217 results about "Map matching" patented technology

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 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 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 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 discloses a sparse GPS map matching method based on confidence and intelligent fragment retrieval, relates to the technical field of map data processing, and realizes real-time adaptive adjustment of weights along with scenes by constructing a three-dimensional dynamic confidence model fusing road complexity, track point quality and historical matching popularity; by constructing a quaternary composite trigger logic of a confidence threshold value + track point spacing + road section topology complexity + GPS signal quality, false triggering or missing triggering caused by a single threshold value is avoided; data reliability is complemented by establishing a credibility scoring system (fusing matching success rate, use frequency and time freshness) of historical track fragments and performing two-dimensional sorting according to similarity and credibility during retrieval; the method does not need multi-sensor fusion and GPU computing power support, only depends on GPS single-sensor data, realizes balance of high-precision completion and low-redundancy calculation through a dynamic strategy, and adapts to resource-limited scenes such as vehicle-mounted edge equipment.

The invention relates to the technical field of fabricated building cost estimation, and discloses an indoor fabricated design parameter intelligent matching cost estimation method. The method comprises the following steps: constructing a dynamic coupling fabricated component spatio-temporal evolution deduction network for bearing and simulating an interaction and transition process of design parameters in a complete assembly period; based on the network, spatio-temporal trajectory simulation is carried out on historical project data, and a component evolution path sequence containing cost correlation is generated. And establishing a semantic mapping channel from the evolution path sequence to the cost element, and analyzing the sequence into a structured cost influence factor map. And for a design scheme input in real time, the description of the design scheme is injected into the network for deduction, and a target component evolution path is obtained. And carrying out map matching and factor mapping on a target path by utilizing the cost influence factor map, and automatically outputting a refined cost estimation entry. According to the method, accurate and automatic estimation of the dynamic influence cost of the design parameters is realized.

The invention relates to the technical field of non-networked indoor autonomous positioning and navigation, in particular to a smart phone positioning and navigation method and system in a non-networked indoor scene, and the method comprises the steps that a smart phone receives data returned by a Bluetooth AOA positioning base station through point-to-point communication, and an AOA position estimation result is obtained through calculation of a single-base-station AOA fusion positioning model; the smart phone collects real-time data of an IMU and a magnetometer, and user coordinates and course angles are recursively estimated through a pedestrian dead reckoning (PDR) method; according to an AOA base station deployment condition of an actual indoor space, multi-base station centroid positioning, AOA fusion positioning and PDR positioning are automatically switched, so that a position result can effectively cover a whole space scene; the method comprises the following steps: constructing an obtained binary grid map through an indoor map model, carrying out position post-processing based on map matching on positioning coordinates to obtain pedestrian coordinates, and carrying out digitization on a map model to realize map display and finally realize pedestrian position display. The method can be reliably operated in a scene lacking network coverage.

The invention discloses a 5G and Beidou fused vehicle positioning navigation system. The system comprises a multi-source heterogeneous positioning module; a high-precision map and a dynamic traffic information base; a self-adaptive multi-layer map matching module; the dynamic path planning module considers steering limitation and real-time position feedback; and a central information fusion and decision unit. According to the invention, multi-source heterogeneous positioning data such as Beidou / GPS, 5G positioning, high-precision IMU, a vehicle-mounted atomic clock and a barometric altimeter are fused, and a tight combination algorithm based on ESKF or a factor graph is adopted, so that the system can realize accurate positioning in an environment with good satellite signals, partial shielding and even serious loss. Continuous, smooth and high-precision vehicle state estimation can be output, particularly when the number of visible satellites is insufficient, double-satellite positioning can be achieved by using the vehicle-mounted atomic clock to predict the clock error and combining the elevation constraint of the barometric altimeter, and the usability and robustness of positioning are improved.

The invention provides a prototype prior and closed-loop condition augmented diffusion model-based vehicle trajectory completion method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the map matching and standardization processing of a sparse trajectory and a road network, and constructing a multi-subspace trajectory product manifold to form a unified geometric measurement; constructing a prototype priori library based on the historical trajectory, and obtaining hybrid prototype priori through retrieval fusion; a closed-loop condition augmentation diffusion model is utilized, observation masks, intermediate prediction and prototype prior are fused in reverse denoising, a track is recovered step by step, and error accumulation is suppressed through unidirectional information flow constraint and data consistency projection; and performing evaluation and rearrangement on the candidate trajectories based on unified measurement, and generating a high-precision complemented trajectory which is reachable in topology, reasonable in direction and consistent in scale. The method effectively relieves the problems of geometric distortion, lack of travel prior, condition diffusion and stiffness and the like, remarkably improves the track recovery precision, stability and diversity in a complex road network, and is suitable for the fields of intelligent transportation, urban calculation and the like.

The invention discloses a multi-source fusion continuous positioning method in combination with a prior feature map, and aims to solve the problem that an existing vision-inertial odometer easily generates accumulative errors in a GNSS denial environment, the method comprises the following steps: constructing a prior feature map comprising key frame poses, feature point descriptors and 3D coordinates based on an open source algorithm; a track displacement difference alignment strategy is adopted to complete visual-inertial system and GNSS joint initialization, and ESIKF is used to replace EKF to improve fusion precision; robust alignment of a local coordinate system and a map coordinate system is achieved through a single-frame geometric constraint accumulation-batch manifold alignment strategy; based on the reverse PnP, the current 3D point cloud and the historical 2D observation constraint are utilized to resolve the relative pose, the map matching result is used as observation to be fused with the GNSS or replace GNSS observation, the ESIKF loose coupling is utilized to eliminate accumulative errors, and continuous high-precision positioning in the GNSS failure scene is achieved.

The invention relates to a digital intelligent trajectory prediction method and system based on logistics transportation order tracking. The method comprises the steps of collecting multi-source transportation data including vehicle GPS data, AI telephone interaction data, ETC passing record data and freight station transfer operation node data; performing space-time alignment, data cleaning and map matching based on road network information on the multi-source transportation data; utilizing the processed multi-source data to generate a continuous digital intelligent track through an interpolation algorithm and a path deduction method; calculating the latest predicted position of the vehicle through a prediction algorithm model based on the digital intelligent track in combination with real-time traffic information and a historical driving mode; residual paths, real-time road conditions, weather factors and driver behavior modes are comprehensively considered, and the predicted arrival time is calculated through a dynamic ETA model. According to the invention, the GPS signal blind area problem is effectively solved, the whole-course blind-area-free tracking and high-precision arrival time prediction are realized, and the transparency and intelligent management level of logistics transportation are greatly improved.

This application relates to an offline map matching method, apparatus, computer device, and storage medium. The method includes: starting from the starting position of the complete GPS trajectory to be matched, selecting a sliding window according to a set sliding window length, and searching for the shortest path set for each sliding window in the road network based on candidate starting and ending points; matching the local GPS trajectory corresponding to each shortest path to the corresponding road segment in the road network to generate the shortest matching path without backtracking, calculating the score of each matching path, and retaining the top k matching paths with the highest scores; performing local non-shortest path travel detection on the top k matching paths, and using a split matching algorithm to correct the travel trajectories of existing local non-shortest paths. The embodiments of this application effectively enhance the robustness of road network matching for local non-shortest path travel, improving the overall efficiency and trajectory matching accuracy of offline map matching.

The application discloses a mobile monitoring carrier networking optimization method, and belongs to the technical field of mobile computing-based monitoring carrier networking optimization. The method solves the problem of insufficient fixed monitoring network coverage in the traditional mobile monitoring networking method in the prior art. The method forms a vehicle operation track based on a map matching algorithm and a GPS observation sequence, determines a vehicle operation space-time distribution rule, and realizes vehicle track space matching. The method analyzes the spatial regularity of all vehicle operation tracks by using a clustering algorithm, obtains a clustering result, and realizes track identification. Based on the clustering result, a stability index is set, a typical track is defined, and vehicle fleet filtering is realized. The track overlap degree between different vehicles is quantitatively obtained, a mobile detection carrier selection model is constructed in combination with the networking target of a mobile monitoring system, and networking vehicle fleet selection is realized. The application effectively improves the fixed monitoring network coverage, reduces the cost, and can be applied to mobile monitoring system networking optimization.

The invention relates to a safety assessment method for a global navigation satellite system (GNSS)-based train positioning system (GNSS train positioning system for short), and aims to improve the operation safety level of a GNSS train positioning-based train operation control system in a complex environment. According to the method, a modularized and hierarchical structure model is constructed by adopting a colored Petri net modeling tool for a GNSS train positioning system, and a data acquisition and interference injection module, a data fusion module, a map matching module and a safety performance evaluation module are included. And integrating indexes such as the position error, the protection level, the alarm limit value and the like to construct a system safety judgment mechanism. Through simulation analysis of various typical operation scenes, the operation safety state and the error evolution trend of the system under the interference condition can be output. According to the method, whole-process formal modeling and safety evaluation of the GNSS train positioning system are achieved, and the method has good universality, visualization and engineering adaptability and is suitable for a railway train operation control system with high safety requirements.

The application provides a map matching method based on GNSS multipath error to distinguish urban road context environment, relates to global navigation satellite system positioning technology field, and includes the following steps: constructing a virtual city and satellite environment, simulating GNSS signal propagation by using a ray tracing algorithm, and generating a GNSS multipath error distribution heat map; based on error statistical characteristics, the road context environment is divided into multiple error level regions; a buffer region is defined with the GNSS trajectory point to be matched as the center, and then all candidate road segments in which the current GNSS trajectory point of the vehicle is located are screened out; through weighted calculation on the candidate road segments, the road segment with the highest path score is finally selected as the best matching road segment. The application quantitatively measures the spatial distribution of urban multipath error in advance, enhances the adaptive ability of the algorithm to the complex road context environment, effectively suppresses the matching deviation caused by the positioning jump in the high error area, and improves the accuracy and robustness of the map matching.

The invention discloses an end-to-end sparse trajectory recovery method and device based on road network constraint, electronic equipment and a storage medium, and solves the problem of error accumulation of an existing map matching and trajectory completion two-stage method. The method comprises the following steps: acquiring a sparse trajectory and road network data; and constructing a unified end-to-end deep learning model, and deeply coupling map matching and track completion tasks. The core is that after a candidate road segment set is generated for each GPS point and the probability is calculated, a differentiable virtual road segment embedding mechanism is introduced, and a gradient return channel from track completion to map matching is constructed through probability weighted candidate road segment embedding. The trajectory decoding module fuses the spatio-temporal features with virtual road segment embedding to generate a complete trajectory. The model performs end-to-end optimization through a joint loss function, so that a trajectory reconstruction supervision signal directly guides an upstream candidate selection process. According to the method, error accumulation caused by one-way information transmission in a traditional method is avoided, and the global precision, continuity and robustness of track recovery are remarkably improved.

The application provides a fusion road feature information adaptive map matching optimization method and device, can adaptively adjust a window of a to-be-matched trajectory according to current inertial trajectory features, fully utilizes road feature information, and can further improve positioning effect of a map matching device.

A map evaluation method and device are used for evaluating the availability of a high-precision map so as to improve the vehicle positioning performance and improve the driving safety. In the method, n frames of scene images of a map to be evaluated can be acquired, and n is an integer greater than 1; map matching is carried out based on the n frames of scene images, first pose information and second pose information are obtained, the first pose information comprises pose information of a target object output by an inertial measurement unit (IMU) associated with the n frames of scene images, and the second pose information comprises pose information of the target object output by a fusion positioning system; and based on the difference information of the first pose information and the second pose information, obtaining the confidence of the to-be-evaluated map, the confidence being used for representing the accuracy of map matching based on the to-be-evaluated map.

The invention provides a GNSS train positioning method and system based on a two-out-of-three redundant structure, and belongs to the technical field of train navigation positioning. GNSS original observation data are independently received, and positioning fusion calculation and map matching processing are executed; performing multi-layer consistency judgment on GNSS original observation data of three channels, fusion positioning coordinates and map matching results; and on the basis of consistency judgment, outputting a final positioning result or an alarm abnormal state by adopting a majority voting mechanism. The method can effectively improve the positioning reliability and fault-tolerant capability of the GNSS train positioning system in a complex environment, has high system integrity and safety, and is suitable for a train positioning scene with a high safety level requirement.

The invention relates to the technical field of vehicle-mounted navigation, and discloses a lane-level navigation method and system based on live-action fusion and an electronic device. The method comprises the following steps: acquiring a current front road live-action image of a vehicle in real time, and acquiring navigation path planning information and map data of the vehicle; determining a current lane position of the vehicle and a current virtual navigation element to be displayed according to the multi-source information; determining a target display position of the virtual navigation element in the world coordinate system; converting a target display position of the virtual navigation element from a world coordinate system position to a display coordinate system position; and fusing the virtual navigation element subjected to coordinate conversion with the real scene image of the front road, and displaying through a target display device. According to the embodiment of the invention, through AI identification and map matching, virtual navigation elements for lane-level positioning display can be generated, and unprecedented accurate guiding experience is provided for a driver; the high-precision registration of the virtual navigation element and the real scene image of the front road is realized, and the hardware cost is reduced.

The invention relates to an image scanning positioning assembly, a control method thereof and a vehicle positioning device, and belongs to the technical field of vehicle positioning devices.The image scanning positioning assembly comprises a fixing base, a laser radar, a camera, a combined navigation mechanism, a switch and an industrial personal computer; the fixed seat is arranged on a vehicle and is in a pyramid shape with a cavity inside; the laser radar and the camera are arranged at the top of the fixed seat; the combined navigation mechanism is arranged in the cavity; the laser radar, the camera and the combined navigation mechanism are respectively used for acquiring positioning information of a vehicle; the switch and the industrial personal computer are arranged in the cavity, and the laser radar, the camera and the combined navigation mechanism are connected with the industrial personal computer through the switch. The laser radar, the camera and the combined navigation mechanism are arranged on the fixing base on the vehicle in a layered mode and used for positioning the vehicle in different environments so as to adapt to image scanning positioning of the vehicle in various weather conditions.