Wall surface adjacent floor height difference out-of-limit identification and positioning method

Abstract

This invention discloses a method for identifying and locating excessive height differences between adjacent floor slabs on walls, comprising the following steps: S1: acquiring indoor point cloud data of a building; S2: detecting different planes in the point cloud formed by the point cloud data, segmenting the indoor point cloud data of the building to obtain point cloud data of each wall surface, including the top of the wall; S3: locating the top point cloud data of the wall surface, specifically including: S31: extracting the center point of the point cloud data of each wall surface. S32: Extract the z-values ​​of the center points of each wall surface, sort the z-values ​​from largest to smallest, and determine the wall surface corresponding to the largest z-value as the wall vertex cloud P; S4: Calculate the height difference between adjacent floor slabs at the top of the wall. This invention proposes a method for identifying and locating the height difference between adjacent floor slabs on the wall surface that exceeds the limit, realizing the rapid measurement of the height difference between adjacent floor slabs on the wall surface. Compared with the manual ruler measurement method, it has the advantages of more measurement points and strong intuitiveness.

Description

A method for identifying and locating excessive height differences between adjacent floor slabs on walls Technical Field

[0001] This invention relates to the field of methods for identifying and locating excessive height differences between adjacent floor slabs on walls. More specifically, this invention relates to a method for identifying and locating excessive height differences between adjacent floor slabs on walls based on point cloud data processing algorithms. Background Technology

[0002] In the technical specifications for prefabricated building construction, the height difference between adjacent floor slabs is one of the important indicators for ensuring building quality and structural safety. Abnormal height differences between adjacent floor slabs not only affect the accuracy of subsequent finishing work but may also lead to safety hazards such as abnormal structural stress. Therefore, accurately identifying and locating the height difference between adjacent floor slabs during construction is of great significance.

[0003] Currently, the detection of floor slab elevation differences mainly relies on traditional surveying equipment such as total stations and levels. This requires manual point-by-point measurement and recording, which is inefficient, labor-intensive, and prone to omissions and errors in large-scale construction projects, making it difficult to meet the demands for high-density and high-precision measurements. With the development of intelligent building construction, point cloud scanning technology has been widely introduced into building structure inspection. By acquiring high-density point cloud data of building surfaces through equipment such as 3D laser scanners, a data foundation has been provided for achieving refined inspection of building structures.

[0004] However, in practical applications, due to unclear boundaries between adjacent floor slabs, numerous noise interference points, and severe structural obstruction, traditional manual extraction and analysis methods are difficult to directly calculate the height difference between adjacent floor slabs, posing a significant challenge to the automatic extraction and over-limit judgment of the height difference between adjacent floor slabs.

[0005] Therefore, there is an urgent need for a method for identifying and locating excessive height differences between adjacent floor slabs based on point cloud data. This method would enable efficient and accurate segmentation of floor slabs from raw point cloud data, calculation of floor slab height differences, and alarm for exceeding limits, thereby improving the digitalization and intelligence of building quality inspection. This invention aims to provide an automated processing and intelligent identification technology and system to reduce human error, improve the efficiency and reliability of large-scale building structure inspection, and meet the higher requirements of modern construction engineering for structural inspection accuracy and construction quality management. Summary of the Invention

[0006] To achieve these objectives and other advantages according to the present invention, a preferred embodiment of the present invention provides a method for identifying and locating excessive height differences between adjacent floor slabs on walls based on a point cloud data processing algorithm, comprising the following steps:

[0007] S1: Acquire point cloud data of the building interior using a 3D laser scanner;

[0008] S2: Detect different planes in the point cloud formed by the point cloud data, segment the point cloud data of the building interior, and obtain the point cloud data of each wall surface, including the top of the wall.

[0009] S3: Location wall vertex cloud data, specifically including:

[0010] S31: Extract the center point of each wall surface point cloud data. ;

[0011] S32: Extract the z-value of the center point of each wall surface, sort the z-values ​​from largest to smallest, and determine the wall surface corresponding to the largest z-value as the wall vertex cloud P;

[0012] S4: Calculate the height difference between adjacent floor slabs at the top of the wall if it exceeds the limit.

[0013] Preferably, S4: Calculate the excessive height difference between adjacent floor slabs at the top of the wall, specifically including:

[0014] S41: Traverse every point in the wall vertex cloud data P Search within a certain radius All neighboring points ;

[0015] S42: Calculation Extract the maximum value from all Z values ​​in the dataset. Minimum value ;

[0016] S43: Calculate elevation difference Set threshold ,when At that time, the judgment The point is where the elevation difference exceeds the limit. Assigned as an attribute , build The data format is .

[0017] S44: Determine if the height difference between adjacent floor slabs exceeds the limit. And determine the location where the elevation difference exceeds the limit. .

[0018] Preferably, S31: Extract the center point of each wall point cloud data using the AABB bounding box algorithm. .

[0019] Preferably, the AABB bounding box algorithm determines the range of the bounding box by calculating the extreme values ​​of the point cloud data of each wall on the x, y, and z axes, and then calculates the coordinates of the center point of each wall.

[0020] Preferably, in step S2, a random consistency plane detection algorithm is used to detect different planes in the point cloud.

[0021] Preferably, the identification result output in step S44 includes the coordinate information of the elevation difference exceeding the limit point, and the positioning result includes the spatial location of the elevation difference exceeding the limit area.

[0022] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0023] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the program to implement the method described thereon.

[0024] The present invention includes at least the following beneficial effects: The present invention acquires indoor point cloud data of a building under construction using a three-dimensional laser scanner, extracts wall point cloud data using a planar segmentation algorithm, extracts wall top point cloud data based on point cloud height information, calculates the height difference of the wall top point cloud data and sets an over-limit value, thereby realizing the identification and positioning of wall top height difference exceeding the limit.

[0025] This invention proposes a method for identifying and locating excessive height differences between adjacent floor slabs on walls, enabling rapid measurement of height differences between adjacent floor slabs on walls. Compared with manual measurement methods, it has advantages such as more measurement points and stronger intuitiveness.

[0026] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0027] Figure 1 is a flowchart illustrating the method for identifying and locating excessive height differences between adjacent floor slabs on walls in this invention.

[0028] Figure 2 shows the indoor point cloud data of the building in step S1 of this invention.

[0029] Figure 3 is a point cloud data segmentation diagram in step S2 of the present invention. Detailed Implementation

[0030] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0031] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious modifications will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0032] Those skilled in the art should understand that, in the disclosure of this invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the above terms should not be construed as limiting this invention.

[0033] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0034] As shown in Figures 1-3, the present invention provides a method for identifying and locating excessive height differences between adjacent floor slabs on walls based on point cloud data processing algorithms, comprising the following steps:

[0035] S1: Acquire point cloud data of the building interior using a 3D laser scanner;

[0036] S2: Detect different planes in the point cloud formed by the point cloud data, segment the point cloud data of the building interior, and obtain the point cloud data of each wall surface, including the top of the wall.

[0037] S3: Location wall vertex cloud data, specifically including:

[0038] S31: Extract the center point of each wall surface point cloud data. ;

[0039] S32: Extract the z-value of the center point of each wall surface, sort the z-values ​​from largest to smallest, and determine the wall surface corresponding to the largest z-value as the wall vertex cloud P;

[0040] S4: Calculate the height difference between adjacent floor slabs at the top of the wall if it exceeds the limit.

[0041] This implementation method improves the efficiency of traditional manual point-by-point measurement by approximately 80% through an automated point cloud processing workflow, allowing a single person to complete the height difference detection of a 1000㎡ building within 2 hours. Compared to manual ruler measurement, which can only acquire discrete point data, this method significantly increases the point cloud density, capturing height difference changes at the 0.5mm level, and greatly improving the accuracy of over-limit identification. By binding the height difference data with point cloud coordinates, a visualized map of the over-limit area can be directly generated, providing precise guidance for construction rectification.

[0042] In another technical solution, S4: calculates whether the height difference between adjacent floor slabs at the top of the wall exceeds the limit, specifically including:

[0043] S41: Traverse every point in the wall vertex cloud data P Search within a certain radius All neighboring points ;

[0044] S42: Calculation Extract the maximum value from all Z values ​​in the dataset. Minimum value ;

[0045] S43: Calculate elevation difference Set threshold ,when At that time, the judgment The point is where the elevation difference exceeds the limit. Assigned as an attribute , build The data format is .

[0046] S44: Determine if the height difference between adjacent floor slabs exceeds the limit. And determine the location where the elevation difference exceeds the limit. .

[0047] This implementation method, through quantifying the nearest neighbor retrieval radius and threshold setting logic, controls the elevation difference calculation error to within ±0.3mm. Compared to the ±2mm error when using a level instrument manually, the accuracy is greatly improved, and the detection cycle is significantly shortened.

[0048] In another technical solution, S31: The center point of each wall point cloud data is extracted using the AABB bounding box algorithm. .

[0049] For walls with complex shapes (such as curved walls), the AABB bounding box algorithm can still quickly determine the center point through axis extremum calculation, avoiding positioning errors caused by irregular wall shapes. The algorithm adopts a parallel computing architecture, which can process point cloud data of multiple walls simultaneously. For a building model containing 100 walls, the center point extraction time is short.

[0050] In another technical solution, in step S2, a random consistency plane detection algorithm is used to detect different planes in the point cloud.

[0051] This implementation method ensures the repeatability and accuracy of the AABB bounding box algorithm by clearly defining the calculation logic of the three-axis extrema and the method for generating the center point coordinates. Testing showed that for regular rectangular walls, the deviation between the center point coordinates and the actual geometric center is less than 0.3 mm; for irregular walls (such as L-shaped walls), the deviation is less than 0.8 mm, meeting the accuracy requirements for building inspection. The algorithm's efficiency allows it to be applied to large-scale point cloud data processing, laying the foundation for subsequent wall top positioning and elevation difference calculation.

[0052] In another technical solution, the identification result output in step S44 includes the coordinate information of the elevation difference exceeding the limit point, and the positioning result includes the spatial location of the elevation difference exceeding the limit area.

[0053] Another technical solution provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0054] "Computer-readable storage medium" refers to a physical carrier capable of storing computer programs, including hard drives, USB flash drives, optical discs, etc., used to persistently store the program code of the height difference over-limit recognition algorithm. "Processor-executed program" refers to the computer's central processing unit (CPU) reading the program code from the storage medium and executing the point cloud processing operations according to the instruction logic.

[0055] Another technical solution provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the program to implement the method.

[0056] "Electronic devices" refer to computing devices that integrate hardware components such as memory and processors, such as laptops and industrial tablets, used for running height difference exceeding limit identification algorithms on-site. "Memory" is used to temporarily store point cloud data and intermediate results of program execution, while "processor" is responsible for executing algorithm instructions.

[0057] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for identifying and locating excessive height differences between adjacent floor slabs on walls, characterized in that, Includes the following steps: S1: Acquire indoor point cloud data of the building; S2: Detect different planes in the point cloud formed by the point cloud data, segment the indoor point cloud data of the building, and obtain point cloud data of each wall surface, including the top of the wall. S3: Locate the vertex cloud data of the wall, specifically including: S31: Extract the center point of the point cloud data of each wall surface. S32: Extract the z-values ​​of the center points of each wall surface, sort the z-values ​​from largest to smallest, and determine the wall surface corresponding to the largest z-value as the wall vertex cloud P; S4: Calculate the height difference exceeding the limit between adjacent floor slabs at the top of the wall; S4: Calculate the height difference exceeding the limit between adjacent floor slabs at the top of the wall, specifically including: S41: Traverse each point of the wall vertex cloud data P Search within a certain radius All neighboring points S42: Calculation Extract the maximum value from all Z values ​​in the dataset. Minimum value S43: Calculate elevation difference Set threshold ,when At that time, the judgment The point is where the elevation difference exceeds the limit. Assigned as an attribute , build The data format is S44: Determine if the height difference between adjacent floor slabs exceeds the limit. And determine the location where the elevation difference exceeds the limit. In S31, the AABB bounding box algorithm is used to extract the center points of the point cloud data of each wall surface. The AABB bounding box algorithm determines the bounding box range by calculating the extreme values ​​of each wall point cloud data on the x, y, and z axes, and then calculates the center point coordinates of each wall. In step S2, the random consistency plane detection algorithm is used to detect different planes in the point cloud. The building interior point cloud data is acquired by a 3D laser scanner.

2. The method for identifying and locating excessive height differences between adjacent floor slabs on walls according to claim 1, characterized in that, The identification results output by step S44 include the coordinate information of the elevation difference exceeding the limit, and the positioning results include the spatial location of the elevation difference exceeding the limit area.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 2.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 2.

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