217 results about "Lidar point cloud" patented technology

The application discloses a container posture alignment detection method and system based on a laser radar, relates to the technical field of alignment detection, and comprises the following steps: a 3D Gaussian template of a container is established, a unified coordinate system is constructed, laser radar point cloud data is adaptively collected, and non-ground point cloud is obtained by ground segmentation; container candidate clusters are generated based on non-ground point cloud clustering, a unique target is selected according to structural consistency, target point cloud is obtained, and three-dimensional center estimation of the target point cloud is carried out by double-plane voting; the template center is used to align the three-dimensional center of the target point cloud, the point cloud is projected to a ground constraint plane and the direction distribution is calculated, a linear mapping matrix is constructed, rotation initial values satisfying orthogonality and environmental constraints are extracted by polar decomposition, and an initial posture is obtained; and high-precision, high-reliability and full-automatic container posture alignment is achieved in a complex, dynamic and unstructured port scene.

This application relates to a robotic method for precise ore sampling in complex mining environments, specifically in the field of ore feature recognition. The method includes: acquiring LiDAR point cloud data and industrial camera image data; after voxelizing the point cloud data, obtaining LiDAR BEV features through an improved SPConv sparse pyramid convolution operator feature extraction network, spatial reconstruction unit, and channel reconstruction unit; extracting semantic features from the image data to generate an image BEV feature map; fusing the two data elements-wise in the BEV space; extracting multi-scale features from the fused BEV feature map using a dual-branch Decoder network; introducing a KA attention mechanism to weight the fused features; and calculating a sampling priority score based on the weighted feature map to determine the optimal sampling point and plan the path. This method can improve ore recognition accuracy in complex mining environments and shorten the decision-making time for the entire process of sampling location determination and path planning.

This invention discloses a method for 3D mesh reconstruction of buildings from multispectral LiDAR point clouds, belonging to the field of multispectral LiDAR point cloud processing technology. The method includes: extracting building instances from the input point cloud using a multispectral LiDAR point cloud classification method; extracting geometric features from the height map obtained from the building point cloud, and then extracting spectral features from the spectral map using a spectral adaptive convolution module; inputting the data into a spectral-geometric fusion module to fuse the features of the spectral map and the geometric map to achieve accurate prediction of 2D corner points; projecting the 2D corner points onto the height index of the height map to obtain 3D corner points, and using an attention mechanism jointly guided by multispectral and geometric features to predict the boundary structure of the 3D corner points, generating a wireframe model; finally, achieving seamless and watertight 3D mesh reconstruction of the building under the guidance of the topological relationships contained in the wireframe model. This invention can effectively reduce artifacts and planar misalignment problems during the reconstruction process.

The application provides a multi-time-phase laser point cloud and three-dimensional real scene model registration method, and belongs to the technical field of point cloud registration, and comprises the following steps: sequentially sorting and automatically registering multi-period laser radar point cloud data in a long time period, and calculating point cloud density values. The initial vertex point cloud of the three-dimensional real scene model is extracted, three-dimensional grid creation is performed according to the calculated density values, the curvature adaptive sampling method and the grid plane interpolation method are adopted to expand the point cloud density, and density-enhanced point cloud data is obtained. Feature point information extraction and matching are performed on the point cloud data obtained by automatically registering the multi-time-phase point cloud and the density-enhanced point cloud data, and the automatic registration of the two types of point clouds is realized. By adjusting the calculation model point cloud to meet the target density and be uniformly distributed, the problem of registration difficulty caused by the large density difference between the three-dimensional real scene model point cloud and the multi-time-phase point cloud is effectively solved.

An example method performed by a processing system includes obtaining a light detecting and ranging point cloud of a building, where the point cloud includes a plurality of points, and where each point is associated with a set of (x,y,z) coordinates. A first point of the plurality of points is assigned to a subset of the plurality of points that is associated with the building, where the subset includes points whose (x,y) coordinates fall within a footprint of the building. The first point is grouped into a first cluster according to at least one of: a (z) coordinate of the first point and a gradient to which the first point belongs. A first prism formed by the first cluster is constructed. A model of the building is stored as a plurality of connected prisms, where the plurality of connected prisms includes the first prism.

This application belongs to the field of computer vision technology and discloses a cross-source point cloud registration method based on adversarial neural networks. The method includes: inputting a multi-frame LiDAR point cloud sequence and a UAV video sequence; segmenting each frame of the LiDAR point cloud into ground points and non-ground points and registering them to obtain a global LiDAR point cloud; performing motion recovery structure reconstruction on the UAV video sequence to generate a dense color point cloud; inputting the global LiDAR point cloud and the SfM dense color point cloud into an MMAlignNet network to construct a shared latent feature space; solving for the rigid transformation matrix from the LiDAR point cloud to the SfM dense color point cloud; and aligning the cross-modal point clouds according to the rigid transformation matrix to obtain a multimodal 3D reconstruction result. This application improves the registration accuracy, completeness, and robustness of large-scale outdoor environment 3D reconstruction by fusing UAV imagery and mobile LiDAR data, combined with cross-modal feature alignment and geometric constraint optimization methods.

This invention discloses a multi-source deformation monitoring data fusion method and system based on scene awareness, belonging to the field of deformation monitoring data fusion technology. The invention involves: S1: acquiring GNSS time-series data, InSAR raster data, LiDAR point cloud data, and ECMWF meteorological data of the dam monitoring area; S2: constructing a multi-dimensional scene feature vector containing signal quality and environmental geometry dimensions based on the acquired data; S3: calculating a first weight, a second weight, and a third weight corresponding to the GNSS time-series data, InSAR raster data, and LiDAR point cloud data respectively, based on the multi-dimensional scene feature vector and a preset physical threshold indication function; S4: calculating a first deformation value, a second deformation value, and a third deformation value of the dam monitoring point based on the acquired data; S5: fusing the first deformation value, the second deformation value, and the third deformation value according to the first weight, the second weight, and the third weight to obtain the fused deformation value of the dam monitoring point.

This invention relates to the field of point cloud computing technology, specifically a hatch positioning method based on multimodal lidar point clouds. First, a multi-dimensional point cloud containing three-dimensional coordinates and reflection intensity is acquired. The coordinate system of the multi-source data is unified and fused through radar extrinsic parameter calibration or point cloud fusion algorithms. Then, the deck area point cloud is segmented based on normal vector filtering and DBSCAN clustering, and converted into a two-dimensional image through orthogonal projection. Next, edges are extracted using an improved Canny operator, and the hatch planar position is determined by least-squares rectangle fitting. Finally, the three-dimensional coordinates of the hatch are restored through inverse projection mapping. This invention overcomes the detection limitations of a single radar, effectively improving the accuracy and stability of hatch positioning under complex conditions. It can provide reliable positional information for the automated control of bulk carrier unloading operations, and has significant industrial application value.

The application provides a ship multi-source perception data fusion in-port target clustering method, and belongs to the technical field of ship berthing perception and ship autonomous positioning. The method first acquires multi-source perception data of a ship-borne camera, a thermal imager and a laser radar and completes time synchronization, then realizes target detection and instance segmentation of a visible light video and target detection of an infrared video through YOLO11n-seg and YOLO11n algorithms respectively, projects laser radar point cloud data to a visible light image coordinate system and extracts effective point cloud indexes after verifying the effectiveness of cross-modal target detection results based on a calculation of an intersection over union, adopts a lightweight clustering algorithm based on local geometric features to construct a target point cloud set and generate a space bounding box, and finally calculates the distance and azimuth angle of the target relative to the ship, and fuses RTK positioning information of the ship to obtain the absolute position of the port target. The application solves the problems of under-segmentation of in-port dense targets, low multi-sensor data fusion efficiency and poor detection stability in poor visibility in the prior art.

This invention discloses a method for synthesizing severe weather lidar point clouds by combining real priors with geometrically guided intensity generation. First, a weather simulation module is used to convert clear weather data into pseudo-severe weather data with physical constraints from the statistical regularities of real datasets, constructing a high-quality training set. Then, using a structure-intensity decomposition diffusion model, a high-fidelity geometric structure is generated first through an asymmetric conditional diffusion process, and then matching reflection intensity information is generated based on the structure. This method effectively overcomes the modeling difficulties caused by geometry-intensity coupling in existing technologies, and achieves high-fidelity severe weather point cloud synthesis in the absence of large-scale real data.

The application discloses a kind of robust camera-lidar multimodal contrast pre-training method and system of anti spacetime bias.The method comprises: generating distance projection map to lidar point cloud;Multi-scale representation is extracted using image encoder and distance projection map encoder respectively;The hierarchical alignment of global vector and regional level block vector is completed by semantic perception branch and geometric perception branch;Space consistency constraint and sparsity regularization are introduced to suppress the representation collapse of low information area;Multi-scale attention aggregation is used to form the final cross-modal representation and is trained with bidirectional contrast loss, and finally a general camera-lidar data representation with robust migration capability for downstream three-dimensional perception tasks is obtained.The method of the application can more robustly provide fused features for downstream three-dimensional perception tasks, and the features can be extended to various severe weather scenarios and road conditions, improving the reliability of autonomous driving in severe scenarios.

The application provides a kind of based on unmanned aerial vehicle's river surface floating object monitoring method and system, belong to water environment protection monitoring field, including: obtaining the river bank boundary line data of target area and utilize the river bank boundary line data to build river bank model;Based on the river bank model, utilize unmanned aerial vehicle to cruise and obtain laser radar point cloud data, visible light image data and infrared thermal imaging data;The visible light image data and the infrared thermal imaging data are fused to obtain fused image data;Utilize the fused image data and the laser radar point cloud data to determine river surface floating object.The application can not only rely on image to determine the appearance characteristics of floating object, but also can accurately obtain its position, volume and other key information by means of point cloud data, greatly improving the accuracy of river surface floating object monitoring.

This invention discloses a LiDAR human motion capture method based on Bézier curve degradation modeling, comprising: acquiring a continuous multi-frame LiDAR point cloud sequence; constructing a trajectory-aware Bézier motion degradation module to fit the original joint trajectory and gradually reduce control points through a trajectory preservation strategy to generate a multi-level motion representation from coarse to fine; designing a progressive motion reconstruction module, using a multi-timescale motion transformer (TMT) to predict Bézier motion curves at multiple time scales based on point cloud features, and using a multi-level motion aggregator (MMA) to adaptively fuse the multi-scale curves to reconstruct a detailed and temporally coherent 3D human posture sequence. This invention effectively alleviates the posture jitter or failure problem caused by occlusion, noise, and sparse point clouds, significantly improving the accuracy and temporal continuity of motion capture, and is suitable for complex open scenarios such as autonomous driving and robotics.

The present application relates to the technical field of unmanned aerial vehicle control, and particularly relates to an unmanned aerial vehicle obstacle avoidance method, system, device and storage medium, comprising obtaining laser radar point cloud data, image data, millimeter wave radar data and environmental thermal imaging data; performing anti-reflection suppression processing on the image data to obtain filtered image data; respectively performing preprocessing on the laser radar point cloud data, the millimeter wave radar data and the environmental thermal imaging data to obtain obstacle point cloud data, distance and speed data of the obstacle and obstacle contour information; performing data fusion on the filtered image data, the obstacle point cloud data, the distance and speed data of the obstacle and the obstacle contour information to obtain obstacle fusion data; constructing a dynamic cost map according to the obstacle fusion data and preset environmental map data; correcting a preset flight path according to the dynamic cost map to obtain a corrected flight path; and eliminating ice and snow reflection interference by using multi-source data to improve the obstacle avoidance accuracy of the unmanned aerial vehicle.

The application provides a loopback test method and device of a laser radar, and belongs to the technical field of radar positioning. The method comprises the following steps: preprocessing laser radar point clouds corresponding to key frames of the acquired laser radar to obtain environment feature point clouds; converting the environment feature point clouds to a polar coordinate system with the laser radar as the center, and dividing the polar coordinate system into a plurality of two-dimensional grids according to radial distances and azimuth angles; determining geometric feature values of the two-dimensional grids according to geometric information of the environment feature point clouds falling into the two-dimensional grids, and determining intensity feature values of the two-dimensional grids according to reflection intensity information of the environment feature point clouds falling into the two-dimensional grids; constructing a multi-modal descriptor matrix according to the geometric feature values and the intensity feature values of the plurality of two-dimensional grids; and performing matching calculation on a current key frame of the laser radar and a historical key frame database according to the multi-modal descriptor matrix, and obtaining a loopback test result according to a matching calculation result. The application improves the loopback test precision and robustness.

This invention proposes a method and apparatus for processing surface clutter using lidar based on unmanned surface vessels (USVs). The method includes: acquiring point cloud data of the USV's surface environment using lidar; performing coordinate system transformation and point cloud correction on the acquired point cloud data; segmenting the corrected data into lidar point cloud clusters using grids; classifying the segmented lidar point cloud clusters by extracting multiple features; and performing real-time frame-by-frame matching and target tracking of USV surface targets using the extracted feature vectors and the positioning information of the point cloud clusters, calculating the target's motion elements, and using the continuity of the target and the randomness of the waves to remove clutter points and accurately track the target. This method can filter out outliers and clutter points caused by plankton, and simultaneously filter out wake waves and spray.

This application relates to the field of equipment condition monitoring technology in fully mechanized coal mining faces, and discloses a method for monitoring the upward and downward movement of a scraper conveyor head based on lidar. The method includes: constructing a global spatial coordinate system and benchmark model of the roadway based on lidar point clouds; secondly, deploying a positioning sensing unit on the transfer machine connected to the scraper conveyor head to calibrate the relative position vector between the machines; then collecting inertial data and local point clouds, and using an error state Kalman filter algorithm to calculate the real-time pose of the carrier; furthermore, using the carrier attitude matrix to perform a spatial rotation transformation on the relative position vector to deduce the absolute coordinates of the scraper conveyor head; finally, calculating the component of this coordinate along the tilt direction, and combining the offset and rate to determine the upward and downward movement state. This invention solves the problems of limited direct measurement and long-term operational drift through indirect measurement and multi-source fusion strategies, achieving high-precision continuous monitoring under harsh working conditions.

The present application relates to the technical field of laser radar point cloud processing and human posture reconstruction, in particular to a human three-dimensional structure modeling method based on single-station line-scan laser radar, in which, according to the determined effective layering meeting the human characteristics in the height direction and the offset of the center of gravity in the horizontal direction, continuity is judged, the effective layering meeting the continuity is clustered to obtain a human candidate region, the point cloud in the height range corresponding to the human candidate region is intercepted to form a human layered point cloud set, and the boundary range of the human in the height direction is determined. The present application does not need multiple sensors to cooperate or a complex learning model, and is suitable for human three-dimensional perception and structure analysis in complex environments.

This invention discloses an online high-precision map building method and system based on positioning fusion, including: acquiring three-axis acceleration data and three-axis gyroscope data through an IMU; obtaining predicted continuous attitude change data based on the changes in velocity and angle; matching and building a local map through LiDAR point cloud data, and acquiring the driving trajectory; matching the driving trajectory with the local map to obtain LiDAR observation pose data; obtaining satellite positioning trajectory data fed back by RTK; obtaining the overlap degree in a unified coordinate system by fitting the predicted continuous attitude change data, LiDAR observation pose data, and satellite positioning trajectory data at the same time, and obtaining the variance value of the relative deviation; judging the positioning accuracy of the predicted continuous attitude change data, LiDAR observation pose data, and satellite positioning trajectory data at the same time based on the obtained variance value of the relative deviation, and obtaining the pose of the sub-map at the same moment.

The application discloses a kind of transmission line tree barrier processing task dispatching method and system based on spatial position, mainly related to overhead transmission line operation and maintenance technical field.Its method includes: obtaining the laser radar point cloud data of tree along transmission line;Point cloud data is analyzed, and the spatial position coordinates of target tree with hidden danger of tree barrier, clearance distance and hidden danger grade are determined;According to spatial position coordinates, target tree is matched with pre-set responsible area, and the responsible operation terminal is determined;Tree barrier processing work order containing hidden danger information is generated and dispatched to the operation terminal;In response to the operation terminal receiving work order, navigation path from current position to target position is provided.The application realizes the automatic identification, accurate positioning and intelligent dispatch of hidden danger of tree barrier, reduces the manual communication link through spatial position matching and navigation guidance, significantly improves the efficiency and accuracy of tree barrier processing.

The application discloses a binocular vision and laser radar-based bionic human eye target recognition system and method, which comprises the following steps: S1, collecting and registering synchronous monocular images and laser radar point clouds; S2, extracting image contours and point cloud attributes to generate feature maps and attribute sets; S3, jointly encoding to generate size-distance representations; S4, constructing an improved Ginzburg-Landau phase field energy model based on the representations and attribute sets, obtaining potential function parameters and anisotropy tensors; S5, introducing SE(2) sub-Riemann geodesic direction continuity constraints into a phase field gradient term to adjust the strength; S6, aligning and optimizing evolution of adjacent frame phase fields in time by using optical flow; S7, extracting zero isograms to generate contours and output parameters; S8, inputting the parameters and size-distance representations into a three-dimensional structure program generation module for matching and screening; S9, performing consistency checking and sorting to output a recognition result. The application fuses visual and point cloud features, and improves the recognition accuracy and stability in a complex scene.

The application discloses a kind of 4D imaging millimeter wave radar stereo traffic dataset construction method and related equipment, comprising: obtaining the multi-source original data collected by data acquisition platform, which is equipped with 4D imaging millimeter wave radar, laser radar, high-definition camera and positioning system etc., and the multi-source original data contains the above-mentioned each sensor data and is collected in the target scene of preset scene library, each sensor data is aligned under unified space-time reference after time synchronization and space calibration;Multi-source original data is analyzed and processed, point cloud is obtained and quality verification is carried out;4D imaging millimeter wave radar point cloud and laser radar point cloud are fused, visual reference is provided according to image data, motion compensation is provided by positioning data, and multi-modal sensing data is formed;Multi-modal sensing data is labeled, and dataset is obtained.The application constructs multi-modal high-quality dataset with 4D imaging millimeter wave radar as core, cooperates multiple sensors, covers multiple stereo traffic scenes, and provides high-precision labeled information with time sequence continuity.

The application belongs to the technical field of unmanned aerial vehicle intelligent inspection, and more particularly relates to a high-precision three-dimensional map construction method and system based on multi-modal feature fusion unmanned aerial vehicle inspection. The method comprises the following steps: acquiring and synchronizing point cloud data and image data, then converting the laser radar point cloud data to the camera coordinate system, and then performing noise reduction processing and distortion correction; dynamically distributing radar and camera sensor weights according to the environmental light intensity and point cloud density; extracting radar point cloud features and camera visual features, then distributing the corresponding features according to the radar and camera sensor weights, and finally performing multi-modal fusion to obtain fusion features; and performing joint SLAM three-dimensional mapping according to the fusion features to generate a three-dimensional map. The application solves the problems of insufficient sensor complementarity, poor environmental adaptability and low map precision in traditional inspection.

This invention discloses a method for registering forest point clouds from UAV-LiDAR during leafy and leafless periods, relating to photogrammetry, remote sensing mapping, and forest resource monitoring. The method extracts ground points from two phases of UAV-LiDAR point clouds, constructs multi-directional radial topographic profiles, and extracts local profile segments using a sliding window. It calculates a comprehensive similarity based on trend consistency, robustness to local shape deformation, and curvature similarity, and selects stable matching segments by combining radial distance and topographic undulation amplitude consistency constraints. Three-dimensional corresponding point pairs are generated from the matching segments, and the initial rigid body transformation is solved using SVD. The registration is then optimized using ICP to obtain a multi-temporal fused point cloud. This invention can achieve reliable registration under conditions of phenological differences, missing vegetation structure, and initial biases, providing support for the reconstruction of three-dimensional forest structures and refined resource monitoring.

The application discloses a hyperspectral and LiDAR data fusion vegetation stress grade evaluation method and system, a storage medium and a product, belongs to the technical field of remote sensing image processing and ecological environment monitoring, solves the problem that vegetation stress grade evaluation only depends on hyperspectral data, is easily interfered by terrain shadow and mixed pixels, and leads to misjudgment; only depends on LiDAR data, although three-dimensional structure information can be obtained, but the stress type cannot be identified. The application comprises obtaining multi-source data of a target region in vegetation and registration, wherein the multi-source data comprises hyperspectral images and LiDAR point cloud data; LiDAR feature maps and hyperspectral feature maps are extracted by using a double-branch encoder network after the registered multi-source data, and are aligned; the LiDAR feature maps and the hyperspectral feature maps are subjected to feature fusion through a terrain-gated feature interaction mechanism, and fusion features are obtained; a trained prediction network of multi-task learning is used to predict the fusion features, and the stress grade of each pixel is output. The application is used for vegetation stress grade evaluation.

The application discloses a kind of unmanned vehicle fast point cloud segmentation algorithm under complex environment, by original laser radar point cloud is encoded as initial equilateral triangle grid chart structure, introduce regularized space representation, to enhance the expression ability of point cloud local geometry;For each point cloud in equilateral triangle grid, the local geometric feature is extracted and the grid flatness is calculated using principal component analysis method, so as to realize the effective distinction of ground area and non-ground area;For the grid area with higher flatness, directly determine the internal point cloud as ground point cloud;For the grid area with lower flatness and complex terrain, then improve the local modeling accuracy by adaptive subdivision operation;In the non-ground point cloud segmentation stage, an adaptive euclidean clustering algorithm based on laser radar angle resolution is used to accelerate the segmentation, and the clustering scale is set adaptively for point clouds in different distance ranges, so as to significantly reduce the computational complexity while ensuring the accuracy of segmentation.

The present application relates to the field of engineering geology and geological disaster prevention technology, in particular to a system and method for evaluating the stability of dangerous rock with steeply inclined fissures in the rear edge, comprising: S1, obtaining high-precision point cloud data of the dangerous rock surface by using unmanned aerial vehicle oblique photography, aerial remote sensing or airborne LiDAR, and constructing a triangular mesh model of the dangerous rock surface; S2, constructing a three-dimensional entity model according to the boundary conditions and sliding surface of the dangerous rock; S3, extracting the volume, barycenter position, sliding surface area and spatial occurrence information of the sliding surface from the three-dimensional entity model, and calculating the self-weight of the dangerous rock by combining the rock mass bulk density and the additional weight of the water in the fissure; the present application constructs a fine three-dimensional scene model of the dangerous rock based on unmanned aerial vehicle oblique photography or LiDAR point cloud, which can accurately extract key geometric parameters such as the volume, barycenter, sliding surface area and inclination of the dangerous rock, avoids the morphological distortion problem caused by two-dimensional simplification, and makes the stability analysis more close to the actual engineering scene.

This invention proposes a real-time calculation method and system for six degrees of freedom of ships based on lidar point clouds, belonging to the field of shipbuilding technology. Addressing the shortcomings of existing ship monitoring technologies, which primarily rely on single-video surveillance and cannot analyze ship motion patterns or comprehensively grasp ship movement trends, this invention utilizes an iterative nearest-point point cloud matching algorithm. Through nearest-point search and singular value decomposition to solve for the optimal transformation matrix, iteratively optimizes the ship's position and attitude, finds the boundaries and centroids of feature data, obtains the main direction of ship motion, and continues iteratively calculating the optimal rigid body transformation between two point clouds. This further yields the core of the ship's motion in any two adjacent frames, achieving high-precision, real-time calculation of the ship's motion under six degrees of freedom (sway, pitch, heave, roll, pitch, and bow). Furthermore, it minimizes errors caused by sea conditions and human operation.

This invention discloses a method for extrinsic parameter calibration of a LiDAR and camera based on directional equalization, comprising the following steps: extracting edge feature maps from the camera image and performing Euclidean distance transformation on the edge feature maps to obtain the range field and its gradient; performing voxel downsampling on the LiDAR point cloud and using a region growing algorithm to segment the principal plane from the downsampled LiDAR point cloud; solving for the intersection lines between adjacent principal planes pairwise to obtain LiDAR edge line features; transforming the LiDAR edge line feature points to the camera coordinate system and projecting them onto the image plane, and performing bilinear sampling of the range field and its gradient at the projected pixel positions; constructing a global orientation histogram and calculating the directional equalization weights; constructing a joint objective function that fuses the range field residuals and the directional equalization weights; and iteratively optimizing the joint objective function to solve for the optimal extrinsic parameters between the LiDAR and the camera. The advantage of this invention is that it improves the stability of extrinsic parameter estimation.

The present disclosure provides a 4DGS model training method and a three-dimensional scene reconstruction method, and relates to the technical fields of three-dimensional modeling and computer vision. The method comprises: acquiring laser radar point cloud data of a target scene; generating depth supervision information based on depth information obtained by projecting the laser radar point cloud data; constructing a geometric supervision loss of a 4DGS model based on the depth supervision information; and training the 4DGS model based on the geometric supervision loss and an image reconstruction loss. The present disclosure can improve the representation ability of the 4DGS model for the spatial structure of the target scene and improve the accuracy of the three-dimensional scene reconstruction result by introducing the depth supervision information generated based on the laser radar point cloud data to train the 4DGS model.