1840 results about "Lidar point cloud" patented technology

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention relates to the technical field of unmanned vehicle control, and discloses a smart city environmental sanitation unmanned vehicle path planning and real-time monitoring method. The method comprises the following steps: firstly, acquiring a multi-source environment sensing data set such as laser radar point cloud data, a camera image sequence and real-time traffic flow information; local road network features are extracted based on laser radar point cloud data, a dynamic target motion prediction map is generated according to a camera image sequence, and real-time traffic flow information is processed to generate a regional traffic efficiency evolution curve. And inputting the data into a path optimization model to generate an initial path sequence, dividing cleaning task priorities, fusing related data and generating a final path planning scheme through a reinforcement learning algorithm. In addition, operation state data of the unmanned vehicle are collected in real time, and a path correction instruction set is generated through an anomaly detection model to update the strategy network. According to the method, the rationality and the operation efficiency of the path planning of the environmental sanitation unmanned vehicle can be improved, real-time monitoring is realized, and the operation safety and the management intelligence level are enhanced.

The invention discloses a multi-view panoramic point cloud splicing method, which comprises the steps of performing joint calibration on a binocular camera and a laser radar through a calibration algorithm, and aligning a coordinate system; a binocular camera is used for collecting a two-dimensional image and carrying out distortion correction, a depth map is generated based on parallax calculation, and three-dimensional point cloud reconstruction is carried out in combination with a triangulation principle; performing deep learning target detection on the two-dimensional image, and screening a line rod assembly area through non-maximum suppression; multi-view point cloud data are collected through a laser radar, point cloud segmentation processing is carried out, and a telegraph pole assembly point cloud subset is reserved; matching the two-dimensional pixel area of the telegraph pole assembly with a laser radar point cloud projection result, and screening point cloud data belonging to the telegraph pole assembly; and carrying out alignment and fusion on the multi-view point clouds through initial registration and fine registration by adopting an improved point cloud splicing algorithm to generate a panoramic point cloud image. According to the method, the point cloud splicing processing time is shortened, and the robustness, precision and integrity of point cloud splicing are improved.

The invention relates to an automatic driving vehicle path planning method and device based on a large language model, equipment and a medium. The method comprises the steps that a traffic scene RGB image, a traffic scene depth image, laser radar point cloud data and inertial measurement unit data of an automatic driving vehicle are acquired; performing feature extraction on the traffic scene RGB image, the traffic scene depth image, the laser radar point cloud data and the inertial measurement unit data to construct multi-modal traffic scene information; calling a preset large language model to determine the motion state of the autonomous vehicle and natural language description of traffic targets around the autonomous vehicle according to the multi-mode traffic scene information; and determining the attention weight of the target node by adopting an attention mechanism in a large language model according to the natural language description of the traffic target, and determining the optimal driving path by adopting a preset path planning algorithm according to the attention weight of the target node, the motion state of the autonomous vehicle and the target position. According to the invention, the adaptive capability of path planning is obviously improved.

The invention relates to the technical field of road crack detection, in particular to a road crack detection method and system based on a fused image. The method comprises the following steps: acquiring road multi-source monitoring data including a visible light image, infrared thermal imaging data and laser radar point cloud data, and performing multi-modal image fusion and road three-dimensional point cloud reconstruction to generate a fused road image and road three-dimensional modeling data; performing crack curvature analysis based on the fused road image to generate crack curvature data; performing reflection crack contour recognition and positioning on the fused road image through the crack curvature data to generate reflection crack initial positioning data; obtaining road base material data; and performing reflection crack stress field reconstruction on the road area according to the reflection crack initial positioning data to obtain a reflection crack stress field. According to the invention, through multi-modal fusion, curvature identification, stress field modeling and crack channel analysis, the accuracy and strain of road reflection crack detection are improved.

The invention provides a flood control project virtual simulation and risk rehearsal method based on digital twinning, and the method comprises the following steps: obtaining drainage basin multi-source data collected by a space-air-ground integrated monitoring network, the multi-source data comprising a satellite remote sensing image, an unmanned aerial vehicle LiDAR point cloud and ground sensor monitoring data; inputting the multi-source data into a pre-constructed digital twin model, wherein the model comprises a spatio-temporal data fusion layer, a physical process modeling layer, a multi-scale simulation deduction layer and a risk assessment decision-making layer which are connected in sequence; the spatio-temporal data fusion layer is used for performing spatio-temporal registration and feature fusion on input multi-source data, and constructing a total element digital backplane; through the space-air-ground integrated monitoring network and the spatio-temporal data fusion technology, high-precision digital mapping of basin total elements is realized. Compared with a traditional single data source, time-space consistency of flood routing simulation is remarkably improved through multi-source data fusion, and prediction errors of key physical processes such as river channel scouring are greatly reduced.

The invention relates to the technical field of building appearance defect detection, in particular to a building appearance defect detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the building information of a target building, and generating a hierarchical scanning path and a three-dimensional obstacle avoidance flight path, a visible light image, an infrared thermodynamic diagram and laser radar point cloud information are collected for space-time alignment processing, and an attention mechanism neural network is used for extracting multi-scale features to generate a detection report containing defect three-dimensional coordinates, damage levels and safety risk assessment. The method achieves the purpose of efficiently and accurately detecting the building appearance defects, can adapt to complex building structures and environmental conditions, supports defect trend prediction and maintenance decision, and remarkably improves the building safety management efficiency.

The invention relates to the technical field of computer vision and three-dimensional reconstruction, and discloses an exhibition hall three-dimensional modeling intelligent optimization system based on multi-modal data fusion, and the system comprises a data collection module which is configured to synchronously obtain laser radar point cloud data, a multispectral image sequence and inertial measurement unit data; the preprocessing module is used for receiving the output of the data acquisition module, aligning a multi-source sensor coordinate system through a space-time calibration algorithm, and separating a static scene from a dynamic interference element by using a dynamic segmentation network; and the multi-modal fusion module is used for receiving the preprocessed data and carrying out adaptive weighted fusion on the geometric features of the laser radar and the visual texture features through a cross-modal attention mechanism. According to the invention, through multi-modal data fusion and a dynamic scene adaptive mechanism, the modeling precision and the real-time updating capability in a complex exhibition hall environment are significantly improved.

The invention discloses an ultra-wideband laser radar inertial navigation cooperative SLAM (Simultaneous Localization and Mapping) method and system, which are suitable for robot positioning and mapping tasks in a GPS (Global Positioning System)-free environment. According to the method, high-frequency motion priori of an IMU, relative pose constraint of LiDAR and absolute ranging information of UWB are fused, a unified factor graph optimization model is constructed, and multi-sensor cooperative positioning is realized; in the system initialization stage, IMU bias calibration, UWB base station coordinate configuration and LiDAR initial attitude estimation are completed; in the operation process, IMU pre-integration is utilized to predict the pose, LiDAR point cloud registration is utilized to obtain the relative motion between key frames, UWB ranging is combined to construct a residual item, a self-adaptive weight mechanism is introduced to suppress ranging abnormity, and the fusion robustness is improved; the system supports geometric feature loopback detection, cross-frame constraints are constructed in combination with UWB to carry out closed-loop optimization, and an optimization result is used for real-time incremental updating of a global map; the method has the advantages of high positioning precision, strong anti-interference capability, wide application scene and the like.

The invention discloses a multi-modal image automatic labeling system and method, and relates to the technical field of image data processing. According to the multi-modal image automatic labeling system and method, time sequence alignment is carried out on video streams and laser radar point cloud data through an asymmetric dynamic time warping algorithm, semantic and geometric features are extracted, and the elastic coefficient of the algorithm is dynamically adjusted. And combining a modal perception attention mechanism, dynamically allocating fusion weights of the video stream and the laser radar according to the features, generating a cross-modal joint feature vector, and outputting a preliminary labeling result. And generating an annotation robustness index by calculating the prediction entropy and the three-dimensional intersection-to-union ratio confidence of the target detection frame, and iteratively optimizing the annotation result. And mapping the cross-modal features and the labeling result into a space-time correlation map, and displaying the three-dimensional positioning, motion trail and modal contribution degree thermodynamic diagram of the target in real time. The problems of time alignment, feature fusion and labeling robustness are effectively solved, and a high-precision and interpretable automatic labeling solution is provided.

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention relates to the technical field of agricultural production data prediction, and discloses a tobacco plant number prediction method based on deep learning, and the method comprises the steps: collecting tobacco field environment data through a soil humidity sensor, a weather sensor, a laser radar point cloud sensor, a multispectral sensor and other multi-source agricultural sensors, carrying out the multi-source feature fusion through a dynamic feature selection network, and generating an environment feature vector. And inputting the model into a multi-modal time sequence prediction model adopting a space-time attention mechanism, and modeling a tobacco plant growth trend. And constructing an adaptive weighted regression model according to a prediction result, and dynamically adjusting the weight by using an improved particle swarm optimization algorithm. And constructing a hierarchical correction model to optimize prediction data. A real-time prediction updating method is provided, and newly-added data can be processed. The multispectral sensor data is subjected to dimension reduction, correlation modeling and interpolation fusion processing. According to the method, multi-source data is fully utilized, the accuracy, real-time performance and stability of tobacco plant number prediction are improved, and powerful support is provided for tobacco planting management.

The invention provides a tree identification method and system based on ground-air fusion three-dimensional laser point cloud, and belongs to the field of environment remote sensing data identification, and the method comprises the steps: obtaining laser radar point cloud data in a set forest region through employing an unmanned plane, and collecting the point cloud data in the set forest region through employing a laser scanning device; preprocessing the two groups of data to obtain preprocessed laser radar point clouds, and performing ground-air data seamless fusion on the preprocessed laser radar point clouds to obtain fused data; generating a canopy height model based on a watershed segmentation algorithm, and performing individual tree segmentation on the fused point cloud data by using the model; extracting a vector boundary after single tree segmentation, distributing a tree ID for each tree, and extracting a single tree point cloud according to the tree ID; the single tree point cloud is further processed to construct a multi-angle point cloud sample data set; and training a deep learning network model by using the multi-angle point cloud sample data set, and detecting the tree species to be detected by using the trained deep learning network model.

Provided in the present invention is a three-dimensional reconstruction method based on LiDAR and vision multi-information fusion. The method comprises: calibrating a coordinate system of a LiDAR detection result and a visual detection coordinate system to unify the coordinate systems; obtaining sparse point cloud data of a LiDAR, and projecting data points onto a visual image; performing feature point extraction and depth estimation on an image collected by a monocular camera, so as to perform feature point extraction and depth estimation on a planar image, and thereby densifying a point cloud; fusing feature points, which are extracted from the image, with LiDAR point cloud data to form a dense point cloud, and mapping the dense point cloud to a three-dimensional space; and performing fast triangulation processing on the processed point cloud data to obtain a three-dimensional plane, performing spatial matching on image pixels and the three-dimensional plane, and restoring colors. In the method of the present invention, the LiDAR and the monocular camera are combined to complement each other, so that device costs are reduced, advantages in both robustness and stability are achieved, and the quality of point cloud data can be effectively improved.

The invention belongs to the technical field of path planning of unmanned equipment, and relates to a medical rescue unmanned aerial vehicle and robot dog cooperative linkage method, which comprises the following steps: integrating LiDAR point cloud, visual images, IMU inertial data and compensated UWB positioning data into a multi-modal data stream subjected to space-time alignment through time alignment and space alignment technologies; respectively extracting LiDAR and visual feature poses through parallel SLAM (Simultaneous Localization and Mapping) calculation, and carrying out graph optimization and joint optimization by utilizing a GTSAM library, so as to construct a globally optimized pose and a global semantic map; the path planning algorithm provides dynamic channel routing for the unmanned aerial vehicle and the robot dog based on a global semantic map, the feature fusion weight is adjusted by considering the environment illumination intensity and the point cloud density, the reverse iris control mechanism detects whether the robot dog enters a shielding area through a UWB signal intensity attenuation value, the unmanned aerial vehicle is switched to a UWB signal follower, and the UWB signal follower is switched to the unmanned aerial vehicle. And through combination with a Kalman filter, penetration positioning in a shielding area is realized, so that stable execution of a rescue task in a complex environment is ensured.

The invention relates to a tower crane operation control system based on complex scene three-dimensional real-time modeling. According to the system, a lifting hook is coarsely positioned through a lifting hook positioning and state sensing unit, a real-time position is positioned by combining a laser radar point cloud clustering algorithm with historical pose data, and visual tracking is synchronously performed by means of a tower top camera AI; converting the real-time point cloud data into a 3D voxel grid map, generating a global path by using a 3DA algorithm, and outputting a hoisting track after smooth processing and track optimization; establishing a sling-lifting hook double-pendulum dynamic model, predicting a state sequence based on a model prediction control algorithm, and adjusting a control signal through a feedforward compensation item and a feedback correction item; and the man-machine interaction and monitoring unit is used for displaying the cantilever angle, the lifting hook height and the three-dimensional map of the tower crane in real time and remotely intervening the operation state of the tower crane. According to the system, multi-source data are fused to construct a high-precision three-dimensional map, lifting hook positioning and full-view tracking are achieved, and lifting safety and trajectory tracking precision are improved through path planning and dynamics control.

The invention discloses a wounded rescue life detection and positioning method based on deep learning. The method comprises the steps that S1, multi-view RGB-D images, laser radar point clouds, millimeter wave vital sign radar data and infrared thermal image data are collected and subjected to time synchronization; s2, outputting a pixel-level semantic mask; s3, generating a semantic point cloud of the wounded; s4, forming a metric-semantic double-map data set; s5, generating vital sign scores and writing the vital sign scores into a metric-semantic double-map data set; s6, updating wounded dynamic anchor point attributes in the measurement-semantic double-map data set; and S7, based on the updated metric-semantic double-map data set, calculating the three-dimensional coordinate and rescue priority sequence of each wounded dynamic anchor point, and generating an obstacle avoidance optimized navigation path for avoiding the risk semantic region. The rescue efficiency and safety are effectively improved.

The invention discloses a concrete crack three-dimensional reconstruction method and system based on multi-modal fusion and a medium, and relates to the technical field of structural engineering detection, and the method comprises the following steps: obtaining a structural image frame sequence and a structural laser radar point cloud frame sequence of a target structure; obtaining a crack mask image frame after crack segmentation; an integral structure de-noised point cloud picture is obtained; obtaining a visible point cloud coloring point cloud map and a visible point cloud semantic segmentation point cloud map; obtaining an overall three-dimensional coloring point cloud map and an overall three-dimensional semantic segmentation point cloud map; obtaining an overall three-dimensional point cloud marking map; and performing three-dimensional attribute measurement on the crack based on the three-dimensional point cloud marking map to obtain three-dimensional geometric information of the crack. According to the method provided by the invention, a multi-frame and multi-modal fused crack structure reconstruction framework is designed, the method can adapt to crack detection of various three-dimensional structures, and simultaneous detection of crack width information, crack position and crack trend information can be realized based on an overall three-dimensional point cloud marking map.

Provided is an individual-tree segmentation method of UAV LiDAR point cloud based on canopy morphology. The method uses woodland data obtained by a UAV LiDAR to: extract initial canopies from a CHM based on a region growing algorithm, determine whether each initial canopy is a correct segmentation canopy according to the number of local density maximum points in each initial canopy, finely segment each wrong segmentation canopy according to canopy morphology to obtain an updated set of tree tops, and finally use each of the updated set of tree tops as a seed point for performing the region growing algorithm to thereby obtain a final individual-tree segmentation result. The method make full use of height information and density information contained in a tree point cloud; and under the guidance of the density information, a wrong segmentation tree can be more accurately identified.

The invention discloses a multi-modal information fusion odometer construction method for star catalogue positioning, and relates to the technical field of star catalogue patroller positioning. The method comprises the following steps: carrying out space joint calibration on a monocular camera, a laser radar and an inertial measurement unit, reconstructing a laser radar point cloud by using a timestamp of a camera image, and realizing time synchronization of the camera image and the laser radar point cloud; establishing an IMU pre-integration error model; and performing motion compensation distortion removal on the laser point cloud by using an IMU pre-integration result, and extracting geometric features of the distorted laser point cloud based on a neighbor region smoothness calculation method of a fixed measurement distance. By researching a multimodal information fusion odometer method, the accumulative error of motion measurement is reduced, the positioning precision and stability are improved, technical support is provided for design and development of a star catalogue navigation system, and the problems that a single-modal star catalogue positioning method is weak in environment adaptive capacity and poor in algorithm generalization are solved.

The invention discloses a laser radar point cloud positioning method in a degraded environment based on feature point enhancement, and the method comprises the following steps: fusing a laser radar and an IMU sensor through employing a tight coupling method, and compensating the motion distortion of laser radar point cloud data; line and plane features of tunnel point clouds and local features in tunnels, such as signs and lighting systems, are extracted in a self-adaptive manner. Feature enhancement is carried out by predicting a virtual point. And carrying out feature matching registration by combining the feature points and fusing the pose matrix pre-integrated by the IUM. Coordinate conversion is carried out to convert the point cloud data from an absolute coordinate system of the laser radar to a world coordinate system by introducing a reference point; and a factor graph optimization algorithm is introduced at the rear end to improve the mapping and positioning precision. According to the method, the laser radar and the IMU sensor are fused, and adaptive feature extraction is adopted for feature matching, so that high-precision mapping and positioning can be completed in a long tunnel degradation environment.

The invention provides a multi-modal three-dimensional target detection method based on an attention mechanism, and the method comprises the steps: obtaining image data and LiDAR point cloud data, carrying out the preprocessing of the image data and the LiDAR point cloud data, carrying out the feature extraction of the processed image data and the processed LiDAR point cloud data, and carrying out the feature extraction of the processed image data and the processed LiDAR point cloud data; and fusing the extracted image features and the point cloud features based on a multimodal feature fusion algorithm of bidirectional information interaction, optimizing the fused features, eliminating redundancy and noise, performing refined processing on the fused features in a specific region of interest, inputting the fused and optimized features into a target detection module, and performing target detection on the target. Through candidate box generation, classification and regression tasks, classification, positioning and identification of a multi-modal three-dimensional target are completed, based on a non-maximum suppression algorithm, a redundant detection box is removed, and a detection result is output, including a category label of the target, a bounding box coordinate and a confidence score. According to the invention, the efficiency and detection precision of multi-modal fusion are improved, and the method is convenient to use.

In various examples, systems and methods are disclosed relating to learning and segmenting objects in lidar point clouds. A system can generate, for an image depicting an environment, a set of segmentation masks using an image segmentation model and the image, and can generate a set of tokens for the image using an image-text encoder model. The system can generate a segmented point cloud by transferring the set of segmentation masks to the point cloud representing at least a portion of the same environment depicted by the image. The system can update, using the segmented point cloud and the set of tokens, a machine-learning model to generate output segmentations for input point clouds.

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.

The invention relates to the technical field of ground mobile unmanned equipment control, and discloses a ground mobile unmanned equipment autonomous obstacle avoidance control system optimized by artificial intelligence. The system comprises an environment perception layer, a bimodal risk assessment layer, a dynamic decision-making layer, a trajectory optimization layer and a feedback optimization layer. The environment sensing layer adopts a retina fovea centralis imitating mechanism to perform non-uniform sampling on laser radar point cloud data to generate dynamic point cloud partitions; the bimodal risk assessment layer fuses two types of radar data to generate static and dynamic obstacle risk assessment diagrams; the dynamic decision-making layer establishes space-time mapping and generates an obstacle confidence coefficient matrix through a graph neural network; the trajectory optimization layer converts the matrix into a control parameter based on a multi-objective evolutionary algorithm, and issues the control parameter through a time-sensitive network protocol; and the feedback optimization layer monitors environment change, calculates deviation, generates an effectiveness index, and dynamically adjusts a point cloud acquisition strategy until the index is optimal. According to the system, the autonomous obstacle avoidance capability and adaptability of the ground mobile unmanned equipment in a complex environment are enhanced.

The invention discloses an unmanned vehicle intelligent obstacle avoidance method and system based on a multi-modal sensor, and relates to the field of automatic driving and intelligent traffic. According to the technical key points of the invention, data of a laser radar and a depth camera are aligned through a space-time registration module; the laser radar point cloud is preprocessed, obstacle point cloud is segmented, fusion clustering is carried out in combination with three-dimensional semantic seed points output by a depth camera, and an obstacle cluster with a semantic tag is generated; and calculating a three-dimensional directed bounding box (OBB) of an obstacle, dynamically adjusting a safety distance according to the speed of the vehicle, expanding the OBB of the vehicle, and carrying out collision detection by adopting a separation axis theorem. When a collision risk is detected, the system sequentially triggers a first-level early warning instruction and a second-level emergency braking instruction, and safe and efficient unmanned vehicle dynamic obstacle avoidance is achieved. According to the method, the advantages of multiple sensors are fully utilized, the obstacle detection precision and the response speed are effectively improved, and reliable technical support is provided for application of the unmanned driving technology in a complex road environment.

The invention discloses an intelligent geographic surveying and mapping data processing analysis method and system, and the method comprises the steps: obtaining satellite image data, LiDAR point cloud data and unmanned aerial vehicle aerial photography data, obtaining multi-source heterogeneous surveying and mapping data, building a DC-CNN dual-channel convolutional neural network model, adding a cross-channel feature fusion module on the basis of a model spectrum channel and a geometric channel, and carrying out the multi-source heterogeneous surveying and mapping data fusion. And utilizing a convolutional layer in a depth separable convolution substitution model, inputting the fused multi-source heterogeneous surveying and mapping data into the target DC-CNN network model for identification, and performing association modeling on the surveying and mapping feature data, topographic parameters and environmental data to obtain a multi-scale geographic knowledge map. The extraction efficiency of the model on key features is improved, so that the model has better real-time performance and expandability while keeping high precision.

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

The invention relates to the technical field of BIM, in particular to a BIM-based construction progress intelligent management and control method and system, and the method comprises the steps: constructing a BIM digital twin cloud model, fusing a panoramic image and LiDAR point cloud data, dynamically selecting a data source update model through an algorithm, extracting engineering topology, building a construction network diagram, and generating a state evolution trajectory in combination with environment and resource data. Predicting and visualizing milestone time, comparing field data with a BIM model to generate progress deviation information, performing stability analysis, triggering resource allocation when a threshold value is exceeded, synchronizing a material supplier and a field manager, dynamically adjusting personnel, equipment and materials, dynamically adjusting a milestone plan based on multiple data, and presenting and guiding allocation through the BIM model. The construction progress is ensured to be consistent with the plan, and the complex interaction relationship and dynamic characteristics in the construction process are captured through nonlinear dynamic system modeling in combination with LiDAR point cloud and other high-precision data.

The invention provides a cross-modal adaptive matching online external parameter calibration method and system for a laser radar and a camera, and belongs to the technical field of online external parameter calibration of the laser radar and the camera, initial external parameters are obtained by using a static calibration result, a laser radar point cloud is projected to a visual angle of a virtual camera, an LIP image aligned with a camera image is generated, and a calibration result is obtained. Performing cross-modal segmentation on the LIP image and the RGB image, extracting a semantic mask, generating a cross-modal feature point pair through a strategy, and performing preliminary external parameter calculation in combination with a PnP algorithm; global optimization is carried out through a multi-modal loss function, and an inter-frame smoothing and increment optimization strategy is introduced to ensure accurate estimation of external parameters. Finally, the optimized external parameter matrix is applied to projection and matching of subsequent frames, and can adapt to environment change and data dynamic update in real time. The method has high precision and real-time performance, overcomes the defects that off-line calibration depends on a specific calibration target and cannot cope with dynamic environment changes, and adapts to application requirements in high-speed motion and complex environments.