Method for detecting relative orientation between projection plane and projection device and related apparatus

Abstract

The present application provides method for detecting a relative orientation between a projection plane and projection device and a related apparatus. The method comprises: obtaining a plurality of normal vector estimation results corresponding to a target projection plane, wherein the normal vector estimation results are used for representing normal vectors of the target projection plane that are obtained by means of estimation, and different normal vector estimation results are estimated on the basis of different sensing data; fusing the plurality of normal vector estimation results to obtain a normal vector detection result of the target projection plane; and determining a relative orientation between the target projection plane and a projection device on the basis of the normal vector detection result, wherein the relative orientation is used for correcting a target picture, and a target picture is a picture projected by the projection device onto the target projection plane. The technical solution can improve the accuracy of normal vector detection of the projection plane, thereby improving the effect of picture trapezoidal correction.

Description

Relative posture detection method and related device between projection plane and projection equipment

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on January 8, 2024, with application number 202410028144.1 and invention name “Relative posture detection method and related device between projection plane and projection device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of plane estimation, for example, to a method, apparatus, device and storage medium for detecting the relative posture between a projection plane and a projection device. Background Art

[0003] During projector use, the following situation often occurs: due to changes in the projector's placement or the projection environment, the projector's position is not perpendicular to the projection plane, resulting in keystone distortion of the projected image, causing distortion of the projected image and affecting the user experience. Therefore, the projector's keystone correction function is particularly important.

[0004] To implement trapezoidal correction, it is necessary to estimate the normal vector of the projection plane, and determine the trapezoidal shape projected onto the projection plane based on the estimated normal vector of the projection plane to achieve correction of the projection image. Summary of the Invention

[0005] The present application provides a method, apparatus, device and storage medium for detecting the relative posture between a projection plane and a projection device to determine the normal vector of the projection plane.

[0006] In a first aspect, a method for detecting a relative posture between a projection plane and a projection device is provided, comprising:

[0007] Obtaining multiple normal vector estimation results corresponding to the target projection plane, wherein the normal vector estimation results are used to represent the estimated normal vector of the target projection plane, and different normal vector estimation results are estimated based on different sensor data;

[0008] Fusing the multiple normal vector estimation results to obtain a normal vector detection result of the target projection plane;

[0009] According to the normal vector detection result, a relative posture between the target projection plane and the projection device is determined, and the relative posture is used to correct a target picture, which is a picture projected by the projection device onto the target projection plane.

[0010] In this technical solution, a plurality of normal vector estimation results corresponding to the target projection plane are obtained, and the plurality of normal vector estimation results are fused to obtain a normal vector detection result of the target projection plane. Since the normal vector estimation result is used to characterize the estimated normal vector of the target projection plane, the normal vector detection result of the target projection plane can be used to characterize the normal vector of the target projection plane, thereby realizing the detection of the normal vector of the projection plane, thereby realizing the detection of the relative posture between the projection plane and the projection device; since different normal vector estimation results are estimated based on different sensor data, the detection of the normal vector of the projection plane is realized by fusing the plurality of normal vector estimation results corresponding to the projection plane, which can avoid the problem of inaccurate normal vector estimation caused by sensor noise, improve the accuracy of the normal vector detection of the projection plane, make the detected relative posture between the projection plane and the projection device sufficiently accurate, and improve the effect of the trapezoidal correction of the picture.

[0011] In conjunction with the first aspect, in one possible implementation, before fusing the multiple normal vector estimation results to obtain the normal vector detection result of the target projection plane, the method further includes: performing validity detection on the multiple normal vector estimation results; and fusing the multiple normal vector estimation results to obtain the normal vector detection result of the target projection plane includes: fusing valid normal vector estimation results to obtain the normal vector detection result of the target projection plane. Before fusing the multiple normal vector estimation results, first detecting the validity of the normal vector estimation results, and then fusing the valid normal vector estimation results to detect the normal vector of the projection plane can further improve the accuracy of normal vector detection.

[0012] In combination with the first aspect, in a possible implementation, each normal vector estimation result includes a pitch angle; the validity detection of the multiple normal vector estimation results includes: obtaining a reference vector detection result, the reference vector detection result is used to characterize the ground normal vector corresponding to the projection device, the ground normal vector is perpendicular to the normal vector of the target projection plane, and the reference vector detection result includes a reference pitch angle; if the angle difference between the pitch angle corresponding to the target normal vector estimation result and the reference pitch angle is greater than a preset angle, then the target normal vector estimation result is determined to be an invalid normal vector estimation result, and the target normal vector estimation result is any normal vector estimation result among the multiple normal vector estimation results; if the angle difference between the pitch angle corresponding to the target normal vector estimation result and the reference pitch angle is less than or equal to the preset angle, then the target normal vector estimation result is determined to be a valid normal vector estimation result. The validity of the normal vector estimation result is detected by comparing the pitch angle in the normal vector estimation result of the projection plane with the pitch angle in the reference vector detection result, and the implementation method is simple and effective.

[0013] In combination with the first aspect, in a possible implementation manner, obtaining the reference vector detection result includes: obtaining a target inertial acceleration of the projection device; and calculating the reference vector detection result according to the target inertial acceleration.

[0014] In conjunction with the first aspect, in one possible implementation, obtaining the target inertial acceleration of the projection device includes: obtaining multiple inertial accelerations of the projection device; filtering out noise inertial accelerations outside the inertial acceleration range of the projection device from the multiple inertial accelerations; and determining the average of the filtered inertial accelerations as the target inertial acceleration. When determining the reference normal vector detection result, filtering out noise inertial accelerations outside the inertial acceleration range can ensure the accuracy of the inertial acceleration, thereby improving the accuracy of the reference normal vector detection result.

[0015] In conjunction with the first aspect, in one possible implementation, each normal vector estimation result includes a pitch angle and a yaw angle; fusing the multiple normal vector estimation results to obtain the normal vector detection result of the target projection plane includes: performing a weighted summation of the pitch angles in the multiple normal vector estimation results to obtain the pitch angle in the normal vector detection result; and performing a weighted summation of the yaw angles in the multiple normal vector estimation results to obtain the yaw angle in the normal vector detection result. Fusion of multiple normal vector estimation results by weighted summation can reduce the normal vector estimation deviation caused by noise and improve the accuracy of normal vector detection of the projection plane.

[0016] In combination with the first aspect, in a possible implementation method, the multiple normal vector estimation results include a first normal vector estimation result, which is a normal vector estimation result estimated based on image sensing data; obtaining the multiple normal vector estimation results corresponding to the target projection plane includes: projecting a target image containing multiple preset feature points onto the target projection plane; obtaining a projection plane image corresponding to the target projection plane, wherein the projection plane image contains the target image; and determining the first normal vector estimation result based on the positions of the multiple preset feature points in the projection plane image.

[0017] In conjunction with the first aspect, in one possible implementation, after obtaining the projection plane image corresponding to the target projection plane, the method further includes: if at least one of the multiple preset feature points is not detected in the projection plane image, skipping the step of determining the first normal vector estimation result based on the positions of the multiple preset feature points in the projection plane image. When all preset feature points are not detected in the projection plane image, it indicates that the target projection plane has stains, which will affect the normal vector detection effect of the target projection plane. Skipping the calculation of the normal vector detection result based on the image sensing data can avoid normal vector detection errors caused by stains on the target projection plane.

[0018] In combination with the first aspect, in a possible implementation method, the multiple normal vector estimation results include a second normal vector estimation result, which is a normal vector estimation result estimated based on distance sensing data; obtaining the multiple normal vector estimation results corresponding to the target projection plane includes: obtaining four target distance data corresponding to the target projection plane, the four target distance data respectively reflecting the distance between the projection device and the upper, lower, left and right four areas in the target projection plane; calculating the second normal vector estimation result based on the four target distance data.

[0019] In conjunction with the first aspect, in one possible implementation, obtaining four target distance data corresponding to the target projection plane includes: obtaining multiple distance data between the projection device and a target area in the target projection plane, where the target area is any one of the four areas: up, down, left, and right; filtering out the distance data that is not within the target distance range corresponding to the target area; and determining the mean of the filtered distance data as the target distance data corresponding to the target area. When determining the normal vector estimation result of the target projection plane, filtering out the distance data that is not within the distance range can ensure the accuracy of the distance data, thereby improving the accuracy of the normal vector estimation result of the target projection plane.

[0020] In conjunction with the first aspect, in one possible implementation, after acquiring the four target distance data corresponding to the target projection plane, the step further includes: if the ratio between the amount of filtered distance data and the total amount of acquired distance data exceeds a preset ratio, skipping the step of calculating the second normal vector estimation result based on the four target distance data. When the ratio between the filtered distance data and the total amount of acquired distance data exceeds the preset ratio, it indicates that the distance data is highly noisy, and skipping the calculation of the normal vector detection result based on the distance data can prevent normal vector detection errors caused by noise.

[0021] In a second aspect, a device for detecting a relative posture between a projection plane and a projection device is provided, comprising:

[0022] A normal vector acquisition module is used to obtain multiple normal vector estimation results corresponding to the target projection plane, wherein the normal vector estimation results are used to represent the estimated normal vector of the target projection plane, and different normal vector estimation results are estimated based on different sensor data;

[0023] A normal fusion module is used to fuse the multiple normal vector estimation results to obtain the normal vector detection result of the target projection plane; based on the normal vector detection result, the relative posture between the target projection plane and the projection device is determined, and the relative posture is used to correct the target picture, and the target picture is the picture projected by the projection device onto the target projection plane.

[0024] In a third aspect, a projection device is provided, comprising a memory, multiple sensors, and one or more processors, wherein the memory and the multiple sensors are connected to the one or more processors, the multiple sensors are used to detect multiple plane sensing data, and the one or more processors are used to execute one or more computer programs stored in the memory. When the one or more processors execute the one or more computer programs, the projection device implements the relative posture detection method between the projection plane and the projection device of the first aspect mentioned above.

[0025] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the relative posture detection method between the projection plane and the projection device of the first aspect.

[0026] The present application can achieve the following technical effects: since the normal vector estimation result is used to characterize the estimated normal vector of the target projection plane, the normal vector detection result of the target projection plane can be used to characterize the normal vector of the target projection plane, thereby realizing the detection of the normal vector of the projection plane, thereby realizing the detection of the relative posture between the projection plane and the projection device; since different normal vector estimation results are estimated based on different sensor data, the normal vector of the projection plane is detected by fusing multiple normal vector estimation results corresponding to the projection plane, which can avoid the problem of inaccurate normal vector estimation caused by sensor noise, improve the accuracy of the normal vector detection of the projection plane, make the detected relative posture between the projection plane and the projection device sufficiently accurate, and improve the effect of the trapezoidal correction of the picture. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0028] FIG1 is a schematic flow chart of a method for detecting a relative posture between a projection plane and a projection device according to an embodiment of the present application;

[0029] FIG2 is a schematic flow chart of a method for detecting a relative posture between a projection plane and a projection device according to an embodiment of the present application;

[0030] 3 is a schematic structural diagram of a device for detecting a relative posture between a projection plane and a projection device provided in an embodiment of the present application;

[0031] FIG4 is a schematic structural diagram of a projection device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and examples. 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. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0033] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other and are all within the scope of protection of the present application. In addition, although the functional modules are divided in the device schematic and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flow chart. Furthermore, the words "first", "second", "third", etc. used in this application do not limit the data and execution order, but only distinguish between the same items or similar items with basically the same functions and effects.

[0034] The technical solution of the present application is applicable to scenarios requiring projection plane estimation, such as a trapezoidal correction scenario for a projector.

[0035] When a projector projects an image onto a projection plane, if the projection device projects with the projection device facing the projection plane, the central ray projected by the projection device is parallel to the normal vector of the projection plane, and the shape of the image projected onto the projection plane by the projection device is consistent with the original shape of the image, appearing as a rectangle. If the projection device projects without facing the projection plane, the central ray projected by the projection device is not parallel to the normal vector of the projection plane. Without image processing, the shape of the image projected onto the projection plane by the projection device will be distorted, appearing as a trapezoid. In order to ensure that the shape of the image projected onto the projection plane appears as a rectangle even when the projection device is not facing the projection plane, the image needs to be calibrated in advance before projecting the image onto the projection plane. The premise of image calibration is to estimate the projection plane, detect the relative posture between the projection device and the projection plane, and then adjust the image to be projected onto the projection plane based on the relative posture between the projection device and the projection plane. The relative posture between the projection plane and the projection device can be represented by estimating the coordinates of the normal vector of the projection plane in the coordinate system of the projection device and converting the coordinates of the normal vector in the coordinate system of the projection device into the posture of the projection device.

[0036] There are two main approaches for estimating the normal vector of the projection plane: time-of-flight (TOF)-based and camera-based. TOF-based approaches estimate the normal vector of the projection plane based on measured distance data, while camera-based approaches estimate the normal vector of the projection plane based on images captured by the camera. TOF-based approaches may suffer from data instability, meaning that the measured distance data is inaccurate, resulting in inaccurate estimated normal vectors of the projection plane. Camera-based approaches also place high demands on the cleanliness of the projection plane. If there are stains on the projection plane, the estimated projection plane will be inaccurate, thus affecting the accuracy of the relative posture detection between the projection plane and the projection device.

[0037] In view of this, the present application proposes a new relative posture detection scheme between the projection plane and the projection device. By acquiring multiple sensor data to respectively estimate the normal vector of the projection plane, multiple normal vector estimation results are obtained, and then the multiple normal vector estimation results are fused to obtain the final normal vector detection result of the projection plane. The fusion method can reduce the detection error caused by inaccurate sensor data, thereby improving the accuracy of the normal vector estimation of the projection plane, so that the detected relative posture between the projection plane and the projection device is sufficiently accurate.

[0038] The technical solution of the present application is introduced below, wherein the technical solution of the present application can be applied to a projection device including multiple sensors, and the multiple sensors in the projection device can include image sensors, distance sensors, inertial sensors, etc.

[0039] Referring to FIG. 1 , FIG. 1 is a flow chart of a method for detecting a relative posture between a projection plane and a projection device provided in an embodiment of the present application. As shown in FIG. 1 , the method includes the following steps:

[0040] S101, obtaining multiple normal vector estimation results corresponding to the target projection plane.

[0041] Here, the target projection plane refers to the plane onto which the projection device needs to project. The target projection plane can be a wall onto which the projection device needs to project, or a screen onto which the projection device needs to project.

[0042] The normal vector estimation result corresponding to the target projection plane is used to characterize the estimated normal vector of the target projection plane. The normal vector estimation result corresponding to the target projection plane is converted from the coordinates of the normal vector of the target projection plane in the coordinate system of the projection device, and can represent the relative posture between the projection plane and the projection device. Each normal vector estimation result includes a pitch angle and a yaw angle. The pitch angle refers to the angle of rotation along the Y axis of the coordinate system of the projection device, and the yaw angle refers to the angle of rotation along the Z axis of the coordinate system of the projection device. The X-axis direction of the coordinate system of the projection device is the orientation of the projection device, the Y-axis direction of the projection device is parallel to the device plane and perpendicular to the X axis, and the Z-axis direction of the projection device is perpendicular to the device plane.

[0043] The multiple normal vector estimation results corresponding to the target projection plane are estimated based on multiple types of sensor data, with different normal vector estimation results estimated based on different sensor data, and each normal vector estimation result corresponds to one type of sensor data. The multiple normal vector estimation results corresponding to the target projection plane may include a normal vector estimation result estimated based on image sensor data (hereinafter referred to as a first normal vector estimation result) and a normal vector estimation result estimated based on distance sensor data (hereinafter referred to as a second normal vector estimation result).

[0044] Image sensing data can be obtained based on a camera, and the first normal vector estimation result can be obtained by detecting and identifying the image of the target projection plane captured by the camera. The first normal vector estimation result can be obtained through the following steps A1-A3:

[0045] A1. Project a target image containing multiple preset feature points onto a target projection plane.

[0046] Here, the preset feature points are feature points that are easily identifiable and detectable in an image, and the target image is a calibration image used for camera calibration. For example, the target image can be a checkerboard image, and the corner points in the checkerboard image are the preset feature points.

[0047] The target image may be projected onto the target projection plane through an optical machine of a projection device. After the target image is projected onto the target projection plane, it is displayed on the target projection plane.

[0048] A2. Obtain a projection plane image corresponding to the target projection plane.

[0049] Here, the target projection plane can be photographed by the camera of the projection device to obtain a projection plane image corresponding to the target projection plane. Since a target image containing multiple preset feature points is projected onto the target projection plane in advance, the projection plane image corresponding to the target projection plane contains the target image, that is, contains the multiple preset feature points.

[0050] A3. Determine a first normal vector estimation result according to positions of the plurality of preset feature points in the projected plane image.

[0051] Here, the plurality of preset feature points can be first detected from the projected plane image; then the positions of the plurality of preset feature points in the projected plane image are determined, and the positions of the plurality of preset feature points in the projected plane image determined are used as the actual positions of the plurality of preset feature points in the projected plane image; then a relationship model is constructed regarding the plane normal vector and the position information of the preset feature points, as well as a position error model regarding the error between the first position corresponding to the preset feature point and the second position corresponding to the preset feature point, wherein the first position corresponding to the preset feature point is the estimated position of the preset feature point in the projected plane image determined based on the position information of the preset feature point, and the second position corresponding to the preset feature point is the actual position of the preset feature point in the projected plane image; finally, the plane normal vector that minimizes the error corresponding to the position error model and satisfies the relationship model is solved as the first plane normal vector. After the first plane normal vector is calculated, the first plane normal vector is expressed in the form of the pitch angle and yaw angle of the projection device to obtain the first normal vector estimation result.

[0052] The relationship model between the plane normal vector and the position information of the preset feature points can be expressed as shown in formula (1): x x i +n y y i +n z z i +1.0=0.0 (1)

[0053] Among them, (n x , n y , n z ) is the vector representation of the plane normal vector; (x i ,y i , z i ) is the position coordinate of the i-th preset feature point.

[0054] The position error model based on the coordinate transformation relationship regarding the error between the first position corresponding to the preset feature point and the second position corresponding to the preset feature point can be expressed as shown in formula (2):

[0055] Where L represents the error between the first position corresponding to the preset feature point and the second position corresponding to the preset feature point; m represents the total number of preset feature points; K cam represents the intrinsic parameter of the camera, K cam Used to reflect the correspondence between the camera coordinate system and the pixel coordinate system, p i Represents the position coordinates of the i-th preset feature point; K cam p i represents the first position corresponding to the i-th preset feature point obtained by the first coordinate transformation method. The first coordinate transformation method means that the position coordinates of the preset feature point are transformed from the camera coordinate system to the pixel coordinate system based on the intrinsic parameters of the camera to obtain the estimated position of the preset feature point in the projected plane image. m cami K represents the second position corresponding to the i-th preset feature point under the first coordinate transformation mode; prj represents the internal parameter of the optical machine, K prj Used to reflect the correspondence between the optical coordinate system and the pixel coordinate system. and represents the external parameters of the optical machine, and Used to reflect the correspondence between the camera coordinate system and the optical machine coordinate system. represents the first position corresponding to the i-th preset feature point obtained by the second coordinate transformation method. The second coordinate transformation method means that the position coordinates of the preset feature point are first transformed from the camera coordinate system to the optical machine coordinate system based on the external parameters of the optical machine, and then the position coordinates of the preset feature point are transformed from the optical machine coordinate system to the pixel coordinate system based on the internal parameters of the optical machine to obtain the estimated position of the preset feature point in the projected plane image. m prji Indicates the second position corresponding to the i-th preset feature point under the second coordinate transformation method.

[0056] According to the above formula (1) and formula (2), determine the (n x , n y , n z ), as the first plane normal vector.

[0057] The calculation formulas for the pitch angle and yaw angle in the first normal vector estimation result can be shown as formulas (3) and (4):

[0058] Among them, pitch cam is the pitch angle in the first normal vector estimation result, yaw cam is the yaw angle in the first normal vector estimation result.

[0059] In some possible situations, before determining the first normal vector estimation result based on the positions of multiple preset feature points in the projection plane image, it can also be determined whether all the preset feature points are detected in the projection plane image corresponding to the target projection plane. If at least one of the multiple preset feature points is not detected in the projection plane image corresponding to the target projection plane, it means that the target projection plane may be dirty, resulting in incomplete detection of the preset feature points, which may cause calculation errors of the normal vector of the target projection plane. The above step A3 can be skipped, that is, the first normal vector estimation result is not calculated; if all the preset feature points of the multiple preset feature points are detected in the projection plane image corresponding to the target projection plane, it means that the target projection plane is clean and tidy, and the above step A3 is executed to complete the calculation of the first normal vector estimation result. Among them, it can be determined whether the number of preset feature points detected in the projection plane image is equal to the preset number. If the number of preset feature points detected in the projection plane image is equal to the preset number, it is determined that all of the multiple preset feature points are detected in the projection plane image corresponding to the target projection plane; if the number of preset feature points detected in the projection plane image is less than the preset number, it is determined that at least one of the multiple preset feature points is not detected in the projection plane image corresponding to the target projection plane. The preset feature points in the projection plane image can be detected by a corner detection algorithm. Corner detection algorithms include but are not limited to corner retrieval algorithms based on binary images, corner detection algorithms based on contour curves, and corner detection algorithms based on grayscale images, etc., and this application does not impose any restrictions.

[0060] When all preset feature points in the target image are not detected in the projection plane image, it means that there are stains on the target projection plane, which will affect the detection effect of the normal vector of the target projection plane. Skipping the calculation of the normal vector detection result based on the image sensing data can avoid the normal vector detection error caused by the stains on the target projection plane.

[0061] The distance sensing data can be measured and calculated based on the TOF method, and the second normal vector estimation result can be obtained by processing the distance sensing data. The second normal vector estimation result can be obtained through the following steps B1-B2:

[0062] B1. Obtain the four target distance data corresponding to the target projection plane.

[0063] Here, the target distance data respectively reflects the distance between the projection device and the four upper, lower, left, and right regions of the target projection plane. A distance sensor can be used to detect multiple distance data between the projection device and the four upper, lower, left, and right regions of the target projection plane, respectively, to obtain multiple distance data corresponding to each of the four upper, lower, left, and right regions of the target projection plane. Based on the multiple distance data corresponding to each of the four upper, lower, left, and right regions of the target projection plane, four target distance data corresponding to the target projection plane are determined.

[0064] For any one of the four areas of the target projection plane (hereinafter referred to as the target area), any one of the multiple distance data between the projection device and the target area of ​​the target projection plane can be selected as the target distance data corresponding to the target area of ​​the target projection plane. Alternatively, the average of the multiple distance data between the projection device and the target area of ​​the target projection plane can be used as the target distance data corresponding to the target area of ​​the target projection plane, that is, D is the target distance data corresponding to the target area of ​​the target projection plane, d j is the jth distance data between the projection device and the target area of ​​the target projection plane, and n is the total number of distance data detected between the projection device and the target area of ​​the target projection plane. It is also possible to filter out the distance data that is not within the target distance range corresponding to the target area corresponding to the target projection plane from the multiple distance data between the projection device and the target area of ​​the target projection plane, and determine the target distance data corresponding to the target area of ​​the target projection plane by taking the average of the filtered distance data. Here, the filtered distance data refers to the remaining distance data after filtering out the distance data that is not within the target distance range corresponding to the target area corresponding to the target projection plane. The two distance boundary values ​​within the target distance range corresponding to the target area corresponding to the target projection plane can be obtained based on the following formulas (5)-(7):

[0065] Among them, d min The lower limit of the target distance range corresponding to the target area corresponding to the target projection plane, d max is the upper limit of the target distance range corresponding to the target area corresponding to the target projection plane, [d min ,d max ] is the target distance range corresponding to the target area corresponding to the target projection plane, σ1 is the preset distance value, σ1 is used to reflect the measurement noise of the distance sensor, and σ1 is usually recorded in the manual of the distance sensor.

[0066] When determining the normal vector of the target projection plane, by filtering out the distance data that is not within the distance range, the accuracy of the distance data can be ensured, thereby improving the accuracy of the normal vector of the target projection plane.

[0067] By processing the multiple distance data corresponding to the four upper, lower, left, and right regions of the target projection plane in the same way, the target distance data corresponding to the four upper, lower, left, and right regions of the target projection plane can be obtained, that is, the four target distance data corresponding to the target projection plane can be obtained. The four target distance data corresponding to the target projection plane can be expressed as d up d down d left and d right .

[0068] B2. Calculate the second normal vector estimation result based on the four target distance data.

[0069] Here, the second normal vector estimation result also includes the pitch angle and the yaw angle, which can be calculated according to the following formulas (8)-(9):

[0070] Among them, pitch tof is the pitch angle in the second normal vector estimation result, yaw tof is the yaw angle in the second normal vector estimation result, and α is the intrinsic angle of the distance sensor.

[0071] In some possible situations, before calculating the second normal vector estimation result based on the four target distance data, the ratio between the total number of distance data filtered out in the process of obtaining the four target distance data and the total number of distance data obtained can also be determined. If the ratio between the total number of filtered distance data and the total number of distance data obtained exceeds a preset ratio, it means that there is a lot of noise data, which will cause the error of the calculated normal vector of the target projection plane to be large. The above step B2 can be skipped, that is, the second normal vector estimation result is not calculated. If the ratio between the total number of filtered distance data and the total number of distance data obtained does not exceed the preset ratio, it means that there is little noise data and the error of the calculated normal vector of the target projection plane is small. The above step B2 is executed to complete the calculation of the second normal vector estimation result. Among them, the ratio of the distance data filtered out of each area in the four upper, lower, left and right areas of the target projection plane to the total number of distance data obtained for each area can be determined respectively. If the ratio of the distance data of the filtered target area to the total number of distance data obtained for the target area is greater than the preset ratio, the above step B2 is skipped, that is, c k / n k>P, 1≤k≤4, skip the above step B2, c k is the number of distance data of the kth region filtered out, n k is the total number of distance data of the kth area obtained, and P is a preset ratio; alternatively, the total number of distance data of each filtered area and the total number of distance data of each area obtained can also be determined. If the total number of distance data of each filtered area and the total number of distance data of each area obtained are greater than the preset ratio, then the above step B2 is skipped, that is, (c1+c2+c3+c4) / (n1+n2+n3+n4)>P, that is, the above step B2 is skipped.

[0072] When the ratio between the filtered distance data and the total number of acquired distance data exceeds a preset ratio, it indicates that the distance data has a lot of noise. Skipping the calculation of the normal vector estimation result based on the distance data can prevent normal vector detection errors caused by noise.

[0073] It should be understood that the multiple normal vector estimation results corresponding to the target projection plane may also include normal vector estimation results estimated based on other sensor data in addition to the above-mentioned first normal vector estimation result and the above-mentioned second normal vector estimation result, and this application does not impose any restrictions.

[0074] S102 , fusing multiple normal vector estimation results corresponding to the target projection plane to obtain a normal vector detection result of the target projection plane.

[0075] In a feasible implementation, after obtaining the pitch angle and yaw angle contained in each of the multiple normal vector estimation results corresponding to the target projection plane, the pitch angles in the multiple normal vector estimation results can be weighted and summed to obtain the pitch angle in the normal vector detection result; and the yaw angles in the multiple normal vector estimation results can be weighted and summed to obtain the yaw angle in the normal vector detection result.

[0076] Taking the multiple normal vector estimation results corresponding to the target projection plane, including the first normal vector estimation result and the second normal vector estimation result introduced in step S101 above, as an example, the pitch angle and yaw angle in the normal vector detection result of the target projection plane can be calculated by the following formulas (10) and (11): Pitch = a*pitch cam +(1-a)*pitch tof (10) Yaw=a*yaw cam +(1-a)*yaw tof (11)

[0077] Wherein, Pitch is the pitch angle in the normal vector detection result, Yaw is the yaw angle in the normal vector detection result, 1-a and a are the weights of the first normal vector estimation result and the second normal vector estimation result, respectively.

[0078] Here, a can be the ratio of the amount of distance data filtered out during the calculation of the second normal vector estimation result to the total amount of distance data acquired, i.e., a = (c1 + c2 + c3 + c4) / (n1 + n2 + n3 + n4). The weight value is positively correlated with the amount of distance data filtered out. The greater the amount of distance data filtered out, the more unstable the distance sensor data. Therefore, a greater weight is assigned to the normal vector estimation result estimated based on the image sensor data, thereby making the calculated normal vector detection result more accurate.

[0079] By fusing multiple normal vector estimation results in a weighted summation manner, the normal vector estimation deviation caused by noise can be reduced and the accuracy of normal vector detection of the target projection plane can be improved.

[0080] S103 : determining the relative posture between the target projection plane and the projection device according to the normal vector detection result of the target projection plane.

[0081] Here, the normal vector detection result is used to reflect the orientation and tilt direction of the projection plane. Since the normal vector detection result includes the pitch angle and yaw angle, the pitch angle is used to reflect the relative tilt degree between the target projection plane and the projection device in the Y-axis direction, and the yaw angle is used to reflect the relative tilt degree between the target projection plane and the projection device in the Z-axis direction.

[0082] After obtaining the normal vector detection result of the target projection plane and determining the relative posture between the projection plane and the projection device, the target image can be corrected. The target image is the image projected by the projection device onto the target projection plane.

[0083] In one embodiment, the relative angle between the target projection plane and the projection device can be calculated based on the pitch angle and the yaw angle, and then the target image can be corrected based on the relative angle between the target projection plane and the projection device. Among them, the distance by which the image needs to be translated left and right can be determined based on the positive and negative values ​​of the relative angle. When the relative angle is positive, the image is translated to the left; when the relative angle is negative, the image is translated to the right. And, based on the positive and negative values ​​of the relative angle, the angle by which the image needs to be tilted upward or downward is determined. When the relative angle is positive, the image is tilted upward; when the relative angle is negative, the image is tilted downward. The calculation formula for the relative angle can be: α = arccos(cosYaw)*cos(pitch)). α is the relative angle.

[0084] In the technical solution corresponding to Figure 1 above, the normal vector detection result of the target projection plane is obtained by obtaining multiple normal vector estimation results corresponding to the target projection plane and fusing the multiple normal vector estimation results. Since the normal vector estimation result is used to characterize the estimated normal vector of the target projection plane, the normal vector detection result of the target projection plane can be used to characterize the normal vector of the target projection plane, thereby realizing the detection of the normal vector of the projection plane, thereby realizing the detection of the relative posture between the projection plane and the projection device; since different normal vector estimation results are estimated based on different sensor data, the normal vector of the projection plane is detected by fusing the multiple normal vector estimation results corresponding to the projection plane, which can avoid the problem of inaccurate normal vector estimation caused by sensor noise, improve the accuracy of the normal vector detection of the projection plane, make the detected relative posture between the projection plane and the projection device sufficiently accurate, and improve the effect of the trapezoidal correction of the picture.

[0085] Referring to FIG. 2 , FIG. 2 is a flow chart of a method for detecting a relative posture between a projection plane and a projection device provided in an embodiment of the present application. As shown in FIG. 2 , the method includes the following steps:

[0086] S201, obtaining multiple normal vector estimation results corresponding to the target projection plane.

[0087] Here, regarding the implementation of step S201, please refer to the description of the aforementioned step S101, which will not be repeated here.

[0088] S202: Perform validity detection on multiple normal vector estimation results corresponding to the target projection plane.

[0089] In a feasible implementation, the validity of multiple normal vector estimation results corresponding to the target projection plane can be checked through the following steps C1-C4:

[0090] C1. Obtain the reference vector detection result.

[0091] Here, the reference vector detection result is used to characterize the ground normal vector corresponding to the projection device. The ground normal vector is perpendicular to the normal vector of the projection plane. The reference vector detection result can be used to represent the relative posture between the ground (i.e., the horizontal plane) and the projection device. The reference vector detection result includes a reference roll angle and a reference pitch angle. The reference roll angle and the reference pitch angle can be expressed as roll imu and pitch imu .

[0092] In a feasible implementation, the reference vector detection result may be determined through the following steps C11-C12:

[0093] C11. Obtain the target inertial acceleration of the projection device.

[0094] Here, multiple inertial accelerations can be detected by an inertial sensor, and the target inertial acceleration of the projection device can be determined based on the multiple inertial accelerations. Among them, any one of the multiple inertial accelerations detected can be selected as the target inertial acceleration of the projection device. Alternatively, the average of the multiple inertial accelerations detected can be used as the target inertial acceleration of the projection device, that is, A is the target inertial acceleration of the projection device, a f is the fth detected inertial acceleration, and N is the total number of detected inertial accelerations. Furthermore, from the multiple detected inertial accelerations, noise inertial accelerations that are not within the inertial acceleration range of the projection device may be filtered out, and the average of the filtered inertial accelerations may be determined as the target inertial acceleration. Here, the filtered inertial acceleration refers to the inertial acceleration remaining after filtering out the noise inertial accelerations that are not within the inertial acceleration range of the projection device.

[0095] The two inertial acceleration boundary values ​​corresponding to the inertial acceleration range of the projection device can be obtained based on the following formulas (12)-(14):

[0096] Among them, a min is the lower limit of inertia acceleration corresponding to the inertia acceleration range, a max is the upper limit of inertia acceleration corresponding to the inertia acceleration range, [a min ,a max ] is the inertial acceleration range of the projection device, σ2 is the preset inertial acceleration value, σ2 is used to reflect the measurement noise of the inertial sensor, and σ2 is usually recorded in the manual of the inertial sensor.

[0097] When determining the reference normal vector detection result, by filtering out the noise inertial acceleration that is not within the inertial acceleration range, the accuracy of the inertial acceleration can be guaranteed, thereby improving the accuracy of the reference normal vector detection result.

[0098] C12. Calculate the reference vector detection result based on the target inertial acceleration of the projection device.

[0099] Here, the reference vector detection result can be calculated according to the following formulas (15)-(16):

[0100] Among them, a y and a x are the Y-axis and X-axis components of the target inertial acceleration respectively.

[0101] C2. Determine whether the angle difference between the pitch angle in the target normal vector estimation result and the reference pitch angle is greater than a preset angle.

[0102] C3. If the angle difference between the pitch angle in the target normal vector estimation result and the reference pitch angle is greater than a preset angle, the target normal vector estimation result is determined to be an invalid normal vector estimation result.

[0103] If the angle difference between the pitch angle in the target normal vector estimation result and the reference pitch angle is greater than the preset angle, it means that the calculation error of the target normal vector estimation result is large.

[0104] C4. If the angle difference between the pitch angle in the target normal vector estimation result and the reference pitch angle is less than or equal to a preset angle, determine that the target normal vector estimation result is a valid normal vector estimation result.

[0105] If the angle difference between the pitch angle corresponding to the target normal vector estimation result and the reference pitch angle corresponding to the ground normal vector is less than or equal to the preset angle, it means that the calculation error of the target normal vector estimation result is small.

[0106] By testing the validity of each normal vector estimation result corresponding to the target projection plane in accordance with steps C1-C4 above, the validity of multiple normal vector estimation results corresponding to the target projection plane can be tested. Testing the validity of the normal vector estimation results is simple and effective by comparing the pitch angle in the normal vector estimation result for the projection plane with the pitch angle in the reference vector detection result.

[0107] S203: Fusing the valid normal vector estimation results to obtain the normal vector detection result of the target projection plane.

[0108] Here, the principle of fusing the valid normal vector estimation results is the same as that of fusing the multiple normal vector estimation results in the aforementioned step S202. The implementation method of the aforementioned step S202 may be referred to and will not be repeated here.

[0109] S204: Determine the relative posture between the target projection plane and the projection device according to the normal vector detection result of the target projection plane.

[0110] Here, regarding the implementation of step S204, please refer to the description of the aforementioned step S103, which will not be repeated here.

[0111] In the technical solution corresponding to Figure 2 above, after obtaining multiple normal vector estimation results corresponding to the target projection plane, the validity of the multiple normal vector estimation results corresponding to the target projection plane are first checked, and then the valid normal vector estimation results are fused to obtain the normal vector detection result of the target projection plane, which can realize the detection of the normal vector of the target projection plane, thereby realizing the detection of the relative posture between the projection plane and the projection device; before fusing the multiple normal vector estimation results, the validity of the normal vector estimation results is first checked, and then the valid normal vector estimation results are fused to detect the normal vector of the projection plane, which can further improve the accuracy of normal vector detection.

[0112] The method of the present application is introduced above, and the device of the present application is introduced below.

[0113] Referring to FIG3 , FIG3 is a schematic diagram of a relative posture detection device between a projection plane and a projection device provided in an embodiment of the present application. As shown in FIG3 , the relative posture detection device 30 between the projection plane and the projection device includes:

[0114] A normal vector acquisition module 301 is used to obtain multiple normal vector estimation results corresponding to the target projection plane, wherein the normal vector estimation results are used to represent the estimated normal vector of the target projection plane, and different normal vector estimation results are estimated based on different sensor data;

[0115] The normal fusion module 302 is used to fuse the multiple normal vector estimation results to obtain the normal vector detection result of the target projection plane; based on the normal vector detection result, the relative posture between the target projection plane and the projection device is determined, and the relative posture is used to correct the target picture, and the target picture is the picture projected by the projection device onto the target projection plane.

[0116] In one possible design, the relative posture detection device 30 between the above-mentioned projection plane and the projection device also includes a validity detection module 303, which is used to perform validity detection on the multiple normal vector estimation results; the above-mentioned normal fusion module 302 is used to: fuse the valid normal vector estimation results to obtain the normal vector detection result of the target projection plane.

[0117] In one possible design, each normal vector estimation result includes a pitch angle; the above-mentioned validity detection module 303 is used to: obtain a reference vector detection result, wherein the reference vector detection result is used to characterize the ground normal vector corresponding to the projection device, the ground normal vector is perpendicular to the normal vector of the target projection plane, and the reference vector detection result includes a reference pitch angle; if the angle difference between the pitch angle corresponding to the target normal vector estimation result and the reference pitch angle is greater than a preset angle, then the target normal vector estimation result is determined to be an invalid normal vector estimation result, and the target normal vector estimation result is any one of the multiple normal vector estimation results; if the angle difference between the pitch angle corresponding to the target normal vector estimation result and the reference pitch angle is less than or equal to the preset angle, then the target normal vector estimation result is determined to be a valid normal vector estimation result.

[0118] In a possible design, the validity detection module 303 is configured to: obtain a target inertial acceleration of the projection device; and calculate the reference vector detection result according to the target inertial acceleration.

[0119] In one possible design, the validity detection module 303 is used to: obtain multiple inertial accelerations of the projection device; filter out noise inertial accelerations that are not within the inertial acceleration range of the projection device among the multiple inertial accelerations; and determine the average of the filtered inertial accelerations as the target inertial acceleration.

[0120] In one possible design, each normal vector estimation result includes a pitch angle and a yaw angle; the above-mentioned normal fusion module 302 is used to: perform weighted summation of the pitch angles in the multiple normal vector estimation results to obtain the pitch angle in the normal vector detection result; perform weighted summation of the yaw angles in the multiple normal vector estimation results to obtain the yaw angle in the normal vector detection result.

[0121] In one possible design, the multiple normal vector estimation results include a first normal vector estimation result, which is a normal vector estimation result estimated based on image sensing data; the above-mentioned normal vector acquisition module 301 is used to: project a target image containing multiple preset feature points onto the target projection plane; obtain a projection plane image corresponding to the target projection plane, wherein the projection plane image contains the target image; determine the first normal vector estimation result based on the positions of the multiple preset feature points in the projection plane image.

[0122] In one possible design, the above-mentioned normal vector acquisition module 301 is also used to: if at least one of the multiple preset feature points is not detected in the projection plane image, skip the step of determining the first normal vector estimation result based on the positions of the multiple preset feature points in the projection plane image.

[0123] In one possible design, the multiple normal vector estimation results include a second normal vector estimation result, which is a normal vector estimation result estimated based on distance sensing data; the above-mentioned normal vector acquisition module 301 is used to: obtain four target distance data corresponding to the target projection plane, and the four target distance data respectively reflect the distance between the projection device and the four upper, lower, left and right areas in the target projection plane; calculate the second normal vector estimation result based on the four target distance data.

[0124] In one possible design, the above-mentioned normal vector acquisition module 301 is used to: obtain multiple distance data between the projection device and the target area in the target projection plane, where the target area is any one of the four areas of up, down, left and right; among the multiple distance data, filter out the distance data that is not within the target distance range corresponding to the target area; and determine the mean of the filtered distance data as the target distance data corresponding to the target area.

[0125] In one possible design, the above-mentioned normal vector acquisition module 301 is also used to: if the ratio between the number of filtered distance data and the total number of acquired distance data exceeds a preset ratio, skip the step of calculating the second normal vector estimation result based on the four target distance data.

[0126] It should be noted that for the contents not mentioned in the embodiment corresponding to FIG3 , reference can be made to the description of the aforementioned method embodiment, which will not be repeated here.

[0127] The above-mentioned device obtains a normal vector detection result of the target projection plane by obtaining multiple normal vector estimation results corresponding to the target projection plane and fusing the multiple normal vector estimation results. Since the normal vector estimation result is used to characterize the estimated normal vector of the target projection plane, the normal vector detection result of the target projection plane can be used to characterize the normal vector of the target projection plane, thereby realizing the detection of the normal vector of the projection plane; since the normal vector estimation result is used to characterize the estimated normal vector of the target projection plane, the normal vector detection result of the target projection plane can be used to characterize the normal vector of the target projection plane, thereby realizing the detection of the normal vector of the projection plane, thereby realizing the detection of the relative posture between the projection plane and the projection device; since different normal vector estimation results are estimated based on different sensor data, the normal vector of the projection plane is detected by fusing the multiple normal vector estimation results corresponding to the projection plane, which can avoid the problem of inaccurate normal vector estimation caused by sensor noise, improve the accuracy of the normal vector detection of the projection plane, make the detected relative posture between the projection plane and the projection device sufficiently accurate, and improve the effect of the trapezoidal correction of the picture.

[0128] 4 is a schematic diagram of the structure of a projection device provided in an embodiment of the present application, wherein the projection device 40 includes a processor 401, a memory 402, and a sensor 403. The memory 402 and the sensor 403 are connected to the processor 401, for example, via a bus.

[0129] The processor 401 is configured to support the projection device 40 in executing the corresponding functions of the method in the above-mentioned method embodiment. The processor 401 can be a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof. The hardware chip can be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or any combination thereof. The PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0130] Memory 402 is used to store program code, etc. Memory 402 may include volatile memory (VM), such as random access memory (RAM); non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); or a combination of the aforementioned types of memory.

[0131] Sensor 403 is used to detect sensor data external to projection device 40. There are multiple types of sensors 403, including but not limited to image sensors, distance sensors, and inertial sensors. Image sensors are used to detect images external to projection device 40 and capture images of the projection plane. Distance sensors are used to detect the distance between projection device 40 and the projection plane. Inertial sensors are used to detect the posture and inertial acceleration of projection device 40.

[0132] The processor 401 may call the program code to perform the following operations:

[0133] According to the normal vector detection result, a relative posture between the target projection plane and the projection device is determined, and the relative posture is used to correct a target picture, which is a picture projected by the projection device onto the target projection plane.

[0134] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes the method as described in the above embodiment.

[0135] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0136] The above disclosures are merely some embodiments of the present application and are certainly not intended to limit the scope of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A method for detecting the relative attitude between a projection plane and a projection device, wherein, Including: Obtaining a plurality of normal vector estimation results corresponding to a target projection plane, where the normal vector estimation results are used to represent the estimated normal vector of the target projection plane, and different normal vector estimation results are estimated based on different sensing data; Fusing the plurality of normal vector estimation results to obtain a normal vector detection result of the target projection plane; Determining a relative pose between the target projection plane and a projection device according to the normal vector detection result, where the relative pose is used to correct a target image, and the target image is an image projected by the projection device onto the target projection plane.

2. The method according to claim 1, wherein, Before fusing the plurality of normal vector estimation results to obtain a normal vector detection result of the target projection plane, it further includes: Performing validity detection on the plurality of normal vector estimation results; The fusing the plurality of normal vector estimation results to obtain a normal vector detection result of the target projection plane includes: Fusing the valid normal vector estimation results to obtain a normal vector detection result of the target projection plane.

3. The method according to claim 2, wherein Each normal vector estimation result includes a pitch angle; the performing validity detection on the plurality of normal vector estimation results includes: Obtaining a reference vector detection result, where the reference vector detection result is used to represent a ground normal vector corresponding to the projection device, the ground normal vector is perpendicular to the normal vector of the target projection plane, and the reference vector detection result includes a reference pitch angle; If the angle difference between the pitch angle in a target normal vector estimation result and the reference pitch angle is greater than a preset angle, determining that the target normal vector estimation result is an invalid normal vector estimation result, where the target normal vector estimation result is any one of the plurality of normal vector estimation results; If the angle difference between the pitch angle in the target normal vector estimation result and the reference pitch angle is less than or equal to the preset angle, determining that the target normal vector estimation result is a valid normal vector estimation result.

4. The method according to claim 3, wherein, The obtaining a reference vector detection result includes: Obtaining a target inertial acceleration of the projection device; Calculating the reference vector detection result according to the target inertial acceleration.

5. The method according to claim 4, wherein The obtaining a target inertial acceleration of the projection device includes: Obtaining a plurality of inertial accelerations of the projection device; Filtering out noise inertial accelerations that are not within the inertial acceleration range of the projection device from the plurality of inertial accelerations; Determining the mean value of the filtered inertial accelerations as the target inertial acceleration.

6. The method according to claim 1, wherein, Each normal vector estimation result includes a pitch angle and a yaw angle; The fusing the plurality of normal vector estimation results to obtain a normal vector detection result of the target projection plane includes: Performing weighted summation on the pitch angles in the plurality of normal vector estimation results to obtain the pitch angle in the normal vector detection result; Performing weighted summation on the yaw angles in the plurality of normal vector estimation results to obtain the yaw angle in the normal vector detection result.

7. The method according to any one of claims 1-6, wherein, The plurality of normal vector estimation results include a first normal vector estimation result, and the first normal vector estimation result is a normal vector estimation result estimated based on image sensing data; The obtaining of multiple normal vector estimation results corresponding to the target projection plane includes: Projecting a target image including multiple preset feature points onto the target projection plane; Obtaining a projection plane image corresponding to the target projection plane, where the projection plane image includes the target image; Determining the first normal vector estimation result according to the positions of the multiple preset feature points in the projection plane image.

8. The method according to claim 7, wherein After obtaining the projection plane image corresponding to the target projection plane, it further includes: If at least one of the multiple preset feature points is not detected in the projection plane image, skip the step of determining the first normal vector estimation result according to the positions of the multiple preset feature points in the projection plane image.

9. The method according to any one of claims 1-6, wherein, The multiple normal vector estimation results include a second normal vector estimation result, and the second normal vector estimation result is a normal vector estimation result estimated based on distance sensing data; The obtaining of multiple normal vector estimation results corresponding to the target projection plane includes: Obtaining four target distance data corresponding to the target projection plane, where the four target distance data respectively reflect the distances between the projection device and the upper, lower, left, and right four regions in the target projection plane; Calculating the second normal vector estimation result according to the four target distance data.

10. The method according to claim 9, wherein, The obtaining of the four target distance data corresponding to the target projection plane includes: Obtaining multiple distance data between the projection device and a target region in the target projection plane, where the target region is any one of the upper, lower, left, and right four regions; Filtering out the distance data that is not within the target distance range corresponding to the target region from the multiple distance data; Determining the mean value of the filtered distance data as the target distance data corresponding to the target region.

11. The method according to claim 10, wherein, After obtaining the four target distance data corresponding to the target projection plane, it further includes: If the ratio between the number of filtered distance data and the total number of obtained distance data exceeds a preset ratio, skip the step of calculating the second normal vector estimation result according to the four target distance data.

12. A relative attitude detection device between a projection plane and a projection device, wherein, It includes: A normal vector obtaining module, configured to obtain multiple normal vector estimation results corresponding to the target projection plane, where the normal vector estimation results are used to represent the estimated normal vector of the target projection plane, and different normal vector estimation results are estimated based on different sensing data; A fusion module, configured to fuse the multiple normal vector estimation results to obtain a normal vector detection result of the target projection plane; Determining a relative pose between the target projection plane and the projection device according to the normal vector detection result, where the relative pose is used to correct a target image, and the target image is an image projected by the projection device onto the target projection plane.

13. A projection device, wherein, It includes a memory, a processor, and a variety of sensors. The memory and the variety of sensors are connected to the processor. The variety of sensors are used to detect a variety of sensing data. The processor is used to execute one or more computer programs stored in the memory. When the processor executes the one or more computer programs, the projection device implements the method according to any one of claims 1-11.

14. A computer-readable storage medium, wherein, The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by the processor, the processor is caused to execute the method according to any one of claims 1-11.

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