457 results about "Map matching" patented technology

The invention discloses a trajectory prediction method and device based on a time-space diagram neural network learning model, and the method comprises the steps: carrying out the data cleaning and trajectory compression based on to-be-classified original trajectory data, and outputting a compressed grid sequence; based on the grid sequence, static track information classification is executed, and track static attributes are extracted; dynamic space-time image track classification is executed, and track dynamic space feature attributes are extracted; the method comprises the following steps: pre-constructing a directed weighted network based on a driving road section connectivity attribute feature, and completing training of a trajectory prediction model based on a trajectory static attribute, a dynamic space feature attribute and the directed weighted network; and based on the trajectory prediction model, inputting spatial-temporal characteristic data of a trajectory to be predicted, and obtaining an optimal trajectory path prediction result. According to the method, uncertain track geographical background information on the road network can be effectively utilized, and meanwhile, errors caused by map matching are avoided.

The invention belongs to the technical field of guidance. The invention provides a self-localization method based on visual inertial feature map multi-source fusion. Comprising the following steps: performing visual inertia fusion positioning on IMU information and visual image information to obtain a visual inertia fusion result; performing feature map matching positioning on the visual inertia fusion result to obtain a feature map matching result; and dynamically correcting the visual inertia fusion result and the feature map matching result to obtain a visual inertia feature map multi-source fusion positioning result. According to the embodiment of the invention, the feature map matching result and the estimation result of visual inertia fusion are dynamically corrected through error state Kalman filtering, and collaborative optimization of global and local information is realized. Performing effective correction on the drift of visual inertial positioning by using global constraints of the feature map; in a dynamic or unknown scene, continuity and real-time performance of positioning are maintained through visual inertia fusion, so that adaptability and stability of the system in a complex dynamic environment are enhanced.

The invention discloses an urban cross-domain multi-mode network space modeling method, and belongs to the technical field of traffic simulation. In order to solve the problem that urban traffic cross-domain spatial modeling is complex, the method comprises the steps that road, hub and track BIM models are classified, traffic semantics in different BIM objects in the BIM models are recognized, and traffic elements are extracted; geometric element abstraction is carried out, spatial topology connection relations and attribute information of geometric elements are defined, and single-mode traffic network model modeling is carried out; coordinate conversion is carried out, and the relative position coordinates of the single-mode traffic network model are converted into 1984 world geodetic surveying geographic coordinates; a walking traffic network is used as an intermediate network, a walking traffic mode and transfer rules of roads, rails, buses and hubs are used as strategies, network fusion is achieved through a map matching algorithm, and a multi-mode network space is modeled; and carrying out topological structure verification on the obtained multi-mode network space. According to the invention, full-link calculation from design to simulation is realized.

Accurately identify a location based on images, even indoors SOLUTION: In this system, a control unit 22 of a server 20 that acquires an image captured by a terminal device 10 identifies the position of the terminal device. The server includes a database 23 that stores a large number of 3D maps showing each area and feature data related to the 3D maps. The feature data includes vectorized images of the 3D maps and related text information, context information of the 3D maps, etc. A map narrowing-down unit 222 of the control unit vectorizes the acquired images, etc., searches the vectors against feature data stored in the database, and also provides the acquired images and context information of the 3D maps to the LLM to narrow down the 3D map candidates (eliminates 3D maps with similar shapes). A map matching unit 221 matches the shapes of the narrowed-down 3D map candidates to accurately identify the position and orientation.

The invention discloses a space-ground integrated dynamic traffic flow analysis and road topology reasoning method and device. A cloud server generates and issues real-time traffic information through multi-level processing such as map matching, traffic flow analysis, prediction and early warning and road topology analysis and updating; specifically, firstly, the high-precision positioning and communication capability of a low-orbit satellite is utilized, data fusion of an inertial measurement unit and a wheel speed sensor is combined, and a multi-source fusion algorithm is constructed to improve the positioning precision; secondly, traffic flow dynamic analysis and prediction are carried out based on a Transform interaction analysis model, and real-time updating of a road topological structure is realized at a cloud end by using a clustering algorithm and a graph theory model; finally, the traffic flow and road topology information is sent to related vehicles and a traffic management system, and real-time monitoring, early warning and optimization of the traffic state are achieved. According to the invention, the accuracy and real-time performance of traffic information acquisition, processing and issuing can be effectively improved, and the intelligent level of traffic management and decision making is enhanced.

The invention aims to provide a speed anomaly detection and fraud identification method and system based on trajectory data, and belongs to the technical field of road traffic safety, the method realizes abnormal trajectory detection and fraud identification through multi-stage data processing: firstly, preprocessing original trajectory data, and removing invalid data; noise points are filtered based on a DBSCAN algorithm; track segments are divided according to vehicle speed changes; matching the moving track segment to a map road through a map matching algorithm; calculating an error ratio between the calculation speed and the equipment uploading speed, and identifying an abnormal track segment; and finally, analyzing the speed distribution of the abnormal track section, and identifying a counterfeit behavior. The system comprises a data preprocessing module, a trajectory noise filtering module, a trajectory division module, a map matching module, an abnormal speed detection module and a speed verification and forgery identification module. According to the method, the accuracy of speed anomaly detection is improved, the recognition capability of a hidden speed forgery behavior is enhanced, and the authenticity and credibility of trajectory data are improved.

The invention discloses an autonomous navigation positioning system and method for an autonomous vehicle, and belongs to the technical field of automatic driving, and the method specifically comprises the steps: receiving a satellite signal through a GPS receiver, preliminarily determining the position of the vehicle, starting a vehicle sensor to carry out real-time measurement, and preprocessing a result; the preprocessed measurement result is converted into position information in a global coordinate system, and the position and attitude of the vehicle are calculated in combination with a sensor fusion algorithm; generating point cloud data by using a laser radar, matching the point cloud data with a high-precision map to determine the accurate position of a vehicle, and identifying obstacle and road features; a vehicle-mounted camera captures an environment image, extracts visual feature information and fuses the visual feature information with environment perception information; on the basis of fusion information and distance information, the obstacle and road conditions are sensed in real time, the optimal driving route is determined by adopting a path planning algorithm, visual navigation and automatic obstacle avoidance are realized, and the safety and reliability of automatic driving are improved.

The invention relates to a dynamic visual map matching autonomous optimization method based on IMU sensing, and belongs to the technical field of positioning and navigation of autonomous mobile equipment. The method comprises the steps of obtaining IMU high-frequency motion data and visual image data, performing noise reduction to obtain carrier short-time attitude change information, performing semantic segmentation on an image to recognize a dynamic target, extracting feature points and performing dynamic target weight evaluation; calculating confidence degree scores of the feature points, and mapping fusion weights of the IMU and the vision through a self-adaptive credibility distribution mechanism; s3, predicting a trajectory and a potential matching area based on carrier short-time attitude change information, incrementally updating a local map, and matching by taking an IMU trajectory as a constraint to obtain an initial result; and detecting the matching validity, reducing the re-matching range by using an IMU short-time track during mismatching, and correcting an initial value to obtain a final result. The map matching precision and stability in a complex dynamic scene are improved, the real-time performance and continuity are balanced, and the problems that a traditional scheme is redundant in calculation power and slow in mismatch recovery are solved.

The invention relates to a deep learning algorithm, and provides an end-to-end sparse trajectory recovery method based on a road network, which is realized based on a framework of a generator and a discriminator, and is characterized in that the generator recovers an input sparse trajectory into a complete trajectory with a high sampling rate; the discriminator evaluates the authenticity of the input track and is used for guiding the generator to update parameters; the fusion of trajectory recovery and map matching is realized in an end-to-end manner through a deep learning technology, and parameter updating of a generator is optimized through a GAN (Generative Adversarial Network) framework, so that the balance between the completeness of the recovered trajectory and the accuracy of single-step interpolation is realized. According to the invention, the precision and speed of track recovery can be obviously improved.

The invention discloses a multi-target track association method and system based on interactive attention map matching and a storage medium, and the method comprises the steps: preprocessing track data, and obtaining an input track; utilizing a track historical feature extraction network to obtain embedded features of each track; a multi-graph structure is utilized to model tracks, nodes represent one track, edges represent message passing between the tracks, and the number of the edges is limited through graph cutting; features of neighbor nodes are aggregated by using a self-attention mechanism and a cross attention mechanism, and finally embedded representation of the nodes is obtained; and constructing a score matrix, and performing a Sinkhorn algorithm on the matrix to obtain an association probability matrix. And track association is carried out by using the multi-target track association model based on interaction attention map matching, and an association task is completed. According to the method, the spatial-temporal characteristics of the track can be fully mined, the track association accuracy is improved, and the track association error rate is reduced.

The invention relates to the technical field of charging piles, and provides a power factor correction method and system for a high-power charging module, and the method comprises the steps: obtaining a power grid input voltage, a power grid input current and an output load current, constructing an input power fluctuation map and a load dynamic response map, carrying out the subgraph matching and joint convolution, and obtaining a power factor correction result; and obtaining a response change index table and a fitting weight vector, generating a dynamic current regulation vector by using the response change index table and the fitting weight vector, and performing phase correction and waveform reconstruction on the power grid input current according to the dynamic current regulation vector. Through phase correction and waveform reconstruction, the amplitude and phase information of the power grid input current are strictly aligned, the power factor of the high-power charging module is improved, the harmonic interference of the input side is reduced, the electric energy transmission efficiency and the module operation stability are improved, and the problem that in a complex power grid environment, the power consumption of the high-power charging module is reduced is solved. The problem that the correction effect of the power factor is not stable due to the fact that fast-changing input current and voltage are difficult to deal with is solved.

The invention discloses a high-frequency vehicle track map matching method, which comprises the following steps of: (1) dividing a track T to be matched into a plurality of sub-tracks based on a reference line distance, and extracting key track points in the sub-tracks; (2) extracting a road where the key track point is located and a road type of the road, filtering the extracted road based on the road type, and storing filtered road data by adopting an R-Tree structure; and (3) matching an optimal matching path of the track T to be matched from the road data stored in the R-Tree structure through a hidden Markov probability matching model. From the perspective of reducing the scale of original data, trajectory and road network data are simplified. Then, space and direction analysis is carried out on the simplified trajectory, a micro-region splicing strategy is adopted to accelerate shortest path search, and a path deviation function is designed to improve the topology of a matching result; in addition, the extraction process of track motion key points is refined by fusing more road semantic information, so that a better matching result is obtained.

The invention discloses a heaven and earth integrated multi-domain fused automobile high-level automatic driving system, which realizes more comprehensive and non-blind area sensing and positioning of an automatic driving automobile through multi-domain information fusion of a satellite platform, a cloud platform, an automobile end platform and the like, and promotes comprehensive upgrading of comprehensive efficiency of an intelligent traffic system. Based on cloud error broadcasting and a high and low orbit satellite fusion enhancement technology, the problem of automobile communication loss caused by the fact that single ground communication is limited by base station coverage is solved; based on a cloud reinforcement learning architecture, the communication network stability of the space-ground integrated system is improved through adaptive switching of a communication network; a road semantic map and a decision control experience pool are established based on vehicle high-precision trajectory data collected by a platform, and the limitation of the perception ability of a bicycle is made up through cloud federal learning scene experience sharing; the vehicle end dynamically updates a cloud road semantic map through local map matching; and information support is provided for realizing a chassis multi-domain fusion technology based on high-precision positioning.

The invention discloses an unmanned aerial vehicle visual positioning method and system based on satellite image map matching, and belongs to the technical field of unmanned aerial vehicle positioning and image processing. A plurality of image pairs are formed by a plurality of obtained satellite image maps and unmanned aerial vehicle images to be matched; determining a matching number corresponding to the feature points in each image pair, and obtaining a satellite image map corresponding to the maximum matching number; obtaining a matching error of the feature points of the satellite image map corresponding to the maximum matching number, and when the obtained matching error of the feature points of the satellite image map corresponding to the maximum matching number is smaller than a preset matching error threshold value, determining the satellite image map after matching error screening; and according to the satellite image map and the unmanned aerial vehicle image after matching error screening, obtaining corresponding pixel coordinates and latitude and longitude coordinates of the unmanned aerial vehicle image in the satellite image map by determining a homography matrix between the two images. Through the method, accurate positioning of the unmanned aerial vehicle can be realized.

Proposed is a mobile robot utilizing the uncertainty of map matching poses for localization. The mobile robot includes a point cloud sensor which acquires point cloud data through scanning, a matching pose calculation part which applies a pre-registered point cloud map and the point cloud data to a pre-registered scan-map matching algorithm to calculate a map-matching pose of the mobile robot, and an uncertainty estimation part which estimates uncertainty of the map-matching pose on the basis of a probability distribution of a preset pose space around the map-matching pose. Through this, the uncertainty of the map-matching pose is estimated to evaluate the reliability of the map-matching pose, enabling more precise localization.

The invention discloses a map updating method, a robot and a storage medium, and the method comprises the steps: carrying out the probability rapid increase and probability slow attenuation of a neighbor key frame and a global map according to a current frame, and then updating a target global map according to the neighbor key frame and the observation probability of a position point in the global map. In the operation of rapid probability increase, the newly-added content in the map has a larger probability increase gradient, so that the newly-added position point in the map is more rapidly kept consistent with the global map, and the problems of positioning deviation and loss caused by inconsistency of the actual environment and the global map are reduced. In the operation of probability slow attenuation, a smaller probability attenuation gradient is set for a map position point, the retention period of historical information by the map is prolonged, the recall rate of a map matching algorithm during subsequent robot positioning is improved, and the influence of positioning deviation caused by inconsistency of an actual environment map and a global map is further reduced.

The invention discloses a path planning method and system based on Beidou satellite positioning, and relates to the technical field of Beidou satellite positioning, and the method comprises the steps: carrying out the high-precision positioning and error correction of Beidou satellite signals through employing a multi-source sensor fusion method, and obtaining the position information; matching the electronic map data with the position information by adopting an electronic map matching method to obtain electronic map data corresponding to the current position; constructing a traffic flow prediction model based on the position information and a machine learning algorithm, and inputting the position information into the traffic flow prediction model to obtain a traffic condition prediction result; based on the traffic condition prediction result, planning a path from the current position to the destination by adopting a path search algorithm to obtain a preliminary path; performing personalized adjustment on the initial path according to user preference setting to obtain a recommended path; and monitoring the final recommended path by adopting a real-time monitoring and feedback mechanism, evaluating the position change and traffic condition in the path, and obtaining a final path planning strategy.

A localization method and apparatus for use in memory parking, a device, and a medium. The method comprises: acquiring a pose of a vehicle relative to an initial position on the basis of a vehicle body sensor; on the basis of the pose of the vehicle, extracting perceptual semantic information of the surrounding of the vehicle at the current moment, and performing map-based matching with map semantic information of the surrounding of the vehicle to obtain pose differences between the perceptual semantic information and the map semantic information; and correcting the pose on the basis of the pose differences to obtain an accurate pose.

The invention discloses a method for calibrating laser optical axis deviation through map matching in the air. Relates to the technical field of photoelectric pod error calibration, in particular to the technical field of calibration of laser optical axis deviation through map matching in the air. The method effectively solves the problems that an existing calibration method needs precise calibration equipment, and secondary deviation in subsequent long-time use is difficult to avoid, and improves the efficiency of calibrating the deviation of the laser optical axis. The method comprises the following steps: aligning data time sequences of an airplane and a pod; preparing map slices around the airport; acquiring a real shot image set and a real shot data set containing a target, and preprocessing the real shot image set; obtaining the real longitude and latitude height of the image center through map matching; and reversely deducing the deviation of the laser optical axis. The method can be applied to the field of photoelectric pod error calibration.

The invention discloses a multi-scale map-based rapid relocation method for an unmanned logistics vehicle in a dynamic factory, and the method comprises the following steps: S1, filtering point cloud dynamic elements based on binary semantic segmentation of a deep convolutional neural network, S2, processing static point cloud, and S3, carrying out the rapid relocation of the unmanned logistics vehicle based on global and local map search. According to the method, movable elements in the cloud are filtered based on the deep convolutional neural network, and interference of a dynamic target on subsequent vehicle positioning is eliminated. The method is also combined with a GOD map descriptor to process the static point cloud, so that the subsequent positioning step is clear and rapid. According to the method, local map matching and global map matching are combined, and rapid relocation of the unmanned vehicle is achieved.

The invention discloses an industrial automobile crane positioning and mapping method and device based on multi-sensing fusion, and relates to the field of machine sensing, and the method comprises the following steps: initializing a pose and offset of an inertial measurement unit by using an input laser point cloud and a pre-integration result of the inertial measurement unit; correcting laser point cloud motion distortion by using an inertial measurement unit, extracting a dynamic object point cloud, and synchronously extracting laser features and visual features with depth; calculating a key frame pose in real time by minimizing a visual re-projection error and an inertial measurement unit pre-integration error, and performing frame-to-map matching by taking the key frame pose as an initial value to further optimize the pose; a visual word bag model is utilized to quickly retrieve a closed-loop candidate frame, after laser passes geometric matching to verify a closed loop, a laser radar factor, a visual factor, an inertial measurement unit factor and a closed-loop factor are fused, a global factor graph is constructed and optimized, and a pose and point cloud map is updated. According to the method, the environment sensing capability of the industrial automobile crane can be improved, and the sensing accuracy and robustness are improved.

The invention discloses a violation person identity recognition method for gas turbine power plant on-site safety supervision, relates to the technical field of person identity recognition, and aims at recognizing specific violation behaviors, positioning the occurrence time and space of the violation behaviors and marking corresponding behavior subjects. The method comprises the following steps: acquiring operation image data, extracting behavior subject image features and equipment acquisition data, generating fusion identity information, matching violation identities, outputting structured records, evaluating violation risk levels, verifying the identities of persons in charge and filing violation records, and automatically identifying dangerous behaviors in a gas turbine power plant through the operation image data acquisition and violation behavior identification model. And in combination with the dynamic attitude vector sequence and equipment acquisition data, generating identity feature fusion data, performing map matching, identifying the identity of a violation person, labeling through a risk level label, pushing to a responsible person terminal, completing identity confirmation and violation behavior recording and archiving, and realizing whole-process supervision and closed-loop management.

The invention discloses a degradation scene-oriented multi-residual fusion laser radar positioning method, which comprises the following steps of: firstly, performing state prediction by adopting an iterative extended Kalman filtering framework and an IMU (Inertial Measurement Unit), and constructing three complementary observation models of a global map matching residual, a local point-to-plane geometry residual and a luminosity residual; secondly, designing a degradation sensing mechanism based on a covariance ellipsoid, representing absolute and relative degradation degrees through a condition number and an information entropy respectively, realizing quantitative evaluation of system observability, and dynamically adjusting fusion weights of observation residuals; meanwhile, a self-adaptive weight strategy based on luminosity Jacobi intensity is introduced; and finally, performing anomaly detection through deviation comparison between the IMU predicted pose and the IEKF estimated pose, inhibiting pose jump, and ensuring continuity of a positioning time sequence. The method effectively overcomes the challenges of geometric constraint deficiency, positioning drift accumulation and the like of the LiDAR positioning system in the geometric degradation environment, does not need to adjust parameters for a specific scene, and improves the precision, robustness and real-time performance of global positioning in the degradation environment.

The invention provides a dynamic environment positioning method based on a laser radar and an IMU (Inertial Measurement Unit). The method comprises the following steps: step 1, obtaining a current environment point cloud through a robot front-end odometer; 2, matching the current environment point cloud with an off-line map, and determining the difference between the current environment point cloud and the off-line map; 3, judging whether the difference value exceeds a preset threshold value or not; if the difference degree does not exceed a preset threshold value, it is considered that the current scene is basically consistent with the environment, no large change occurs, and the environment point cloud is transferred to a robot coordinate system and stored in a real-time local map based on incremental voxels; if the difference degree exceeds a preset threshold value, it is considered that the current scene and the environment change greatly; the environment point cloud is matched with a real-time local map, and positioning information at the moment is obtained on the basis of an initial pose obtained through IMU pre-integration. According to the method provided by the invention, accurate positioning in a dynamic environment can be realized, the positioning stability of the autonomous platform is improved, and the occurrence of a positioning jump condition is prevented.

A positioning method and a related device are applied to the technical field of positioning. The electronic device may acquire a map of the travelable space, the map indicating a screen of the travelable space. In the positioning process, the electronic equipment can obtain the first picture, the obtained picture is matched with the map of the marching space to determine which position the currently obtained picture is in the marching space, and therefore the position of the electronic equipment is determined. Therefore, positioning can be carried out in real time based on the picture acquired by the electronic equipment, and the accuracy and the real-time performance are relatively high. Moreover, the scheme is simple in operation process, shortens the operation time of the positioning process, can remarkably improve the use experience of the user during positioning, and achieves the instant-on and instant-use.

A vehicle localization system and method includes acquiring, from a first sensor, first data indicative of a first navigation state of a vehicle; acquiring, from a second sensor, second data indicative of an environment in the vicinity of the vehicle; generating, using a processor communicatively coupled to the first sensor and the second sensor, a second navigation state based on matching the second data to a known map of the environment; and, generating, using the processor, a current navigation state based on the first navigation state and the second navigation state.

The invention relates to an unmanned aerial vehicle navigation decoy defense method based on visual map matching, and solves the technical problems that a traditional relative positioning mode is easy to cause positioning drift and an unmanned aerial vehicle is easy to decoy and interfere under long-term and large-scene conditions. The absolute positioning method based on the scene coordinate regression network is adopted, positioning drift is avoided in mechanism, and the scene coordinate regression network based on deep learning is used for performing positioning information mapping on the visual image and the scene coordinates. And inputting a visual image shot by the unmanned aerial vehicle into the optimized scene coordinate regression network to obtain a scene coordinate predicted value, and calculating the scene coordinate predicted value by using an RANSAC algorithm to obtain a pose estimation result. And comparing the pose estimation result with global navigation satellite system (GPS) positioning information received by the unmanned aerial vehicle to complete positioning signal decoy recognition, so that the unmanned aerial vehicle can effectively defend against GPS decoy signals.

The invention discloses an end-to-end automatic driving long tail scene data acquisition and automatic labeling method. The method comprises the following steps: firstly, collecting multi-modal time series data including images, point cloud and odometer information; thirdly, calculating a comprehensive long-tail scene judgment coefficient by constructing an occupation map and fusing perception uncertainty and prediction uncertainty, thereby automatically identifying a high-risk and high-uncertainty long-tail scene; then, only for the data judged as the long-tail scene, starting a particle filtering-based track truth value generation process, fusing the motion speculation of the odometer and the map matching of the laser radar point cloud in the process, and carrying out state prediction, observation updating, resampling and weighted averaging to obtain a track truth value of the long-tail scene; and generating a high-quality self-vehicle track point sequence conforming to the vehicle kinematics constraint as a truth value label. According to the method, accurate screening and true value automatic generation of the long-tail scene are realized, the data acquisition efficiency and the labeling quality are improved, and the safety and generalization ability of an end-to-end automatic driving model in a complex scene are enhanced.

The invention relates to the technical field of intelligent traffic, in particular to a traffic abnormal event dynamic detection method and system based on trajectory analysis, and the method comprises the steps: receiving original space-time trajectory data; preprocessing the original spatio-temporal trajectory data, and mapping the original spatio-temporal trajectory data to a road section sequence of a road network through a map matching algorithm; constructing a behavior model based on the track sequence after map matching, learning the track sequence of the normal behavior mode on the road section, and establishing an observation emission model and a state transition matrix; in the online stage, the log-likelihood value of an observation sequence and / or the similarity between the observation sequence and a normal behavior pattern cluster are / is calculated for a real-time track in a set sliding window, and when the log-likelihood value and / or the similarity exceed a set threshold value, the track is marked as abnormal; performing time-space aggregation on abnormal trajectories of the same road section or intersection in unit time according to single vehicle abnormality judgment, and triggering group abnormal event alarm when aggregation data exceed a preset value.

The invention discloses an improved particle filter map matching method based on rail transit 5G positioning, and the method mainly comprises the steps: 1) collecting original trajectory data obtained through 5G positioning, carrying out the sliding average processing according to a preset sliding window, generating a smooth new trajectory point sequence, and reducing the data redundancy; 2) constructing an improved particle filter map matching algorithm on the basis of the new track points, combining with subway tunnel fine map structure generation, considering space prior information such as a track center line and a track boundary, endowing particles with differentiated weights through a track center line judgment function and a track area judgment function, and determining the track area according to the differentiated weights; high-robustness matching of the track points and the tunnel environment is realized; and 3) in a particle filtering iteration process, setting an effective particle number threshold value, triggering a resampling operation when particles are seriously degraded, selecting a new particle set in a probability mode, avoiding falling into a degradation endless loop, and outputting a state posteriori estimation result in combination with a particle weight to generate an accurate trajectory.