376 results about "Map matching" patented technology

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.

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 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 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 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.

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.

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.

The invention belongs to the technical field of road network generation, and discloses an overpass multilayer structure generation method considering a directed topological connection relationship, which comprises the following steps: obtaining overpass track data, generating a density grid map and a skeleton line based on the data, and obtaining a road geometric shape; defining a direction domain and center line rule, matching the direction cluster density grid map to the direction domain, and vectorizing according to the rule to obtain a directed center line in each direction; connecting the direction lines by the integral track center line to obtain an initial overpass map; setting an entrance and exit range, and screening a high-confidence track. And in combination with the high-confidence trajectory, matching the initial map with a map to obtain trajectory point road matching information. And carrying out topological check and correction on the matched information, and finally generating the directed three-dimensional vector flyover road. According to the method, the problem of complexity of overpass generation in road network construction is solved, information such as geometric morphology, topological connection and road direction of the overpass is extracted, and powerful support is provided for automatic generation of the road network.

The invention discloses a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation System) / map tightly integrated navigation positioning method and system based on road marking matching, and belongs to the technical field of navigation positioning, and the method comprises the following steps: acquiring priori road map data, foresight road image data, a GNSS original observation value and an INS original observation value; a semantic segmentation network is adopted to extract road marking semantic features in the foresight road image data, and a road marking feature instance is obtained; performing nearest neighbor search on the road marking feature instance and map elements in the prior road map data, and obtaining corresponding map matching observation based on a nearest neighbor search result; and coupling the GNSS original observation value, the INS original observation value and map matching observation to obtain a navigation positioning result. According to the method, a tight combination model based on GNSS / INS and map matching original observation values is provided, the positioning performance of a navigation system in a complex city scene is improved, and omni-directional pose constraint is provided for a vehicle.

The invention discloses a quadruped robot space precise positioning method and system based on deep learning. The quadruped robot space precise positioning method specifically comprises the steps that S1, a laser radar data stream and an inertial measurement unit data stream are aligned to generate a synchronous sensing data sequence in a unified mode; s2, determining a mapping pose sequence through consistency evaluation of poses output by the offline point cloud registration algorithm pool; s3, performing parallel loopback detection to generate a unified loopback constraint set; s4, the mapping pose sequence, the loopback constraint set and the pre-integration constraint are written into the factor graph model to generate a three-dimensional point cloud reference map; s5, the local point cloud frame and the three-dimensional point cloud reference map are registered, and a map matching pose is output; s6, the map matching pose and the pre-integration result are written into a factor graph model to continuously solve and output a continuous-time six-degree-of-freedom pose result; and S7, outputting a six-degree-of-freedom pose result in continuous time. According to the invention, direction constraint consistency point cloud registration and dual-channel loopback optimization are introduced, and stable and high-precision positioning of the closed space is realized.

The invention discloses a hydropower station corridor autonomous mobile robot path navigation method, system and device and a storage medium, and the method comprises the steps: collecting hydropower station multi-source environment data, constructing a laser two-dimensional map, and fusing image semantic segmentation labels to generate a two-dimensional semantic map; the current sensor data is matched with the two-dimensional semantic map, and the initial pose of the robot is determined; a navigation path is generated on the basis of the initial pose and the target point, and the pose of the robot is dynamically adjusted and updated in the navigation process; path planning control is carried out based on the updated pose, obstacles are recognized in real time, and a driving path is optimized; recording path driving navigation data, updating the two-dimensional semantic map, and returning to the starting point based on the updated map after the task is completed. According to the method, a two-dimensional semantic map with space consistency and semantic definition is constructed, key geometric structure extraction and navigation structure index generation are combined, accurate recognition and path topology understanding of passable areas in the hydropower station gallery are achieved, and the accuracy and robustness of autonomous path navigation are improved.

The invention discloses an unplanned operation early warning method and system fused with multi-source data mining. According to the method, vehicle tracks, operation plans and electronic fence data are collected, cleaning and map matching preprocessing are carried out on the tracks, and then track sequences are mapped into low-dimensional embedded vectors through a track representation learning model; recognizing an abnormal behavior mode through clustering based on the vector, and constructing a dynamic risk index and predicting the future state of the vehicle in combination with real-time data; inputting the abnormal mode, the risk index and the prediction result into a fuzzy logic decision maker for multi-source fusion, outputting a comprehensive risk level and triggering graded early warning; a reinforcement learning mechanism is adopted to dynamically optimize decision maker parameters according to the early warning effect, and finally early warning information is pushed to a management platform and feedback is received to form closed-loop management. According to the invention, accurate and adaptive early warning of unplanned operation behaviors is realized, and the intelligent level of operation safety supervision is significantly improved.

The invention belongs to the field of tourism data processing, and provides a GIS-based culture and tourism spatio-temporal feature fusion system, which comprises a data acquisition module used for acquiring culture and tourism spatio-temporal data; the data fusion module is used for establishing semantic association between the cultural tourism data based on the knowledge graph; the GIS topological structure construction module is used for constructing a topological graph of the culture and tourism data and constructing a topological relation between the culture and tourism data based on the similarity of the topological graph and the semantic association relation; the culture and tourism time-space feature fusion module is used for constructing a culture and tourism fusion map; matching the tourist trajectory data with the cultural tourism resources; determining an influence range of the cultural activity; the prediction module is used for predicting a tourist diffusion thermodynamic diagram of a preset destination in a preset time; and the visual display module is used for displaying the cultural tourism fusion map and dynamically displaying the tourist diffusion thermodynamic diagram on the cultural tourism fusion map.

The present invention discloses a mobile robot navigation system based on a solid-state lidar and a point cloud map, comprising: a point cloud map preprocessing module for generating an occupancy grid map for path planning; a lidar inertial odometer module for removing motion distortion from lidar point clouds and providing a relatively accurate odometer; a map matching and positioning module for matching the accumulated distortion-removed point clouds with the point cloud map to update the pose of the robot in the map system; and a path planning module for comprehensively planning a path to a target point based on map and positioning information. The present invention solves the contradiction that the two-dimensional grid map does not describe the complex environment completely, and the three-dimensional point cloud map is not convenient for path planning of mobile robots. By integrating the tightly coupled odometer of the solid-state lidar and the IMU and the method of matching and updating the pose with the point cloud map, the positioning and navigation of the robot have high precision and robustness in complex scenarios.

An apparatus for estimating a position in an automated valet parking system includes a front camera processor processing a front image of a vehicle, a surround view monitor (SVM) processor recognizing a short-distance lane and stop line by processing a surround view image of the vehicle, a map data unit storing a high definition map, and a controller downloading a map including an area set as a parking zone from the map data unit when the entry of the vehicle to a parking lot is identified and correcting a position measurement value of the vehicle by performing map matching based on results of the recognition and processing of the front camera processor and SVM processor and the parking lot map of the map data unit when an automated valet parking start position is recognized based on the recognized short-distance lane and stop line.

The invention provides a map matching method and device, equipment, a medium and a product, and relates to artificial intelligence, in particular to the field of automatic driving and the field of map navigation. The specific implementation scheme is as follows: for a track point of a to-be-matched track, determining the emission probability of the track point relative to each road section in a target road section combination in a map; wherein the target road section combination is a combination formed by all road sections meeting a preset rule in the initial candidate road sections of the track point, and the combination formed by all the road sections meeting the preset rule comprises a continuous road section in which the tail part of the previous road section is connected with the head part of the next road section; determining candidate road sections of the road section combination according to the emission probability; and according to the candidate road sections, determining a matched road section sequence corresponding to the to-be-matched track in a map. Through the method, the map matching efficiency can be effectively improved, and the matching calculation amount is reduced.