Method and system for collecting and managing data for three-dimensional visualization of radiation source

Abstract

The present invention relates to a method and a system for collecting and managing data for three-dimensional visualization of a radiation source, the method comprising the steps of: collecting radiation data through a sensor provided in at least a part of a robot; processing the radiation data by applying at least one of filtering, clustering and plane projection to the radiation data; and visualizing and providing a radiation map created on the basis of the radiation data.

Description

Data Collection, Management Method, and System for 3D Visualization of Radiation Sources

[0001] The present invention relates to a method and system for collecting and managing data for three-dimensional visualization of a radiation source, and more specifically, to a method and system for collecting and managing data for three-dimensional visualization of a radiation source that visualizes radiation data collected in real time through a robot by reflecting it on point cloud data, and in particular, selectively calculates radiation intensity for a region of interest during the radiation intensity visualization process, thereby drastically reducing the calculation load for radiation intensity that changes in real time and enabling efficient and accurate visualization processing.

[0002] Recently, unmanned surveillance systems using drones or quadruped robots are being utilized to explore areas contaminated with radiation or areas at risk from radiation exposure, or to measure radiation.

[0003] In this situation, the radiation measurement method used by robots deployed in such areas relies on mounting a dosimeter on the robot; therefore, it can only determine the dose rate at the point the robot has moved to (i.e., the point where the dosimeter is located). Since the dosimeter displays dose information at its own location, it cannot pinpoint the direction of the radiation source, making it difficult to locate the approach path to the points necessary for the various operations required to contain the accident reactor.

[0004] In addition, in the case of such conventional unmanned detection systems, there is a problem in that when the threshold value is arbitrarily changed for noise filtering during the process of calculating the amount of radiation emitted from a radiation source in space and visualizing the radiation intensity, the number of radiation sources predicted may change according to the changed threshold value.

[0005] In addition, point cloud data of a building is utilized through sensors mounted on a robot in the process of visualizing radiation intensity. However, in order to calculate and display the radiation intensity of a cell based on the number of cells and radiation sources constituting the point cloud data, numerous calculations must be repeated even for areas unrelated to radiation sources, which causes a problem where the data processing speed becomes significantly slow.

[0006] Prior art literature

[0007] Patent Document Korean Registered Patent No. 10-2608026 (2023.11.27)

[0008] The present invention aims to solve the aforementioned problems by providing a method and system for collecting and managing data for the three-dimensional visualization of a radiation source, which visualizes radiation data collected in real time through a robot in three dimensions by reflecting it on point cloud data, and in particular, selectively calculates radiation intensity for a region of interest during the radiation intensity visualization process, thereby drastically reducing the calculation load for radiation intensity that changes in real time and enabling efficient and accurate visualization processing.

[0009] A method for collecting and managing data for three-dimensional visualization of a radiation source according to an embodiment of the present invention includes the steps of: collecting radiation data through a sensor provided in at least a part of a robot; processing the radiation data by applying at least one of a filtering process, a clustering process, and a planar projection process; and providing a radiation map created based on the radiation data. The step of processing by applying at least one of the filtering process, the clustering process, and the planar projection process includes removing noise by obtaining only matching data based on a threshold value set during radiation measurement in the filtering process; estimating the number and location of radiation sources for the radiation data in the clustering process; and deriving values ​​related to the radiation emission amount of the radiation source using an inverse square law in the planar projection process and performing projection processing. The step of providing the radiation map may include reducing the calculation load by hierarchically dividing a three-dimensional space in the visualization process and subdividing a space set as a region of interest among the divided spaces to calculate radiation intensity.

[0010] The step of collecting radiation data through a sensor provided in at least a part of the robot according to one embodiment of the present invention may include the step of estimating a real-time position along the movement path of the robot by obtaining an estimated value from each of the attitude and position estimator and the marker position estimator mounted on the robot, the step of collecting the radiation data through a radiation sensor and a distance sensing sensor mounted on the robot, and the step of matching the real-time position data of the robot with the radiation data.

[0011] In the filtering process according to one embodiment of the present invention, the step of removing noise by obtaining only matching data based on a preset threshold value during radiation measurement, estimating the number and location of radiation sources for the radiation data in the clustering process, and deriving a value related to the radiation emission amount of the radiation source using the inverse square law in the planar projection process and performing projection processing may include the step of clustering a plurality of radiation measurement values ​​using a k-means clustering technique in the clustering process and estimating the number and location of radiation sources for the clustered radiation measurement area.

[0012] In the filtering process according to one embodiment of the present invention, the step of removing noise by obtaining only matching data based on a preset threshold value during radiation measurement, estimating the number and location of radiation sources for the radiation data in the clustering process, and deriving values ​​related to the radiation emission amount of the radiation source using the inverse square law in the planar projection process and performing projection processing may further include the step of generating the radiation map after obtaining point cloud data for the space in advance through a laser measuring instrument or obtaining point cloud data for the space in real time through a 3D sensor mounted on the robot.

[0013] In the filtering process according to one embodiment of the present invention, the step of removing noise by obtaining only matching data based on a preset threshold value during radiation measurement, estimating the number and location of radiation sources for the radiation data in the clustering process, and deriving a value related to the radiation emission amount of the radiation source using the inverse square law in the planar projection process may further include the step of processing by projecting the location of the radiation source for the clustered radiation measurement area onto the point cloud data for the space obtained in the above example so that the location and radiation emission amount of the radiation source are reflected on a 2D plane.

[0014] In the filtering process according to one embodiment of the present invention, the step of removing noise by obtaining only matching data based on a preset threshold value during radiation measurement, estimating the number and location of radiation sources for the radiation data in the clustering process, and deriving a value related to the radiation emission amount of the radiation source using the inverse square law in the planar projection process may further include the step of calculating the radiation emission amount for the radiation source at a location spaced apart from the radiation sensor mounted on the robot through the following mathematical formula 1.

[0015] [Mathematical Formula 1]

[0016]

[0017] (Here, I d,j is the radiation emission amount of the above radiation sensor for the j-th radiation source, and P j (a j , Z j ) is the object characteristic function, and f(θ j ) is the bearing angle f(θ) between the contact surface of the radiation sensor and the point of the radiation source in three-dimensional space)

[0018] In the visualization process according to one embodiment of the present invention, the step of reducing the calculation load by hierarchically dividing a three-dimensional space and subdividing a space set as a region of interest among the divided spaces to calculate radiation intensity may include: a step of dividing point cloud data for the space in which the location of the radiation source and the amount of radiation emitted are projected and reflected into a plurality of cells; a step of setting a region where the clustered radiation measurement areas overlap among the plurality of cells over which the clustered radiation measurement areas span as a region of interest; and a step of subdividing each of the plurality of cells set as a region of interest into voxel units having a preset volume to calculate radiation intensity.

[0019] According to one embodiment of the present invention, the step of setting the area where the clustered radiation measurement areas overlap among the plurality of cells spanned by the clustered radiation measurement areas as the area of ​​interest may include the step of determining the plurality of cells not spanned by the clustered radiation measurement areas as noise cells and excluding them when calculating radiation intensity for a space divided into a plurality of cells.

[0020] The step of providing a radiation map created based on the radiation data according to one embodiment of the present invention may include the step of visualizing the location of the radiation source, the exposure path of the radiation source, and the radiation intensity by reflecting them on a 3D spatial map of the space acquired in real time through a laser measuring instrument or a 3D sensor mounted on the robot, based on 3D coordinate values ​​for the location of the radiation source.

[0021] A data collection and management system for three-dimensional visualization of a radiation source according to another embodiment of the present invention includes a radiation data collection unit that collects radiation data measured through a sensor frame provided in at least part of a robot, a radiation data processing unit that processes the radiation data by applying at least one of a filtering process, a clustering process, and a planar projection process, and a radiation map visualization unit that visualizes and provides a radiation map created based on the radiation data. The radiation data processing unit removes noise by acquiring only data that matches based on a threshold value set during radiation measurement in the filtering process, estimates the number and location of radiation sources for the radiation data in the clustering process, and derives values ​​related to the radiation emission amount of the radiation source using an inverse square law in the planar projection process and performs projection processing. The radiation map visualization unit can reduce the calculation load by hierarchically dividing the three-dimensional space in the visualization process and subdividing the space set as a region of interest among the divided spaces to calculate the radiation intensity.

[0022] According to one embodiment of the present invention, radiation data collected in real time through a robot can be reflected on point cloud data to be visualized in three dimensions. In particular, during the radiation intensity visualization process, radiation intensity calculation is selectively performed for a region of interest, thereby significantly reducing the calculation load for radiation intensity that changes in real time and providing the advantage of performing visualization processing efficiently and accurately.

[0023] FIG. 1 is a flowchart showing a method for collecting and managing data for three-dimensional visualization of a radiation source according to an embodiment of the present invention in a series of sequences.

[0024] FIG. 2 is a conceptual diagram conceptually illustrating the overall process of measuring radiation using a data collection and management method for three-dimensional visualization of a radiation source according to an embodiment of the present invention.

[0025] FIG. 3 is a schematic diagram showing the configuration of a robot for measuring radiation according to one embodiment of the present invention.

[0026] Figure 4 is a conceptual diagram illustrating the process of clustering multiple radiation measurements using the k-means clustering technique.

[0027] Figure 5 is a conceptual diagram illustrating the process of projecting the location and radiation emission amount of a radiation source onto point cloud data.

[0028] Figure 6 is a conceptual diagram conceptually illustrating the process of subdividing the space set as the region of interest based on the Octomap algorithm.

[0029] Hereinafter, specific details for implementing the present invention will be described in detail with reference to the attached drawings. However, in the following description, specific descriptions regarding widely known functions or configurations will be omitted if there is a risk that the gist of the present invention may be unnecessarily obscured.

[0030] In the attached drawings, identical or corresponding components are given the same reference numerals. Additionally, in the description of the following embodiments, the description of identical or corresponding components may be omitted. However, even if a description of a component is omitted, it is not intended that such component is not included in any embodiment.

[0031] The advantages and features of the invented embodiments and the methods for achieving them will become clear by referring to the embodiments described below together with the accompanying drawings. However, the present invention is not limited to the embodiments described below but can be implemented in various different forms, and these embodiments are provided merely to make the present invention complete and to fully inform a person skilled in the art of the scope of the invention.

[0032] The terms used in this specification will be briefly explained, and the invented embodiments will be described in detail. The terms used in this specification have been selected to be as generally used as possible, taking into account their functions in the present invention; however, these terms may vary depending on the intent of those skilled in the relevant field, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms have been arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant description of the invention. Therefore, the terms used in this invention should be defined not merely by their names, but based on the meanings they possess and the content of the invention as a whole.

[0033] In this specification, singular expressions include plural expressions unless the context clearly specifies them as singular. Additionally, plural expressions include singular expressions unless the context clearly specifies them as plural. Throughout the specification, when a part is described as including a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0034]

[0035] FIG. 1 is a flowchart showing a method for collecting and managing data for three-dimensional visualization of a radiation source in a series of sequences according to an embodiment of the present invention, and FIG. 2 is a conceptual diagram conceptually showing the overall process for measuring radiation using the method for collecting and managing data for three-dimensional visualization of a radiation source according to an embodiment of the present invention.

[0036] Referring to FIGS. 1 and 2, the radiation data collection unit of the data collection and management system for three-dimensional visualization of a radiation source for performing the data collection and management method for three-dimensional visualization of a radiation source according to one embodiment of the present invention collects radiation data measured through a sensor frame provided on at least a part of a robot (S101). At this time, the structure of the robot for measuring radiation is schematically as follows.

[0037] FIG. 3 is a schematic diagram showing the configuration of a robot for measuring radiation according to one embodiment of the present invention.

[0038] Looking at Fig. 3, the radiation measuring robot may include an attitude / position estimator, a marker, an IMU device, a camera, and a radiation sensor.

[0039] Attitude / position estimators can estimate the attitude and position of radiation sensors and can utilize various techniques such as marker recognition, inertial navigation system-based SLAM, and camera-based odometry estimation.

[0040] In addition, for radiation collection and data processing algorithms used in radiation measuring robots, the output values ​​(attitude, position) of the attitude / position estimator and the radiation measurements can be used.

[0041] In addition, the radiation data collection unit can collect radiation data through radiation sensors and distance sensors mounted on the robot, and by matching the robot's real-time position data with the radiation data, it becomes possible to generate an accurate radiation map based on the precise location of the radiation source.

[0042]

[0043] Next, a radiation data processing unit according to one embodiment of the present invention processes radiation data collected through a radiation data collection unit by applying at least one of a filtering process, a clustering process, and a planar projection process (S102).

[0044] In step S102, the radiation data processing unit can remove noise by acquiring only matching data based on a preset threshold value during radiation measurement in the filtering process, estimate the number and location of radiation sources for the radiation data in the clustering process, and derive values ​​related to the radiation emission amount of the sources using the inverse square law in the planar projection process to perform projection processing. This is examined in more detail as follows.

[0045] Figure 4 is a conceptual diagram illustrating the process of clustering multiple radiation measurements using the k-means clustering technique, and Figure 5 is a conceptual diagram illustrating the process of projecting the location and radiation emission amount of a radiation source onto point cloud data.

[0046] Referring to FIGS. 4 and 5, a radiation data processing unit according to one embodiment of the present invention filters and removes noise within radiation data based on a threshold value directly specified by a user. In this process, radiation values ​​below the threshold value are filtered out as noise, leaving only effective radiation values.

[0047] In addition, the radiation data processing unit clusters multiple radiation measurements using the k-means clustering technique during the clustering process and estimates the number and location of radiation sources for the clustered radiation measurement areas.

[0048] In this process, the radiation data processing unit can predict the point (location) of the radiation source (x) through clustering.

[0049] Meanwhile, the method of clustering radiation sources using k-means clustering can be performed through unsupervised learning algorithms. Here, clustering radiation sources is a process of grouping sources with similar characteristics into the same group to facilitate various analyses or predictions, and it can be performed in the following order.

[0050] The radiation data processing unit prepares data regarding the radiation source, such as radiation intensity, energy spectrum, location, or other characteristics. At this time, the radiation source can be represented as a vector defined by various characteristics.

[0051] In addition, the radiation data processing unit can pre-set the number of clusters (k). Here, the number of clusters corresponds to the number of groups of radiation sources to be clustered, and to select the optimal k, the radiation data processing unit may use methods such as the elbow method or silhouette analysis.

[0052] Additionally, the radiation data processing unit can randomly select k initial centroids from the radiation data. Here, each centroid represents the center of a cluster, and clustering of points representing radiation values ​​is performed based on the centroids.

[0053] In addition, the radiation data processing unit assigns each radiation source data point to the nearest center point. This can be achieved by calculating the distance between each radiation source and the center point using techniques such as the Euclidean distance, and assigning the corresponding radiation source to the center point with the shortest distance.

[0054] In addition, the radiation data processing unit can calculate the average position of the radiation source in each cluster after the radiation source is assigned and set a new center point, update the new center point with the average coordinates of all radiation sources within the cluster, and if a new center point is set, the radiation source is reassigned to match the new center point and the process of updating the center point can be repeated until the center point no longer moves.

[0055] In addition, the radiation data processing unit can acquire point cloud data (PCD) for space in real time through a laser measuring instrument that has acquired point cloud data for space in advance or a 3D sensor mounted on the robot for radiation measurement described earlier, and generate a radiation map.

[0056] In addition, during this process, the radiation data processing unit can calculate the amount of radiation emitted from a radiation source located at a distance from the radiation sensor mounted on the robot through the following mathematical formula 1.

[0057]

[0058] [Mathematical Formula 1]

[0059]

[0060] Here, I d,j is the radiation emission amount of the above radiation sensor for the j-th radiation source, and P j (a j , Z j ) is the object characteristic function, and f(θ j ) is the bearing angle f(θ) between the contact surface of the radiation sensor and the point of the radiation source in three-dimensional space.

[0061] Therefore, finally, through Equation 1, the radiation data processing unit can calculate the total radiation dose from multiple radiation sources, and can process the position of the radiation source for the clustered radiation measurement area onto the previously acquired point cloud data so that the position of the radiation source and the amount of radiation emitted are reflected on a 2D plane.

[0062] Here, point cloud data (PCD) for space is data containing location information of radiation sources, and each radiation source can be represented by three-dimensional coordinates (x,y,z). In addition to location information of radiation sources, other metadata such as energy intensity and time information may be included.

[0063] The radiation data processing unit may select an orthographic projection method that converts to 2D coordinates by ignoring one axis (e.g., the z-axis) in 3D coordinates, a principal component analysis (PCA) method that visualizes the radiation data along the direction with the greatest data variance by converting it along the major axis, or a t-SNE or UMAP algorithm that projects high-dimensional data into low-dimensional data while maintaining a non-linear pattern as a projection method.

[0064] In addition, the radiation data processing unit can perform Min-Max scaling and normalization on the data before visualization when the coordinate data of the radiation source is distributed over a very large range.

[0065]

[0066] Next, a radiation map visualization unit according to one embodiment of the present invention visualizes and provides a radiation map created based on radiation data (S103).

[0067] In step S103, the radiation map visualization unit can reduce the computation load by hierarchically dividing the 3D space during the visualization process and subdividing the space set as the region of interest among the divided spaces to calculate radiation intensity. This is examined in more detail as follows.

[0068] Figure 6 is a conceptual diagram conceptually illustrating the process of subdividing the space set as the region of interest based on the Octomap algorithm.

[0069] Referring to FIG. 6, a radiation map visualization unit according to one embodiment of the present invention divides point cloud data for a space in which the location of a radiation source and the amount of radiation emitted are projected and reflected into a plurality of cells, and sets the area where the clustered radiation measurement areas overlap among the plurality of cells spanned by the clustered radiation measurement areas as a region of interest. Subsequently, the radiation map visualization unit can calculate radiation intensity by subdividing each of the plurality of cells set as the region of interest into voxel units having a preset volume.

[0070] In addition, during this process, the radiation map visualization unit can significantly increase processing speed by simplifying the radiation intensity calculation process by determining multiple cells that are not overlapping with clustered radiation measurement areas as noise cells for a space divided into multiple cells and excluding them when calculating radiation intensity.

[0071] For example, the radiation map visualization unit can filter out and exclude areas where multiple clustered radiation measurement areas overlap as noise, and among the areas where clustered radiation measurement areas overlap, particularly areas where clustered radiation measurement areas overlap each other, set them as regions of interest, and further subdivide the regions of interest to calculate radiation intensity and highlight the results.

[0072] In addition, the radiation map visualization unit can visualize the location of the radiation source, the exposure path of the radiation source, and the radiation intensity by reflecting them on a 3D spatial map of the space acquired in real time through a laser measuring instrument or a 3D sensor mounted on a robot, based on 3D coordinate values ​​of the location of the radiation source.

[0073] In this process, the radiation map visualization unit displays the color, size, etc., of each clustered radiation measurement area differently so that the radiation exposure path and intensity can be identified in 3D space, and the radiation intensity changing in real time can be processed efficiently, and safe path setting and radiation blocking can be enabled.

[0074]

[0075] Although the embodiments described above have been described as utilizing aspects of the subject matter currently invented in one or more standalone computer systems, the present invention is not limited thereto and may be implemented in conjunction with any computing environment, such as a network or a distributed computing environment. Furthermore, aspects of the subject matter in the present invention may be implemented in a plurality of processing chips or devices, and storage may be similarly affected across a plurality of devices. Such devices may include PCs, network servers, and portable devices.

[0076] Although the present invention has been described in relation to some embodiments, various modifications and changes may be made without departing from the scope of the invention as understood by a person skilled in the art to which the invention pertains. Furthermore, such modifications and changes should be considered to fall within the scope of the claims appended to this specification.

[0077]

[0078] Explanation of the symbols

[0079] 100: Automated Routine Inspection System for Current Collectors

[0080] 110: Captured Image Collection Unit

[0081] 120: Abnormal condition detection unit

[0082] 121: Current collector detection unit

[0083] 122: Calibration Process

[0084] 122-1: Distortion Correction Section

[0085] 122-2: Distance determination unit

[0086] 122-3: Angle identification section

[0087] 123: Deformation detection unit

[0088] 124: Foreign substance detection unit

[0089] 130: Abnormal condition notification unit

Claims

1. A step of collecting radiation data through a sensor equipped on at least a part of the robot; A step of processing the above radiation data by applying at least one of a filtering process, a clustering process, and a planar projection process; and The method includes the step of visualizing and providing a radiation map created based on the above radiation data; The step of processing by applying at least one of the above filtering process, clustering process, and planar projection process is, The method includes the step of removing noise by acquiring only matching data based on a preset threshold value during radiation measurement in the filtering process, estimating the number and location of radiation sources for the radiation data in the clustering process, and deriving values ​​related to the radiation emission amount of the radiation sources using the inverse square law in the planar projection process and performing projection processing. The step of visualizing and providing the above radiation map is: A step of reducing the calculation load by hierarchically dividing a three-dimensional space during the above visualization process and subdividing the space set as a region of interest among the divided spaces to calculate radiation intensity; Data collection and management method for three-dimensional visualization of a radiation source.

2. In Paragraph 1, The step of collecting radiation data through a sensor equipped on at least a part of the robot is, A step of obtaining estimated values ​​from each of the attitude and position estimator and the marker position estimator mounted on the robot to estimate the real-time position according to the movement path of the robot; A step of collecting radiation data through a radiation sensor and a distance sensing sensor mounted on the robot; and A step of matching the real-time position data of the robot and the radiation data with each other; comprising Data collection and management method for three-dimensional visualization of a radiation source.

3. In Paragraph 2, The step of removing noise by acquiring only matching data based on a preset threshold value during radiation measurement in the filtering process, estimating the number and location of radiation sources for the radiation data in the clustering process, and deriving values ​​related to the radiation emission amount of the radiation sources using the inverse square law in the planar projection process and performing projection processing is A step comprising: clustering multiple radiation measurements using a k-means clustering technique in the above clustering process, and estimating the number and location of radiation sources for the clustered radiation measurement area; Data collection and management method for three-dimensional visualization of a radiation source.

4. In Paragraph 3, The step of removing noise by acquiring only matching data based on a preset threshold value during radiation measurement in the filtering process, estimating the number and location of radiation sources for the radiation data in the clustering process, and deriving values ​​related to the radiation emission amount of the radiation sources using the inverse square law in the planar projection process and performing projection processing is The method further comprises the step of generating the radiation map after acquiring point cloud data of the space in advance through a laser measuring instrument, or acquiring point cloud data of the space in real time through a 3D sensor mounted on the robot. Data collection and management method for three-dimensional visualization of a radiation source.

5. In Paragraph 4, The step of removing noise by acquiring only matching data based on a preset threshold value during radiation measurement in the filtering process, estimating the number and location of radiation sources for the radiation data in the clustering process, and deriving values ​​related to the radiation emission amount of the radiation sources using the inverse square law in the planar projection process and performing projection processing is The method further comprises the step of projecting the location of the radiation source for the clustered radiation measurement area onto the previously acquired point cloud data for the space, processing so that the location of the radiation source and the amount of radiation emitted are reflected on a 2D plane. Data collection and management method for three-dimensional visualization of a radiation source.

6. In Paragraph 5, The step of removing noise by acquiring only matching data based on a preset threshold value during radiation measurement in the filtering process, estimating the number and location of radiation sources for the radiation data in the clustering process, and deriving values ​​related to the radiation emission amount of the radiation sources using the inverse square law in the planar projection process and performing projection processing is The method further comprises the step of calculating the amount of radiation emitted from the radiation source at a position spaced apart from the radiation sensor mounted on the robot using the following mathematical formula 1. Data collection and management method for three-dimensional visualization of a radiation source. [Mathematical Formula 1] (Here, I d,j is the radiation emission amount of the above radiation sensor for the j-th radiation source, and P j (a j , Z j ) is the object characteristic function, and f(θ j ) is the bearing angle f(θ) between the contact surface of the radiation sensor and the point of the radiation source in three-dimensional space) 7. In Paragraph 6, The step of reducing the calculation load in the above visualization process by hierarchically dividing the three-dimensional space and subdividing the space set as the region of interest among the divided spaces to calculate radiation intensity is: A step of dividing point cloud data for the space in which the location and radiation emission amount of the radiation source are projected and reflected into a plurality of cells; A step of setting a region of interest where the clustered radiation measurement areas overlap among a plurality of cells spanned by the clustered radiation measurement areas; and A step of calculating radiation intensity by subdividing each of the plurality of cells set as the region of interest into voxel units having a preset volume; Data collection and management method for three-dimensional visualization of a radiation source.

8. In Paragraph 7, The step of setting the area where the clustered radiation measurement areas overlap among the plurality of cells spanned by the clustered radiation measurement areas as the region of interest is: A step comprising, for a space divided into multiple cells, determining multiple cells that are not overlapping with the clustered radiation measurement area as noise cells and excluding them when calculating radiation intensity; Data collection and management method for three-dimensional visualization of a radiation source.

9. In Paragraph 8, The step of visualizing and providing a radiation map created based on the above radiation data is: A step comprising: visualizing the location of the radiation source, the exposure path of the radiation source, and the radiation intensity based on 3D coordinate values ​​for the location of the radiation source on a 3D spatial map of the space acquired in real time through the laser measuring instrument or the 3D sensor mounted on the robot; Data collection and management method for three-dimensional visualization of a radiation source.

10. A radiation data collection unit that collects radiation data measured through a sensor frame provided in at least a part of the robot; A radiation data processing unit that processes the above radiation data by applying at least one of a filtering process, a clustering process, and a planar projection process; and A radiation map visualization unit that visualizes and provides a radiation map created based on the above radiation data; is included. The above radiation data processing unit is, In the above filtering process, only matching data is obtained based on a preset threshold value during radiation measurement to remove noise, in the above clustering process, the number and location of radiation sources for the radiation data are estimated, and in the above planar projection process, values ​​related to the radiation emission amount of the radiation sources are derived using the inverse square law and projected. The above radiation map visualization unit is, In the above visualization process, the calculation load is reduced by hierarchically dividing the 3D space and subdividing the space set as the region of interest among the divided spaces to calculate radiation intensity. Data collection and management system for 3D visualization of radiation sources.

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