CN-116430366-B - Temperature drift detection method and depth camera

CN116430366BCN 116430366 BCN116430366 BCN 116430366BCN-116430366-B

Abstract

The application discloses a temperature drift detection method and a depth camera. The temperature drift detection method is applied to a depth camera, a board to be detected is arranged in the light emitting direction of the depth camera, the board to be detected is provided with a surface to be detected facing the depth camera, the temperature drift detection method comprises the steps of adjusting the position of the depth camera, enabling the light emitting surface of the depth camera to be parallel to the surface to be detected, controlling the depth camera to project an image on the surface to be detected, acquiring a first depth image of the image at the initial moment when the depth camera is started, acquiring a first normal vector of a plane where the first depth image is located, acquiring a second depth image of the image after the preset time of the initial moment, acquiring a second normal vector of the plane where the second depth image is located, determining a temperature drift angle according to the first normal vector and the second normal vector, and detecting whether temperature drift of the depth camera is qualified or not according to the temperature drift angle. According to the technical scheme, temperature drift detection can be completed, and whether the temperature drift of the depth camera is qualified or not is judged.

Inventors

Chen Zhanyao
ZHONG WEIXIN
HU DAYI
Qian Zhehong

Assignees

银牛微电子(无锡)有限责任公司

Dates

Publication Date: 20260512
Application Date: 20230329

Claims (8)

1. The temperature drift detection method is applied to a depth camera, the depth camera comprises a light emitting surface for emitting light and a light receiving surface for receiving the light, the light emitting surface is parallel to the light receiving surface, a board to be detected is arranged in the light emitting direction of the depth camera, the board to be detected is provided with a surface to be detected facing the depth camera, and the temperature drift detection method comprises the following steps: adjusting the position of a depth camera to enable the light-emitting surface of the depth camera to be parallel to the surface to be measured; controlling the depth camera to project an image on the surface to be detected; Acquiring a first depth map of the image at the initial moment when the depth camera is started, and acquiring a first normal vector of a plane where the first depth map is located; acquiring a second depth map of the image after the preset time of the initial moment, and acquiring a second normal vector of a plane where the second depth map is located; Determining a temperature drift angle according to the first normal vector and the second normal vector, wherein the temperature drift angle comprises the following steps of performing the Rodrigues transformation on the first normal vector to obtain a first rotation matrix; obtaining a first change angle of the first normal vector according to the first rotation matrix; Performing the Rodrigues transformation on the second normal vector to obtain a second rotation matrix; Obtaining a second change angle of the second normal vector according to the second rotation matrix; Determining a temperature drift angle according to the first change angle and the second change angle; and detecting whether the temperature drift of the depth camera is qualified or not according to the temperature drift angle.
2. The method of claim 1, wherein the step of obtaining a first normal vector of a plane in which the first depth map is located comprises: Performing plane fitting on the first depth map to obtain a first plane, and obtaining a first normal vector according to the first plane; the step of obtaining the second normal vector of the plane where the second depth map is located includes: and carrying out plane fitting on the second depth map to obtain a second plane, and obtaining a second normal vector according to the second plane.
3. The temperature drift detection method of claim 2, wherein the first depth map comprises first point cloud data and the second depth map comprises second point cloud data; the step of performing plane fitting on the first depth map to obtain a first plane comprises the following steps: preprocessing the first point cloud data to remove invalid points and noise; fitting according to the preprocessed first point cloud data to obtain a first plane; performing plane fitting on the second depth map to obtain a second plane, including: preprocessing the second point cloud data to remove invalid points and noise; And fitting according to the preprocessed second point cloud data to obtain a second plane.
4. A temperature drift detection method according to claim 3, characterized in that the step of preprocessing said first point cloud data comprises: Removing points with invalid points and depth values of zero in the first point cloud data to obtain residual first point cloud data; performing direct distribution processing on the residual first point cloud data; Removing first point cloud data with the percentage smaller than a first preset percentage, removing first point cloud data with the percentage larger than a second preset percentage, and obtaining the removed point cloud data as first intermediate data; the step of preprocessing the second point cloud data comprises the following steps: Removing points with invalid points and depth values of zero in the second point cloud data to obtain remaining second point cloud data; performing direct distribution processing on the remaining second point cloud data; Removing second point cloud data with the percentage smaller than the first preset percentage, removing second point cloud data with the percentage larger than the second preset percentage, and obtaining the removed point cloud data as second intermediate data.
5. The method of claim 1, wherein the step of determining a temperature drift angle from the first normal vector and the second normal vector is preceded by: Constructing a reference coordinate system by using the light-emitting surface of the depth camera, wherein the reference coordinate system comprises a first coordinate axis extending along the horizontal direction of the light-emitting surface, a second coordinate axis extending along the vertical direction of the light-emitting surface and a third coordinate axis perpendicular to the light-emitting surface; The step of determining the temperature drift angle according to the first normal vector and the second normal vector further comprises: Converting the first rotation matrix into an Euler space to obtain a first rotation angle of the first normal vector relative to the first coordinate axis, a second rotation angle of the first normal vector relative to the second coordinate axis, and a third rotation angle of the first normal vector relative to the third coordinate axis, wherein the first change angle comprises the first rotation angle, the second rotation angle and the third rotation angle; Converting the second rotation matrix into an Euler space to obtain a fourth rotation angle of the second normal vector relative to the first coordinate axis, a fifth rotation angle of the second normal vector relative to the second coordinate axis, and a sixth rotation angle of the second normal vector relative to the third coordinate axis, wherein the second change angle comprises the fourth rotation angle, the fifth rotation angle and the sixth rotation angle; Comparing the first rotation angle with the fourth rotation angle to obtain a first drift angle, comparing the second rotation angle with the fifth rotation angle to obtain a second drift angle, and comparing the third rotation angle with the sixth rotation angle to obtain a third drift angle, wherein the temperature drift angle comprises the first drift angle, the second drift angle and the third drift angle.
6. The temperature drift detection method according to claim 5, wherein the step of detecting whether the temperature drift of the depth camera is acceptable or not according to the temperature drift angle comprises: respectively comparing the first drift angle, the second drift angle and the third drift angle with a preset standard angle; if the first drift angle, the second drift angle and the third drift angle are all smaller than or equal to the preset standard angle, the temperature drift of the depth camera is qualified; And if at least one of the first drift angle, the second drift angle and the third drift angle is larger than the preset standard angle, the temperature drift of the depth camera is not qualified.
7. The method for detecting temperature drift according to claim 1, wherein the length of the surface to be detected is L, the width of the surface to be detected is W, and the distance between the light emitting surface of the depth camera and the surface to be detected is D, then: Where fov_l is the field angle in the L direction and fov_w is the field angle in the W direction.
8. A depth camera comprising a mounting plate, a light emitter and a light receiver, wherein the light emitter and the light receiver are arranged on the same surface of the mounting plate, the light emitter has a light emitting surface for emitting light, the light receiver has a light receiving surface for receiving light, and the depth camera is detected by the temperature drift detection method according to any one of claims 1 to 7.

Description

Temperature drift detection method and depth camera Technical Field The application belongs to the technical field of depth cameras, and particularly relates to a temperature drift detection method and a depth camera. Background The depth camera (TOF) generates a lot of heat during operation, and the heat can deform the optical structure of the depth camera to a certain extent, thereby causing the optical performance of the optical structure to change, and affecting the accuracy of the depth camera. Therefore, the deformation size of the depth camera needs to be known, the degree of influence of heat on the depth camera can be reflected by detecting the temperature drift size, and whether the design of a product is qualified or not is further determined. However, a detection means for temperature drift is lacking at present, and whether the temperature drift of the depth camera is qualified or not is difficult to determine. Disclosure of Invention The application aims to provide a temperature drift detection method and a depth camera, which can finish temperature drift detection and further judge whether the temperature drift of the depth camera is qualified. Other features and advantages of the application will be apparent from the following detailed description, or may be learned by the practice of the application. According to an aspect of an embodiment of the present application, the present application provides a temperature drift detection method applied to a depth camera, the depth camera including a light emitting surface for emitting light and a light receiving surface for receiving light, the light emitting surface being parallel to the light receiving surface, a board to be measured being disposed in a light emitting direction of the depth camera, the board to be measured having a surface to be measured facing the depth camera, the temperature drift detection method including: adjusting the position of a depth camera to enable the light-emitting surface of the depth camera to be parallel to the surface to be measured; controlling the depth camera to project an image on the surface to be detected; Acquiring a first depth map of the image at the initial moment when the depth camera is started, and acquiring a first normal vector of a plane where the first depth map is located; acquiring a second depth map of the image after the preset time of the initial moment, and acquiring a second normal vector of a plane where the second depth map is located; determining a temperature drift angle according to the first normal vector and the second normal vector; and detecting whether the temperature drift of the depth camera is qualified or not according to the temperature drift angle. In one aspect, the step of obtaining the first normal vector of the plane in which the first depth map is located includes: Performing plane fitting on the first depth map to obtain a first plane, and obtaining a first normal vector according to the first plane; the step of obtaining the second normal vector of the plane where the second depth map is located includes: and carrying out plane fitting on the second depth map to obtain a second plane, and obtaining a second normal vector according to the second plane. In one aspect, the first depth map comprises first point cloud data and the second depth map comprises second point cloud data; the step of performing plane fitting on the first depth map to obtain a first plane comprises the following steps: preprocessing the first point cloud data to remove invalid points and noise; fitting according to the preprocessed first point cloud data to obtain a first plane; performing plane fitting on the second depth map to obtain a second plane, including: preprocessing the second point cloud data to remove invalid points and noise; And fitting according to the preprocessed second point cloud data to obtain a second plane. In one aspect, the step of preprocessing the first point cloud data includes: Removing points with invalid points and depth values of zero in the first point cloud data to obtain residual first point cloud data; performing direct distribution processing on the residual first point cloud data; Removing first point cloud data with the percentage smaller than a first preset percentage, removing first point cloud data with the percentage larger than a second preset percentage, and obtaining the removed point cloud data as first intermediate data; the step of preprocessing the second point cloud data comprises the following steps: Removing points with invalid points and depth values of zero in the second point cloud data to obtain remaining second point cloud data; performing direct distribution processing on the remaining second point cloud data; Removing second point cloud data with the percentage smaller than the first preset percentage, removing second point cloud data with the percentage larger than the second preset percentage, and obtaining the removed point cloud dat