and the de-centroid coordinates in the world coordinate system respectively. and M c are the center of mass coordinates in the camera coordinate system. Let H = [M c ] T ·[M w ], perform SVD decomposition on H, H=U∑V T , so the rotation matrix R0 and translation vector T0 can be obtained by the following formula, let pose init =[R0,T0].

[0065] Using EPnP, we assign initial values ​​R0 and T0 and perform an optimization solution. For cooperative targets with known spatial feature points, image-space reprojection is often used for optimization. Bundle adjustment optimization aims to minimize all reprojection errors, so the pixel errors at all points are summed, as shown in the following equation.

[0066]

[0067] A is the camera internal parameter, k is the distortion coefficient, R iw ,T iw is the relative position relationship between the camera and the world coordinate system. During the iterative solution of the nonlinear equation, the small increment Δx is a six-dimensional column vector, which is expressed as shown in the following equation.

[0068] Δx=[δρ,δφ] T =[ΔT X ,ΔTY ,ΔT Z ,ΔA X ,ΔA Y ,ΔA Z ] T (5)

[0069] The change in increment Δx causes a change in the pixel coordinate point e. Let e ​​be a function of x. According to the Taylor expansion, we have the following equation.

[0070]

[0071] Where H = J T J. J is a 2×6 Jacobian matrix, H is a 6×6 Hessian matrix, and the J Jacobian matrix is ​​shown below.

[0072]

[0073] The solution of the H matrix is ​​shown in the following formula.

[0074] H=J T J (8)

[0075] In Levenberg-Marquadt optimization, the optimization problem of the objective function is an optimization problem with inequalities. The constraints are added to the objective function using Lagrange multipliers to form a Lagrange function.

[0076]

[0077] λ is the Lagrange multiplier, I is the identity matrix, and μ is a constant. The derivative of the Lagrange function with respect to Δx is set to zero. The core is still the linear equation for calculating the increment, as shown in the following formula.

[0078] (H+λI)·Δx=J T e (10)

[0079] Here, e represents the column vector of the reprojection disparity of all points. Assuming there are n observation points, the dimension of J is 2n×6, the dimension of H is 6×6, and the dimension of e is 2n×1.

[0080] The present invention actually adopts 10 control points, namely, four endpoints of two groups of ellipse major and minor axes and the coordinates of the circle center, so that the algorithm has higher solution accuracy.

[0081] In the above-mentioned method for estimating the pose of a quasi-cylindrical target based on narrowband filtering, two non-coplanar circular rings with reflective coatings are pre-marked on the quasi-cylindrical target by design, and a narrowband filtered image of the quasi-cylindrical target is obtained by a pre-processing camera equipped with an imaging light source of a specific wavelength and a narrowband filter of the same wavelength. Based on the narrowband filtered image, ellipse detection is performed on the quasi-cylindrical target using a fast ellipse detection algorithm based on an arc segment adjacency matrix to obtain two sets of ellipse information. Based on the two sets of ellipse information, the pose information of the quasi-cylindrical target is obtained by solving the EPnP algorithm. The present invention uses narrowband filtering to ensure that only light of the corresponding wavelength can be used for imaging, which can effectively reduce the interference of complex backgrounds in complex scenes. Combined with the fast ellipse detection algorithm based on the arc segment adjacency matrix, ellipse detection can also be achieved when the target is obscured, with higher detection accuracy and stronger robustness.

[0082] In one embodiment, the method further includes: obtaining predicted three-dimensional skeleton model information of a cylindrical-like target; reprojecting the three-dimensional skeleton model of the cylindrical-like target onto a narrow-band filtered image based on the posture information and three-dimensional skeleton model information of the cylindrical-like target; and determining the accuracy of the obtained posture information based on the reprojection result.

[0083] like Figure 5 The solid line in the middle is a schematic diagram of a three-dimensional skeleton of a cylindrical target in a specific embodiment. Figure 6 This is the reprojection result of projecting the 3D skeleton model onto the image. It can be seen that the 3D skeleton model is well matched with the target on the image, verifying that the obtained pose is accurate.

[0084] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0085] In one embodiment, Figure 7 As shown, a cylindrical target pose estimation device based on narrowband filtering is provided, including: a narrowband filtering module 702, an ellipse detection module 704 and a pose estimation module 706, wherein:

[0086] Narrowband filtering module 702 is configured to obtain a narrowband filtered image of the cylindrical target to be detected using a pre-processing camera; the pre-processing camera is pre-installed with an imaging light source of a specific wavelength and a narrowband filter of the same wavelength; the cylindrical target includes two pre-marked, non-coplanar rings with a reflective coating; the wavelengths of the imaging light source and the narrowband filter are determined by the material properties of the reflective coating;

[0087] The ellipse detection module 704 is used to perform ellipse detection on the cylindrical target based on the narrow-band filtered image using a fast ellipse detection algorithm based on an arc segment adjacency matrix to obtain two sets of ellipse information;

[0088] The pose estimation module 706 is used to obtain the pose information of the cylindrical target through the EPnP algorithm based on the two sets of ellipse information.

[0089] The pose estimation module 706 is also used to obtain the predicted three-dimensional skeleton model information of the cylindrical-like target; based on the pose information and three-dimensional skeleton model information of the cylindrical-like target, the three-dimensional skeleton model of the cylindrical-like target is reprojected onto the narrow-band filtered image; and the accuracy of the obtained pose information is determined based on the reprojection result.

[0090] The ellipse detection module 704 is also used to extract edge lines based on the narrowband filtered image, divide the edge lines into elliptical arcs, and construct a directed arc segment adjacency matrix based on the elliptical arcs; obtain candidate arc segment combinations and a cumulative matrix based on the cumulative factor by bidirectionally traversing the arc segment adjacency matrix; perform secondary eigendecomposition on the cumulative matrix through the Jacobi algorithm to fit candidate ellipses; verify the candidate ellipse matrix by calculating the verification score, eliminate false ellipses, and obtain the ellipse detection result and the ellipse information corresponding to the two circular rings.

[0091] The pose estimation module 706 is also used to obtain the coordinate information of the four endpoints of the major and minor axes of two sets of ellipses and the coordinate information of the center point of the ellipse to determine the coordinate information of the control point; obtain the three-dimensional skeleton model information of the cylindrical target; establish the 2D-3D correspondence relationship of the elliptical features of the cylindrical target based on the control point coordinate information and the three-dimensional skeleton model information; and obtain the pose information of the cylindrical target by solving the EPnP algorithm based on the 2D-3D correspondence relationship of the elliptical features of the cylindrical target.

[0092] Regarding the specific limitations of the cylindrical target pose estimation device based on narrowband filtering, please refer to the limitations of the cylindrical target pose estimation method based on narrowband filtering above, which will not be repeated here. The various modules in the above-mentioned cylindrical target pose estimation device based on narrowband filtering can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0093] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for estimating the pose of a cylindrical target based on narrowband filtering is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0094] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0095] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiment when executing the computer program.

[0096] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0097] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0098] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for estimating the pose of a cylindrical target based on narrowband filtering, characterized in that: The method comprises: A narrowband filtered image of a cylindrical target to be detected is acquired through a pre-processing camera; the pre-processing camera is pre-installed with an imaging light source of a specific wavelength and a narrowband filter of the same wavelength; the cylindrical target includes two pre-marked non-coplanar rings with a reflective coating; the wavelengths of the imaging light source and the narrowband filter are determined by the material properties of the reflective coating; According to the narrowband filtered image, ellipse detection is performed on the cylindrical target using a fast ellipse detection algorithm based on an arc segment adjacency matrix to obtain two sets of ellipse information; According to the two sets of ellipse information, the position information of the cylindrical target is obtained by solving the EPnP algorithm; After obtaining the position information of the cylindrical target by solving the two sets of ellipse information through the EPnP algorithm, the method further includes: Acquiring predicted three-dimensional skeleton model information of the cylindrical target; Reprojecting the three-dimensional skeleton model of the cylindrical target onto the narrowband filtered image according to the pose information of the cylindrical target and the three-dimensional skeleton model information; Determine the accuracy of the obtained pose information based on the reprojection results; According to the narrowband filtered image, ellipse detection is performed on the cylindrical target using a fast ellipse detection algorithm based on an arc segment adjacency matrix to obtain two sets of ellipse information, including: Extracting edge lines from the narrowband filtered image, dividing the edge lines into elliptical arcs, and constructing a directed arc segment adjacency matrix based on the elliptical arcs; By bidirectionally traversing the arc segment adjacency matrix, a candidate arc segment combination and a cumulative matrix based on a cumulative factor are obtained; Performing a secondary eigendecomposition on the cumulative matrix using the Jacobi algorithm to fit a candidate ellipse; The candidate ellipse matrix is ​​verified by calculating the verification score, false ellipses are eliminated, and the ellipse detection result and the ellipse information corresponding to the two rings are obtained; Based on the two sets of ellipse information, the pose information of the cylindrical target is obtained by solving the EPnP algorithm, including: Obtain the coordinate information of the four endpoints of the major and minor axes of the two ellipses and the coordinate information of the center point of the ellipse to determine the coordinate information of the control point; Obtaining three-dimensional skeleton model information of the cylindrical target; Establishing a 2D-3D correspondence relationship of the cylindrical target elliptical features according to the control point coordinate information and the three-dimensional skeleton model information; The EPnP algorithm is used to solve the 2D-3D correspondence relationship of the elliptical features of the cylindrical target to obtain the pose information of the cylindrical target.

2. The method according to claim 1, characterized in that The ellipse information includes the coordinate information of the four endpoints of the major and minor axes of the ellipse and the coordinate information of the center point of the ellipse.

3. The method according to claim 1 or 2, characterized in that The wavelength of the imaging light source and the narrowband filter is 850 nm.

4. A cylindrical target pose estimation device based on narrowband filtering applied to the method according to any one of claims 1 to 3, characterized in that: The device comprises: A narrowband filtering module is configured to acquire a narrowband filtered image of a cylindrical target to be detected through a pre-processing camera; the pre-processing camera is pre-installed with an imaging light source of a specific wavelength and a narrowband filter of the same wavelength; the cylindrical target includes two pre-marked, non-coplanar rings with a reflective coating; the wavelengths of the imaging light source and the narrowband filter are determined by the material properties of the reflective coating; an ellipse detection module, configured to perform ellipse detection on the cylindrical target using a fast ellipse detection algorithm based on an arc segment adjacency matrix according to the narrowband filtered image, thereby obtaining two sets of ellipse information; The pose estimation module is used to obtain the pose information of the cylindrical target by solving the two sets of ellipse information through the EPnP algorithm.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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