593 results about "Global Map" patented technology

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

The invention discloses a polling path dynamic planning method, medium and equipment, and the method comprises the steps: obtaining environment information in real time, dynamically switching an RTK-GPS mode, a visual inertial navigation mode and a laser SLAM positioning mode, generating the current positioning information of a robot in combination with a global map, and constructing a local environment map; and planning an adaptive second preset inspection path based on the first preset inspection path. Environmental feature information is actively collected in an outdoor environment, a real-time obstacle avoidance path is generated by dynamically updating a local environment map, and the real-time obstacle avoidance path is fused to a second preset inspection path, so that smooth switching and dynamic obstacle avoidance cooperation of a positioning mode in an indoor and outdoor scene are realized; the problem of planning failure caused by multiple positioning source conflicts, local path oscillation and environment sudden change in a complex scene is solved, and the continuity of the inspection process and the path planning robustness are guaranteed.

The invention provides a robot semantic navigation method and system, a terminal and a storage medium, and the method comprises the steps: inputting a received natural language command and a pre-constructed semantic map into a pre-trained large language model, so that the large language model extracts a target navigation object in the natural language command, matching the target navigation object with a semantic map to determine a target position point of the target navigation object on the semantic map; performing path planning based on the current position of the robot and the target position point to obtain a corresponding target path so that the robot navigates to the target position point based on the target path; the semantic map is a global map which corresponds to the current space environment and contains semantic information. The robot can execute accurate positioning and navigation tasks in a complex environment through the semantic map containing semantic information and the large language model, the requirement for high spatial precision is met, and the robot can understand natural languages of human beings.

The invention discloses a robot vision-inertia SLAM method and device and a medium, and belongs to the technical field of computer vision and robot navigation. The method comprises the following steps: synchronously acquiring images and inertial data through a robot binocular camera and an IMU, performing feature enhancement on an original image in combination with a pre-trained deep learning model, introducing an adaptive brightness compensation mechanism and designing an image light supplementing module based on a generative adversarial network (GAN), recovering low-illumination image details, and improving the image quality of a low-illumination area; an entropy-based adaptive dynamic interference rejection algorithm is provided, and dynamic interference feature points are rejected in combination with IMU (Inertial Measurement Unit) data; a low-rank approximate improved graph optimization algorithm is adopted, and global map construction and pose optimization are accelerated; and through entropy-based nonlinear dynamic smoothing coefficient adjustment, the track stability is improved. According to the method, the positioning precision and robustness in a low-light environment are improved, the calculation efficiency is improved, and dynamic interference is effectively resisted.

The invention relates to the technical field of laser SLAM, and particularly discloses a laser SLAM method based on ground segmentation, and the method comprises the steps: obtaining laser radar point cloud data information, carrying out the point cloud preprocessing of the laser radar point cloud data, and obtaining ground point cloud features and non-ground point cloud features; respectively carrying out feature extraction, feature matching and pose estimation on the ground point cloud features and the non-ground point cloud features to obtain a pose nonlinear optimization constraint result; selecting a key frame according to a pose nonlinear optimization constraint result, constructing a local map according to pose information of the key frame, and obtaining a local map construction result; loopback detection is carried out according to the local map construction result, and a loopback detection constraint result is obtained; and performing global pose optimization processing according to a loopback detection constraint result and a local map construction result to obtain a global pose and global map construction result. The laser SLAM method based on ground segmentation provided by the invention can improve the positioning precision and the operation stability.

The invention discloses a cooperative positioning and trajectory planning method based on an air-ground robot system, and the method comprises the steps: enabling unmanned vehicles to construct a global environment map through a laser radar, achieving the positioning of the unmanned vehicles, obtaining the distances between the unmanned vehicles through the combination of an IMU (Inertial Measurement Unit) and a UWB (Ultra Wideband) distance measurement chip of the unmanned vehicles, and carrying out the real-time pose estimation. Based on the fused pose and global map information, a method of combining mixed A * search and nonlinear trajectory optimization is adopted, a collaborative trajectory planning problem is converted into a multi-constraint optimization model, the sight distance quality between air-ground robots, an unmanned vehicle team-shaped geometric structure and an unmanned aerial vehicle rapid navigation problem are comprehensively considered, and the space-ground robot cooperative trajectory planning method is established. And generating a collision-free optimized cooperative trajectory meeting multi-robot kinematics constraints in real time. According to the method, the positioning precision and the coordination degree of the air-ground coordination system in a complex dynamic environment are remarkably improved, and a safe, efficient and real-time heterogeneous multi-robot trajectory planning method is provided for various application scenes such as routing inspection, search and rescue, environment monitoring and the like.

The invention provides a mobile robot active positioning method and device for an indoor dynamic scene, and the method comprises the steps: collecting indoor environment data, and constructing an indoor global map which comprises the geometric structure and semantic information of an environment; sensing data collected by the mobile robot in real time are matched with an indoor global map, the pose of the mobile robot in the three-dimensional space is estimated in real time, and the uncertainty index of pose estimation is calculated; evaluating information gains of different potential observation points based on uncertainty indexes of pose estimation, and selecting the observation point with the maximum information gain as a candidate target point; planning a path to the candidate target point; the mobile robot collects sensing data at the candidate target points, the sensing data are matched with the indoor global map again, and the pose of the mobile robot is updated; the indoor environment change is monitored in real time, the indoor global map is dynamically updated, and the indoor global map is regularly corrected and optimized.

A laser SLAM system for slope compensation by using IMU and an implementation method thereof are provided. The system includes: a data preprocessing and slope detection module for acquiring and processing original point cloud data in a roadway to obtain slope information; acquiring and processing IMU data of a mobile vehicle to obtain initial values of a position and posture of the mobile vehicle and the IMU factors; a laser point cloud position and posture estimation module for classifying feature points based on the slope information and the initial values of the position and posture of the mobile vehicle to obtain a joint error; performing inter-frame matching position and posture estimation and obtaining key frame factors; and a high-precision mapping module for optimizing a factor map, and obtaining a global map of a ramp to complete the slope compensation.

The invention belongs to a synchronous localization and mapping (SLAM) method in the field of robots, and discloses a binocular vision SLAM method based on a deep learning feature extraction and matching algorithm. The system comprises three modules: a front-end tracking module, a local mapping module and a loopback detection module. For each frame of input binocular image, firstly, feature points of the image are extracted, and then frame-to-frame matching is used to track a current image frame and determine whether the current frame is set as a key frame. In the local mapping module, matching from a key frame to a local map is used to obtain a more accurate pose and a global map, and in addition, the loopback detection module is used for inhibiting accumulative errors of a large scene. Experiments on a public data set and a data set collected by a robot platform show that robust, accurate and globally consistent pose estimation and mapping are realized by the method, and quick real-time operation can be realized on an embedded platform at a speed exceeding 10 FPS.

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on sensing fusion and reinforcement learning, and belongs to the technical field of aviation flight control, and the method comprises the following steps: S1, sensor fusion time-space synchronization; s2, sensing fusion obstacle recognition and positioning; s3, obstacle avoidance route planning based on reinforcement learning; the low-resolution global map and a local occupation grid map with the unmanned aerial vehicle as the center are superposed to serve as multi-channel input, and the current state and the historical action sequence of the unmanned aerial vehicle are combined to be input into an intelligent agent model fused by a trained convolutional neural network and a long-short-term memory network; and an obstacle avoidance decision is output through a reward function of the agent model, and the airborne computer executes the obstacle avoidance decision to control the unmanned aerial vehicle to complete an obstacle avoidance action. Through sensor fusion, the obstacle sensing capability under different weather conditions and different flight environments is enhanced, the sensing distance is also increased, and the flight safety under extreme conditions can be ensured.

The invention belongs to the field of computer vision, and particularly relates to a method, a device and a system for monitoring suction-scrabbling falling of a cleaning robot, and the method comprises the following steps: 1) a rear-view camera arranged on the cleaning robot collects suction-scrabbling images, and carries out data labeling; 2) building a deep learning labeling and training environment in a server or a workstation, selecting a YOLOv8 target detection network model, sending labeled suction and scrabbling image data into the model for training, and performing format conversion and quantization on the model after the training is completed; 3) inputting an image acquired in real time into the trained target detection model, and judging whether the suction rake falls off or not; (4) if the suction scrabbling device falls off, the upper computer sends falling abnormal information to a robot controller of the cleaning robot, and the robot controller controls a hub motor to brake emergently; and meanwhile, the upper computer transmits the abnormal information of the suction-scrabbling falling and the position coordinate information of the robot in the global map to the operation and maintenance personnel through the cloud platform and then transmits the abnormal result and the position information of the suction-scrabbling falling to the operation and maintenance personnel.

The invention discloses a multi-level hybrid obstacle avoidance control method adaptive to environment complexity, and relates to the technical field of robot control, and the method comprises the steps: obtaining the global map information of an inspection environment, dividing a map into grid units, marking static obstacles, and calculating a global path of an inspection robot from a starting position to a target position through an A * algorithm; position and speed information of a dynamic obstacle is obtained in real time through a laser radar, a gravitational field model and a repulsive force field model are established, a gravitational field is generated by a target point, and a repulsive force field is generated by the detected dynamic obstacle. According to the invention, by establishing an obstacle classification mechanism based on the distance threshold, quantitative evaluation of the threat degree is realized, and the problem of low control efficiency caused by adopting a unified processing strategy for all obstacles in a traditional method is avoided.

The invention discloses a construction robot positioning and mapping method in a long tunnel environment, and relates to the technical field of computer vision, and the method comprises the steps that a robot obtains data information in the long tunnel environment through a carried sensor, and the data information comprises IMU information, an RGB image and a depth image; the method comprises the following steps: calculating IMU pre-integration according to IMU information; carrying out adaptive ORB point feature extraction and EDlines line feature extraction and optimization on an RGB image; constructing a deep learning network to carry out feature matching on extracted point-line features to obtain a matching result; constructing a point-line feature re-projection error model based on the matching result; preliminarily estimating an inter-frame pose based on the depth image and a point-line feature re-projection error model; the method comprises the following steps: selecting a key frame and detecting an identification code based on a depth image and a preliminarily estimated inter-frame pose, constructing a factor graph model based on the key frame, an identification code detection result and IMU pre-integration, updating global positioning based on a factor graph optimization result, and constructing a global map.

A detection device, such as an unmanned vehicle, is adapted to traverse a search area and generate sensor data associated with objects that may be present in the search area. The generated sensor data is used by a system including object detection inference models configured to receive the sensor data and output object data, a local object tracker configured to track detected objects in a local map, and a global object tracker configured to track detected objects on a global map. The local object tracker is configured to fuse object detections from the object detection inference models to identify locally tracked objects, and a Kalman filter processes frames of fused object data to resolve duplicates and / or invalid object detections. The global object tracker includes a pose manager, configured to track global objects in the global map and update the pose based on a map optimization process. User-in-the-loop processing includes a user interface for displaying and manual editing of detected object data.

The invention relates to an unmanned aerial vehicle dynamic environment sensing method based on a visual large model, electronic equipment and a medium, and the method comprises the steps: carrying out the instance segmentation and optical flow estimation of multiple frames of original images based on the visual large model and an optical flow estimation network, and obtaining a dynamic mask and inter-frame optical flow information; performing feature point classification on the depth information, the dynamic masks and the inter-frame optical flow information corresponding to the multiple frames of images to obtain a classification result; constructing a re-projection error model based on the classification result to obtain an estimated pose; evaluating the quality of the feature points based on the estimated pose to obtain an optimized pose; and constructing a feature point global map based on the world coordinates of the continuous frame road sign points and the optimized poses. Through a multi-information fusion and step-by-step optimization mechanism, the method has relatively high robustness, can adapt to various dynamic environments, and improves environment perception performance and operation reliability in different scenes.

The invention provides a land unmanned equipment semantic preserving large model deployment method and system, and relates to the technical field of navigation control. According to the deployment method, a preliminary control suggestion is directly output through a lightweight fusion model, optimization fusion is carried out through an MPC framework, underlying dynamics and environmental constraints, and seamless connection from semantics to control is achieved; in combination with a VILO odometer and instance segmentation, a global map containing dynamic obstacle semantic information is constructed and updated in real time, and an accurate context is provided for planning and decision making; model pruning, knowledge distillation and edge calculation scheduling are adopted, so that a complex multi-modal semantic model can run in real time on an embedded platform; the decision basis from instruction analysis to control execution is recorded and visualized in the whole process, and the transparency and credibility of the system are improved; on-line re-planning, multi-stage fault detection and switching strategies are integrated, and the robustness of the system in a dynamic environment and an abnormal condition is remarkably improved.

The invention discloses a tight coupling laser inertial vision fusion method based on radiation reconstruction, and relates to the technical field of vision fusion. Comprising the following steps: removing laser radar point cloud motion distortion based on IMU measurement, dividing the laser radar point cloud into multi-scale voxel grids, and obtaining a plane normal vector of the laser radar point cloud contained in each voxel grid to calculate a point-surface residual error; real radiation information is obtained through photometric calibration, meanwhile, a global map is projected to a current frame to obtain a tracking point, the obtained tracking point is tracked through an LK optical flow method, and a frame-to-frame re-projection error and a radiation error are obtained; a multi-constraint factor graph containing IMU pre-integration, laser radar constraint and visual constraint is constructed based on the factor graph, and robustness enhancement of the system in a degradation environment is realized in combination with a key frame sliding window optimization mechanism. The method provided by the invention has high positioning precision and robustness in a highly challenged degradation scene.

A method for calculating an optimum route for a first vehicle to travel from a current location to a selected parking space includes receiving map data of a parking zone, the map data including local map data and global map data of the parking zone, sensor data, parking space data indicating availability of parking spaces within the parking zone, and vehicle dynamics and location data, selecting, as the selected parking space, at least one available parking space based on the parking space data, determining a plurality of target positions between the current location and the selected parking space, calculating, in response to the selecting of the selected parking space and based on the plurality of target positions, the optimum route, including calculating trajectories of the first vehicle along an entire route between the current location and the selected parking space, and controlling the first vehicle to travel from the current location to the selected parking space.

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 an unmanned aerial vehicle image seamless rapid splicing method based on multi-view geometry, and particularly relates to the technical field of unmanned aerial vehicle image splicing, and the method comprises the steps: extracting feature points of video frames through a deep learning model, and carrying out the inter-frame matching, and obtaining a matching point set; based on the matching point set, solving the three-dimensional coordinates of the 3D map points, determining the initial pose of the key frame and initializing the global map, matching the feature points of the current frame with the 3D map points in the global map, and solving the camera pose of the current frame; a new key frame is selected according to the feature coverage rate, a local optimization window is constructed for 3D map points jointly observed by the newly added key frame and the associated key frame, error correction is carried out on key frame poses and 3D map point coordinates in the window through local light beam adjustment optimization, and a global map is updated; and based on the optimized global 3D map point fitting reference surface, projecting the key frame image to the fitting reference surface, generating a panoramic image by adopting a multi-scale pyramid fusion method, and outputting a final image spliced image.

The invention provides a map updating method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps of performing prediction processing based on historical change data of a global map and current environment data through a first convolutional neural network model to obtain a first prediction result of map elements in the global map; performing first fusion processing on the global map and the part of the local map at the plurality of collection times to obtain a fusion result of the plurality of collection times, and performing prediction processing based on the fusion result of the plurality of collection times through a recurrent neural network model to obtain a second prediction result of map elements in the global map; wherein the local map is determined according to the vehicle perception data of the vehicle driving on the global map; and performing map updating processing on the global map according to the first prediction result and the second prediction result of the map elements in the global map. According to the method, the timeliness and accuracy of map updating can be remarkably improved.

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 relates to the technical field of real-time positioning and mapping (SLAM), in particular to an SLAM method for tightly coupling a laser odometer and an inertial odometer, which comprises the following steps of: S1, receiving IMU (Inertial Measurement Unit) data and laser radar point cloud data, carrying out pre-integration processing on the IMU data and compensating laser frame motion distortion; s2, feature point selection is carried out on the laser point cloud based on curvature calculation, and angular points and plane points are distinguished; s3, uniform filtering is carried out on the feature points to optimize the point cloud distribution density; s4, calculating a lower pose change matrix of the laser speedometer according to the filtered feature point matching, and correcting IMU data of the inertial speedometer through the pose change matrix; and S5, adding multiple factors into the factor graph to optimize the global pose, and constructing a global map. Through a tight coupling bidirectional correction mechanism, the advantages of a laser odometer and an inertia odometer are combined, deep bidirectional compensation of sensor errors is realized, the pose estimation precision is effectively improved, and particularly, accumulative errors are remarkably inhibited in a dynamic interference scene.

The invention provides an online map construction method and device fusing time sequence prior and historical global map prior, and relates to the technical field of online map construction.The method comprises the steps that time sequence BEV features and online generated BEV features are efficiently fused through multi-channel cross attention based on masks; adaptively fusing the time sequence enhanced BEV features and local BEV features provided by a historical map through a spatial prior gating network; a mixed prior ROI mask is generated through prior information provided by a time sequence and a historical global map, and initialization of map query in map decoding at the current moment is assisted, so that the reliability of online map construction is improved.

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 discloses a quadruped robot system for indoor fire rescue. The quadruped robot system comprises a quadruped robot body, a main control unit carried by the robot, a sensing unit, a navigation unit, an execution unit and a motion control unit. The main control unit comprises two high-performance industrial personal computers which respectively process a motion control algorithm and a visual perception algorithm. The industrial personal computer communicates with other sensors through an IP and a serial port, and wirelessly communicates with the rear end of the fire-fighting command center through a TCP. After the fire-fighting command center issues a rescue task to the quadruped robot, the robot constructs a global map through the laser radar, and searches a fire source and trapped persons through the depth camera. When the robot recognizes the target, the robot sends the target position to the rear end of the fire-fighting command center, automatically avoids obstacles, and navigates to the target position to execute fire extinguishing and rescue work. The firefighter can be helped to quickly find a fire source and trapped persons, determine the positions of the trapped persons in the map, and guide the firefighter to reach the area to carry out rescue.

The invention relates to a sweeper path planning method, system and device suitable for a complex environment and a medium. The method comprises the steps that a global map of a sweeping area is acquired, feature vertexes are determined to decompose the global map, and a structural sub-area and a special sub-area are generated; determining a starting point of the structure sub-region according to the feature vertex, and generating a first path covering the structure sub-region by adopting an internal spiral method based on the starting point; determining a non-linear constraint region of the special sub-region, converting the non-linear constraint region based on coordinate system transformation, and generating a second path of the special sub-region; and determining a starting point and a target point of any path, connecting the starting point and the target point based on an improved path search algorithm, and generating a target path of the cleaning area. Therefore, according to the invention, through accurate path planning and dynamic adaptability optimization, the path planning level and the completion quality of the sweeping operation are improved, so that the sweeping vehicle can adapt to the actual scene of a complex environment, and the application range is greatly expanded.

The invention provides a positioning method and system based on multi-sensor fusion and a storage medium. Through strategies of local dynamic adaptation, global static correction and multi-source fusion, the problem of laser SLAM positioning degradation caused by warehouse environment changes is effectively solved. Local sub-graph matching adapts to a dynamic environment, and frequent reconstruction of a global map is avoided. And the dynamic weight distribution ensures the positioning accuracy in different environments. The redundant design of multiple sensors such as a laser radar / a wheel type encoder / a two-dimensional code ensures continuous operation in a complex scene. When laser matching fails, the two-dimensional code landmark provides redundant positioning. And flexible addition of new sensors is supported, and requirements of different storage scenes are met. Through combination of local subgraph matching, global observation constraint, an encoder speedometer, a two-dimensional code landmark and multi-sensor weighted fusion, the scheme not only significantly improves the positioning precision and robustness of the AGV in a dynamic environment, but also reduces the dependence on environmental consistency.

The invention belongs to the technical field of underwater navigation, and particularly relates to a multi-AUV (Autonomous Underwater Vehicle) collaborative SLAM (Simultaneous Localization and Mapping) method based on multi-beam water depth measurement data, which comprises the following steps: a plurality of AUVs synchronously run in a task area, and a multi-beam depth sounding sonar is respectively used for acquiring local water depth data and generating an internal water depth sub-graph set; each AUV performs sparse processing on the internal water depth sub-graph; the AUV receiving the sub-graph performs interpolation reconstruction on the sparse sub-graph by using a GPR algorithm; each AUV constructs a collaborative SLAM factor graph containing a pushing factor, an internal water depth closed-loop factor, an external water depth closed-loop factor and an inter-AUV distance observation factor based on self navigation data, the internal water depth sub-graph and the external water depth sub-graph; and solving the factor graph through a distributed optimization algorithm, and outputting an optimized pose and a global map of each AUV. According to the method, the positioning precision and reliability of the underwater SLAM are remarkably improved.

The invention relates to a semantic collaborative SLAM map fusion and splicing method based on a multi-modal large model. The method comprises the following steps: deploying a plurality of intelligent agents, collecting original data and preprocessing the original data to obtain multi-modal perception data; inputting the data into a local SLAM (Simultaneous Localization and Mapping) module, combining with a semantic consistency loss fine-tuning multi-modal large model, and constructing a local geometric and semantic map of each agent; fusing the features and mapping to obtain a local and global semantic feature map, and calculating relative poses between the intelligent agents to register the map; and finally, optimizing, reasoning and integrating the initial global map to form a final global semantic map with complete semantics and standard geometric accuracy. According to the method, the problems of insufficient precision, poor robustness, lack of semantic information, difficulty in adapting to dynamic scenes and the like in the existing map splicing process can be effectively solved, and the efficiency, the accuracy and the intelligent level of global map construction are effectively improved.