Multi-angle traceability surface area measuring method and system under non-contact optical scanning

Abstract

The invention discloses a multi-angle traceability surface area measurement method and system under non-contact optical scanning, and belongs to the technical field of object surface measurement, and the method comprises the steps: employing a built-in depth induction camera, and obtaining three-dimensional point cloud data; a processor is arranged in the equipment, a synchronous positioning and map building algorithm and a three-dimensional reconstruction algorithm are operated according to the three-dimensional point cloud data, and a three-dimensional grid model is generated; based on the three-dimensional grid model, determining overall surface area calculation data corresponding to the target object and local surface area calculation data corresponding to the user designated area through a computer graphics algorithm; a man-machine interaction module is configured through a micro display, an indicator light and a trigger button, and overall surface area calculation data and local surface area calculation data are visually output. According to the method, the technical problems that the irregular surface sampling area cannot be accurately obtained and effective quantitative risk assessment cannot be carried out in a traditional surface sampling method are solved.

Description

Technical Field

[0001] This invention relates to the field of object surface measurement technology, specifically to a surface area measurement method and system for multi-angle traceability under non-contact optical scanning. Background Technology

[0002] Surface microbial contamination detection is a crucial component in disease prevention and control, food safety supervision, and infection control in healthcare institutions. Accurate quantitative assessment of environmental surface microbial contamination levels is essential for scientific infection risk assessment, developing effective disinfection strategies, and evaluating the effectiveness of interventions. The core development need in this field is to move from traditional qualitative or semi-quantitative detection to fully quantitative analysis that provides accurate and comparable data.

[0003] However, current mainstream surface microbial sampling methods, such as those using standard-sized swabs or contact plates, suffer from a fundamental technical bottleneck: the inability to accurately determine the actual sampling area of ​​irregular surfaces. For regular planes, estimations can be made based on the size of the swab head or contact plate; however, for complex, irregular surfaces, which constitute the majority of practical applications, the sampling area is difficult to measure and is often ignored or roughly estimated. This directly leads to subsequent colony counting results failing to accurately reflect the microbial contamination density per unit area, resulting in a lack of comparability between sample data collected at different times, locations, and by different operators, severely hindering the application of true quantitative risk assessment. Therefore, there is an urgent need in this field for a technical solution that can quickly, accurately, and on-site measure the area of ​​any irregular surface, and seamlessly integrate with the sampling operation. Summary of the Invention

[0004] This application provides a surface area measurement method and system for multi-angle traceability under non-contact optical scanning, aiming to solve the technical problem that traditional surface sampling methods cannot accurately determine the sampling area of ​​irregular surfaces and cannot conduct effective quantitative risk assessment.

[0005] In view of the above problems, this application provides a surface area measurement method and system for multi-angle traceability under non-contact optical scanning.

[0006] The first aspect disclosed in this application provides a surface area measurement method for multi-angle traceability under non-contact optical scanning. The method includes: using a built-in depth sensing camera to acquire three-dimensional point cloud data of a target object; the device's built-in processor running a simultaneous localization and mapping algorithm and a three-dimensional reconstruction algorithm based on the three-dimensional point cloud data of the target object to generate a three-dimensional mesh model; based on the three-dimensional mesh model, determining the overall surface area calculation data corresponding to the target object and the local surface area calculation data corresponding to the user-specified area through computer graphics algorithms; and configuring a human-computer interaction module with a micro-display, indicator lights, and trigger buttons to visualize and output the overall surface area calculation data and the local surface area calculation data.

[0007] Another aspect of this application discloses a surface area measurement system for multi-angle traceability under non-contact optical scanning. This system includes: a point cloud data acquisition module for acquiring three-dimensional point cloud data of a target object using a built-in depth-sensing camera; a mesh model generation module for generating a three-dimensional mesh model by running a synchronous positioning and mapping algorithm and a three-dimensional reconstruction algorithm based on the three-dimensional point cloud data of the target object using a built-in processor; a target area calculation module for determining the overall surface area calculation data corresponding to the target object and the local surface area calculation data corresponding to a user-specified area based on the three-dimensional mesh model using computer graphics algorithms; and a visualization output module for configuring a human-computer interaction module with a micro-display, indicator lights, and trigger buttons to visualize and output the overall surface area calculation data and the local surface area calculation data.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: This technical solution, which acquires 3D point cloud data through a built-in depth-sensing camera, generates a 3D mesh model by running simultaneous localization and mapping (SLAM) and 3D reconstruction algorithms on a processor, calculates the overall or local surface area based on this model using computer graphics algorithms, and finally outputs the visualization through a human-computer interaction module, solves the technical problem that traditional surface sampling methods cannot accurately determine the sampling area of ​​irregular surfaces and cannot conduct effective quantitative risk assessment. It achieves the technical effect of transforming complex geometric measurements into automated, high-precision digital calculations, thereby providing reliable core data for various quantitative analysis applications.

[0009] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0010] Figure 1A flowchart illustrating a surface area measurement method for multi-angle tracing under non-contact optical scanning is provided for embodiments of this application.

[0011] Figure 2 A schematic diagram of the surface area measurement system for multi-angle tracing under non-contact optical scanning is provided for the embodiments of this application.

[0012] Figure labeling: Point cloud data acquisition module 11, mesh model generation module 12, target area calculation module 13, visualization output module 14. Detailed Implementation

[0013] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0014] The overall concept of the technical solution provided in this application is as follows: This application provides a method and system for measuring the surface area of ​​objects using non-contact optical scanning for multi-angle tracing. The method acquires 3D point cloud data of an object through non-contact optical scanning; utilizes simultaneous localization and mapping (SLAM) algorithms and 3D reconstruction algorithms to transform the discrete point cloud into a precise 3D mesh model; calculates the overall or partial surface area using graphics methods based on this model; and finally visualizes the results through a human-computer interaction module, thereby achieving rapid and accurate measurement of the surface area of ​​irregular objects.

[0015] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0016] Example 1, as Figure 1 As shown in the embodiments of this application, a surface area measurement method for multi-angle traceability under non-contact optical scanning is provided, the method comprising: Step S100: Use a built-in depth sensing camera to acquire 3D point cloud data of the target object.

[0017] Specifically, a built-in depth-sensing camera is a special imaging device. Unlike ordinary cameras that can only capture color two-dimensional images, it can actively acquire distance information between each pixel in the scene and the camera, i.e., "depth". Common implementation technologies include structured light, time-of-flight, and stereo vision. 3D point cloud data is a direct result output by the depth-sensing camera. It can be understood as a set of thousands of discrete three-dimensional spatial coordinate points (X, Y, Z). Each point precisely represents a position on the surface of an object. When these points are dense enough, they can outline the three-dimensional contour and shape of the object's surface. The target object refers to the object being measured and scanned; in this invention, it specifically refers to objects with irregular surfaces, such as door handles, faucets, and medical devices.

[0018] Specifically, after the user starts the device, they hold the device so that its built-in depth-sensing camera faces the target object and performs a slow and steady scan. The distance data is combined with the spatial positioning information of the camera itself, and a set of data points with three-dimensional coordinates is generated in real time, namely three-dimensional point cloud data. This completes the first high-precision digital replication of the geometric shape of the object's surface in digital space, laying the data foundation for subsequent three-dimensional model reconstruction.

[0019] This step enables the rapid and automatic acquisition of high-precision raw data on the surface geometry of irregular objects in a non-contact manner, transforming the complex shapes of the physical world into a digital point cloud set that can be processed by computers, laying the foundation for subsequent accurate modeling and calculation.

[0020] Step S200: The device's built-in processor runs a simultaneous localization and mapping algorithm and a 3D reconstruction algorithm based on the 3D point cloud data of the target object to generate a 3D mesh model.

[0021] Specifically, a device's built-in processor refers to a high-performance computing unit integrated within the device, such as a CPU, GPU, or dedicated ASIC chip. It is responsible for running complex algorithms and processing massive amounts of data. Simultaneous localization and mapping (SLAM) algorithms are computer vision algorithms. Their core function is to solve the "chicken or the egg" problem in real time: while the device is moving, it simultaneously performs two tasks: localization: calculating and tracking the device's position and orientation in space in real time; and mapping: building and updating the surrounding environment, i.e., the geometric map of the target object, in real time based on camera data. 3D reconstruction algorithms are algorithms that transform discrete, unordered data into a continuous, complete model. Receiving data processed by the SLAM algorithm, through a series of steps such as point cloud registration, Poisson reconstruction, and surface fitting, it "stitches" countless individual 3D points into a continuous, watertight surface model. A 3D mesh model is a standardized digital representation of a 3D model. It consists of a large number of vertices, edges, and faces, forming a polygonal "mesh" to accurately describe the surface geometry of an object. It is the cornerstone of visualization, interaction, and computation in computer graphics.

[0022] Specifically, after receiving the 3D point cloud data transmitted in real time from the depth sensing camera, the device's built-in processor runs synchronous positioning and mapping algorithms and 3D reconstruction algorithms to "weave" the discrete spatial point set into a complete, continuous 3D mesh model composed of countless triangular facets, thereby completing the transformation from a real object to its digital 3D twin model.

[0023] This step uses simultaneous localization and mapping (LAN) and 3D reconstruction algorithms to transform dynamically acquired, discrete, multi-view point cloud data into an accurate, complete, and readily available 3D mesh model for subsequent quantitative calculations in real time and automatically.

[0024] Step S300: Based on the three-dimensional mesh model, determine the overall surface area calculation data corresponding to the target object and the local surface area calculation data corresponding to the user-specified area using computer graphics algorithms.

[0025] Specifically, computer graphics algorithms refer to the mathematical methods and programs used to process, analyze, and calculate 3D model data. In this context, it specifically refers to algorithms used to calculate the area of ​​curved surfaces. Total surface area calculation data refers to the precise numerical result representing the sum of the areas of all the outer surfaces of the entire target object, usually expressed in square centimeters (cm²). 2 Area units are used to represent the area. User-specified area: refers to a specific sub-region marked by the user on the 3D mesh model through human-computer interaction, such as by projecting a light spot or selecting a box on a touch screen.

[0026] Specifically, based on the generated 3D mesh model, the model surface or the subset of the model surface corresponding to the local area specified by the user through the interactive device is used to calculate the overall surface area data reflecting the total surface area of ​​the object, as well as the local surface area data reflecting the area of ​​interest of the user, for visualization output or control of the sampling device.

[0027] This step utilizes computer graphics methods to transform the complex and irregular three-dimensional surface area calculation problem into a precise mathematical summation of a large number of simple geometric shapes. This enables automated, high-precision quantitative measurement of the surface area of ​​any complex-shaped object as a whole or any local area thereof, providing the core data foundation for subsequent precise quantitative sampling.

[0028] Step S400: Configure the human-computer interaction module with a miniature display, indicator lights and trigger buttons to visualize the overall surface area calculation data and the local surface area calculation data.

[0029] Specifically, a human-computer interaction module refers to a collection of hardware and software components installed on a device to enable information exchange and control between the user and the device. Its core function is to establish two-way communication: the user issues commands to the device, and the device provides feedback on its status and results to the user. A micro-display refers to a small display screen integrated into the device, such as an OLED or LCD screen. It is used to intuitively display information to the user in text, numbers, or graphics. Indicator lights typically refer to LED lights. They intuitively convey the current status of the device to the user through different colors or flashing patterns. A trigger button refers to a physical switch; the user issues explicit commands to the device by pressing it, such as starting a scan, confirming a selection, or initiating sampling.

[0030] Specifically, the device, through its configured human-machine interaction module, transforms the calculated overall or partial surface area data into a form that users can intuitively perceive; it sends the final numerical results to a micro display screen, displaying them with clear numbers and units along with possible 3D model diagrams; the processor controls indicator lights to emit steady-state light of a specific color to indicate that the measurement task has been completed; throughout the process, the user can trigger buttons to start scanning, confirm the sampling area, or execute sampling commands, thus completing a complete interactive loop from instruction input to result confirmation.

[0031] This step integrates a micro-display, indicator lights, and trigger buttons to create an intuitive and efficient human-machine interface. It transforms complex internal data processing results into clear and easy-to-understand visual and tactile feedback, greatly reducing the operational threshold of the equipment, improving the efficiency of on-site measurement and user experience, and ensuring the accuracy and traceability of sampling data.

[0032] Furthermore, a built-in depth-sensing camera is used to acquire three-dimensional point cloud data of the target object. The method includes: projecting an invisible laser speckle pattern onto the target object; reading the laser speckle pattern through the depth-sensing camera and performing structured light analysis to determine depth data; performing time-of-flight analysis based on the round-trip time of the laser pulse to determine distance data; and performing stereo vision analysis based on the depth data and the distance data to obtain the three-dimensional point cloud data.

[0033] Specifically, laser speckle patterns are patterns composed of random, high-contrast tiny spots projected by a laser. Due to their irregularity and invisibility, when this pattern is projected onto an object's surface, the surface's unevenness causes unique deformation of the pattern. Structured light analysis is a 3D measurement technique. Its principle is to analyze the deformation of a known pattern projected by a projector onto the surface of the object being measured, and to calculate the depth information of each point on the object's surface, i.e., the distance from the object's surface to the camera, using triangulation. Time-of-flight analysis is another 3D measurement technique. Its principle is to emit laser pulses towards the target and accurately measure the round-trip time between the pulse's emission and its reception by the camera. Since the speed of light is known, precise distance data can be directly calculated by calculating the "time difference." Stereo vision analysis refers to a technique that simulates the binocular parallax of the human eye. It involves simultaneously capturing the same scene from different angles using two or more cameras, matching corresponding points in the two images, and calculating depth information based on the pixel position differences (parallax) of these points.

[0034] Specifically, the device first activates its depth sensing module, projecting an invisible laser speckle pattern onto the surface of the target object. This pattern deforms due to the three-dimensional contours of the object's surface. Simultaneously, the depth sensing camera reads this deformed pattern and uses a structured light analysis algorithm to convert the pattern's deformation into depth data of each point on the object's surface relative to the camera. To improve the robustness and accuracy of the measurement, the device may also integrate time-of-flight technology, which independently obtains a set of direct distance data by emitting laser pulses and calculating their round-trip time. Finally, the processing unit, based on the depth data determined by structured light analysis and the distance data determined by time-of-flight analysis, and possibly combining multi-camera perspectives for stereo vision analysis, generates a more complete and accurate set of three-dimensional point cloud data through data fusion and complementary algorithms.

[0035] This step integrates multiple depth measurement technologies such as structured light and time-of-flight method, and is supplemented by stereo vision analysis. This enables the acquisition of high-precision and high-reliability 3D point cloud data under different ambient lighting and object surface characteristics, providing a solid guarantee for the accuracy of subsequent 3D reconstruction.

[0036] Furthermore, the device has a built-in processor that runs a simultaneous localization and mapping (SMR) algorithm and a 3D reconstruction algorithm based on the 3D point cloud data of the target object to generate a 3D mesh model. The method includes: the processor running the SMR algorithm to track the target object in real time and determine its movement trajectory; and the processor running the 3D reconstruction algorithm to stitch and fuse the 3D point cloud data of the target object with multiple 3D point cloud frame nodes acquired during the movement process, the movement trajectory, and the pose of the target object to generate the 3D mesh model.

[0037] Specifically, the movement trajectory refers to the device's movement path in three-dimensional space during the scanning process. Simultaneous localization and mapping (SMR) algorithms calculate the device's spatial position at every moment in real time, and connecting these positions sequentially forms the movement trajectory. A 3D point cloud frame node refers to a single frame of 3D point cloud data acquired at different times and poses during continuous scanning. It can be understood as taking a "photo" of the entire object, where each "photo" is a frame node. These frame nodes partially capture the shape of the object from different perspectives.

[0038] Specifically, the device processor runs a synchronous localization and mapping (SLAM) algorithm. By continuously analyzing the changes in feature points in the continuously incoming 3D point cloud data stream, it calculates and outputs the device's own movement trajectory and pose information during the scanning process in real time. The processor then initiates a 3D reconstruction algorithm. This algorithm takes multiple discrete 3D point cloud frame nodes acquired during the scanning process as input and uses the precise movement trajectory and device pose provided by the SLAM algorithm as key spatial constraints. Through iterative nearest-point registration algorithms, it precisely stitches together these point cloud frame nodes from different perspectives with a large number of overlapping areas, aligning them to a unified global coordinate system. The stitched massive point cloud is then fused, denoised, and reconstructed to generate a watertight, high-precision 3D mesh model composed of continuous triangular patches.

[0039] This step provides motion trajectory and pose constraints by explicitly defining the synchronous localization and mapping algorithm, and the 3D reconstruction algorithm uses these constraints to perform precise inter-frame stitching and fusion, ensuring that the final generated 3D mesh model has high geometric consistency and accuracy. This effectively avoids model misalignment or distortion caused by the instability of manual scanning, and provides a reliable model foundation for the subsequent accurate calculation of surface area.

[0040] Furthermore, based on the three-dimensional mesh model, the overall surface area calculation data corresponding to the target object is determined by computer graphics algorithms. The method includes: decomposing the mesh surface into multiple triangular facets on the three-dimensional mesh model; calculating the area data of each triangular facet based on the coordinates of its three vertices; traversing the multiple triangular facets and accumulating the area data to obtain the overall surface area calculation data corresponding to the target object.

[0041] Specifically, triangular facets are the most basic and commonly used building blocks in 3D mesh models. They are triangular planes composed of three vertices and three sides. Any complex 3D surface can be infinitely approximated by a sufficient number of sufficiently small triangular facets. Vertex coordinates refer to the precise positions of the three vertices of each triangular facet in 3D space, usually represented by (X, Y, Z) coordinate values. These coordinates define the shape, size, and spatial orientation of the triangular facet. Area data here specifically refers to the area value of a single triangular facet. Based on the spatial coordinates of its three vertices, the true area of ​​this triangle can be accurately calculated using vector operations, such as the modulus of the cross product.

[0042] Specifically, based on the generated 3D mesh model, the geometric data of the model is accessed, its surface mesh is identified and decomposed into a set of all triangular facets that make it up; each triangular facet is traversed, and its area data is calculated based on the coordinate values ​​of its three vertices in 3D space using the standard computer graphics formula of half the magnitude of the cross product formed by two edge vectors; after calculating the area of ​​all individual triangular facets, a loop accumulator is used to sum the area data of all triangular facets, and the final sum is the total surface area of ​​the target object, i.e., the overall surface area calculation data.

[0043] This step transforms the complex geometric problem of solving the surface area of ​​any irregular shape into a series of deterministic mathematical calculations by decomposing it into triangular facets, calculating the area of ​​each facet, and then summing them up. This ensures the accuracy and objectivity of the final result.

[0044] Furthermore, the human-computer interaction module is configured with a miniature display, indicator lights, and trigger buttons. The method also includes: the human-computer interaction module further includes a projector; wherein the projector is used to project a virtual sampling frame onto the surface of the target object.

[0045] Specifically, a virtual sampling frame refers to a visible light spot generated by a projector and projected onto the surface of an object. Its shape is typically a regular geometric shape such as a rectangle or circle, used to visually delineate an area in the physical world that needs to be measured or sampled. It serves as a bridge between user intent and machine recognition.

[0046] Specifically, in addition to basic human-computer interaction functions, the device further utilizes its integrated projector to aim at the surface of the scanned target object and project a clearly visible virtual sampling frame onto it. This allows the user to intuitively define the area of ​​interest in the physical space. At the same time, the device's internal processing system continuously senses the precise position of the projection frame on the 3D mesh model through a camera, automatically identifying the subset of triangular facets corresponding to the selected local area, providing the target range for subsequent dedicated local area calculations.

[0047] This step, by adding a projector and projecting a virtual sampling frame, upgrades the functionality from "measuring the overall surface area" to "precisely specifying and measuring the surface area of ​​any local area," providing an intuitive, natural, and accurate human-computer interaction method and enhancing the ease of use of the device and the flexibility of application scenarios.

[0048] Furthermore, the method for visualizing the overall surface area calculation data and the local surface area calculation data includes: identifying a subset of triangular facets corresponding to the projected area within the virtual sampling frame; and visualizing the local surface area calculation data corresponding to the user-specified area based on the subset of triangular facets, wherein the virtual sampling frame corresponds one-to-one with the user-specified area.

[0049] Specifically, the projection area within the virtual sampling frame refers to the physical area covered by the light spot projected by the projector onto the actual surface of the target object. It is visible to the user's naked eye.

[0050] Specifically, after the projector projects a virtual sampling frame onto the surface of the target object, its built-in camera captures the image of the object with the sampling frame in real time. Combining this with the generated 3D mesh model and the device's own spatial positioning, coordinate transformation and image recognition algorithms are used to accurately identify the subset of triangular faces covered by the virtual sampling frame on the 3D mesh model. For the identified subset of triangular faces, the same triangular face accumulation method used to calculate the overall surface area is applied to calculate the local surface area of ​​the user-specified area. This calculation result is then visualized and output through a micro-display, thus completing a closed loop from the selection operation in the physical world to the precise calculation and feedback in the digital world, ensuring a one-to-one correspondence between the virtual sampling frame, the user-specified area, and its calculation result.

[0051] This step establishes a precise spatial mapping relationship between the virtual sampling frame and the subset of triangular facets in the 3D mesh model, enabling seamless integration between the user's intuitive selection operation on the front-end physical surface and the automatic, accurate identification and calculation of the back-end digital model, fundamentally ensuring the accuracy and reliability of the local surface area measurement results.

[0052] Furthermore, the method includes: integrating a sampling head based on a built-in depth-sensing camera; and having the user trigger the sampling head to perform non-contact sampling configuration.

[0053] Specifically, the sampling head refers to the actuator integrated at the front end of the device for performing physical sampling operations. It is a replaceable, modular component.

[0054] Specifically, after the device completes the three-dimensional scanning of the target object's surface and the surface area calculation of the specified area (which can be the whole area or a local area specified through a virtual sampling frame), the user aligns the device probe with the area to be sampled. The user issues a sampling command by pressing a trigger button or other means. Upon receiving the command, the device immediately activates the micro-pump and other mechanisms integrated in the sampling head to generate a controllable negative pressure airflow. This airflow non-contactly draws in and captures target substances such as bioaerosols on the surface of the sampling area into the built-in collection tube or petri dish, thereby completing a quantitative sampling that is linked to the precisely calculated area data.

[0055] This step integrates a non-contact sampling head into the measuring device, achieving a closed loop of precise area measurement and quantitative physical sampling, providing a precise quantitative solution for environmental microbial detection with "known area and traceable sample".

[0056] In summary, the surface area measurement method for multi-angle traceability under non-contact optical scanning provided in this application has the following technical effects: 1. Through a complete automated process of "scanning-reconstruction-calculation-output," the irregular and difficult-to-measure surfaces of real-world objects are transformed into a precise and quantifiable numerical value of surface area. This is not merely the output of a single data point, but rather the realization of a new measurement paradigm: transforming complex geometric measurement problems into a reliable black-box operation automatically completed by the equipment, laying the core data foundation for subsequent quantitative analysis.

[0057] 2. By leveraging the spatiotemporal context provided by the simultaneous localization and mapping (SMR) algorithm, the correct spatial location was found for each frame of discrete point cloud data. Furthermore, a stitching and fusion algorithm eliminated accumulated errors and redundancy in overlapping areas during the scanning process. The final computational output is a single, watertight 3D mesh model with strict geometric consistency.

[0058] 3. By projecting a virtual sampling frame, an intuitive interface connecting the physical and digital worlds is established. The system can calculate the precise position and extent of the virtual frame in 3D space in real time, and accurately map the user's intuitive command "sample here" into a series of triangular faces to be calculated on the 3D mesh model, providing target boundaries for accurate measurement of local areas.

[0059] Example 2, based on the same inventive concept as the surface area measurement method for multi-angle traceability under non-contact optical scanning in the foregoing examples, such as... Figure 2 As shown in the embodiment of this application, a surface area measurement system for multi-angle traceability under non-contact optical scanning is provided. The system includes: a point cloud data acquisition module 11, used to acquire three-dimensional point cloud data of a target object using a built-in depth sensing camera; a mesh model generation module 12, used by the device's built-in processor to generate a three-dimensional mesh model by running a synchronous positioning and mapping algorithm and a three-dimensional reconstruction algorithm based on the three-dimensional point cloud data of the target object; a target area calculation module 13, used to determine the overall surface area calculation data corresponding to the target object and the local surface area calculation data corresponding to the user-specified area based on the three-dimensional mesh model using computer graphics algorithms; and a visualization output module 14, used to configure a human-computer interaction module with a micro display, indicator lights, and trigger buttons to visualize and output the overall surface area calculation data and the local surface area calculation data.

[0060] Furthermore, the point cloud data acquisition module 11 is also used to perform the following steps: projecting an invisible laser speckle pattern onto the target object; reading the laser speckle pattern through the depth sensing camera and performing structured light analysis to determine the depth data; performing time-of-flight analysis based on the round-trip time of the laser pulse to determine the distance data; and performing stereo vision analysis based on the depth data and the distance data to obtain the three-dimensional point cloud data.

[0061] Furthermore, the mesh model generation module 12 is also used to perform the following steps: the processor runs a synchronous localization and mapping algorithm to track the target object in real time and determine its movement trajectory; the processor runs a three-dimensional reconstruction algorithm to stitch and fuse the three-dimensional point cloud data of the target object with multiple three-dimensional point cloud frame nodes obtained during the movement process, the movement trajectory, and the pose of the target object to generate the three-dimensional mesh model.

[0062] Furthermore, the target area calculation module 13 is also used to perform the following steps: on the three-dimensional mesh model, the mesh surface is decomposed into multiple triangular facets; for each triangular facet, the area data is calculated based on the coordinates of the three vertices; the multiple triangular facets are traversed, and the area data is accumulated to obtain the overall surface area calculation data corresponding to the target object.

[0063] Furthermore, the visualization output module 14 is also used to perform the following steps: the human-computer interaction module further includes a projector; wherein the projector is used to project a virtual sampling frame onto the surface of the target object.

[0064] Furthermore, the visualization output module 14 is also used to perform the following steps: in the virtual sampling frame, identify the subset of triangular facets corresponding to the projected area within the virtual sampling frame; based on the subset of triangular facets, visualize the local surface area calculation data corresponding to the user-specified area, wherein the virtual sampling frame corresponds one-to-one with the user-specified area.

[0065] Furthermore, the system is also used to perform the following steps: integrating a sampling head based on a built-in depth sensing camera; and having the user trigger the sampling head to perform non-contact sampling configuration.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A surface area measurement method for multi-angle traceability under non-contact optical scanning, characterized in that, The method includes: It uses a built-in depth-sensing camera to acquire 3D point cloud data of the target object; The device has a built-in processor that runs a simultaneous localization and mapping algorithm and a 3D reconstruction algorithm based on the 3D point cloud data of the target object to generate a 3D mesh model. Based on the three-dimensional mesh model, computer graphics algorithms are used to determine the overall surface area calculation data of the target object and the local surface area calculation data of the user-specified area. A human-computer interaction module is configured with a miniature display, indicator lights, and trigger buttons to visualize the overall surface area calculation data and the local surface area calculation data.

2. The method as described in claim 1, characterized in that, The method for acquiring 3D point cloud data of a target object using a built-in depth-sensing camera includes: An invisible laser speckle pattern is projected onto the target object; The depth data is determined by analyzing the laser speckle pattern using the depth-sensing camera and performing structured light analysis. Distance data is determined by analyzing the round-trip time of the laser pulse. Based on the depth data and the distance data, stereoscopic vision analysis is performed to obtain the three-dimensional point cloud data.

3. The method as described in claim 1, characterized in that, The device has a built-in processor that, based on the 3D point cloud data of the target object, runs a simultaneous localization and mapping (SMR) algorithm and a 3D reconstruction algorithm to generate a 3D mesh model. The method includes: The processor runs a simultaneous localization and mapping algorithm to track the target object in real time and determine its movement trajectory. The processor runs a 3D reconstruction algorithm, which stitches and fuses the 3D point cloud data of the target object with multiple 3D point cloud frame nodes acquired during the movement process, the movement trajectory, and the pose of the target object to generate the 3D mesh model.

4. The method as described in claim 3, characterized in that, Based on the aforementioned three-dimensional mesh model, the overall surface area calculation data corresponding to the target object is determined using computer graphics algorithms. The method includes: On the three-dimensional mesh model, the mesh surface is decomposed into multiple triangular facets; For each triangular facet, calculate the area data based on the coordinates of its three vertices; By traversing the multiple triangular facets and accumulating the area data, the overall surface area of ​​the target object is obtained.

5. The method as described in claim 4, characterized in that, The method further includes configuring a human-computer interaction module with a miniature display, indicator lights, and trigger buttons, and further comprising: The human-computer interaction module also includes a projector; The projector is used to project a virtual sampling frame onto the surface of the target object.

6. The method as described in claim 5, characterized in that, The method for visualizing the overall surface area calculation data and the local surface area calculation data includes: In the virtual sampling frame, identify the subset of triangular facets corresponding to the projected area within the virtual sampling frame; Based on the triangular patch subset, the local surface area calculation data corresponding to the user-specified region is visualized and output, and the virtual sampling frame corresponds one-to-one with the user-specified region.

7. The method as described in claim 1, characterized in that, The method includes: Based on a built-in depth-sensing camera, with an integrated sampling head; The user triggers the sampling head to configure non-contact sampling.

8. A surface area measurement system for multi-angle traceability under non-contact optical scanning, characterized in that, The system is used to perform the surface area measurement method for multi-angle traceability under non-contact optical scanning as described in any one of claims 1 to 7, the system comprising: The point cloud data acquisition module is used to acquire the three-dimensional point cloud data of the target object using a built-in depth sensing camera; The mesh model generation module is used by the device's built-in processor to run a simultaneous localization and mapping algorithm and a 3D reconstruction algorithm based on the 3D point cloud data of the target object to generate a 3D mesh model. The target area calculation module is used to determine the overall surface area calculation data of the target object and the local surface area calculation data of the user-specified area based on the three-dimensional mesh model and through computer graphics algorithms. The visualization output module is used to configure the human-computer interaction module with a miniature display, indicator lights and trigger buttons to visualize the overall surface area calculation data and the local surface area calculation data.

Images