A measuring device for fast measuring of a desktop horizontal reference

Abstract

The present application relates to the technical field of measurement, and relates to a kind of fast measurement of desktop horizontal reference, and it is used to carry out horizontal reference and flatness measurement to the plane to be measured of hand-held scanning, processing unit is used to obtain the depth perception unit of the spatial point depth information data stream of the local area of the plane to be measured, inertial measurement unit is used to obtain the data stream of the motion state of measurement device and the attitude information reflecting measurement device relative to gravity direction, processing unit is connected with depth perception unit and inertial measurement unit, the pose of measurement device is estimated based on depth information data stream and inertial measurement unit and constructs the three-dimensional point cloud map of the plane to be measured, and global horizontal calibration is executed based on the three-dimensional point cloud map constructed, geometric analysis is carried out to the three-dimensional point cloud map after calibration, to determine the overall levelness and local flatness of the plane to be measured, the present application realizes the rapid three-dimensional modeling of the plane to be measured and accurate level and flatness analysis.

Description

Technical Field

[0001] This invention relates to the field of measurement technology, and specifically to a desktop level reference rapid measurement device. Background Technology

[0002] In modern architectural decoration, furniture installation, and industrial manufacturing, the levelness and flatness of flat surfaces such as tabletops and countertops are crucial indicators of installation quality and performance. Currently, the main tools used to detect whether such surfaces are level or flat include bubble levels, laser line projectors, and digital levels based on electronic sensors. However, these traditional measurement methods and devices have many limitations in practical applications.

[0003] Traditional bubble levels rely on the positional changes of bubbles in liquid alcohol to determine whether a surface is level. They are simple in structure and inexpensive, but can only reflect the tilt in a single direction and cannot detect local unevenness. They also have low accuracy and cannot meet the needs of precision construction.

[0004] Laser levels set a horizontal reference by emitting visible laser lines. While they are intuitive and have a wide range of applications, they can only provide reference lines when inspecting irregular surfaces or tabletops with local undulations. They lack the ability to make an overall assessment of the surface morphology and are sensitive to external lighting conditions, which limits their application in complex environments.

[0005] Electronic digital levels, which have become increasingly popular in recent years, use inertial sensors such as accelerometers or gyroscopes to achieve digital measurement, providing high-precision angle measurement data. However, these devices are essentially still single-point or multi-point angle measurement systems, which can only reflect the overall tilt and cannot detect microscopic surface height differences, making it difficult to accurately judge the local flatness of a tabletop.

[0006] Furthermore, existing technologies generally lack intelligent analysis capabilities, requiring users to interpret measurement results and make judgments themselves, which presents a significant barrier for non-professionals. Additionally, most devices lack graphical feedback functions, failing to intuitively display the state of the entire measured surface, thus impacting user experience and work efficiency.

[0007] In summary, current desktop leveling devices on the market generally suffer from problems such as limited detection dimensions, poor adaptability, low level of intelligence, and weak visualization effects. There is an urgent need for a new type of detection device with novel structure, unique principle, comprehensive functions, and convenient operation to achieve simultaneous and rapid detection of the overall levelness and local flatness of the desktop. Summary of the Invention

[0008] Therefore, the purpose of this invention is to provide a desktop level benchmark rapid measurement device that integrates depth sensing, inertial measurement and optional visual sensing data, and utilizes advanced Simultaneous Localization and Mapping (SLAM) technology and gravity vector-based global level calibration to achieve rapid 3D modeling and accurate level and flatness analysis of the plane to be measured.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A desktop level reference rapid measurement device for measuring the levelness and flatness of a handheld scanning surface, comprising:

[0011] The depth sensing unit is used to acquire spatial point depth information data streams of a local area of ​​the plane to be measured during the movement of the user's handheld measuring device;

[0012] An inertial measurement unit (IMU) is used to acquire the motion state of a measuring device and a data stream reflecting the attitude information of the measuring device relative to the direction of gravity.

[0013] The processing unit, connected to the depth sensing unit and the inertial measurement unit, is configured as follows:

[0014] Based on the depth information data stream and the motion state and attitude information data stream of the inertial measurement unit, a simultaneous localization and mapping algorithm is executed to estimate the pose of the measuring device and construct a three-dimensional point cloud map of the plane to be measured.

[0015] Based on the attitude information provided by the inertial measurement unit, reflecting the direction of gravity, a global horizontal calibration is performed on the constructed 3D point cloud map to align the calibrated point cloud map with the true horizontal reference plane; and

[0016] Geometric analysis is performed on the calibrated 3D point cloud map to determine the overall levelness and local flatness of the plane to be measured.

[0017] The present invention is further configured such that the processing unit is configured to perform nonlinear optimization based on a multi-sensor data joint model in the synchronous positioning and mapping algorithm, the model including at least one measurement residual term based on inertial measurement unit readings, the calculation of which is related to the gravity vector.

[0018] The present invention is further configured such that: the nonlinear optimization based on the multi-sensor data joint model aims to minimize the sum of errors including the measurement residuals of the inertial measurement unit and the measurement residuals of the depth sensing unit. The construction of the measurement residual term of the inertial measurement unit takes into account the motion state, attitude change and gravity vector of the measuring device.

[0019] The present invention is further configured such that: the measuring device further includes a visual perception unit connected to the processing unit for acquiring an image data stream of the plane to be measured, and the processing unit is configured to fuse the image data stream in a synchronous positioning and mapping algorithm to estimate the pose of the measuring device.

[0020] The present invention is further configured such that: when performing global horizontal calibration, the processing unit determines the rotation relationship of the coordinate system of the three-dimensional point cloud map relative to the real horizontal reference plane based on the attitude estimate of the measuring device relative to the direction of gravity obtained from the information of the inertial measurement unit, and applies the rotation relationship to transform the point cloud map.

[0021] The present invention is further configured such that: when performing geometric analysis, the processing unit performs plane fitting on the calibrated three-dimensional point cloud map, and determines the overall levelness based on the relationship between the fitted plane and the true horizontal reference plane.

[0022] The present invention is further configured such that: when performing geometric analysis, the processing unit calculates the vertical deviation of points in the calibrated point cloud map relative to the plane fitting result, and calculates the local flatness based on the vertical deviation.

[0023] The present invention is further configured such that: the processing unit is configured to calculate the difference between the maximum and minimum values ​​of the vertical deviation as the peak-valley difference index of the local flatness, and to calculate the root mean square of the vertical deviation as the root mean square deviation index of the local flatness.

[0024] The present invention is further configured such that: the measuring device includes a communication unit connected to the processing unit, for wirelessly transmitting the measurement results and the three-dimensional point cloud map to an external computing unit, the external computing unit being configured to further analyze or display the results or map.

[0025] The present invention is further configured such that: the measuring device includes a housing for integrating a sensing unit, a processing unit and a communication unit, and the housing is designed to facilitate the user to hold the measuring device and move it above the plane to be measured for scanning.

[0026] Compared with the shortcomings of the prior art, the beneficial effects of the present invention are as follows:

[0027] By fusing IMU data and performing global level calibration based on gravity vectors, this device measures the orientation of the plane under test relative to the real physical level, rather than simply relative to the starting position or scan path. This provides a physically meaningful and highly accurate assessment of levelness.

[0028] It not only provides the overall levelness (tilt angle), but also gives detailed local flatness quantifications for the entire scanned area.

[0029] Generates dense 3D point cloud maps that can be visualized, exported, and used for further detailed analysis, archiving, or quality control recording, going beyond simple numerical readings. Attached Figure Description

[0030] Fig. 1 This is a schematic diagram of the framework of the present invention;

[0031] Fig. 2 This is a flowchart illustrating the processing unit of the present invention. Detailed Implementation

[0032] Reference Figs. 1-2 The present invention provides a desktop level reference rapid measurement device, comprising: a housing, a depth sensing unit integrated within the housing, an inertial measurement unit, a processing unit, and a communication unit.

[0033] The housing is made of lightweight, high-strength engineering plastics (such as ABS or PC) and features an ergonomic design to ensure a comfortable grip and stable center of gravity, facilitating smooth one-handed operation for scanning. The internal structure is designed to ensure the relative positions of each sensor unit are fixed, guaranteeing the validity of calibration parameters. It includes a power switch, status indicator lights (such as power, scanning in progress, completed, and error), and a possible USB-C port (for charging or wired data transfer).

[0034] Depth sensing unit: A depth camera employing active structured light or time-of-flight (ToF) technology.

[0035] Parameters: Outputs 640x480 resolution depth and infrared images (or color images, if integrated), frame rate 30FPS, effective ranging range 0.2m-3m, accuracy in the millimeter range.

[0036] During the scanning process, the three-dimensional spatial coordinates (relative to the camera coordinate system) of a local area of ​​the plane to be measured within the field of view of the measuring device are acquired in real time, forming a depth information data stream.

[0037] ,in .

[0038] The depth information data stream consists of a series of timestamped depth maps continuously captured at a certain frame rate by the depth sensing unit during scanning with the user's handheld device. Each depth map contains 3D distance information of a local area of ​​the target plane within the sensor's field of view at that moment. This continuous data stream is one of the fundamental inputs for subsequent SLAM algorithms to construct a complete 3D point cloud map.

[0039] Inertial Measurement Unit (IMU): A six-axis MEMSMU integrating a three-axis accelerometer and a three-axis gyroscope. Examples include the Bosch BMI088 or TDK InvenSense MPU-6050. It is hardware-synchronized with the depth sensing unit or has precise timestamps.

[0040] Parameters: Accelerometer range ±8g, gyroscope range ±2000dps, data output rate 200Hz or higher. Low noise density and good bias stability.

[0041] Function: Real-time measurement of linear acceleration in the device's body coordinate system. and angular velocity Data stream. Accelerometer readings can be used to sense the direction of the gravity vector under static or quasi-static conditions, thus providing attitude information (such as pitch and roll) of the device relative to the earth (gravity) coordinate system.

[0042] Processing Unit (PU): A high-performance, low-power embedded computing platform. It has at least 4GB of RAM and sufficient computing power (CPU + GPU) to run SLAM algorithms and subsequent processing in real time. It runs an embedded Linux operating system and custom-developed measurement software. It connects to the DPU, IMU, and VPU via MIPICSI, USB, or internal bus.

[0043] Communication Unit (CU): Wi-Fi (802.11ac) and / or Bluetooth (BLE5.0) module.

[0044] Function: Wirelessly transmits the calculated levelness / flatness results and calibrated 3D point cloud maps (such as .ply or .pcd format) to smartphone apps, tablets, or PC software for visualization, report generation, or further analysis.

[0045] Power supply unit: Built-in rechargeable lithium-ion battery (e.g., 3000mAh) provides at least 1 hour of continuous operation. Includes power management circuitry.

[0046] Core Algorithm and Working Principle:

[0047] After the device is started, it begins synchronous data acquisition. The data stream, with precise timestamps, is sent to the processing unit. The IMU data undergoes preliminary filtering and noise reduction, while the depth image requires denoising and removal of invalid points.

[0048] Simultaneous Localization and Mapping (SLAM): Employing a tightly coupled vision-inertial-depth SLAM framework, the estimated state vector X contains at least a series of keyframe moments of the measurement device pose (position). And attitude, attitude is usually represented by quaternions or rotation matrix In this representation, W represents the world coordinate system, B represents the IMU coordinate system, and the velocity... and the IMU's accelerometer bias and gyroscope bias .

[0049] ,

[0050] in It is the first The state of each keyframe It is an optional set of 3D map points (landmarks).

[0051] Factor graph construction and optimization:

[0052] IMU pre-integration factor: connecting two consecutive keyframes and +1 state Utilize all IMU measurements between two keyframes. Relative motion constraints are calculated using IMU pre-integration theory. And its covariance, the IMU measurement model is:

[0053]

[0054]

[0055] in It is the actual acceleration in the world coordinate system. It is the gravity vector in the world coordinate system (usually set as...). (or optimized estimation). It is a rotation from the world to the entity. It is the body angular velocity. , It's noise.

[0056] Pre-integrated measurement residuals In optimization, it is used to constrain adjacent states. The key is that the calculation of this residual explicitly or implicitly depends on the gravity vector. For example, the influence of gravity needs to be subtracted from the state transition equation, or the direction of gravity may be included as part of the variable to be estimated in the optimization. The optimization objective is to minimize the squared Mahalanobis norm of the residual: .

[0057] Depth / Visual Factor: For each keyframe k, the observed 3D points (from DPU) or 2D feature points (from VPU) If the point already exists on the map. In the middle, the reprojection error factor is constructed.

[0058] For depth points (Camera coordinate system) Transform to world coordinate system ( It is a homogeneous transformation matrix. (External parameters from IMU to camera).

[0059] For visual feature points (Pixel coordinates), their corresponding 3D map points Back-projecting onto the image plane of the current frame k, we obtain the predicted pixel coordinates. ( (For camera projection model).

[0060] residuals or Calculate the difference between the measured value and the predicted value. For example... The optimization objective is to minimize .

[0061] Joint optimization: The optimization objective is to minimize the weighted sum of squares of the residuals of all factors (based on the covariance matrix). Mahalanobis distance):

[0062]

[0063] The optimal state X^ is solved iteratively using a nonlinear least squares solver (such as Gauss-Newton). This joint optimization framework ensures that the motion and gravity information provided by the IMU and the geometric information provided by depth / vision can mutually constrain and correct each other, resulting in more accurate pose estimation and mapping.

[0064] Map building: The depth points observed in each keyframe are used to optimize the pose based on that frame.

[0065] Transform to a unified world coordinate system Below, a dense 3D point cloud map of the plane to be measured is constructed. .

[0066] Global horizontal calibration: Point cloud map built with SLAM (The Z-axis direction of its coordinate system W is determined by SLAM initialization and is usually not aligned with the direction of gravity.) Rotate to a new coordinate system. , making The XY plane is the real horizontal plane, and the Z axis is perpendicular to the horizontal plane (i.e., aligned in the opposite direction to the gravity vector).

[0067] During SLAM optimization, IMU acceleration readings (especially at low dynamics) provide information about the gravity vector. Estimation in world coordinate system W. The optimized IMU attitude can be utilized. And bias, combined with raw accelerometer readings, to estimate at multiple times Then, an average or filter is applied to obtain a robust estimate. .

[0068] Calculating rotation relationships requires finding a rotation matrix. , making (Assume that the Z-axis of the H system is positive upwards).

[0069] Let the target vector ;calculate unit vector ;

[0070] Calculate the axis of rotation ; Calculate the rotation angle .

[0071] According to axis angle Calculate the rotation matrix (For example, using the Rodriguez rotation formula).

[0072] Application transformation: for point cloud maps Each point in Perform rotational transformation:

[0073]

[0074] The calibrated point cloud map .at this time The coordinates of the points in the image are referenced to the actual horizontal plane.

[0075] Geometric Analysis: Input calibrated 3D point cloud map ;

[0076] The RANSAC (Random Sample Consensus) algorithm is employed. A minimum set of 3 points is randomly selected to fit a planar model ax + by + cz + d = 0. The number of points in the point cloud whose distance to this plane is within a threshold (inliers) is then counted. This process is repeated, and the plane with the most inliers is selected as the best-fit plane. RANSAC can effectively resist noise points or outliers that may occur during the scanning process.

[0077] Output the parameters (a, b, c, d) and the set of interior points of the fitted plane. .

[0078] Overall levelness calculation: normal vector of the fitted plane .

[0079] The normal vector of the true horizontal plane (in the H frame) is: (or To maintain consistency in direction.

[0080] Levelness index: Calculates the angle between two normal vectors. :

[0081] The smaller the angle, the closer the measured plane is to being horizontal. The results can be reported in degrees or radians.

[0082] Local flatness calculation:

[0083] Calculate the vertical deviation: for the set of interior points of the fitted plane Each point in Calculate its path to the fitted plane The vertical distance (i.e., the deviation along the Z-axis, since the plane is approximately horizontal):

[0084]

[0085] (Assuming) ,like The plane is approximately perpendicular, making it unsuitable for levelness measurements. More precisely, it's the orthogonal distance from a point to the plane: However, after horizontal calibration, attention is usually paid to... Directional deviation.

[0086] Flatness index:

[0087] Peak-to-valley difference: Calculate the difference between the maximum and minimum vertical deviations of all interior points.

[0088]

[0089] Root mean square deviation: Calculate the root mean square value of the perpendicular deviation of all interior points.

[0090]

[0091] The smaller these two indicators are, the less undulation the surface of the plane being measured has, meaning it is smoother.

[0092] Result Output and Transmission: The processing unit calculates... Numerical results, etc.

[0093] These numerical results and (optionally) calibrated point cloud data are transmitted via a communication unit (CU). Wireless transmission to paired external computing devices (such as mobile apps).

[0094] The software on the external device is responsible for receiving data, visualizing it (such as displaying levelness angles, flatness values, rendering 3D point cloud maps and mapping deviations with colors, etc.), and generating measurement reports.

[0095] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any ordinary changes and substitutions made by those skilled in the art within the scope of the technical solutions of the present invention should be included within the protection scope of the present invention.

Claims

1. A measurement device for fast measurement of horizontal reference and flatness of a handheld-scanned plane under test, comprising: a depth perception unit configured to obtain a spatial point depth information data stream of a local area of the plane under test during a user handheld movement of the measurement device; an inertial measurement unit configured to obtain a motion state and an attitude information data stream reflecting an orientation of the measurement device relative to a gravity direction; a processing unit connected to the depth perception unit and the inertial measurement unit, and configured to: execute a simultaneous localization and mapping algorithm based on the depth information data stream and the motion state and the attitude information data stream of the inertial measurement unit, to estimate a pose of the measurement device and construct a three-dimensional point cloud map of the plane under test; perform a global horizontal calibration on the constructed three-dimensional point cloud map based on the attitude information reflecting the orientation relative to the gravity direction provided by the inertial measurement unit, to align the calibrated point cloud map with a real horizontal reference plane; and perform a geometric analysis on the calibrated three-dimensional point cloud map to determine an overall levelness and a local flatness of the plane under test; wherein the processing unit is configured to execute a multi-sensor data fusion based nonlinear optimization in the simultaneous localization and mapping algorithm, the nonlinear optimization is configured to minimize an error sum including an inertial measurement unit measurement residual and a depth perception unit measurement residual, the inertial measurement unit measurement residual is constructed considering the motion state, the attitude change and the gravity vector of the measurement device; wherein the processing unit is configured to determine a rotation relationship of a coordinate system of the three-dimensional point cloud map relative to the real horizontal reference plane based on a pose estimation of the measurement device relative to the gravity direction obtained from the inertial measurement unit information, and apply the rotation relationship to transform the point cloud map in performing the global horizontal calibration; wherein the processing unit is configured to fuse an image data stream obtained by a vision perception unit connected to the processing unit to estimate the pose of the measurement device in the simultaneous localization and mapping algorithm; wherein the processing unit is configured to perform a plane fitting on the calibrated three-dimensional point cloud map in performing the geometric analysis, and determine the overall levelness based on a relationship between the fitted plane and the real horizontal reference plane; wherein the processing unit is configured to calculate a vertical deviation of points in the calibrated point cloud map relative to the plane fitting result in performing the geometric analysis, and determine the local flatness based on the vertical deviation; wherein the processing unit is configured to calculate a difference between a maximum value and a minimum value of the vertical deviation as a peak-to-valley difference indicator of the local flatness, and calculate a root mean square of the vertical deviation as a root mean square deviation indicator of the local flatness; and wherein the measurement device further comprises a communication unit connected to the processing unit, and configured to wirelessly transmit the measurement results and the three-dimensional point cloud map to an external computing unit configured to further analyze or display the results or the map. The method of measuring, characterized by, ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 2. The measuring device for fast measurement of a desktop level reference according to claim 1, characterized in that, ​ ​ 3. The apparatus for fast measurement of a horizontal reference of a desktop according to claim 1, wherein ​ ​ 4. The apparatus for fast measurement of a horizontal reference of a desktop according to claim 3, wherein ​ ​ 5. The apparatus for fast measurement of a horizontal reference of a desktop according to claim 4, wherein ​ 6. A measuring device for fast measurement of a table top level reference according to claim 5, characterized in that ​ 7. The apparatus of claim 1, wherein, ​ 8. The measuring device for fast measurement of a horizontal reference of a desktop according to claim 7, characterized in that, The measuring device comprises a housing for integrating a sensing unit, a processing unit and a communication unit, the housing being designed to facilitate the user to hold the measuring device and move it over the plane to be measured for scanning.

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