Point cloud data correction method and related equipment

Abstract

The invention discloses a point cloud data correction method and related equipment, and relates to the technical field of point cloud data, and the method comprises the steps: obtaining RTK data; filtering the RTK data to obtain filtered RTK data; based on the filtered RTK data, performing time synchronization on the POS data to generate aligned POS data; correcting the aligned POS data to generate corrected POS data; and performing point cloud calculation on the corrected POS data to generate target point cloud data.

Description

Technical Field

[0001] The present application relates to the field of point cloud data technology, and in particular to a point cloud data correction method and related equipment. Background Art

[0002] In the process of high-precision point cloud data processing, RTK technology has been widely used in positioning systems. However, RTK data is often affected by environmental factors such as signal shielding, multipath effects, atmospheric delays, etc., and often contains errors and noise, resulting in reduced data accuracy.

[0003] Existing point cloud data correction methods usually rely on simple time synchronization and linear error correction, but these methods have a large risk of error accumulation, especially in dynamic and nonlinear environments. To solve this problem, traditional methods often find it difficult to accurately capture complex error characteristics, thus affecting the quality and accuracy of point cloud data. Therefore, there is an urgent need for a point cloud data correction method that can adapt to complex environmental changes. Summary of the invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0005] In a first aspect, the present application provides a point cloud data correction method, comprising:

[0006] Get RTK data;

[0007] Filtering the above RTK data to obtain filtered RTK data;

[0008] Based on the above filtered RTK data, the POS data is time synchronized to generate aligned POS data;

[0009] Correcting the aligned POS data to generate corrected POS data;

[0010] Perform point cloud solution on the above-mentioned corrected POS data to generate target point cloud data.

[0011] In some implementations, filtering the RTK data to obtain filtered RTK data includes:

[0012] For each time point, calculate the elevation error and horizontal error of the above RTK data;

[0013] Based on the elevation error threshold and the plane error threshold, it is determined whether the elevation error and the horizontal error meet the preset conditions, and the RTK data meeting the preset conditions are screened out to obtain filtered RTK data.

[0014] In some implementations, the above-mentioned time synchronization of POS data based on the above-mentioned filtered RTK data to generate aligned POS data includes:

[0015] Define RTK data sequence and POS data sequence;

[0016] Based on the RTK data sequence and the POS data sequence, calculating the Euclidean distance;

[0017] Based on the above Euclidean distance, a cumulative distance matrix is ​​constructed and the optimal alignment path is determined by backtracking;

[0018] Based on the above optimal alignment path, the POS data is aligned to the above filtered RTK data to generate aligned POS data.

[0019] In some implementations, the above-mentioned correction of the aligned POS data to generate the corrected POS data includes:

[0020] Using a dynamic spatiotemporal transformation network to correct the aligned POS data and determine correction parameters;

[0021] Based on the above correction parameters, preliminary POS data is obtained;

[0022] Based on the above preliminary POS data, an error vector is constructed;

[0023] An adaptive weight matrix is ​​defined to optimize the above error vector and generate an optimized error vector;

[0024] Construct a Bayesian network model to predict the above-mentioned optimized error vector to obtain a predicted error vector;

[0025] Based on the above-mentioned prediction error vector, corrected POS data is generated.

[0026] In some embodiments, performing point cloud calculation on the corrected POS data to generate target point cloud data includes:

[0027] Collecting original point cloud data, and converting the original point cloud data from the sensor coordinate system to the global coordinate system;

[0028] Splice and fuse continuously collected point cloud frames to form a continuous 3D point cloud model;

[0029] The voxel grid filtering method and Poisson surface reconstruction algorithm are used to reconstruct the continuous surface of the point cloud and generate the target point cloud data.

[0030] In some embodiments, the above-mentioned use of a dynamic spatiotemporal transformation network to correct the above-mentioned aligned POS data and determine the correction parameters includes:

[0031] Extracting the spatiotemporal features of the filtered RTK data and the aligned POS data through multi-layer convolution and recurrent neural networks;

[0032] Based on the above-mentioned spatiotemporal characteristics, the correction parameters are determined.

[0033] In some embodiments, it further comprises:

[0034] After generating the target point cloud data, setting the point cloud data quality assessment index, wherein the above-mentioned quality assessment index includes point density and surface smoothness;

[0035] Based on the above quality assessment indicators, the overall quality of the above target point cloud data is evaluated;

[0036] Based on the point cloud quality assessment results, the RTK data filtering threshold, correction parameters and filtering weight are dynamically adjusted to optimize the point cloud data generation process.

[0037] In a second aspect, the present application proposes a point cloud data correction device, comprising:

[0038] RTK data acquisition unit, used for acquiring RTK data;

[0039] An RTK data filtering unit, used for filtering the above RTK data to obtain filtered RTK data;

[0040] A POS data alignment unit, configured to time synchronize the POS data based on the filtered RTK data to generate aligned POS data;

[0041] A POS data correction unit, used to correct the aligned POS data to generate corrected POS data;

[0042] The point cloud data generating unit is used to perform point cloud calculation on the above-mentioned corrected POS data to generate target point cloud data.

[0043] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the point cloud data correction method of any one of the first aspects when executing the computer program stored in the memory.

[0044] In a fourth aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the point cloud data correction method of any one of the first aspects is implemented.

[0045] In summary, the point cloud data correction method proposed in this application realizes high-precision RTK data filtering, POS data alignment and correction by combining a dynamic space-time transformation network with a Bayesian network. This method can not only effectively remove low-quality data, but also accurately calculate and optimize the error vector, thereby greatly improving the overall quality and accuracy of the point cloud data. By modeling and predicting the error vector field, combined with voxel grid filtering and Poisson surface reconstruction algorithms, the generated target point cloud data is smoother and noise-free, suitable for applications such as high-precision three-dimensional modeling and environmental perception, and has greater practicality and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0047] Figure 1 A schematic diagram of a point cloud data correction method provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of the structure of a point cloud data correction device provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of the structure of an electronic device for point cloud data correction provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0051] See also Figure 1 , is a schematic diagram of a point cloud data correction method provided in an embodiment of the present application, which may specifically include:

[0052] S110, acquiring RTK data;

[0053] Exemplarily, step S110 involves the acquisition of RTK (Real-Time Kinematic) data. RTK is a high-precision positioning technology that achieves centimeter-level positioning accuracy through carrier phase differentiation between a base station and a mobile station. The base station is located at a known high-precision position, continuously receives signals from the Global Navigation Satellite System (GNSS), and transmits the carrier phase information of these signals to the mobile station in real time. The mobile station receives GNSS signals from the base station and its own GNSS signals, and uses carrier phase differentiation technology to calculate its precise position coordinates in three-dimensional space in real time. This high-precision positioning capability makes RTK data a key reference information source in the point cloud data processing process.

[0054] The acquired RTK data not only includes the three-dimensional coordinate information of the current position, but also includes VDOP (Vertical Dilution of Precision) and VDOP (Vertical Dilution of Precision) related to positioning accuracy. VDOP reflects the geometric accuracy of the positioning system in the vertical direction. The smaller the VDOP value, the higher the positioning accuracy in the vertical direction; HDOP reflects the geometric accuracy in the horizontal direction. The smaller the HDOP value, the higher the positioning accuracy in the horizontal direction. In addition, each RTK data is also equipped with a timestamp to record the specific time of data collection to ensure the accuracy of subsequent data synchronization and time alignment processes. By acquiring and recording these high-precision RTK data in real time, a reliable positioning benchmark can be provided for subsequent point cloud data filtering, time synchronization and correction steps, significantly improving the overall accuracy and reliability of point cloud data processing.

[0055] S120, filtering the RTK data to obtain filtered RTK data;

[0056] Exemplarily, the RTK data filtering step first calculates the elevation error and horizontal error of the RTK data at each time point. The elevation error reflects the positioning deviation of the RTK data in the vertical direction, while the horizontal error represents the positioning deviation in the horizontal direction. Then, based on the preset elevation error threshold and plane error threshold, the error evaluation model is used to evaluate the calculated error. The elevation error threshold and plane error threshold are set by dynamically adjusting the formula in combination with the vertical precision factor and the horizontal precision factor to ensure that the error tolerance can be flexibly adapted under different environments and positioning conditions.

[0057] During the screening process, if the errors of the RTK data at a certain point in time in both the elevation and horizontal directions are lower than or equal to the corresponding dynamic thresholds, the data point is judged as high-quality data and retained for subsequent point cloud correction. Conversely, if the error in any direction exceeds the set dynamic threshold, the data point will be eliminated to avoid low-quality data from having a negative impact on subsequent processing steps. Through this error screening method based on dynamic thresholds, the present application can effectively filter out unreliable RTK data and ensure that subsequent time synchronization and correction steps are based on high-precision data, thereby significantly improving the overall accuracy and reliability of point cloud data processing.

[0058] First, real-time differential positioning data is obtained from the RTK device, including timestamp, which records the specific time of data collection to ensure the time alignment of subsequent data; X, Y, and Z coordinates, which represent the three-dimensional coordinate information of the current position; VDOP and HDOP values, which are used to evaluate the geometric factors of positioning accuracy.

[0059] Based on the preset elevation error threshold and plane error threshold, the system judges and screens the RTK data at each time point. z (t) and plane error threshold T xy The dynamic adjustment formula of (t) is as follows:

[0060] T z (t) = α·VDOP(t) β

[0061] T xy (t) = γ·HDOP(t) δ

[0062] Among them, α, β, γ and δ are custom parameters used to adjust the sensitivity of the error threshold and control the response degree of the error threshold to the changes of VDOP and HDOP; T z (t) is the elevation error threshold at time t; T xy (t) is the horizontal error threshold at time t.

[0063] During the screening process, for each time point t, the following screening conditions are executed,

[0064] |Z(t)-Z ref (t)|≤T z (t)

[0065] (X(t)-X ref (t)) 2 +(Y(t)-Y ref (t)) 2 ≤T xy (t)

[0066] Where Z(t), X(t) and Y(t) are the elevation and horizontal coordinates of the RTK device at time t respectively; Z ref (t), X ref (t) and Y ref (t) are the corresponding elevation and horizontal coordinates of the reference point (from a reference station or a known high-precision position).

[0067] If the errors of the RTK data in both elevation and horizontal directions meet the above screening conditions, the data point will be retained for subsequent point cloud correction. If the error of the RTK data in any direction exceeds the corresponding dynamic threshold, the data point will be removed to avoid the negative impact of low-quality data on subsequent processing.

[0068] S130, based on the filtered RTK data, time-synchronize the POS data to generate aligned POS data;

[0069] Exemplarily, the filtered RTK data is time synchronized with the POS (Position and Orientation System) data to generate aligned POS data. Since there may be slight differences in the sampling frequency and timestamps of the RTK data and the POS data, direct data alignment may lead to error accumulation and inaccurate synchronization. Therefore, the use of the DTW (Dynamic Time Warping) algorithm to accurately align the two time series is a key step to ensure the accuracy of the subsequent correction steps.

[0070] Filtered RTK data M i =(X m,i , Y m，i , Z m,i ) and POS data N j =(X n，j , Y n，j , Z n，j ) are aligned in time. Since there may be slight differences in the sampling frequency and timestamps between the two, a dynamic time warping algorithm is required for time synchronization. The specific steps include:

[0071] Define two time series and Calculate the Euclidean distance of each pair of moments (i, j), expressed as:

[0072]

[0073] Where D(i, j) is the RTK data point M i and POS data point N j The Euclidean distance between m,iis the coordinate of the RTK data in the X direction at the i-th time step; Y m，i is the Y coordinate of the RTK data at the i-th time step; Z m,i is the coordinate of the RTK data in the Z direction at the i-th time step; n,j is the X-coordinate of the POS data at the jth time step; Y n，j is the Y-coordinate of the POS data at the jth time step; Z n,j is the coordinate of the POS data in the Z direction at the jth time step.

[0074] Based on the calculated Euclidean distance, the cumulative distance matrix C(i, j) is defined, and its calculation formula is:

[0075] C(i,j)=D(i,j)+min{C(i-1,j),C(i,j-1),C(i-1,j-1)}

[0076] stC(0,0)=0∩C(i,0)=C(0,j)=∞for i,j>0

[0077] Among them, C(0,0)=0 is the starting point, indicating that both sequences have not started; C(i,0)=∞for i>o means that the first i elements of sequence 1 cannot be aligned with the 0th element of sequence 2; C(0,j)=∞for j>0 means that the 0th element of sequence 1 cannot be aligned with the first j elements of sequence 2.

[0078] Starting from C(N, N), select the minimum value among C(i-1, j), C(i, j-1), C(i-1, j-1) in each step, move to the corresponding position, trace back to C(1, 1), determine the optimal time alignment path, and thus achieve time synchronization of the two sequences. Among them, C(N, N) is located in the lower right corner of the cumulative distance matrix, indicating the minimum cumulative distance required to align the first N elements of sequence 1 with the first N elements of sequence 2, and represents the minimum total cost required in the entire DTW alignment process. Through C(N, N), the optimal alignment path of the two sequences can be obtained. This path represents the minimum cumulative distance required to align the POS data to the filtered RTK data, ensuring the best temporal match of the two sequences.

[0079] According to the optimal alignment path, the POS data N j Align to RTK data M i , generate aligned POS data N′ i =(X′ n，i , Y′ n，i , Z′ n，i ).

[0080] It should be noted that in the embodiment of the present application, i represents the index of the aligned matching point, that is, each i corresponds to a time step or sampling point. In the alignment process, the RTK data M i and POS data N′ i are matched as a pair of corresponding points; once the alignment is completed, each time step i corresponds to a matching point i, so in the subsequent error calculation and analysis, i naturally becomes the index of the matching point.

[0081] S140, correcting the aligned POS data to generate corrected POS data;

[0082] Exemplarily, the aligned POS data is first corrected using a dynamic spatiotemporal transformation network. The network consists of a multi-layer convolutional neural network and a recurrent neural network, which can extract the spatiotemporal features of the filtered RTK data and the aligned POS data, and generate parameters for correction. These correction parameters are applied to the POS data through a nonlinear mapping function to obtain preliminary corrected POS data. On this basis, the system constructs an error vector to represent the three-dimensional error component between the corrected POS data and the RTK data. In order to further optimize the error vector, step S140 defines an adaptive weight matrix, performs weighted processing on the error vector, and generates an optimized error vector. By introducing a Bayesian network model, the optimized error vector is modeled and predicted for high-order statistical features, and the network parameters are dynamically updated using the variational Bayesian inference VBI (Variational Bayesian Inference) method to achieve accurate prediction and correction of future error vectors. Finally, the predicted error vector is applied to the preliminary corrected POS data to generate the final corrected POS data vector, ensuring the high accuracy and consistency of the POS data in position and posture.

[0083] Generate position and translation correction parameters Θ using a dynamic spatiotemporal transformer network i , the specific steps include:

[0084] Θ i =f DTM (STE(M i , N′ i ))

[0085] Among them, STE is a spatiotemporal encoder, which is composed of multi-layer convolution and recurrent neural networks and is used to extract the spatiotemporal characteristics of data; DTM is the nonlinear mapping function of the dynamic transformation network.

[0086] Based on the correction parameter Θ i , the POS data after preliminary correction X c,i =(X c,i , Y c,i ,Z c，i) is expressed as:

[0087] X c，i =X′ n，i +θ i，x ·cos(φ i )-θ i,y ·sin(ψ i )

[0088] Among them, X c，i is the X coordinate of the corrected POS data at the i-th time step; X′ n，i is the X coordinate of the aligned POS data at the i-th time step; θ i，x ·cos(φ i ) is the position correction value θ i，x After the attitude angle φ i The cosine adjustment represents the effective correction amount in the X direction; θ i，y ·sin(ψ i ) is the position correction value θ i,y Through the attitude angle ψ i The sinusoidal adjustment represents the effect in the X direction due to the Y direction correction.

[0089] Y c，i =Y′ n，i +θ i,y ·cos(ψ i )+θ i，z ·sin(ω i )

[0090] Among them, Y c，i is the Y coordinate of the corrected POS data at the i-th time step; Y′ n，i is the Y coordinate of the aligned POS data at the i-th time step; θ i，y ·cos(ψ i ) is the position correction value θ i,y Through the attitude angle ψ i The cosine adjustment represents the effective correction amount in the Y direction; θ i，z ·sin(ω i ) is the position correction value θ i，z After attitude angle ω i The sinusoidal adjustment represents the effect in the Y direction due to the Z direction correction.

[0091] Z c，i =Z′ n，i +θ i，z ·cos(ω i )

[0092] Among them, Z c，iis the Z coordinate of the corrected POS data at the i-th time step; Z′ n，i is the Z coordinate of the aligned POS data at the i-th time step; θ i，z ·cos(ω i ) is the position correction value θ i，z After attitude angle ω i The cosine adjustment represents the effective correction in the Z direction.

[0093] Define the error vector, expressed as:

[0094] E i =X c,i -M i =(X c，i -X m,i , Y e,i -Y m，i , Z c，i -Z m，i )=(E x，i , E y,i ,E z，i )

[0095] Among them, E i is the overall error vector of the i-th matching point; X c,i =(X c,i , Y c,i ,Z c，i ) is the three-dimensional position of the corrected POS data vector at the i-th time step; M i =(X m,i , Y m，i , Z m,i ) is the three-dimensional position of the RTK data at the i-th time step; (E x，i , E y,i , E z,i ) are the error components of the ith matching point in the X, Y and Z directions.

[0096] Define the adaptive weight matrix W i =diag(w x，i , w y,i , w z，i ), where the adaptive weight matrix of the i-th matching point is a diagonal matrix; w x,i , w y,i , w z,i are the weights in the X, Y, and Z directions respectively.

[0097] The weight calculation formula is expressed as:

[0098]

[0099] In the formula, κ dTo adjust the parameters, control the steepness of the weight adjustment, and affect the sensitivity of the weight function; σ d is the error variance in direction d; θ d is the error threshold in the dth direction.

[0100] The error vector field is weighted using an adaptive weight matrix to calculate the corrected error vector E′ i , expressed as:

[0101]

[0102] Among them, E′ i is the weighted error vector of the i-th matching point;

[0103] Optimize the weighted error vector field E′ i , improve the accuracy of error prediction and correction, establish a Bayesian network that can capture the complex dependencies between POS and RTK data, model the high-order statistical features in the error vector field, more effectively capture the complex dependencies and uncertainties in the error vector field, and achieve accurate prediction and correction of the error vector. The specific steps include:

[0104] Building a Bayesian Network Where V is a node set, each node represents the error vector field E′ i A component E′ d，i , d∈{x,y,z}, represents the errors in the X, Y, and Z directions; E is the dependency between nodes. For example, the error in the X direction may be related to the error in the Y direction, and this relationship is represented by edges.

[0105] Dynamically update the parameters of the Bayesian network to adapt to changes in the error vector field, including:

[0106] For each node v∈V, set the prior distribution p(E′ d，i ), usually using Gaussian distribution, expressed as:

[0107]

[0108] Among them, μ d is the mean; is the variance.

[0109] Based on the VBI method, the posterior distribution q(E′ d，i ), expressed as:

[0110]

[0111] Among them, μ′ d and It is iteratively updated by the VBI to maximize the variational lower bound.

[0112] Using the optimized Bayesian network model, the future error vector Make a prediction, expressed as:

[0113]

[0114] in, For the Bayesian network Next, the error vector E′ i expected value.

[0115] The predicted error vector E′ i Applied to the POS data after preliminary correction, it is expressed as:

[0116]

[0117] Generate the corrected POS data vector, expressed as:

[0118] X 校正,i =(X 校正，i , Y 校正,i , Z 校正，i )

[0119] S150, performing point cloud solution on the corrected POS data to generate target point cloud data.

[0120] Exemplarily, step S150 involves solving the point cloud data using the corrected POS data to generate high-precision target point cloud data. Specifically, this step includes converting the original point cloud data collected by the sensor from the sensor coordinate system to the global coordinate system, splicing and fusing the continuously collected point cloud frames, and optimizing the point cloud data using a voxel grid filtering method and a Poisson surface reconstruction algorithm.

[0121] First, the collected raw point cloud data is converted to the global coordinate system through the sensor coordinate system. This conversion is based on the corrected POS data and attitude angles (including roll angle, pitch angle and yaw angle) to ensure the accurate positioning of the point cloud data in three-dimensional space. The rotation transformation is achieved through the rotation matrix to reflect the change in the orientation of the sensor; the translation transformation adjusts the position of the point cloud according to the corrected POS data. This process ensures the consistency and accuracy of the point cloud data in the global coordinate system, laying the foundation for subsequent point cloud stitching and fusion.

[0122] Subsequently, the continuously collected point cloud frames are spliced ​​and fused to form a continuous three-dimensional point cloud model. In this process, the iterative closest point algorithm (ICP) or feature matching method is used to accurately align the point clouds of different time frames to ensure the geometric consistency between the frames. In order to improve the quality and processing efficiency of point cloud data, the voxel grid filtering method is used to downsample the point cloud, remove outliers and reduce the point cloud density, retaining key geometric features. Finally, the Poisson surface reconstruction algorithm is used to perform continuous surface reconstruction on the filtered point cloud, enhance the geometric details of the point cloud, and generate high-precision target point cloud data. Through the above steps, the present invention can effectively generate a three-dimensional point cloud model with high precision and high quality, which is widely applicable to various high-precision positioning and three-dimensional modeling application scenarios.

[0123] In some examples, it also includes:

[0124] After generating the target point cloud data, setting the point cloud data quality assessment index, wherein the above-mentioned quality assessment index includes point density and surface smoothness;

[0125] Based on the above quality assessment indicators, the overall quality of the above target point cloud data is evaluated;

[0126] Based on the point cloud quality assessment results, the RTK data filtering threshold, correction parameters and filtering weight are dynamically adjusted to optimize the point cloud data generation process.

[0127] Exemplarily, this step first sets the quality assessment indicators of the point cloud data, mainly including point density and surface smoothness. The point density indicator is used to measure the number of points per unit volume, ensuring the uniformity and integrity of the spatial distribution of the point cloud data, thereby avoiding information loss or redundancy caused by sparse or overly dense data. The surface smoothness indicator is used to evaluate the continuity and detail performance of the point cloud surface, ensuring the accuracy and realism of the geometric features of the point cloud data.

[0128] Subsequently, based on the set quality assessment indicators, the system conducts an overall quality assessment of the generated target point cloud data. By comparing the point density and surface smoothness of the actual point cloud data with the preset quality standards, the system can objectively determine whether the point cloud data meets the expected accuracy requirements. If the evaluation results show that the point cloud data fails to meet the standards in certain indicators, the system will automatically trigger the dynamic adjustment mechanism to optimize the RTK data filtering threshold, correction parameters, and filtering weights accordingly. This adjustment process responds to changes in point cloud quality in real time through feedback control, ensuring that the subsequently generated point cloud data can be continuously optimized and gradually reach higher quality standards.

[0129] Finally, based on the results of the point cloud quality assessment, the system dynamically adjusts relevant parameters to optimize the point cloud data generation process. Specifically, when the point density of the point cloud data is too low or the surface smoothness is insufficient, the system will appropriately relax the RTK data filtering threshold to increase the number of valid data points; otherwise, the threshold will be tightened to reduce the interference of noise data. At the same time, the correction parameters and filtering weights will also be adjusted according to the feedback from the quality assessment to further improve the accuracy and stability of the point cloud data. This dynamic optimization mechanism not only improves the adaptability and robustness of point cloud data generation, but also ensures that the point cloud data always maintains high quality and high reliability under different environmental conditions, thereby significantly enhancing the technical advantages and practical value of the present invention in practical applications.

[0130] See also Figure 2 , is a schematic diagram of the structure of a point cloud data correction device provided in an embodiment of the present application, comprising:

[0131] An RTK data acquisition unit 21, used for acquiring RTK data;

[0132] The RTK data filtering unit 22 is used to filter the RTK data to obtain filtered RTK data;

[0133] A POS data alignment unit 23, configured to perform time synchronization on the POS data based on the filtered RTK data to generate aligned POS data;

[0134] A POS data correction unit 24, used to correct the above-mentioned aligned POS data to generate corrected POS data;

[0135] The point cloud data generating unit 25 is used to perform point cloud calculation on the corrected POS data to generate target point cloud data.

[0136] See also Figure 3 The embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any method of point cloud data correction are implemented.

[0137] Since the electronic device introduced in this embodiment is a device used to implement a point cloud data correction device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, the technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by the technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0138] During the specific implementation process, when the computer program 311 is executed by a processor, any implementation method in the embodiments corresponding to the first aspect can be implemented.

[0139] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application adopts the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application adopts the form of a computer program product implemented on one or more computer-readable storage media that include computer-readable program code.

[0141] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0142] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0144] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes Figure 1 A process of a point cloud data correction method in the corresponding embodiment.

[0145] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired or wireless means. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium can be a magnetic medium, an optical medium, or a semiconductor medium, etc.

[0146] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0147] In the several embodiments provided in the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0148] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0149] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0150] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.

[0151] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

[0152] Although the preferred embodiments of this specification have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of this specification.

[0153] Obviously, those skilled in the art can make various changes and modifications to this specification without departing from the spirit and scope of this specification. Thus, if these modifications and modifications of this specification fall within the scope of the claims of this specification and their equivalents, this specification is also intended to include these modifications and modifications.

Claims

1. A point cloud data correction method, characterized in that: The method comprises: Get RTK data; Filtering the RTK data to obtain filtered RTK data; Based on the filtered RTK data, time synchronizing the POS data to generate aligned POS data; Correcting the aligned POS data to generate corrected POS data; Point cloud solution is performed on the corrected POS data to generate target point cloud data.

2. The point cloud data correction method according to claim 1, characterized in that: The filtering of the RTK data to obtain filtered RTK data includes: For each time point, calculating the elevation error and horizontal error of the RTK data; Based on the elevation error threshold and the plane error threshold, it is determined whether the elevation error and the horizontal error meet the preset conditions, and the RTK data meeting the preset conditions are screened out to obtain filtered RTK data.

3. The point cloud data correction method according to claim 1, characterized in that: The step of performing time synchronization on the POS data based on the filtered RTK data to generate aligned POS data includes: Define RTK data sequence and POS data sequence; Calculating the Euclidean distance based on the RTK data sequence and the POS data sequence; Based on the Euclidean distance, a cumulative distance matrix is ​​constructed and an optimal alignment path is determined by backtracking; Based on the optimal alignment path, the POS data is aligned to the filtered RTK data to generate aligned POS data.

4. The point cloud data correction method according to claim 1, characterized in that: The step of correcting the aligned POS data to generate corrected POS data includes: Correcting the aligned POS data using a dynamic spatiotemporal transformation network to determine correction parameters; Based on the correction parameters, preliminary POS data is obtained; constructing an error vector based on the preliminary POS data; defining an adaptive weight matrix, optimizing the error vector, and generating an optimized error vector; Constructing a Bayesian network model to predict the optimized error vector to obtain a predicted error vector; Based on the prediction error vector, corrected POS data is generated.

5. The point cloud data correction method according to claim 1, characterized in that: The step of performing point cloud calculation on the corrected POS data to generate target point cloud data includes: Collecting original point cloud data, and converting the original point cloud data from a sensor coordinate system to a global coordinate system; Splice and fuse continuously collected point cloud frames to form a continuous 3D point cloud model; The voxel grid filtering method and Poisson surface reconstruction algorithm are used to reconstruct the continuous surface of the point cloud and generate the target point cloud data.

6. The point cloud data correction method according to claim 4, characterized in that: The step of correcting the aligned POS data using a dynamic spatiotemporal transformation network and determining correction parameters includes: Extracting the spatiotemporal features of the filtered RTK data and the aligned POS data through multi-layer convolution and recurrent neural networks; Based on the spatiotemporal characteristics, correction parameters are determined.

7. The point cloud data correction method according to claim 1, characterized in that: Also includes: After generating the target point cloud data, setting the point cloud data quality assessment index, wherein the quality assessment index includes point density and surface smoothness; Based on the quality assessment index, evaluating the overall quality of the target point cloud data; Based on the point cloud quality assessment results, the RTK data filtering threshold, correction parameters and filtering weight are dynamically adjusted to optimize the point cloud data generation process.

8. A point cloud data correction device, characterized in that: include: RTK data acquisition unit, used for acquiring RTK data; An RTK data filtering unit, used for filtering the RTK data to obtain filtered RTK data; a POS data alignment unit, configured to perform time synchronization on the POS data based on the filtered RTK data, and generate aligned POS data; A POS data correction unit, used for correcting the aligned POS data to generate corrected POS data; The point cloud data generating unit is used to perform point cloud calculation on the corrected POS data to generate target point cloud data.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the point cloud data correction method according to any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the point cloud data correction method according to any one of claims 1 to 7 is implemented.

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