Oral implant miniature binocular camera optimization method based on view space segmentation DLT

Abstract

The invention discloses an oral implant miniature binocular camera optimization method based on view space segmentation DLT, and the method comprises the steps: 1, segmenting the view space of a miniature camera into eight subspaces composed of crystal lattices, and further segmenting the subspaces for n times, and dividing the subspaces into n layers; 2, calculating a projection matrix of each lattice of each layer according to a DLT algorithm, and completing n-layer calibration of the camera; step 3, acquiring pixel coordinates of a target point, and reconstructing a three-dimensional world coordinate p0 by using the projection matrix calculated by the sampling points of the whole view space in the step 2; 4, searching 8 crystal lattices of the next layer, selecting the crystal lattice with the space center closest to p0, and reconstructing the three-dimensional world coordinate p1 of the next layer of the target point by utilizing the crystal lattice projection matrix calculated in the step 2; and step 5, repeating the step 4, and obtaining a finally reconstructed three-dimensional world coordinate pn of the target point by using the projection matrix of the crystal lattice where the last layer is located, namely a positioning result after optimization of the oral implant miniature binocular camera.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer vision, and in particular relates to an optimization method for an oral implant micro binocular camera based on field of view spatial segmentation DLT. Background Art

[0002] Tooth loss and defect is one of the important diseases that affect health and quality of life. Implant restoration has become the main method to solve the problem of tooth loss and defect due to its long-term reliable effect, natural and beautiful appearance, high comfort and other advantages. Considering that traditional implant surgery is performed manually by doctors in narrow oral space and non-direct vision areas, it is easy to affect the accuracy of the implant, resulting in insufficient contact area of ​​bone tissue around the implant, which in turn affects the stability of the implant, causing damage to periodontal tissues, abnormal occlusal function, and increasing the incidence of postoperative infection, pain and discomfort.

[0003] The use of oral implant robots can reduce operational errors and improve the accuracy of implant placement. The oral implant robot system includes surgical accessories, visual systems, robotic arms and end-effector systems, and display and software control systems. The surgical process includes: using cone beam CT and intraoral scanning to obtain three-dimensional image data inside the oral cavity, wearing personalized guides and locators in the mouth, installing robotic equipment, calibrating robots and probes according to prompts, checking, registering positioning plates, robotic cavity preparation, and installing implants. Multiple dynamic coordinates are rigidly fixed on the teeth or jaws and the robotic arm, and the relative position relationship between the implant site and the implant mobile phone is obtained based on matrix conversion.

[0004] In the oral implant robot system, a miniature binocular camera is used to locate the markers on the positioning guide to determine the patient's position and quickly and accurately identify any small displacement of the patient. Considering the small size of the miniature camera, the quality problems of its lens and components may cause radial and tangential distortion, which will reduce the accuracy of the imaging results, thereby causing deviations in the positioning of the implant site and affecting the implant accuracy. At the same time, the root of the positioning guide needs to be fixed inside the patient's mouth. Wearing it for a long time during surgery will cause discomfort to the patient, and the positioning guide needs to be designed and manufactured according to the patient's specific situation, which requires a lot of time and cost. Summary of the invention

[0005] In view of the above problems, the present invention proposes a field of view space segmentation DLT algorithm to solve the influence of possible tangential and radial distortion of the oral implant micro binocular camera on the positioning accuracy, so as to obtain the precise three-dimensional coordinates of the marker in the field of view, thereby achieving the effect of optimizing the positioning accuracy of the oral implant micro binocular camera. At the same time, a positioning ball is fixed inside the oral cavity to replace the positioning guide plate, and a micro binocular camera is used in conjunction to reduce the camera's field of view. The three-dimensional coordinates of the positioning marker are used for registration to calibrate the robotic arm to the implantation point in real time, ultimately achieving the goal of improving the accuracy of oral implants.

[0006] The technical solution adopted by the present invention is an optimization method for an oral implant micro binocular camera based on field of view spatial segmentation DLT, comprising the following steps:

[0007] Step 1: Field of view space segmentation. The field of view space of the micro camera is divided into 8 equal parts, and 8 subspaces composed of lattices are obtained. The subspaces are further segmented, and the field of view space is segmented n times in total, and divided into n layers;

[0008] Step 2: Using the sampling points in each lattice layer, the projection matrix M of each lattice layer is calculated according to the DLT algorithm to complete the n-layer calibration of the camera;

[0009] Step 3: The micro binocular camera obtains the pixel coordinates p of the target point p , and use the projection matrix calculated from the sampling points of the entire field of view space in step 2 to reconstruct the three-dimensional world coordinates p0 of the target point;

[0010] Step 4: Find the next 8 lattices of the current lattice, select the lattice whose spatial center is closest to p0, and use the projection matrix of the lattice calculated in step 2 to reconstruct the 3D world coordinate p1 of the next layer of the target point;

[0011] Step 5: Repeat step 4 until there is no next layer. Use the projection matrix of the lattice where the last layer is located to obtain the final reconstructed three-dimensional world coordinates p of the target point n .

[0012] Furthermore, in step 1, the camera's field of view is ideally a rectangular space, that is, at this time, it is layer 0, 1 lattice. The present invention selects a positioning ball as a sampling point, and uses a three-dimensional coordinate machine to record the three-dimensional world coordinates of the positioning ball. Select (2 n +1)×(2 n +1)×(2 n +1) sampling points, the field of view space can be divided into n layers, and the camera field of view space can be divided into 2 n ×2 n ×2 n A minimum lattice.

[0013] In the step 2, the present invention calculates the projection matrix of the subspace using the internal sampling points of the segmented subspace, thereby obtaining a projection matrix with higher accuracy in the subspace.

[0014] In step 4, the present invention uses a full octree data structure to store the projection matrix and spatial center coordinates of each lattice at each layer. Each node of the full octree represents a lattice, and while storing its projection matrix and spatial center coordinates, it also points to its 8 subspaces. The root node represents the entire field of view space, and the leaf node represents the smallest lattice. The time complexity of searching using the full octree structure is O(n), where n is the number of layers of the field of view space, which can achieve the purpose of quickly searching for the projection matrix and spatial center coordinates of each lattice at each layer.

[0015] Compared with the prior art, the beneficial effects of the present invention include: using a DLT algorithm of field of view space segmentation to approximate the actual performance of the lens, completing n-layer calibration of the oral implant miniature binocular camera, greatly reducing the influence of radial and tangential distortion and imaging distortion of the oral implant miniature binocular camera, optimizing and acquiring more accurate three-dimensional world coordinates, thereby effectively improving the accuracy of oral implants. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the process of optimizing the oral implant miniature binocular camera based on field of view spatial segmentation DLT.

[0017] Figure 2 Schematic diagram of the process of dividing the camera field of view space into n layers (n=2 in this case). DETAILED DESCRIPTION

[0018] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown, the present invention discloses a flow chart of an optimization method for an oral implantation micro binocular camera based on field of view spatial segmentation DLT. The present invention combines the three-dimensional world coordinates and pixel coordinates of the positioning ball to complete n-layer calibration of the micro binocular camera, reconstruct and optimize the three-dimensional world coordinates of the target point, including the following steps:

[0020] Step 1: Field of view space segmentation. The present invention idealizes the field of view space of the camera as a rectangular space, that is, at this time, it is the 0th layer, 1 lattice. The field of view space of the micro camera is divided into 8 equal parts to obtain 8 subspaces composed of lattices. Figure 2 As shown, the subspace is further divided, and the field of view space is divided n times in total, into n layers.

[0021] The present invention selects a positioning ball as a sampling point and uses a three-dimensional coordinate meter to record the three-dimensional world coordinates of the positioning ball. n +1)×(2 n +1)×(2 n +1) sampling points, the field of view space can be divided into n layers according to the segmentation method in step 1. At this time, the camera field of view space can be divided into 2n ×2 n ×2 n A minimum lattice.

[0022] Step 2: Using the sampling points in each lattice layer, the projection matrix M of each lattice layer is calculated according to the DLT algorithm to complete the n-layer calibration of the camera. The present invention uses the internal sampling points of the segmented subspace to calculate the projection matrix of the subspace, thereby obtaining a projection matrix with higher accuracy in the subspace.

[0023] The three-dimensional world coordinates of the positioning ball are recorded as p w (x w ,y w ,z w ), the pixel coordinate is p p (u, v). Let the camera's projection matrix be And write it in vector form, let m = [m 11 , m 12 , m 13 , m 14 , m 21 , m 22 , m 23 , m 24 ,m 31 , m 32 , m 33 , m 34 ] T According to the direct linear transformation algorithm (i.e., DLT algorithm, which is based on the camera projection model and estimates the camera projection matrix through the correspondence between the known three-dimensional points and their projection points on the image, so as to realize the camera calibration and the reconstruction of the three-dimensional target points), m 34 =1, and the linear equation system can be obtained:

[0024] Collect n sets of data (n is as many as possible, at least 6). When the number of equations exceeds the number of unknowns, an overdetermined system of equations can be obtained:

[0025] Simplifying, we can get Am = U, and using the least squares method to solve, we can get m = (A T A)- 1 A T U, the camera’s projection matrix can be found.

[0026] Step 3: The micro binocular camera obtains the pixel coordinates p of the target point p , and use the projection matrix calculated from the sampling points of the entire field of view space in step 2 to reconstruct the three-dimensional world coordinates p0 of the target point.

[0027] From the conversion relationship between the world coordinate system and the pixel coordinate system, we can know that the three-dimensional coordinates in the world coordinate system corresponding to the two-dimensional coordinates of any point in the pixel coordinate system can be obtained through the projection matrix. l and M r They are: The pixel coordinates of the target point on the left and right cameras are p l (u l , v l ), p r (u r , v r ). Assume the three-dimensional world coordinates of the target point are (x, y, z), and the above relationship can be used to obtain the overdetermined equations: Simplifying, we can get AP = Q. Similarly, according to the least squares method, we can get P = (A T A) -1 A T Q, the three-dimensional world coordinates can be reconstructed.

[0028] Step 4: Find the next 8 lattices in the next layer of the current lattice, select the lattice whose spatial center is closest to p0, and use the projection matrix of the lattice calculated in step 2 to reconstruct the 3D world coordinate p1 of the next layer of the target point.

[0029] The present invention uses a full octree data structure to store the projection matrix and spatial center coordinates of each lattice at each layer. Each node of the full octree represents a lattice, and while storing its projection matrix and spatial center coordinates, it also points to its 8 subspaces. The root node represents the entire field of view space, and the leaf node represents the smallest lattice. The time complexity of searching using the full octree structure is O(n), where n is the number of layers of the field of view space, and the purpose of quickly searching for the projection matrix and spatial center coordinates of each lattice at each layer can be achieved.

[0030] Step 5: Repeat step 4 until there is no next layer. Use the projection matrix of the lattice where the last layer is located to obtain the final reconstructed three-dimensional world coordinates p of the target point n , which is the three-dimensional world coordinates optimized for the oral implant micro binocular camera.

[0031] The above description is only a preferred specific implementation mode of the present invention, which cannot be used to limit the scope of rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope covered by the present invention.

Claims

1. An optimization method for oral implantation miniature binocular camera based on field of view spatial segmentation DLT, characterized in that: The following steps are involved: Step 1: Field of view space segmentation. The field of view space of the micro camera is divided into 8 equal parts, and 8 subspaces composed of lattices are obtained. The subspaces are further segmented, and the field of view space is segmented n times in total, and divided into n layers; Step 2: Using the sampling points in each lattice layer, calculate the projection matrix M of each lattice layer according to the DLT algorithm to complete the n-layer calibration of the camera: Step 3: The micro binocular camera obtains the pixel coordinates p of the target point p , and use the projection matrix calculated from the sampling points of the entire field of view space in step 2 to reconstruct the three-dimensional world coordinates p0 of the target point; Step 4: Find the next 8 lattices of the current lattice, select the lattice whose spatial center is closest to p0, and use the projection matrix of the lattice calculated in step 2 to reconstruct the 3D world coordinate p1 of the next layer of the target point; Step 5: Repeat step 4 until there is no next layer. Use the projection matrix of the lattice where the last layer is located to obtain the final reconstructed three-dimensional world coordinates p of the target point n , which is the three-dimensional world coordinates optimized for the oral implant micro binocular camera.

2. The method for optimizing an oral implantation miniature binocular camera based on field of view spatial segmentation DLT according to claim 1, characterized in that: In step 1, the camera's field of view is ideally a cuboid space, that is, at this time, it is layer 0, 1 lattice. Select the positioning ball as the sampling point, and use a three-dimensional coordinate machine to record the three-dimensional world coordinates of the positioning ball. Select (2 n +1)×(2 n +1)×(2 n +1) sampling points, the field of view space can be divided into n layers, and the camera field of view space can be divided into 2 n ×2 n ×2 n A minimum lattice.

3. The method for optimizing an oral implantation miniature binocular camera based on field of view spatial segmentation DLT according to claim 1, characterized in that: In step 2, the projection matrix of the subspace is calculated using the internal sampling points of the segmented subspace, so as to obtain a projection matrix with higher accuracy in the subspace.

4. The method for optimizing an oral implantation miniature binocular camera based on field of view spatial segmentation DLT according to claim 1, characterized in that: In step 4, a full octree data structure is used to store the projection matrix and spatial center coordinates of each lattice at each layer. Each node of the full octree represents a lattice, and while storing its projection matrix and spatial center coordinates, it also points to its 8 subspaces. The root node represents the entire field of view space, and the leaf node represents the smallest lattice. The time complexity of searching using the full octree structure is O(n), where n is the number of layers of the field of view space, which can achieve the purpose of quickly searching for the projection matrix and spatial center coordinates of each lattice at each layer.

Images