SLAM (Simultaneous Localization and Mapping) technology-based geological logging system and method and program product

Abstract

The invention relates to the technical field of geological logging, and discloses a geological logging system and method based on the SLAM technology and a program product. Uniform and stable illumination conditions can be provided for an adit to be recorded through a light source module, meanwhile, mobile scanning is carried out through an SLAM module, traditional single-station measurement is replaced, and the working efficiency is improved. And efficient collection of adit three-dimensional data is realized in combination with an SLAM technology. Furthermore, control point coordinates are obtained based on an SLAM image measurement technology. Furthermore, the original three-dimensional point cloud data set is processed through a point cloud processing module, so that the quality of the target three-dimensional color point cloud data set is improved. And finally, efficient and accurate geological recording is realized in a data analysis and calculation module through a target three-dimensional color point cloud data set and a preset geological attribute information set, and the problems of low efficiency, poor precision, weak environmental adaptability and dependence on manpower in traditional adit geological recording are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological logging, and in particular to a geological logging system, method and program product based on SLAM technology. Background Art

[0002] Engineering geological cataloging, equivalent to large-scale geological mapping, is the most important and fundamental task in construction geology. It involves the technical process of observing and describing the rock excavation surface, recording strata, structural surfaces, and geological characteristics. Traditional geological cataloging relies primarily on geological sketching, relying on manual on-site surveys to draw geological structural lines and measure occurrences. This method is outdated, the internal work is cumbersome, and the quality and personal habits of field geologists vary, resulting in delayed and sometimes incomplete geological cataloging results. Furthermore, due to the influence of on-site construction conditions and the working environment, the time available for on-site geological cataloging is increasingly limited, sometimes exposing field workers to potential safety hazards and risks. Furthermore, the cataloging process requires a significant investment of manpower, material resources, and time.

[0003] In order to change the traditional means of geological cataloging, relevant researchers have developed a geological cataloging system based on photogrammetry technology, which has achieved satisfactory results to a certain extent. However, in a flat tunnel environment, on the one hand, poor lighting conditions make image capture difficult, and on the other hand, the texture inside the flat tunnel is single, making it difficult to achieve ideal results in image recognition or image modeling. 3D laser scanning technology is a surveying and mapping technology developed in recent years. It is also known as "real-scene replication technology". With the help of collected point cloud data, it can completely and accurately reconstruct the spatial three-dimensional form of the scanned object. Some relevant researchers also use it for engineering geological cataloging. However, in a flat tunnel environment, stand-type 3D laser scanning requires a large number of survey stations, the scanning efficiency is relatively low, and absolute positioning requires a large number of control points, further increasing the operation time. Summary of the Invention

[0004] In view of this, the present invention provides a geological logging system, method and program product based on SLAM technology to solve the problems existing in traditional geological logging methods in flat tunnel environments.

[0005] In a first aspect, the present invention provides a geological logging system based on SLAM technology, the system comprising: a light source module, a SLAM module, a positioning module, a point cloud processing module and a data analysis and calculation module;

[0006] The light source module is used to provide lighting conditions for the flat hole to be recorded; the SLAM module is used to perform mobile scanning on the flat hole to be recorded, obtain the original three-dimensional point cloud dataset and image dataset of the flat hole to be recorded, and send the original three-dimensional point cloud dataset and image dataset to the point cloud processing module; the positioning module is used to obtain the coordinate dataset of multiple control points in the flat hole to be recorded based on the SLAM image measurement technology, and send the coordinate dataset to the point cloud processing module; the point cloud processing module is used to process the original three-dimensional point cloud dataset based on the coordinate dataset and the image dataset to obtain the target three-dimensional color point cloud dataset of the flat hole to be recorded, and send the target three-dimensional color point cloud dataset to the data analysis and calculation module; the data analysis and calculation module is used to record the geological conditions of the flat hole to be recorded based on the target three-dimensional color point cloud dataset and the preset geological attribute information set of the flat hole to be recorded, and obtain the geological recording dataset of the flat hole to be recorded.

[0007] The geological cataloging system based on SLAM technology provided by the present invention can provide uniform and stable lighting conditions for the flat tunnel to be cataloged through the light source module, thus solving the problem of "insufficient lighting" in the flat tunnel, ensuring that the images captured by the SLAM module during the scanning process are clear and discernible, and avoiding image blur caused by dim light. At the same time, it ensures the visibility of workers passing through the flat tunnel, reduces the safety risks of on-site operations, and indirectly improves work efficiency. Furthermore, the use of the SLAM module for mobile scanning replaces traditional single-station measurement, and does not require frequent setting up of measurement stations. It can scan continuously while moving, significantly reducing the operation time in the narrow and long environment of the flat tunnel, and solving the problem of "low efficiency and multiple measurement stations" of station-based scanning. At the same time, by synchronously acquiring the original three-dimensional point cloud dataset and the image dataset, it not only preserves the spatial three-dimensional morphological information of the flat tunnel, but also records the surface texture characteristics of the flat tunnel. Furthermore, it breaks through the limitation of "single texture" of the flat tunnel. At the same time, combined with the real-time positioning and map construction capabilities of SLAM technology, it avoids the modeling errors caused by insufficient texture in traditional photogrammetry and realizes the efficient collection of three-dimensional data of the flat tunnel. Furthermore, the coordinates of control points are acquired based on SLAM imaging measurement technology, replacing the traditional "complex measurement" mode, reducing the measurement workload and shortening the operation cycle. Furthermore, the original 3D point cloud dataset is processed through the point cloud processing module, improving the quality of the target 3D color point cloud dataset. Finally, in the data analysis and calculation module, efficient and accurate geological cataloging is achieved through the target 3D color point cloud dataset and the preset geological attribute information set. Therefore, the implementation of the present invention solves the problems of traditional flat-hole geological cataloging, namely "low efficiency, poor accuracy, weak environmental adaptability, and reliance on manual labor," and realizes fast, accurate, safe, and automated flat-hole geological cataloging.

[0008] In an optional embodiment, the positioning module includes: a laser RTK device, which is used to scan multiple control points using SLAM image measurement technology to obtain multiple scanned images; the laser RTK device is also used to obtain a coordinate data set of multiple control points based on the multiple scanned images through a preset processing algorithm.

[0009] The geological cataloging system based on SLAM technology provided by the present invention, relying on SLAM image measurement technology, breaks through the limitation of "no satellite signal" in flat holes, solves the application blind spot of traditional positioning technology in closed environments, and ensures that the control point measurement can still be carried out normally when the satellite is locked. At the same time, through mobile scanning and combined with the real-time image processing capability of SLAM technology, the spatial characteristics of the control points can be quickly captured, reducing manual operation errors. Furthermore, by analyzing the scanned image through a preset processing algorithm, the coordinates of multiple control points can be quickly calculated, replacing traditional manual data processing, greatly shortening the calculation time, and improving efficiency. Therefore, by implementing the present invention, the preset processing algorithm and the spatial correlation information of the SLAM image are combined, which solves the problem of "sudden drop in accuracy when there is no satellite signal" in the traditional technology.

[0010] In an optional embodiment, the point cloud processing module includes: a registration unit, an orientation unit and a pre-processing unit;

[0011] The registration unit is used to perform registration processing on the original three-dimensional point cloud dataset and the image dataset to obtain a first three-dimensional color point cloud dataset, and send the first three-dimensional color point cloud dataset to the orientation unit; the orientation unit is used to use the coordinate dataset to locate the first three-dimensional color point cloud dataset to obtain a second three-dimensional color point cloud dataset, and send the second three-dimensional color point cloud dataset to the preprocessing unit; the preprocessing unit is used to preprocess the second three-dimensional color point cloud dataset to obtain a target three-dimensional color point cloud dataset.

[0012] The SLAM-based geological cataloging system provided by this invention aligns image datasets with point clouds and generates a three-dimensional color point cloud, addressing the shortcomings of traditional point clouds, such as the lack of color information and the low visibility of geological details. Furthermore, the coordinate dataset enables automatic absolute orientation of the three-dimensional point cloud, resolving the disconnect between the relative positioning of traditional point clouds and actual space. Furthermore, preprocessing removes interfering data, improving point cloud quality and preventing noise from affecting the accuracy of geological cataloging.

[0013] In an optional embodiment, the data analysis and calculation module is further used to project the target three-dimensional color point cloud data set to obtain a two-dimensional cavern expansion diagram.

[0014] The geological logging system based on SLAM technology provided by the present invention realizes the conversion of three-dimensional form to two-dimensional visualization by projecting three-dimensional color point clouds into two-dimensional cavern expansion maps, thus solving the problems of incomplete information and disconnection with the site in traditional two-dimensional maps.

[0015] In an optional embodiment, the data analysis and calculation module is further used to generate a geological logging map of the adit to be logged based on the two-dimensional cavern expansion map and the geological logging data set.

[0016] The SLAM-based geological logging system provided by this invention combines a two-dimensional cavern expansion map with a geological logging dataset to generate a geological logging map, ensuring consistency between the map and actual site conditions. Furthermore, the geological logging map integrates the spatial information of a three-dimensional point cloud and geological attribute data, replacing the traditional "hand-drawing + manual annotation" model. This improves the objectivity and accuracy of the results, helps the expert team quickly understand the geological structure of the flat cave (such as lithologic zoning and structural distribution), and provides an intuitive and reliable visualization basis for engineering decision-making.

[0017] In an optional embodiment, the SLAM module is a portable mobile three-dimensional laser scanner using SLAM technology.

[0018] The SLAM-based geological cataloging system provided by the present invention replaces station-based surveying with a portable mobile 3D laser scanner using SLAM technology, enabling continuous mobile scanning within flat tunnels, reducing the number of survey stations and resolving the issue of excessive station setup and low efficiency. Furthermore, the device's portability adapts to the narrow and complex working environment of flat tunnels, easing the difficulty of on-site transportation and installation and reducing the labor intensity of operators. Furthermore, mobile scanning allows for rapid coverage of the entire tunnel, avoiding the blind spots of traditional single-station scanning, ensuring the integrity of the point cloud data, and providing a comprehensive 3D foundation for subsequent cataloging.

[0019] In a second aspect, the present invention provides a geological logging method based on SLAM technology, which is used in a data analysis and calculation module in a geological logging system based on SLAM technology according to the first aspect or any corresponding embodiment thereof; the method comprises:

[0020] Obtain a target three-dimensional color point cloud dataset and a preset geological attribute information set for the flat hole to be cataloged; catalog the geological elements of the flat hole to be cataloged using the target three-dimensional color point cloud dataset and the preset geological attribute information set to obtain a plurality of geological element cataloging data; catalog the geological information of different dimensions of the flat hole to be cataloged using the target three-dimensional color point cloud dataset and the preset geological attribute information set to obtain a plurality of geological information cataloging data; determine the geological cataloging dataset of the flat hole to be cataloged based on the plurality of geological element cataloging data and the plurality of geological information cataloging data.

[0021] The geological cataloging method based on SLAM technology provided by the present invention provides a three-dimensional visualization scene consistent with the site for cataloging by acquiring a target three-dimensional color point cloud data set, replacing the fuzzy basis of traditional "site memory + sketching", and solving the problem that flat-hole geological information recording is not intuitive and details are easily missed. At the same time, by acquiring a preset geological attribute information set, the cataloging dimension is unified, avoiding the defects of non-standard attribute description and incomplete information in traditional manual cataloging, and ensuring data structuring and standardization. Furthermore, the geological element cataloging is automatically associated with the three-dimensional point cloud coordinates, avoiding the errors of manually recorded coordinates and improving data accuracy. Furthermore, by cataloging geological information of different dimensions, traditional paper records are replaced, and data structured storage is achieved.

[0022] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the geological cataloging method based on SLAM technology of the second aspect mentioned above by executing the computer instructions.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the geological cataloging method based on SLAM technology according to the second aspect.

[0024] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the geological cataloging method based on SLAM technology according to the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 is a structural block diagram of a geological logging system based on SLAM technology according to an embodiment of the present invention;

[0027] Figure 2 1 is a flow chart of a geological logging method based on SLAM technology according to an embodiment of the present invention;

[0028] Figure 3 is an architectural diagram of a geological logging system based on SLAM technology according to an embodiment of the present invention;

[0029] Figure 4is a flow chart of a geological logging method based on SLAM technology according to an embodiment of the present invention;

[0030] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0032] The embodiment of the present invention provides a geological cataloging system based on SLAM technology. Through the collaborative work of different modules, it solves the problems of traditional flat-hole geological cataloging such as "low efficiency, poor accuracy, weak environmental adaptability, and dependence on manual labor", and realizes fast, accurate, safe and automated flat-hole geological cataloging.

[0033] In this embodiment, a geological logging system based on SLAM technology is provided. Figure 1 As shown, the geological cataloging system 1 based on SLAM technology includes: a light source module 2, a SLAM module 3, a positioning module 4, a point cloud processing module 5 and a data analysis and calculation module 6.

[0034] Optionally, the light source module 2 represents an equipment component that provides uniform and stable lighting conditions for the flat tunnel to be cataloged. Its purpose is to make up for the lack of natural light inside the flat tunnel, ensure that the SLAM module 3 can capture clear images during the scanning process, and at the same time ensure the visibility of the workers in the flat tunnel.

[0035] Specifically, the flat tunnel is a closed or semi-closed space where natural light cannot enter, and there may be local uneven light brightness (such as direct light spots from temporary construction lights).

[0036] In this embodiment, the light source module 2 outputs uniform visible light (such as white light) through distributed or wide-angle lighting design, which can cover the effective range of scanning and image capture of the SLAM module 3, thereby helping to eliminate shadows and highlight areas, and ensuring that the texture details of the inner wall of the flat tunnel and geological structure surfaces (such as rock layers and cracks) can be clearly recorded.

[0037] Furthermore, the basic lighting provided by the light source module 2 can also ensure that the operators can clearly see the road conditions in the flat tunnel (such as obstacles and height differences), reducing the safety risks such as bumps and falls during on-site mobile scanning, and indirectly improving work efficiency.

[0038] Optionally, the SLAM module 3 is used to perform mobile scanning on the flat hole to be cataloged, obtain the original three-dimensional point cloud dataset and image dataset of the flat hole to be cataloged, and send the original three-dimensional point cloud dataset and image dataset to the point cloud processing module 5.

[0039] The SLAM module 3 in this embodiment is a portable mobile three-dimensional laser scanner using SLAM technology.

[0040] Furthermore, SLAM (Simultaneous Localization and Mapping) technology refers to a technology that enables a device to determine its own position in real time while moving in an unknown environment through its own sensors (such as lidar, cameras, etc.) and simultaneously build a three-dimensional map of the surrounding environment.

[0041] Specifically, after the SLAM module 3 is started, it automatically completes the calibration of the internal sensors (3D laser scanner, camera, IMU inertial measurement unit) to ensure time synchronization (alignment of timestamps of laser scanning and image capture) and spatial coordinate system one.

[0042] At the same time, the laser scanning frequency (such as 100-200 lines / second) and camera frame rate (such as 15-30 frames / second) can be automatically adjusted according to the preset parameters of the flat hole (such as estimated diameter and length) to adapt to the scanning requirements of narrow and closed environments.

[0043] Furthermore, the 3D laser scanner rotates and emits laser beams, covering a 360-degree cross-section of the flat tunnel. Each laser beam is reflected by the rock wall and captured by a receiver. Furthermore, the laser's time of flight (TOF) or phase difference is calculated, generating the spatial coordinates (X, Y, Z) of each scan point in real time. This forms a continuous "point cloud stream" that reflects the 3D geometry of the flat tunnel's inner wall (such as curvature, protrusions, and depressions).

[0044] At the same time, under the uniform illumination provided by the light source module 2, the camera synchronously captures color images of the inner wall of the flat tunnel. Each frame of the image covers the corresponding area of ​​the laser scan, recording the texture details of the rock wall (such as rock type differences, structural surface direction, and degree of weathering), ensuring the spatial overlap between the image and the point cloud (usually ≥70%).

[0045] Furthermore, the IMU can record the module's movement speed, acceleration, and attitude angles (pitch, roll, and heading) in real time, and can temporarily store position information through inertial navigation to avoid positioning interruptions when feature matching fails temporarily (such as in a single area of ​​rock wall texture).

[0046] Furthermore, every time the SLAM module 3 moves 0.1-0.5 meters, it automatically performs feature matching between the currently collected point cloud / image ("current frame") and the data at the previous moment ("historical frame"):

[0047] (1) Point cloud feature matching: The algorithm is used to extract key geometric features (such as planes, edge lines, and curvature mutation points) from the two frames of point clouds, calculate the spatial transformation relationship (rotation matrix + translation vector) of the feature points, and determine the displacement of the module's current relative historical position.

[0048] (2) Image feature matching: Match the texture features (such as corners and edges) of the two frames of images, verify the spatial position relationship through triangulation calculation, and correct the errors that may be caused by pure point cloud matching (especially in areas with rich rock wall textures, the image matching accuracy is higher).

[0049] Furthermore, combined with IMU data, the position coordinates and posture of the SLAM module 3 in the flat hole global coordinate system are output in real time, ensuring the continuity of "positioning while moving".

[0050] Furthermore, as the module moves along the horizontal hole axis, the continuously collected point cloud frames are spatially stitched through the real-time positioning results.

[0051] Among them, each frame of point cloud is uniformly converted to the global coordinate system based on the current position of the module, and the coordinate information of all laser scanning points on the inner wall of the flat hole covering the scanned area is accumulated, but the original three-dimensional point cloud dataset is not yet assigned color (grayscale or attributeless point cloud).

[0052] Furthermore, the images taken by the camera are stored in chronological order, and each frame of the image is associated with the corresponding module position information (i.e., the coordinates and posture of the module in the global coordinate system at the time of shooting), forming an image dataset that records the visual texture of the inner wall of the flat hole.

[0053] Furthermore, the SLAM module 3 may send the collected original three-dimensional point cloud dataset and image dataset to the point cloud processing module 5 .

[0054] Optionally, the positioning module 4 is used to obtain a coordinate data set of multiple control points in the horizontal hole to be cataloged based on SLAM image measurement technology, and send the coordinate data set to the point cloud processing module 5.

[0055] The positioning module 4 includes a laser RTK device 41 .

[0056] Furthermore, the laser RTK device 41 represents a composite surveying device that integrates laser scanning technology and RTK, which can quickly obtain high-precision three-dimensional coordinates of targets (such as control points, geological structures) during dynamic movement, and has both the detail capture capability of laser scanning and the real-time positioning accuracy of RTK.

[0057] Furthermore, control points represent reference points that are pre-selected or arranged in the horizontal tunnel to be cataloged, have clear spatial positions and strong stability. Their role is to provide a spatial coordinate reference for the entire measurement process, ensuring the absolute position accuracy and global consistency of subsequent data (such as three-dimensional point clouds and images).

[0058] For example, the control points can be determined by the following screening requirements:

[0059] (1) Distribution requirements: Cover key areas of the flat tunnel (such as tunnel entrances, turns, and sections with complex geological structures) to ensure the uniformity of the subsequent absolute orientation of the point cloud;

[0060] (2) Feature requirements: Control points must be located in stable and easily identifiable locations (such as flat rock walls, fixed marking points), and should avoid being located in cracks, seepage, or weathered areas to ensure scanning stability;

[0061] Furthermore, the laser RTK device 41 can use SLAM image measurement technology to scan multiple control points and obtain multiple scanned images.

[0062] Among them, SLAM image measurement technology refers to a composite technology that integrates SLAM (Simultaneous Localization and Mapping) technology and image measurement technology. Its core is to analyze sequential images, calculate the device's own motion trajectory in real time in an unknown environment, build a three-dimensional spatial model of the environment, and provide high-precision spatial coordinate information for target points (such as control points).

[0063] Furthermore, due to the lack of satellite signals in the flat tunnel, the laser RTK device 41 can use its own technical characteristics based on SLAM image measurement to scan or photograph the control points and obtain corresponding multiple scanned images.

[0064] Finally, the laser RTK device 41 obtains a coordinate data set of multiple control points based on the multiple scanned images through a preset processing algorithm.

[0065] Specifically, after obtaining multiple scanned images, the laser RTK device 41 can also quickly calculate and obtain the precise coordinates (including three-dimensional coordinate information) of the control point through internal processing algorithms combined with environmental features obtained by SLAM technology.

[0066] In some optional embodiments, after obtaining multiple scanned images, the laser RTK device 41 performs distortion correction (based on camera intrinsic parameters) and enhancement processing (such as deblurring and contrast adjustment) on the multiple scanned images to highlight the characteristic details of the control points.

[0067] At the same time, the laser point cloud fragments can be denoised (scanning noise points are removed) and plane fitting (if the control point is a plane, its three-dimensional plane equation is fitted).

[0068] Furthermore, SLAM image measurement technology can be used to convert feature points from image pixel coordinates into three-dimensional space coordinates (based on the real-time positioning results of the device) through multi-view geometry calculation and obtain the corresponding coordinate data sets of multiple control points.

[0069] Furthermore, the obtained coordinate data set can be sent to the point cloud processing module 5 .

[0070] The control unit uniformly acquires and manages multiple control points, ensuring that the selection of control points is representative and avoiding the arbitrariness of traditional manual control point selection. Furthermore, sending targeted control instructions based on the control points makes the measurement process of the laser RTK equipment more purposeful, avoids invalid scanning, and reduces the energy consumption and data redundancy of the equipment. Furthermore, relying on SLAM image measurement technology, it breaks through the limitation of "no satellite signal" in flat holes, solves the application blind spots of traditional positioning technology in closed environments, and ensures that control point measurement can still proceed normally when the satellite loses lock. At the same time, through mobile scanning and combined with the real-time image processing capabilities of SLAM technology, the spatial characteristics of the control points can be quickly captured, reducing manual operation errors. Furthermore, by analyzing the scanned images through a preset processing algorithm, the coordinates of multiple control points can be quickly calculated, replacing traditional manual data processing, significantly shortening calculation time, and improving efficiency.

[0071] Optionally, the point cloud processing module 5 is used to process the original three-dimensional point cloud dataset based on the coordinate dataset and the image dataset to obtain the target three-dimensional color point cloud dataset of the flat hole to be cataloged, and send the target three-dimensional color point cloud dataset to the data analysis and calculation module 6.

[0072] The point cloud processing module 5 includes an orientation unit 51 , a registration unit 52 and a pre-processing unit 53 .

[0073] First, the registration unit 52 is used to perform registration processing on the original 3D point cloud dataset and the image dataset to obtain a first 3D color point cloud dataset, and the first 3D color point cloud dataset is sent to the orientation unit 51 .

[0074] Specifically, general point cloud processing software can be used to identify common features in images and point clouds (such as geological structure edges, specific marker points, etc.) through algorithms, establish a spatial correspondence between the two, and ensure that the pixel information of the image is accurately associated with the three-dimensional coordinates of the point cloud. This can then realize the fusion of flat hole images and three-dimensional laser point clouds, and form the corresponding fused three-dimensional color point clouds.

[0075] Secondly, in the orientation unit 51 , the coordinate data set is used to locate the first three-dimensional color point cloud data set to obtain a second three-dimensional color point cloud data set, and the second three-dimensional color point cloud data set is sent to the preprocessing unit 53 .

[0076] Specifically, the coordinate data set can be associated with the corresponding control point positions in the first three-dimensional color point cloud data set, and then the conversion parameters (such as translation, rotation, scaling, etc.) between the initial coordinates and absolute coordinates of the point cloud can be calculated through an algorithm, and the precise absolute coordinate information of the control point can be passed to the entire three-dimensional point cloud, thereby realizing the absolute positioning of the point cloud.

[0077] By applying the obtained coordinate data sets of multiple control points to the point cloud that has completed image matching, the absolute orientation of the point cloud is achieved, so that the three-dimensional point cloud can obtain real and accurate spatial coordinates, ensuring the accuracy of the point cloud in absolute position.

[0078] Furthermore, after absolute orientation, the three-dimensional point cloud, which was originally based only on SLAM relative positioning, is given real and precise absolute spatial coordinates, ensuring the position accuracy of the point cloud in the global coordinate system, and providing a reliable spatial benchmark for subsequent geological point coordinate recording, occurrence analysis and other work in geological cataloging.

[0079] Finally, the second three-dimensional color point cloud dataset is preprocessed by the preprocessing unit 53 to obtain a target three-dimensional color point cloud dataset.

[0080] Specifically, the second three-dimensional color point cloud dataset may be pre-processed by denoising, classification, extraction, etc. to generate a corresponding target three-dimensional color point cloud dataset.

[0081] Furthermore, the obtained target three-dimensional color point cloud data set can be sent to the data analysis and calculation module 6.

[0082] The coordinate dataset enables automatic absolute orientation of the original 3D point cloud, resolving the disconnect between the relative positioning of traditional point clouds and real-world space. Furthermore, the image dataset is registered with the point cloud to generate a 3D color point cloud, addressing the shortcomings of traditional point clouds, such as the lack of color information and the low visibility of geological details. Furthermore, preprocessing removes interfering data, improving point cloud quality and preventing noise from impacting the accuracy of geological cataloging.

[0083] Optionally, the data analysis and calculation module 6 is used to catalog the geological conditions of the adit to be cataloged based on the target three-dimensional color point cloud dataset and the preset geological attribute information set of the adit to be cataloged, and obtain a geological catalog dataset of the adit to be cataloged.

[0084] Specifically, after receiving the target three-dimensional color point cloud dataset and the preset geological attribute information set of the horizontal hole to be cataloged, indoor geological cataloging work such as adding geological points and geological lines, judging and describing lithology, identifying structural surfaces and measuring occurrence, describing structural and fracture development, and describing hydrogeological conditions can be carried out and the corresponding geological cataloging dataset can be obtained.

[0085] Furthermore, while recording geological data, the cataloged data can also record actual coordinate elevation data, reducing measurement work.

[0086] Optionally, the data analysis and calculation module 6 is further configured to project the target three-dimensional color point cloud data set to obtain a two-dimensional cavern expansion diagram.

[0087] Specifically, the central axis of the flat tunnel can be calculated based on the target three-dimensional color point cloud dataset, and vertical sections can be generated along the central axis at fixed intervals (such as 1 meter). The contour point cloud of each section (i.e., the rock wall boundary points) can be extracted and fitted into a closed polygon.

[0088] Furthermore, the flat hole is regarded as an approximate cylindrical surface, and the point on each cross section is projected onto the cylindrical surface with the central axis as the reference (the radial distance and angle from the point to the central axis are calculated).

[0089] Furthermore, the cylindrical surface is "unfolded" along the central axis, and the three-dimensional point coordinates are converted into two-dimensional coordinates (U, V), where U is the distance along the central axis and V is the angle in the circumferential direction, to generate a preliminary unfolding diagram.

[0090] Furthermore, elastic correction (e.g. based on TPS thin plate spline interpolation) is performed on the local distortion of the unfolded image caused by the bending of the flat hole to maintain the true proportion of the geological features.

[0091] Furthermore, the RGB values ​​of the 3D color point cloud are mapped to the corresponding pixels of the 2D unfolded image, while the edge features are enhanced to form the final 2D cavern unfolded image.

[0092] By projecting the three-dimensional color point cloud into a two-dimensional cavern expansion diagram, the conversion from three-dimensional form to two-dimensional visualization is achieved, solving the problem of incomplete information and disconnection with the site in traditional two-dimensional diagrams.

[0093] Optionally, the data analysis and calculation module 6 is further configured to generate a geological logging map of the adit to be logged based on the two-dimensional cavern development map and the geological logging data set.

[0094] Specifically, a complete geological catalog map is drawn using the two-dimensional cavern expansion map as the base map and combined with the stored geological data, including all entered geological features, coordinate information and analysis results.

[0095] In some optional implementations, the geological catalog dataset may be spatially aligned with the two-dimensional cavern development map, and data consistency may be ensured by matching control point coordinates.

[0096] Furthermore, the geological attributes (such as lithology code and structural surface inclination) in the cataloging data can be associated with the corresponding area of ​​the two-dimensional cavern development map and a corresponding geological cataloging map of the adit to be cataloged can be generated.

[0097] By combining the 2D cavern development map with the geological catalog dataset to generate a geological catalog map, consistency between the map and actual site conditions was ensured. Furthermore, the geological catalog map integrates the spatial information of the 3D point cloud and geological attribute data, replacing the traditional "hand-drawn + manual annotation" model. This improved the objectivity and accuracy of the results, helped the expert team quickly understand the geological structure of the flat cavern (such as lithologic zoning and structural distribution), and provided an intuitive and reliable visual basis for engineering decision-making.

[0098] The SLAM-based geological cataloging system provided in this embodiment uses a light source module to provide uniform and stable lighting conditions for the flat tunnels to be cataloged. This solves the problem of "insufficient lighting" in flat tunnels and ensures clear images captured by the SLAM module during scanning, avoiding image blur caused by dim lighting. At the same time, it ensures visibility for workers within the flat tunnels, reduces safety risks during on-site operations, and indirectly improves operational efficiency. Furthermore, the use of the SLAM module for mobile scanning replaces traditional single-station measurement and eliminates the need for frequent station setup. Continuous scanning can be performed while in motion, significantly reducing operation time in the narrow and long environments of flat tunnels and addressing the "low efficiency and large number of stations" associated with station-based scanning. Simultaneously acquiring the original 3D point cloud dataset and the image dataset, the system preserves the spatial 3D morphological information of the flat tunnels and records their surface texture characteristics. Furthermore, this overcomes the limitation of flat tunnels' "single texture" and, combined with the real-time positioning and mapping capabilities of SLAM technology, avoids modeling errors caused by insufficient texture in traditional photogrammetry, achieving efficient acquisition of 3D data for flat tunnels. Furthermore, the coordinates of control points are acquired based on SLAM imaging measurement technology, reducing the measurement workload and shortening the operation cycle. Furthermore, the original 3D point cloud dataset is processed through the point cloud processing module, improving the quality of the target 3D color point cloud dataset. Finally, the data analysis and calculation module uses the target 3D color point cloud dataset and the preset geological attribute information set to achieve efficient and accurate geological cataloging. Therefore, the implementation of this invention solves the problems of traditional flat-hole geological cataloging, namely low efficiency, poor accuracy, poor environmental adaptability, and reliance on manual labor, and realizes fast, accurate, safe, and automated flat-hole geological cataloging.

[0099] According to an embodiment of the present invention, an embodiment of a geological cataloging method based on SLAM technology is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0100] In this embodiment, a geological logging method based on SLAM technology is provided, which can be used in the data analysis and calculation module 6 of the geological logging system 1 based on SLAM technology provided in the above embodiment. Figure 2 : is a flow chart of a geological logging method based on SLAM technology according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0101] Step S201: Obtain a target three-dimensional color point cloud data set and a preset geological attribute information set of a flat hole to be cataloged.

[0102] Specifically, the preset geological attribute information set may include geological point number, geological line identifier, lithologic age, lithologic and lithofacies characteristics, lithologic comprehensive description, groundwater type, water level, weathering grade and other information.

[0103] Among them, the geological point number can be set according to the cataloging requirements (such as by region, sequence, etc.); information such as lithologic age, lithologic and lithofacies characteristics, and comprehensive lithologic description can be determined based on geological professional knowledge and on-site observation results; information such as structural type, fracture development density, and filling characteristics can be determined based on actual on-site conditions and geological judgment results; information such as groundwater type, water level, and seepage phenomenon description can be determined based on on-site observation data; information such as weathering degree and collapse scale can be determined based on on-site conditions and point cloud visualization features.

[0104] Furthermore, the acquisition of the target three-dimensional color point cloud data set can refer to the functional description of the light source module 2, SLAM module 3, positioning module 4 and point cloud processing module 5 in the geological cataloging system 1 based on SLAM technology in the above embodiment, which will not be repeated here.

[0105] Step S202 : cataloging the geological elements of the adit to be cataloged using the target three-dimensional color point cloud data set and the preset geological attribute information set, to obtain a plurality of geological element cataloging data.

[0106] Among them, the geological elements can be geological points or geological lines.

[0107] Specifically, the corresponding geological point number can be obtained according to the preset geological attribute information set. Further, the coordinates and elevation information of the geological point corresponding to the geological point number can be extracted according to the target three-dimensional color point cloud data set.

[0108] Furthermore, by combining the geological point number, coordinates and elevation information, the geological point of the adit to be cataloged can be cataloged and the corresponding geological point cataloging data can be obtained, that is, the geological point cataloging data can include the geological point number, the coordinates and elevation information of the corresponding geological point.

[0109] Furthermore, the corresponding geological line identifier can be obtained based on the preset geological attribute information set. Furthermore, based on the geological line identifier, nodes can be continuously selected along the geological line trend (such as lithologic boundary line, fault line) in the target 3D color point cloud dataset to complete the cataloging of the geological line of the adit to be cataloged.

[0110] Furthermore, during the cataloging process, the coordinates of each node can be automatically obtained and the spatial form of the geological line, ie, the geological line cataloging data, can be generated.

[0111] Furthermore, the geological point catalog data and the geological line catalog data are integrated to form corresponding multiple geological element catalog data.

[0112] Step S203 , using the target three-dimensional color point cloud data set and the preset geological attribute information set, the geological information of different dimensions of the adit to be cataloged is cataloged to obtain a plurality of geological information cataloging data.

[0113] Among them, geological information of different dimensions can include lithology information, structure and fracture information, hydrogeological information, physical geological information, etc.

[0114] Specifically, a point cloud screenshot at the corresponding geological point can be obtained based on the target three-dimensional color point cloud dataset, and the lithology automatic recognition tool based on the neural network can be used to identify the lithology to obtain the corresponding lithology name.

[0115] Furthermore, three non-collinear points on the structural surface can be selected in the target three-dimensional color point cloud dataset to ensure that the three points can uniquely determine the spatial posture of the plane, and then the occurrence elements (strike, dip, and inclination) can be automatically calculated using the three-point method.

[0116] Furthermore, by combining the lithologic name, occurrence elements and the lithologic age, lithologic lithofacies characteristics and lithologic comprehensive description in the preset geological attribute information set, the lithologic information of the adit to be cataloged can be cataloged.

[0117] Furthermore, the structure and fracture information of the horizontal hole to be cataloged can be cataloged by identifying and analyzing the structural surface in the target three-dimensional color point cloud data set.

[0118] In some optional implementations, the point cloud morphology of the inner wall of the flat tunnel can be observed based on the target three-dimensional color point cloud data set to identify structural surfaces with obvious continuity and ductility (such as crack surfaces, joint surfaces, fault interfaces, etc.).

[0119] Furthermore, for a single identified structural surface (such as a crack), feature points that can characterize its spatial morphology are selected from the point cloud data:

[0120] (1) If the occurrence (strike, dip, and inclination) needs to be calculated, select three points on the structural surface that are not on the same straight line (non-collinear points) to ensure that the three points can uniquely determine the spatial posture of the plane;

[0121] (2) If it is necessary to analyze the length and extension range of the crack, select feature points at the starting point, end point and key turning point of the crack and record their coordinate information.

[0122] Furthermore, the three-point method can be used to automatically calculate the attitude elements (strike, dip, and inclination) of the corresponding structural surface and directly use them as the attitude data of the fracture or structural surface.

[0123] Furthermore, the area to be counted (e.g., the sidewalls or roof of a horizontal tunnel section) can be delineated in the point cloud data to identify all fractured structural surfaces within it. Next, the number of fractures within the area is counted, and the area of ​​the region is calculated based on the spatial scale of the point cloud. Finally, the fracture density can be automatically or manually calculated using the "number of fractures per unit area" or "total fracture length per unit area" as indicators.

[0124] Furthermore, based on the coordinates of the feature points, parameters such as the length (the straight-line distance between two points) and width (the maximum distance perpendicular to the direction of the crack) can be calculated. Simultaneously, combined with the color texture information of the point cloud, the characteristics of the crack filling (such as filling type and distribution range) can be observed and recorded, and its spatial position can be located using the point cloud coordinates.

[0125] Furthermore, the calculated quantitative data such as the occurrence, density, length, and other information, as well as the observed structural type, fracture distribution characteristics, etc., are bound to the corresponding point cloud coordinates (feature point coordinates or structural surface spatial range), and the cataloging of the structural and fracture information of the adit to be cataloged is completed.

[0126] Furthermore, the corresponding groundwater outcrop locations, seepage points, etc. can be obtained based on the target three-dimensional color point cloud dataset, and then the distribution boundaries of features such as seepage range and water flow traces can be assisted by combining the visualization of the target three-dimensional color point cloud dataset.

[0127] Furthermore, by combining the determined groundwater outcrop location, seepage range, water flow traces, flow rate and other information with the preset geological attribute information set of groundwater type, water level and other information, the cataloging of the hydrogeological information of the cataloged flat cave can be completed.

[0128] Furthermore, the surface morphology of the rock wall of the adit to be cataloged can be determined based on the target 3D color point cloud dataset. Specifically, features such as weathering and exfoliation and surface roughness can be captured from the target 3D color point cloud dataset to assist in determining the weathering grade. Furthermore, the boundaries of the weathered area can be delineated in the point cloud data, and its spatial distribution can be determined using point cloud coordinates.

[0129] Furthermore, the collapse area can be delineated in the point cloud based on the target three-dimensional color point cloud dataset, and the volume of the collapse body can be estimated or parameters such as the length and width of the collapse range can be measured through the three-dimensional coordinates and density information of the point cloud.

[0130] Furthermore, by combining the obtained parameters such as the surface morphology of the rock wall, the volume of the collapsed body, the length and width of the collapsed range with the weathering grade and other information in the preset geological attribute information set, the physical geological information of the adit to be cataloged can be completed.

[0131] Furthermore, the obtained cataloging data of different dimensional information such as lithologic information, structural and fracture information, hydrogeological information, and physical geological information of the adit to be cataloged are integrated to obtain corresponding multiple geological information cataloging data.

[0132] Step S204 : determining a geological cataloging data set of the adit to be cataloged based on the plurality of geological element cataloging data and the plurality of geological information cataloging data.

[0133] Specifically, the obtained multiple geological element cataloging data and multiple geological information cataloging data are integrated to obtain a final geological cataloging data set of the adit to be cataloged.

[0134] The geological cataloging method based on SLAM technology provided in this embodiment provides a three-dimensional visualization scene consistent with the site for cataloging by acquiring the target three-dimensional color point cloud data set, replacing the fuzzy basis of traditional "site memory + sketching", and solving the problem that the geological information recording of flat holes is not intuitive and details are easily missed. At the same time, by acquiring the preset geological attribute information set, the cataloging dimension is unified, avoiding the defects of non-standard attribute description and incomplete information in traditional manual cataloging, and ensuring data structuring and standardization. Furthermore, the geological element cataloging is automatically associated with the three-dimensional point cloud coordinates, avoiding the errors of manually recorded coordinates and improving data accuracy. Furthermore, by cataloging geological information of different dimensions, traditional paper records are replaced and data structured storage is realized.

[0135] In one embodiment, a geological logging system based on SLAM technology is provided, the system comprising:

[0136] [SLAM module], providing SLAM 3D laser scanning;

[0137]

Light source module

[0138]

Positioning module

[0139] [Point cloud processing module] pre-processes the scanned 3D laser point cloud to remove noise;

[0140] [Data Analysis and Calculation Module] uses the scanned 3D point cloud to perform geological cataloging, structural occurrence analysis, calculate occurrence elements using the three-point method, and draw cataloging maps.

[0141] Among them, the SLAM module is a portable mobile 3D laser scanner using SLAM technology;

[0142] The light source module provides lighting conditions for scanning operations in flat tunnels, ensuring visibility while assisting in acquiring scanning images.

[0143] The positioning module uses laser RTK, based on SLAM image measurement technology and Android-based high-performance laser point cloud and image processing. With just one photo, it can achieve 5cm high positioning accuracy even if the satellite loses lock for 10 minutes in a flat tunnel or within a 100m radius.

[0144] The point cloud processing module uses general point cloud processing software to first match the image with the point cloud, then perform absolute orientation of the point cloud based on the control points collected by laser RTK, and finally perform pre-processing such as denoising, classification, and extraction on the point cloud.

[0145] Among them, the data analysis and calculation module adopts the independently developed three-dimensional geological survey system. After directly importing the three-dimensional point cloud data, it can perform indoor geological cataloging work such as adding geological points and geological lines, lithology judgment and description, structural surface identification and occurrence measurement, structure and fracture development description, and hydrogeological description. While recording geological data, the cataloged data can also record the actual coordinate elevation data to reduce measurement work.

[0146] Furthermore, in actual application, first click on the "Geological Mapping" module in the three-dimensional geological survey system, then click on "Add geological point" (you can also choose "Add geological line", etc.), and the "Geological point" interface will pop up. First, enter the geological point number in the "Basic Information" module (note that the number cannot be repeated), and then you need to enter the coordinates and elevation of the geological point. The coordinates and elevation can be entered manually or you can select a point in the imported geological point cloud data and automatically obtain the relevant data (the geological line requires entering the coordinates and elevations of each node or manually outlining the geological line). The geological point number and the geological point coordinate elevation are required. After entering these two pieces of information, you can catalog other geological information. In addition to "Basic Information", there are also modules such as "Lithology", "Structure", "Joints and Fractures", "Hydrogeology", and "Physical Geological Phenomenon" under the geological point. To enter lithologic information, manually enter the lithologic name, lithologic age, lithologic and lithofacies characteristics, stratum occurrence, and a comprehensive description in the "Lithologic" module for the geological point. In addition to manually entering the "Lithologic Name," you can also take a photo of the geological point or capture a screenshot of the point cloud. The neural network-based automatic lithologic identification tool will identify the lithologic type and automatically load the result into the "Lithologic Name" field. In addition to manually entering the "Stratum Occurrence" field, you can also select three points in the point cloud data to automatically measure the occurrence and return the result to the "Stratum Occurrence" field. To identify structures and fractures, structural planes, or describe hydrogeology, you can add these details in different modules, similar to adding "Lithologic" information. Once all information has been entered, click "Save" or "Exit" to save the geological point or exit the cataloging process.

[0147] Furthermore, the cataloged data is stored in a structured form in the survey system, and the geological data in the three-dimensional scene can also be displayed in the cavern expansion diagram in the two-dimensional scene.

[0148] Furthermore, this embodiment also provides a geological logging method based on SLAM technology, which includes:

[0149] (1) The SLAM laser scanner obtains the three-dimensional point cloud and image in the flat tunnel under uniform light source conditions, fuses them into a three-dimensional color point cloud, accurately reconstructs the three-dimensional shape of the flat tunnel, and fully presents any geological structure surface consistent with the site;

[0150] (2) Laser RTK is based on SLAM image measurement technology. It can quickly and accurately measure coordinates even when the satellite signal is lost in a flat tunnel, providing real coordinates for the absolute positioning of the 3D laser point cloud.

[0151] (3) Based on the three-dimensional color point cloud with real coordinates, carry out indoor geological cataloging work such as adding geological points and geological lines, judging and describing lithology, identifying structural surfaces and measuring occurrence, describing structural and fracture development, and describing hydrogeological conditions;

[0152] (4) Displaying geological data in a three-dimensional scene in a cavern expansion diagram in a two-dimensional scene;

[0153] (5) The three-dimensional point cloud and geological cataloging results are quickly shared with the expert group in the survey system, and the quality of the cataloging results is guaranteed through expert opinions.

[0154] The geological logging system and method based on SLAM technology provided in this example have the following effects:

[0155] (1) Based on SLAM technology, it breaks through the single-station measurement method and realizes self-positioning during movement. It can continuously move and measure and scan, improve the efficiency of 3D point cloud scanning, and shoot flat hole images under light source assistance conditions for fusion into 3D color point clouds. At the same time, laser RTK is used to quickly measure precise coordinates in the flat hole without satellite signals, providing control points for absolute positioning of 3D point clouds.

[0156] (2) It can quickly and accurately obtain the three-dimensional color point cloud inside the flat tunnel, accurately reconstruct the three-dimensional shape of the flat tunnel, and fully present any geological structural surface consistent with the site, which can be used for geological cataloging and provided to the expert group for analysis. Ultimately, it can save manpower, improve efficiency, and reduce risks.

[0157] (3) Based on SLAM technology, the focus is on solving the difficult problems of geological cataloging in flat tunnel environments, giving full play to the advantages of high efficiency of SLAM scanning technology, signal loss-free positioning capability of laser RTK, and uniform fill light in weak light environment, quickly and efficiently obtaining 3D point clouds and images in flat tunnels, integrating them into 3D color point clouds, combining with the precise real coordinates of laser RTK measurement, accurately reconstructing the 3D shape of flat tunnels, and fully presenting any geological structural surface consistent with the site. By using the independently developed 3D survey system and importing 3D color point clouds, indoor geological cataloging work such as adding geological points and geological lines, judging and describing lithology, identifying structural surfaces and measuring occurrence, describing structural and fracture development, and describing hydrogeological conditions can be carried out efficiently.

[0158] In some optional embodiments, such as Figure 3 As shown, the geological cataloging system based on SLAM technology includes a SLAM scanning module 10, a light source device module 11, a positioning module 12, a point cloud processing module 13 and a data analysis and calculation module 14.

[0159] Specifically, the SLAM scanning module 10 is used for point cloud scanning and image capture in a flat tunnel.

[0160] Furthermore, the light source module 2 is used to assist the SLAM module in capturing images in the flat tunnel, thereby providing uniform lighting conditions.

[0161] Furthermore, the positioning module 12 is used to measure the coordinates of control points in the flat tunnel. The laser RTK adopted in the present invention is based on SLAM image measurement technology, which can quickly and accurately measure the coordinates even when the satellite signal is lost in the flat tunnel, providing real coordinates for the absolute positioning of the three-dimensional laser point cloud.

[0162] Furthermore, the point cloud processing module 13 uses general point cloud processing software to pre-process the scanned flat hole three-dimensional point cloud, including absolute orientation, image registration, and noise removal.

[0163] Furthermore, the data analysis and calculation module 14 is used to perform geological cataloging based on the 3D point cloud. Using a self-developed 3D survey system, it performs indoor geological cataloging tasks such as adding geological points and lines, determining and describing lithology, identifying structural planes and measuring their occurrence, describing structural and fracture development, and describing hydrogeological conditions. While recording geological data, the cataloged data also includes actual coordinate and elevation data, reducing measurement work. The cataloged data is stored in a structured format within the survey system, and the geological data in the 3D scene can also be displayed in the 2D cavern expansion diagram.

[0164] In some optional embodiments, such as Figure 4 As shown in FIG, the geological logging method based on SLAM technology includes the following steps.

[0165] In step 20, SLAM laser scanning is performed on the flat hole.

[0166] In step 21, with the assistance of light source equipment, the flat hole is imaged.

[0167] In step 22, the absolute coordinates of the control points are collected.

[0168] In step 23, the scanned three-dimensional point cloud is absolutely oriented, and the real coordinates of the control points are transferred to the point cloud.

[0169] In step 24 , the captured image is registered with the point cloud to form a three-dimensional color point cloud.

[0170] In step 25, noise processing is performed on the formed three-dimensional point cloud.

[0171] In step 26, the structural surface is identified on the three-dimensional point cloud, three points that are not on a straight line are selected on the structural surface, and the three-point method is used to calculate the occurrence elements.

[0172] In step 27, the three-dimensional color point cloud of the flat hole can be projected into a two-dimensional cavern expansion diagram.

[0173] In step 28, the cavern expansion map obtained in step 27 is used as a base map to draw a geological catalog map.

[0174] The embodiment of the present invention also provides a computer device for executing the above Figure 2 The geological logging method based on SLAM technology is shown.

[0175] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of a GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0176] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0177] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0178] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0179] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0180] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0181] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0182] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0183] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A geological logging system based on SLAM technology, characterized in that: The system includes: a light source module, a SLAM module, a positioning module, a point cloud processing module and a data analysis and calculation module; The light source module is used to provide lighting conditions for the flat hole to be recorded; The SLAM module is used to perform mobile scanning on the flat hole to be cataloged, obtain an original three-dimensional point cloud dataset and an image dataset of the flat hole to be cataloged, and send the original three-dimensional point cloud dataset and the image dataset to the point cloud processing module; The positioning module is used to obtain a coordinate data set of multiple control points in the flat hole to be cataloged based on SLAM image measurement technology, and send the coordinate data set to the point cloud processing module; The point cloud processing module is configured to process the original three-dimensional point cloud dataset based on the coordinate dataset and the image dataset to obtain the target three-dimensional color point cloud dataset of the flat hole to be cataloged, and send the target three-dimensional color point cloud dataset to the data analysis and calculation module; The data analysis and calculation module is used to catalog the geological conditions of the adit to be cataloged based on the target three-dimensional color point cloud data set and the preset geological attribute information set of the adit to be cataloged, so as to obtain the geological cataloging data set of the adit to be cataloged.

2. The system according to claim 1, wherein: The positioning module includes: A laser RTK device is used to scan multiple control points using the SLAM image measurement technology to obtain multiple scanned images; The laser RTK device is further configured to obtain the coordinate data sets of the multiple control points based on the multiple scanned images through a preset processing algorithm.

3. The system according to claim 1, wherein: The point cloud processing module includes: a registration unit, an orientation unit and a pre-processing unit; The registration unit is configured to perform registration processing on the original three-dimensional point cloud dataset and the image dataset to obtain a first three-dimensional color point cloud dataset, and send the first three-dimensional color point cloud dataset to the orientation unit; The orientation unit is configured to locate the first three-dimensional color point cloud dataset using the coordinate dataset to obtain a second three-dimensional color point cloud dataset, and send the second three-dimensional color point cloud dataset to the preprocessing unit; The preprocessing unit is used to preprocess the second three-dimensional color point cloud dataset to obtain the target three-dimensional color point cloud dataset.

4. The system according to claim 1, wherein: The data analysis and calculation module is further used to project the target three-dimensional color point cloud data set to obtain a two-dimensional cavern expansion diagram.

5. The system according to claim 4, characterized in that The data analysis and calculation module is further used to generate a geological logging map of the adit to be logged based on the two-dimensional cavern development map and the geological logging data set.

6. The system according to claim 1, wherein: The SLAM module is a portable mobile three-dimensional laser scanner using SLAM technology.

7. A geological logging method based on SLAM technology, characterized in that: A data analysis and calculation module in a geological logging system based on SLAM technology according to any one of claims 1 to 6; the method comprising: Obtain the target three-dimensional color point cloud data set and preset geological attribute information set of the adit to be cataloged; Using the target three-dimensional color point cloud data set and the preset geological attribute information set, the geological elements of the adit to be cataloged are cataloged to obtain a plurality of geological element cataloging data; Using the target three-dimensional color point cloud data set and the preset geological attribute information set, the geological information of different dimensions of the adit to be cataloged is cataloged to obtain a plurality of geological information cataloging data; A geological cataloging data set of the adit to be cataloged is determined based on the multiple geological element cataloging data and the multiple geological information cataloging data.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the geological cataloging method based on SLAM technology as described in claim 7 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the geological cataloging method based on SLAM technology as described in claim 7.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the geological logging method based on SLAM technology as claimed in claim 7.

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