Indoor bay depth measurement method, device and computer equipment

Abstract

The present invention relates to a method, device and computer equipment for measuring the depth of an indoor bay. The device includes: a laser radar arranged on the longitudinal or transverse axis of a movable platform for collecting 2D point cloud data; an inclinometer arranged at the geometric center of the movable platform chassis plane for measuring the attitude parameters of the movable platform; an industrial computer for converting the 2D point cloud data into room outline point cloud data based on the movable platform coordinate system, converting the room outline point cloud data based on the movable platform coordinate system into horizontal plane room outline point cloud data according to the attitude parameters, and determining the bay depth measurement result based on the horizontal plane room outline point cloud data; an Internet of Things device is communicatively connected to the industrial computer for uploading the bay depth measurement result to a background server. Through this application, the problems of low bay depth measurement accuracy, complex data processing and time-consuming in the related art are solved, and the effect of improving measurement accuracy and efficiency is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of building quality monitoring, and in particular to a method, a device and a computer device for measuring the depth of an indoor bay. Background Art

[0002] According to relevant national regulations and standards, residential projects require a quality inspection of each unit upon completion. Bay depth is a key indicator of residential project quality inspection. Bay and depth refer to the distances between walls in the same room, respectively, reflecting the room's squareness and spatial dimensions. Bay and depth measurements are typically performed by surveyors using a tape measure or laser rangefinder. The typical bay depth measurement method is as follows: select a wall in the room, place a laser rangefinder or tape measure at a certain elevation on the wall, and measure the vertical distance from that point to the opposite wall. Repeat the measurement multiple times at different locations along the same elevation. The range between the maximum and minimum distances is typically used as the measurement indicator and compared to the design dimensions and the range standard. Any value exceeding the standard is considered unacceptable. Using a tape measure cannot guarantee the accuracy of the measured data. While a laser rangefinder can ensure measurement accuracy, it also requires continuous measurement work, which is labor-intensive and inefficient, and data processing is cumbersome and time-consuming.

[0003] At present, no effective solution has been proposed to address the problems of low span depth measurement accuracy, complex data processing and time-consuming problems in related technologies. Summary of the Invention

[0004] The purpose of this application is to address the deficiencies in the prior art and provide a method, device, computer equipment and computer-readable storage medium for measuring the depth of an indoor bay, so as to at least solve the problems in the related art of low bay depth measurement accuracy, complex data processing and time-consuming.

[0005] To achieve the above objectives, the technical solutions adopted in this application are:

[0006] In a first aspect, an embodiment of the present application provides an indoor bay depth measurement device, comprising:

[0007] A movable platform and an inclinometer, a laser radar, an industrial computer, and an Internet of Things device arranged on the movable platform, wherein:

[0008] The laser radar is arranged on the longitudinal axis or the transverse axis of the movable platform and is used to collect 2D point cloud data;

[0009] The inclinometer is arranged at the geometric center position of the chassis plane of the movable platform and is used to measure the posture parameters of the movable platform;

[0010] The industrial computer is used to convert the 2D point cloud data into room outline point cloud data based on the movable platform coordinate system, convert the room outline point cloud data based on the movable platform coordinate system into horizontal plane room outline point cloud data according to the posture parameters, and determine the bay depth measurement result based on the horizontal plane room outline point cloud data;

[0011] The Internet of Things device is communicatively connected to the industrial computer and is used to upload the bay depth measurement result to a background server.

[0012] In some embodiments, the movable platform base plane, the inclinometer installation plane, and the lidar installation plane are in the same plane or parallel to each other.

[0013] In some embodiments, determining the bay depth measurement result based on the horizontal room outline point cloud data includes:

[0014] Extracting four parts of point cloud in the longitudinal and transverse directions of the movable platform coordinate system according to the horizontal plane room outline point cloud data, and fitting the four parts of point cloud into straight lines respectively;

[0015] Determine the target point cloud closest to the fitting straight line from the four point clouds;

[0016] Calculate the average value of the distance from the target point cloud to the opposite fitting straight line, and determine the average value as the bay depth measurement result.

[0017] In some embodiments, the industrial computer is further used to:

[0018] Perform multiple measurements to obtain multiple bay depth measurement results;

[0019] Determining a maximum value and a minimum value from the plurality of bay depth measurement results, and calculating a range based on the maximum value and the minimum value;

[0020] Determine whether the range is within a preset value range;

[0021] Determine whether the engineering indicators are qualified based on the results of the judgment.

[0022] In a second aspect, an embodiment of the present application provides an indoor bay depth measurement method for the indoor bay depth measurement device described in the first aspect, comprising:

[0023] Acquire 2D point cloud data collected by a laser radar and attitude parameters of the movable platform measured by an inclinometer;

[0024] Converting the 2D point cloud data into room outline point cloud data based on the movable platform coordinate system;

[0025] Converting the room outline point cloud data based on the movable platform coordinate system into horizontal plane room outline point cloud data according to the posture parameters;

[0026] Determine the bay depth measurement result based on the horizontal plane room outline point cloud data.

[0027] In some embodiments, determining the bay depth measurement result based on the horizontal room outline point cloud data includes:

[0028] Extracting four parts of point cloud in the longitudinal and transverse directions of the movable platform coordinate system according to the horizontal plane room outline point cloud data, and fitting the four parts of point cloud into straight lines respectively;

[0029] Determine the target point cloud closest to the fitting straight line from the four point clouds;

[0030] Calculate the average value of the distance from the target point cloud to the opposite fitting straight line, and determine the average value as the bay depth measurement result.

[0031] In some embodiments, further comprising:

[0032] Perform multiple measurements to obtain multiple bay depth measurement results;

[0033] Determining a maximum value and a minimum value from the plurality of bay depth measurement results, and calculating a range based on the maximum value and the minimum value;

[0034] Determine whether the range is within a preset value range;

[0035] Determine whether the engineering indicators are qualified based on the results of the judgment.

[0036] In some embodiments, before extracting the four-part point cloud in the longitudinal and transverse directions of the movable platform coordinate system, the method further includes:

[0037] The horizontal plane room outline point cloud data is preprocessed to remove point clouds that exceed the room size and noise points within the preset area of ​​the movable platform.

[0038] In a third aspect, an embodiment of the present application provides a computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for measuring the depth of an indoor bay as described in the second aspect above is implemented.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the indoor bay depth measurement method as described in the second aspect above.

[0040] The present application adopts the above technical solution. Compared with the existing technology, the embodiment of the present application provides an indoor bay depth measurement device, which includes a movable platform and an inclinometer, a laser radar, an industrial computer and an Internet of Things device arranged on the movable platform, wherein: the laser radar is arranged on the longitudinal axis or the transverse axis of the movable platform, and is used to collect 2D point cloud data; the inclinometer is arranged at the geometric center position of the chassis plane of the movable platform, and is used to measure the posture parameters of the movable platform; the industrial computer is used to convert the 2D point cloud data into room contour point cloud data based on the coordinate system of the movable platform, and convert the room contour point cloud data based on the coordinate system of the movable platform into horizontal plane room contour point cloud data according to the posture parameters, and determine the bay depth measurement result according to the horizontal plane room contour point cloud data; the Internet of Things device is communicated with the industrial computer and is used to upload the bay depth measurement result to the background server. The embodiment of the present application solves the problems of low bay depth measurement accuracy, complex data processing and time-consuming in the related technology, and achieves the effect of improving the accuracy of bay depth measurement and improving measurement efficiency.

[0041] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0043] Figure 1 is a structural block diagram of a mobile terminal according to an embodiment of the present application;

[0044] Figure 2 2 is a schematic structural diagram of an indoor bay depth measurement device according to an embodiment of the present application;

[0045] Figure 3 2 is a schematic diagram of a measurement process performed by an indoor bay depth measurement device according to an embodiment of the present application;

[0046] Figure 4 is a schematic diagram of point cloud extraction in the bay depth direction according to an embodiment of the present application;

[0047] Figure 5 is a schematic diagram of the least squares fitting result according to an embodiment of the present application;

[0048] Figure 6 is a flow chart of a method for measuring the depth of an indoor bay according to an embodiment of the present application;

[0049] Figure 7 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0051] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0052] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0053] Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by a person of ordinary skill in the technical field to which this application belongs. The words "one", "a", "the" and the like used in this application do not indicate a limit on quantity and may indicate the singular or plural. The terms "include", "comprise", "have" and any variations thereof used in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units that are inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The word "multiple" used in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0054] This embodiment provides a mobile terminal. The form of the mobile terminal is not specifically limited here, and it can be the industrial computer in the embodiment of the present application. Figure 1 : is a structural block diagram of a mobile terminal according to an embodiment of the present application. Figure 1 As shown, the mobile terminal includes components such as a radio frequency (RF) circuit 110, a memory 120, an input unit 130, a display unit 140, a sensor 150, an audio circuit 160, a wireless fidelity (WiFi) module 170, a processor 180, and a power supply 190. Those skilled in the art will understand that Figure 1 The structure of the mobile terminal shown in the figure does not constitute a limitation to the mobile terminal, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0055] The following combination Figure 1 A detailed introduction to the various components of the mobile terminal is given below:

[0056] The RF circuit 110 can be used to receive and send signals during information transmission or calls. In particular, after receiving downlink information from the base station, it is sent to the processor 180 for processing; in addition, the designed uplink data is sent to the base station. Generally, the RF circuit includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, etc. In addition, the RF circuit 110 can also communicate with the network and other devices through wireless communication. The above-mentioned wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobile Communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.

[0057] The memory 120 can be used to store software programs and modules. The processor 180 executes various functional applications and data processing of the mobile terminal by running the software programs and modules stored in the memory 120. The memory 120 may mainly include a program storage area and a data storage area. The program storage area may store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created based on the use of the mobile terminal (such as audio data, a phone book, etc.). In addition, the memory 120 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0058] The input unit 130 can be used to receive input digital or character information, and to generate key signal input related to the user settings and function control of the mobile terminal. Specifically, the input unit 130 may include a touch panel 131 and other input devices 132. The touch panel 131, also known as a touch screen, can collect user touch operations on or near it (such as operations performed by the user using any suitable object or accessory such as a finger, stylus, etc. on or near the touch panel 131) and drive the corresponding connection device according to a pre-set program. Optionally, the touch panel 131 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch direction, detects the signal caused by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device and converts it into touch point coordinates, which are then sent to the processor 180, and can receive commands sent by the processor 180 and execute them. In addition, the touch panel 131 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 131, the input unit 130 may further include other input devices 132. Specifically, the other input devices 132 may include but are not limited to one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick.

[0059] The display unit 140 can be used to display information input by the user or information provided to the user and various menus of the mobile terminal. The display unit 140 may include a display panel 141. Optionally, the display panel 141 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. Further, the touch panel 131 may cover the display panel 141. When the touch panel 131 detects a touch operation on or near it, it is transmitted to the processor 180 to determine the type of touch event. Subsequently, the processor 180 provides corresponding visual output on the display panel 141 according to the type of touch event. Although in Figure 1 In the embodiment, the touch panel 131 and the display panel 141 are used as two independent components to implement the input and output functions of the mobile terminal. However, in some embodiments, the touch panel 131 and the display panel 141 can be integrated to implement the input and output functions of the mobile terminal.

[0060] The mobile terminal may also include at least one sensor 150, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 141 according to the brightness of the ambient light, and the proximity sensor may turn off the display panel 141 and / or the backlight when the mobile terminal is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors that can be configured in the mobile terminal, such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be described here.

[0061] The speaker 161 and microphone 162 in the audio circuit 160 provide an audio interface between the user and the mobile terminal. The audio circuit 160 converts received audio data into electrical signals and transmits them to the speaker 161, which then converts the signals into sound signals for output. Furthermore, the microphone 162 converts the collected sound signals into electrical signals, which are then received by the audio circuit 160 and converted into audio data. The audio data is then processed by the processor 180 and transmitted to, for example, another mobile terminal via the RF circuit 110, or the audio data is output to the memory 120 for further processing.

[0062] WiFi is a short-range wireless transmission technology. Mobile terminals can help users send and receive emails, browse web pages, and access streaming media through the WiFi module 170. It provides users with wireless broadband Internet access. Figure 1 A WiFi module 170 is shown, but it is understandable that it is not an essential component of the mobile terminal and can be omitted as needed without changing the essence of the invention, or replaced with other short-range wireless transmission modules, such as a Zigbee module or a WAPI module.

[0063] The processor 180 is the control center of the mobile terminal, connecting all components of the mobile terminal using various interfaces and circuits. By running or executing software programs and / or modules stored in the memory 120 and accessing data stored in the memory 120, it executes various functions of the mobile terminal and processes data, thereby providing overall monitoring of the mobile terminal. Optionally, the processor 180 may include one or more processing units; preferably, the processor 180 may integrate an application processor and a modem processor. The application processor primarily handles the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into the processor 180.

[0064] The mobile terminal also includes a power supply 190 (such as a battery) for supplying power to various components. Preferably, the power supply can be logically connected to the processor 180 through a power management system, thereby managing charging, discharging, and power consumption through the power management system.

[0065] Although not shown, the mobile terminal may further include a camera, a Bluetooth module, etc., which will not be described in detail here.

[0066] In this embodiment, the processor 180 is configured to:

[0067] Acquire 2D point cloud data collected by a laser radar and attitude parameters of the movable platform measured by an inclinometer;

[0068] Converting the 2D point cloud data into room outline point cloud data based on the movable platform coordinate system;

[0069] Converting the room outline point cloud data based on the movable platform coordinate system into horizontal plane room outline point cloud data according to the posture parameters;

[0070] Determine the bay depth measurement result based on the horizontal plane room outline point cloud data.

[0071] In some embodiments, the processor 180 is further configured to:

[0072] Extracting four parts of point cloud in the longitudinal and transverse directions of the movable platform coordinate system according to the horizontal plane room outline point cloud data, and fitting the four parts of point cloud into straight lines respectively;

[0073] Determine the target point cloud closest to the fitting straight line from the four point clouds;

[0074] Calculate the average value of the distance from the target point cloud to the opposite fitting straight line, and determine the average value as the bay depth measurement result.

[0075] In some embodiments, the processor 180 is further configured to:

[0076] Perform multiple measurements to obtain multiple bay depth measurement results;

[0077] Determining a maximum value and a minimum value from the plurality of bay depth measurement results, and calculating a range based on the maximum value and the minimum value;

[0078] Determine whether the range is within a preset value range;

[0079] Determine whether the engineering indicators are qualified based on the results of the judgment.

[0080] In some embodiments, the processor 180 is further configured to:

[0081] The horizontal plane room outline point cloud data is preprocessed to remove point clouds that exceed the room size and noise points within the preset area of ​​the movable platform.

[0082] This embodiment provides a device for measuring the depth of an indoor bay. Figure 2 : is a schematic diagram of the structure of the indoor bay depth measurement device according to an embodiment of the present application. Figure 2 As shown, the device includes:

[0083] A movable platform 20 and an inclinometer 21, a laser radar 22, an industrial computer 23 and an Internet of Things device 24 arranged on the movable platform 20, wherein:

[0084] The laser radar 22 is arranged on the longitudinal axis or the transverse axis of the movable platform 20 and is used to collect 2D point cloud data;

[0085] The inclinometer 21 is arranged at the geometric center of the chassis plane of the movable platform 20 and is used to measure the posture parameters of the movable platform 20;

[0086] The industrial computer 23 is used to convert the 2D point cloud data into room outline point cloud data based on the coordinate system of the movable platform 20, convert the room outline point cloud data based on the coordinate system of the movable platform 20 into horizontal plane room outline point cloud data according to the posture parameters, and determine the bay depth measurement result based on the horizontal plane room outline point cloud data;

[0087] The Internet of Things device 24 is in communication with the industrial computer 23 and is used to upload the bay depth measurement result to a backend server.

[0088] The movable platform 20 in the embodiment of the present application refers to a platform that can navigate and move autonomously or can be manually remotely controlled, and may include but is not limited to unmanned vehicles, drones, etc. The embodiment of the present application controls the movable platform 20 to collect 2D point cloud data and convert it into room contour point cloud data to determine the span depth measurement result, thereby improving the accuracy and measurement efficiency of the span depth measurement.

[0089] In some embodiments, the base plane of the movable platform 20, the installation plane of the inclinometer 21, and the installation plane of the laser radar 22 are in the same plane or parallel to each other.

[0090] In some embodiments, determining the bay depth measurement result based on the horizontal room outline point cloud data includes:

[0091] Extracting four parts of point cloud in the vertical and horizontal directions of the coordinate system of the movable platform 20 according to the horizontal plane room outline point cloud data, and fitting the four parts of point cloud into straight lines respectively;

[0092] Determine the target point cloud closest to the fitting straight line from the four point clouds;

[0093] Calculate the average value of the distance from the target point cloud to the opposite fitting straight line, and determine the average value as the bay depth measurement result.

[0094] In some embodiments, the industrial computer 23 is further used to:

[0095] Perform multiple measurements to obtain multiple bay depth measurement results;

[0096] Determining a maximum value and a minimum value from the plurality of bay depth measurement results, and calculating a range based on the maximum value and the minimum value;

[0097] Determine whether the range is within a preset value range;

[0098] Determine whether the engineering indicators are qualified based on the results of the judgment.

[0099] Through the above steps, this application solves the problems of low bay depth measurement accuracy, complex data processing, and time-consuming in related technologies, and achieves the effect of improving the accuracy of bay depth measurement and improving measurement efficiency.

[0100] Optionally, the inclinometer 21 refers to an instrument that can measure the inclination angle of a plane, and is mounted at the geometric center of the chassis plane of the movable platform 20. The laser radar 22 refers to a device that can collect 2D room contour point clouds, and is horizontally mounted on the longitudinal or transverse axis of the movable platform 20. The industrial computer 23 refers to a device that can run the laser radar 22 driver and the point cloud processing algorithm program, and can be installed on the movable platform 20 at any location. The chassis plane of the movable platform 20, the installation plane of the inclinometer 21, and the installation plane of the laser radar 22 are in the same plane or parallel to each other. The Internet of Things device 24 is installed on the movable platform 20, can communicate with the industrial computer 23, and can upload measurement-related data and information of the movable platform 20 to the background in real time.

[0101] In actual application scenarios, the specific implementation process of the indoor bay depth measurement device is as follows: Figure 3 As shown, the details are as follows:

[0102] (1) Control the movable platform 20 to the room to be measured so that the movable platform 20 is approximately oriented toward the direction of the span or depth.

[0103] (2) Send the measurement command, the laser radar 22 starts to collect 2D point cloud data, and converts the point cloud data into the coordinate system of the movable platform 20. The conversion process is as follows: the coordinates of the point cloud data obtained by the laser radar 22 are relative to the laser radar 22 coordinate system. In order to make the coordinates of the final contour point cloud data in the vehicle coordinate system, the coordinates of each point in the point cloud need to be converted. Let the rotation matrix between the laser radar 22 coordinate system and the movable platform 20 coordinate system be R, the translation matrix be T, and let the coordinates of the point under the movable platform 20 coordinate system be: (x c ,y c , z c ), the coordinates of the point under the laser radar 22 are: (x l ,y l , z l ), the coordinate transformation form is:

[0104]

[0105] Through this conversion process, a room outline point cloud based on the coordinate system of the movable platform 20 is generated.

[0106] (3) Based on the data from the inclinometer 21, the room outline is projected onto the horizontal plane to obtain the horizontal plane room outline point cloud. The inclinometer 21 is a dynamic inclinometer that can measure the attitude parameters (roll, pitch, and azimuth) of the moving carrier and is suitable for inclinometer measurement in a moving or vibrating state. The inclinometer 21 sensor only outputs sensor data without data processing. Assume that the coordinates of the point under the horizontal movable platform 20 are: (x h ,y h , z h ), the coordinates of the original movable platform 20 are: (x c ,y c , z c ), the coordinate transformation form is:

[0107]

[0108] where R h The coordinate system rotation matrix between the original point cloud plane and the water surface is calculated from the sensor data (roll angle, pitch angle) of the inclinometer 21. The specific coordinate conversion program is run on the industrial computer 23.

[0109] (4) Preprocess the horizontal room outline point cloud to remove point clouds that are obviously larger than the size of a regular room and noise points around the movable platform 20.

[0110] (5) Four parts of point cloud are extracted in the longitudinal and transverse directions of the movable platform 20. The extraction process is as follows: Figure 4As shown in the figure, specifically: extract point clouds with a length of 1 meter (the length can be determined by yourself) respectively in the front, rear, left, and right of the movable platform 20, and use the least squares method [(also known as the method of least squares) is a mathematical optimization technique. It finds the best function match for the data by minimizing the sum of the squares of the errors. Using the least squares method, unknown data can be easily obtained, and the sum of the squares of the errors between the obtained data and the actual data is minimized. The least squares method can also be used for curve fitting. Some other optimization problems can also be expressed by minimizing energy or maximizing entropy using the least squares method] to fit the four parts of point clouds into straight lines.

[0111] (6) Find the point clouds closest to the fitted straight line in the four parts of the extracted point clouds respectively, calculate the distances from the point clouds to the opposite straight lines respectively and find the average value, and the average value is the measurement result of the bay width and depth of this time. The process of determining the closest point cloud and the process of calculating the distance to the opposite straight line are as Figure 5 shown in the figure. Use the least squares method to fit the straight line L1, calculate the distances from each point cloud to the fitted straight line respectively. Let the equation of the straight line L1 be Ax + By + C = 0, and the coordinates of the point P be (xi, yi). Then the distance from the point Pi to the straight line L1 is defined as the length of the perpendicular line segment from it to the straight line L1, and di = │Axi + Byi + C│ / √(A2 + B2) can be obtained by calculating the length of the perpendicular line segment from the point Pi to the straight line L1. Let the threshold be T. When di < T, it is considered that the point is closest to the fitted straight line L1 and is added to the set S1. Let the fitted straight line of the point cloud on the right side be L2, calculate the distances from the points Pi in the set S1 to the fitted straight line L2 respectively, and find the average value of the distances, then the average value is the measured value of the bay width or depth.

[0112] (7) Conduct multiple measurements in the bay width or depth direction, obtain the range (in statistics, the range refers to the difference between the maximum value and the minimum value in a set of data, and it is a measure of the degree of data dispersion) in the bay width or depth direction. The range should not exceed a certain standard value. Generally, for decoration projects, it is required to be less than or equal to 10 mm. Moreover, the deviation value between the actual measured distance and the designed size should also not exceed a certain standard value. Generally, for decoration projects, it is required to be less than or equal to 10 mm. Otherwise, it is considered that this index is unqualified. Conduct multiple measurements and calculate the maximum and minimum extreme values of the bay width and depth, compare them with the designed size, and obtain the measurement conclusion.

[0113] (8) Automatically write the results into the report file through the program, store them in the industrial control computer 23 or upload the relevant data to the background server through the Internet of Things device 24.

[0114] It should be noted that when the above lidar 22 and inclinometer 21 are installed horizontally, they can be used to measure the bay width and depth of indoor rooms. On the other hand, when the lidar 22 and inclinometer 21 are installed vertically, they can be used to measure the floor height of indoor rooms.

[0115] This application can achieve the following technical effects:

[0116] 1. The measuring equipment is mounted on a movable platform and can be moved autonomously or remotely controlled, which is easy to operate and saves time and effort.

[0117] 2. Using inclinometer and lidar, and fitting the point cloud data, the measurement accuracy is high.

[0118] 3. Send the measurement command and get the measurement results immediately. The results will be automatically saved and uploaded to the background in real time. There is no need for surveyors to record, process and analyze the data, which greatly improves the measurement efficiency.

[0119] An embodiment of the present application also provides a method for measuring the depth of an indoor bay, which is executed by the industrial computer in the above embodiment. Figure 6 is a flow chart of a method for measuring the depth of an indoor bay according to an embodiment of the present application. Figure 6 As shown, the process includes the following steps:

[0120] Step S601, acquiring 2D point cloud data collected by a laser radar and attitude parameters of the movable platform measured by an inclinometer;

[0121] Step S602, converting the 2D point cloud data into room outline point cloud data based on the movable platform coordinate system;

[0122] Step S603, converting the room outline point cloud data based on the movable platform coordinate system into horizontal plane room outline point cloud data according to the posture parameters;

[0123] Step S604: determining a bay depth measurement result based on the horizontal plane room outline point cloud data.

[0124] In some embodiments, the step S604 of determining the bay depth measurement result based on the horizontal room outline point cloud data includes:

[0125] Extracting four parts of point cloud in the longitudinal and transverse directions of the movable platform coordinate system according to the horizontal plane room outline point cloud data, and fitting the four parts of point cloud into straight lines respectively;

[0126] Determine the target point cloud closest to the fitting straight line from the four point clouds;

[0127] Calculate the average value of the distance from the target point cloud to the opposite fitting straight line, and determine the average value as the bay depth measurement result.

[0128] In some embodiments, the method further comprises:

[0129] Perform multiple measurements to obtain multiple bay depth measurement results;

[0130] Determining a maximum value and a minimum value from the plurality of bay depth measurement results, and calculating a range based on the maximum value and the minimum value;

[0131] Determine whether the range is within a preset value range;

[0132] Determine whether the engineering indicators are qualified based on the results of the judgment.

[0133] In some embodiments, before extracting the four-part point cloud in the longitudinal and transverse directions of the movable platform coordinate system, the method further includes:

[0134] The horizontal plane room outline point cloud data is preprocessed to remove point clouds that exceed the room size and noise points within the preset area of ​​the movable platform.

[0135] The embodiment of the present application is based on a method for measuring the depth of an indoor span. The method obtains a horizontal plane room outline point cloud by collecting 2D point cloud data and using inclinometer data to horizontally project the room outline point cloud collected by a lidar, extracts the span and depth direction point clouds and uses the least squares method to fit a straight line, calculates the average distance of the fitted straight line from multiple point clouds on the straight line to the opposite wall as measurement data, and automatically saves the measurement results and uploads them to the background in real time through an Internet of Things device. The measuring equipment is highly mobile and can be remotely operated, and can realize automatic navigation measurement operations, thereby improving measurement accuracy and efficiency.

[0136] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0137] The embodiment provides a computer device. In conjunction with the embodiment of the present application, the indoor bay depth measurement method can be implemented by the computer device. Figure 7 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present application.

[0138] The computer device may include a processor 71 and a memory 72 storing computer program instructions.

[0139] Specifically, the processor 71 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0140] Among them, the memory 72 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 72 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 72 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 72 may be inside or outside the data processing device. In a specific embodiment, the memory 72 is a non-volatile memory. In a specific embodiment, the memory 72 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0141] The memory 72 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 71 .

[0142] The processor 71 reads and executes computer program instructions stored in the memory 72 to implement any one of the indoor bay depth measurement methods in the above embodiments.

[0143] In some embodiments, the computer device may further include a communication interface 73 and a bus 70. Figure 7 As shown, the processor 71, the memory 72, and the communication interface 73 are connected via a bus 70 and communicate with each other.

[0144] The communication interface 73 is used to implement communication between the various modules, devices, units, and / or devices in the embodiments of the present application. The communication interface 73 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0145] Bus 70 includes hardware, software, or both, and couples components of a computer device to each other. Bus 70 includes, but is not limited to, at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 70 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of the above. Bus 70 may include one or more buses, where appropriate. Although embodiments herein describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

[0146] In addition, in conjunction with the indoor bay depth measurement method in the above embodiments, embodiments of the present application may provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the indoor bay depth measurement methods in the above embodiments is implemented.

[0147] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. An indoor bay depth measuring device, characterized in that: include: A movable platform and an inclinometer, a laser radar, an industrial computer, and an Internet of Things device arranged on the movable platform, wherein: The laser radar is arranged on the longitudinal axis or the transverse axis of the movable platform and is used to collect 2D point cloud data; The inclinometer is arranged at the geometric center position of the chassis plane of the movable platform and is used to measure the posture parameters of the movable platform; The industrial computer is used to convert the 2D point cloud data into room outline point cloud data based on the movable platform coordinate system, convert the room outline point cloud data based on the movable platform coordinate system into horizontal plane room outline point cloud data according to the posture parameters, and determine the bay depth measurement result based on the horizontal plane room outline point cloud data; The Internet of Things device is in communication with the industrial computer and is used to upload the bay depth measurement result to the backend server; Determining the bay depth measurement result according to the horizontal room outline point cloud data includes: Extracting four parts of point cloud in the longitudinal and transverse directions of the movable platform coordinate system according to the horizontal plane room outline point cloud data, and fitting the four parts of point cloud into straight lines respectively; Determine the target point cloud closest to the fitting straight line from the four point clouds; Calculate the average value of the distance from the target point cloud to the opposite fitting straight line, and determine the average value as the bay depth measurement result.

2. The device according to claim 1, characterized in that The movable platform chassis plane, the inclinometer installation plane, and the laser radar installation plane are in the same plane or parallel to each other.

3. The device according to claim 1, characterized in that The industrial computer is also used for: Perform multiple measurements to obtain multiple bay depth measurement results; Determining a maximum value and a minimum value from the plurality of bay depth measurement results, and calculating a range based on the maximum value and the minimum value; Determine whether the range is within a preset value range; Determine whether the engineering indicators are qualified based on the results of the judgment.

4. A method for measuring the depth of an indoor bay used in the indoor bay depth measuring device according to any one of claims 1 to 3, characterized in that: include: Acquire 2D point cloud data collected by a laser radar and attitude parameters of the movable platform measured by an inclinometer; Converting the 2D point cloud data into room outline point cloud data based on the movable platform coordinate system; Converting the room outline point cloud data based on the movable platform coordinate system into horizontal plane room outline point cloud data according to the posture parameters; Determine the span depth measurement result based on the horizontal plane room outline point cloud data; Determining the bay depth measurement result according to the horizontal room outline point cloud data includes: Extracting four parts of point cloud in the longitudinal and transverse directions of the movable platform coordinate system according to the horizontal plane room outline point cloud data, and fitting the four parts of point cloud into straight lines respectively; Determine the target point cloud closest to the fitting straight line from the four point clouds; Calculate the average value of the distance from the target point cloud to the opposite fitting straight line, and determine the average value as the bay depth measurement result.

5. The method according to claim 4, characterized in that Also includes: Perform multiple measurements to obtain multiple bay depth measurement results; Determining a maximum value and a minimum value from the plurality of bay depth measurement results, and calculating a range based on the maximum value and the minimum value; Determine whether the range is within a preset value range; Determine whether the engineering indicators are qualified based on the results of the judgment.

6. The method according to claim 4, characterized in that Before extracting the four-part point cloud in the longitudinal and transverse directions of the movable platform coordinate system, the method further includes: The horizontal plane room outline point cloud data is preprocessed to remove point clouds that exceed the room size and noise points within the preset area of ​​the movable platform.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 4 to 6 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 4 to 6 is implemented.

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