3D camera temperature drift adaptive compensation method

Abstract

The application discloses a 3D camera temperature drift self-adaptive compensation method, collects a depth map data set; a discrete point set is made for the depth map data set; a fitting function is established according to the discrete point set; and a self-adaptive compensation coefficient is determined according to the fitting function. The application can reduce the depth map temperature drift and improve the 3D camera measurement precision.

Description

Technical Field

[0001] This invention belongs to the field of 3D camera depth image correction technology, specifically relating to a 3D camera temperature drift adaptive compensation method. Background Technology

[0002] Compared to ordinary 2D cameras, 3D cameras, because they contain depth information of the target object, are widely used in machine vision fields such as autonomous driving, 3D reconstruction, and industrial inspection. Furthermore, with technological advancements and increasing inspection demands, the required measurement accuracy of 3D cameras is also rising.

[0003] However, due to limitations in its hardware and the influence of the external environment, 3D cameras inevitably exhibit measurement errors when measuring depth. Among these, changes in ambient temperature and the camera's internal module and chip heat generation are significant factors contributing to these errors. Furthermore, at the same temperature, different distances will result in varying drift errors. Therefore, a solution that adaptively compensates for depth changes with temperature and distance can significantly improve the camera's measurement accuracy. Summary of the Invention

[0004] In view of this, the main objective of the present invention is to provide a method for adaptive compensation of temperature drift in 3D cameras.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0006] This invention also provides a 3D camera temperature drift adaptive compensation method, the method comprising:

[0007] Collect depth map datasets;

[0008] Create a discrete point set from the aforementioned depth map dataset;

[0009] Establish a fitting function based on the discrete point set;

[0010] The adaptive compensation coefficients are determined based on the fitting function.

[0011] In the above scheme, the acquisition of the depth map dataset specifically involves: fixing the 3D camera parallel to a vertical smooth plane, and acquiring the depth map dataset within the temperature range at intervals from near to far.

[0012] In the above scheme, within a distance range of 300mm-3000mm, at intervals of 300mm, a depth map dataset with a temperature range of -10℃ to 60℃ at the current distance is collected as the depth map dataset.

[0013] In the above scheme, the step of creating a discrete point set for the depth map dataset specifically involves: arbitrarily selecting a depth map in the depth map dataset, reading the depth value at any coordinate point, determining the error between this depth value and the actual depth value, and combining it with the temperature and depth value of the current depth map to form a three-dimensional point; then, performing the same operation on this coordinate position for each depth map in the depth map dataset to obtain three-dimensional points, and finally forming a discrete point set for all three-dimensional points corresponding to this coordinate position.

[0014] In the above scheme, the step of establishing a fitting function based on the discrete point set is specifically as follows: the fitting function is z = a*x + b*y + c, where z represents the error, x represents the currently read 3D camera temperature, and y represents the depth value measured by the 3D camera.

[0015] In the above scheme, determining the adaptive compensation coefficients based on the fitting function specifically involves: determining the parameters a, b, and c in the fitting function z = a*x + b*y + c using the Random Sample Consensus Algorithm (RANSAC), where the parameters a, b, and c are the adaptive compensation coefficients for the current coordinate point.

[0016] The above scheme further includes: traversing each coordinate point in the depth map, calculating the adaptive compensation coefficient for all coordinate points in turn, and forming a compensation coefficient table based on this resolution map.

[0017] Compared with existing technologies, the present invention can reduce depth map temperature drift and improve the measurement accuracy of 3D cameras. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and, together with their descriptions, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 The flowchart illustrates a 3D camera temperature drift adaptive compensation method according to an embodiment of the present invention.

[0020] Figure 2 This invention provides a diagram showing the positional relationship between the 3D camera and the measurement surface during the data acquisition process in a 3D camera temperature drift adaptive compensation method, as provided in this embodiment of the invention.

[0021] Figure 3 This is a comparison image showing the effects before and after warming and tonifying. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] In the accompanying drawings of this embodiment, the same or similar reference numerals correspond to the same or similar components. In the description of this invention, it should be understood that the terms "upper," "lower," "left," "right," "inner," "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the accompanying drawings are only for illustrative purposes and should not be construed as limiting this patent. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0024] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, article, or apparatus that includes that element.

[0025] This invention provides a 3D camera temperature drift adaptive compensation method, such as... Figure 1 As shown, the method includes:

[0026] Step 101: Collect depth map dataset;

[0027] Specifically, a 3D camera is fixed parallel to a vertical smooth plane, and depth map datasets are collected at intervals from near to far, within the temperature range at the current distance.

[0028] like Figure 2 As shown, within a distance range of 300mm-3000mm, at intervals of 300mm, depth map datasets with temperatures ranging from -10℃ to 60℃ at the current distance are collected as depth map datasets.

[0029] Step 102: Create a discrete point set for the depth map dataset;

[0030] Specifically, in the depth map dataset, any depth map is selected, the depth value at any coordinate point is read, the error between the depth value and the actual depth value is determined, and a three-dimensional point is formed by combining the temperature of the current depth map and the depth value of the depth map; then, the same operation is performed on this coordinate position of each depth map in the depth map dataset to obtain three-dimensional points, and finally all the three-dimensional points corresponding to this coordinate position form a discrete point set.

[0031] Step 103: Establish a fitting function based on the discrete point set;

[0032] Specifically, the fitting function is z = a*x + b*y + c, where z represents the error, x represents the currently read 3D camera temperature, and y represents the depth value measured by the 3D camera.

[0033] Step 104: Determine the adaptive compensation coefficients based on the fitted function.

[0034] Specifically, the parameters a, b, and c in the fitting function z = a*x + b*y + c are determined by the Random Sample Consensus Algorithm (RANSAC), and these parameters a, b, and c are the adaptive compensation coefficients for the current coordinate point.

[0035] Furthermore, the method also includes: traversing each coordinate point in the depth map, sequentially calculating the adaptive compensation coefficient for all coordinate points, and forming a compensation coefficient table based on this resolution map.

[0036] Furthermore, the current depth map and current temperature value of the 3D camera are read and used as x and y values ​​in z = a*x + b*y + c; the parameter table is traversed, and the parameter values ​​of each coordinate point in the compensation coefficient table, namely a, b, and c, are substituted into the equation z = a*x + b*y + c to calculate the z value, which is the error value under each coordinate.

[0037] The correct depth value is obtained by subtracting the error value from the current depth value.

[0038] Tables 1 and 2 present partial experimental data results. The data in the tables represent the angle values ​​between the depth point cloud plane and the ideal horizontal plane. Table 1 shows the original depth map point cloud data before temperature compensation, and Table 2 shows the depth map point cloud data after temperature compensation.

[0039] Table 1 shows the point cloud angle data of the original depth map before partial temperature compensation.

[0040]

[0041] Table 2 shows the point cloud angle data of the depth map after partial temperature compensation.

[0042]

[0043] like Figure 3As shown, the image shows the front view of the depth map point cloud before and after temperature compensation at a distance of 1500mm and a temperature of 50℃. Point cloud plane 1 is the original depth map point cloud plane, and the angle between point cloud plane 1 and the ideal horizontal plane is -7.34 degrees. Point cloud plane 2 is the depth map point cloud after temperature compensation, and the angle between point cloud plane 2 and the ideal horizontal plane is 0.558 degrees.

[0044] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A method for adaptive compensation of temperature drift in a 3D camera, characterized in that, The method includes: Collect depth map datasets; Create a discrete point set from the aforementioned depth map dataset; Establish a fitting function based on the discrete point set; The adaptive compensation coefficients are determined based on the fitting function; The process of creating a discrete point set for the depth map dataset is as follows: Randomly select a depth map from the depth map dataset, read the depth value at any coordinate point, determine the error between this depth value and the actual depth value, and combine it with the temperature and depth value of the current depth map to form a three-dimensional point; then, perform the same operation on this coordinate position for each depth map in the depth map dataset to obtain three-dimensional points, and finally, all three-dimensional points corresponding to this coordinate position form a discrete point set. The step of establishing a fitting function based on the discrete point set is as follows: the fitting function is z=a*x+b*y+c, where z represents the error, x represents the currently read 3D camera temperature, and y represents the depth value measured by the 3D camera. The step of determining the adaptive compensation coefficients based on the fitting function specifically involves: determining the parameters a, b, and c in the fitting function z=a*x+b*y+c using the Random Sample Consensus Algorithm (RANSAC), where the parameters a, b, and c are the adaptive compensation coefficients for the current coordinate point.

2. The 3D camera temperature drift adaptive compensation method according to claim 1, characterized in that, The acquisition of the depth map dataset specifically involves fixing the 3D camera parallel to a vertical smooth plane, and acquiring a depth map dataset within the temperature range at intervals from near to far.

3. The 3D camera temperature drift adaptive compensation method according to claim 2, characterized in that, Within a distance range of 300mm-3000mm, at 300mm intervals, depth map datasets with temperatures ranging from -10℃ to 60℃ at the current distance are collected as depth map datasets.

4. The 3D camera temperature drift adaptive compensation method according to claim 1, characterized in that, The method also includes: traversing each coordinate point in the depth map, calculating the adaptive compensation coefficient for all coordinate points in turn, and forming a compensation coefficient table based on this resolution map.

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