468 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 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.

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 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 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.

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 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 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 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.

The invention belongs to the technical field of artificial intelligence and robot autonomous navigation, and discloses an open vocabulary topology semantic graph construction and navigation method and system for an unknown environment. A topological map composed of nodes and edges is constructed in an online incremental mode, the nodes represent key positions in the environment, and the edges represent passable connection between the positions; for each node in the topological map, coding the corresponding visual observation image into a semantic feature vector by using a pre-trained visual language model; performing graph neural network fusion on the semantic feature vectors, executing cross-modal alignment with a natural language instruction, and generating global map representation; and based on the global map representation, generating a navigation action, and driving an intelligent agent to move towards the target. According to the method, an environment representation which deeply fuses spatial topology and semantic information is constructed, and intelligent decision making is carried out according to the environment representation.

The invention relates to the technical field of automatic driving positioning, and particularly discloses a laser radar SLAM method, device and system for an automatic driving vehicle, and the method comprises the steps: obtaining the original point cloud data of a laser radar, and carrying out the adaptive roughness evaluation and feature screening of the original point cloud data of the laser radar, and obtaining feature information; performing dynamic outlier detection on the feature information to screen out outlier feature points to obtain effective feature information; attitude estimation is carried out on the effective feature information according to a double-peak geometric primitive constraint mechanism, a six-degree-of-freedom pose is obtained, and the double-peak geometric primitive constraint mechanism is obtained through construction according to the feature information of the current frame and corresponding feature information in a local map; and according to the six-degree-of-freedom poses, local map construction and global map construction are carried out in sequence, and positioning information of the autonomous vehicle is obtained. According to the laser radar SLAM method for the autonomous vehicle provided by the invention, the precision and reliability of laser radar SLAM can be improved.

The invention discloses a plane 2D point cloud map updating method based on dynamic value evaluation, which belongs to the technical field of intelligent navigation, and comprises the following steps: acquiring sensor data of a current frame, and matching the sensor data with a currently maintained global map to obtain a matching result; based on the matching result, updating and evaluating the target unit in the map, and fusing an instant cleaning criterion based on space consistency and a long-term value criterion based on historical state information; according to a result of the update evaluation, determining an update action on the target unit, the update action at least comprising a deletion operation; an update action is performed to update the global map. According to the method, the accurate map updating decision is realized through intelligent fusion of instant and long-term double criteria, and the global scoring and fault-tolerant control mechanism is combined, so that the long-term consistency and high precision of the map are ensured, the operation efficiency and stability of the system are remarkably improved, and reliable and adaptive navigation support is provided for the robot in a dynamic environment.

The invention provides a centralized multi-robot collaborative SLAM (Simultaneous Localization and Mapping) method based on an FPGA (Field Programmable Gate Array) platform. The method comprises the following steps: acquiring a color image and IMU (Inertial Measurement Unit) data of a surrounding environment by using a motion camera The color image is input into an FPGA platform of the single robot and converted into a grey-scale map, image pyramid scaling, corner detection and feature description are carried out, and the image is transmitted to a processing system of the single robot; the processing system generates observation information and further generates an image frame; generating a key frame and sending the key frame to a central server; the central server side carries out loopback detection on the key frame, carries out map fusion on local maps established by different single robots to obtain a global map, and optimizes the global map; and constructing a three-dimensional dense point cloud map. According to the method, cooperative positioning and three-dimensional mapping of multiple robots can be carried out in a large-scale and complex environment scene, the problem that computing resources and a detectable range of a single robot in the complex scene are limited is solved, and the scale, the precision and the robustness of map construction are improved.

The invention provides a robot obstacle avoidance method, a robot obstacle avoidance device and a computer storage medium. The robot obstacle avoidance method comprises the following steps: dividing a current obstacle point cloud from current point cloud data; mapping the current obstacle point cloud to a map coordinate system, and updating an obstacle grid of a global map; acquiring a current obstacle grid of a local map based on the current position of the robot; determining an actual obstacle distance according to the distance between the current obstacle point cloud and the robot; determining a virtual obstacle distance according to the distance between the current obstacle grid and the robot; and making an obstacle avoidance decision for the robot based on the actual obstacle distance and the virtual obstacle distance. When an obstacle avoidance decision is made through the robot obstacle avoidance method, comprehensive evaluation can be carried out based on the actual obstacle distance and the virtual obstacle distance, and the reasonability and accuracy of obstacle avoidance opportunity selection are improved.

The invention discloses an unmanned aerial vehicle video live-action map generation method and device, a medium and equipment, and belongs to the field of map generation. According to the method, the real-time video data and the pose information of the multiple unmanned aerial vehicles are acquired, projection conversion is carried out, the pose estimation is carried out by utilizing the visual SLAM algorithm and combining the converted pose information, and the camera pose matrix is constructed. And further, processing the video data by adopting a preset densification and orthorectification method in combination with the camera attitude matrix to obtain each video frame. A video frame is divided into a non-overlapping area and an overlapping area, the non-overlapping area is directly integrated to a global map, the overlapping area is divided into sub-maps, overlapping information is processed and then integrated to the global map, and finally a panoramic base map is formed. And finally, integrating the panoramic base map with the 3D GIS through a projection texture mapping technology, and generating an unmanned aerial vehicle video live-action map. The problem that the video live-action map of the unmanned aerial vehicle cannot be accurately and efficiently generated in the prior art is effectively solved.

The invention discloses a track planning system for an unmanned intelligent vehicle. The track planning system comprises an environment sensing module, a global map module, a vehicle state monitoring module, a risk degree evaluation module, a track planning module and a dynamic adjustment module, by integrating the modules of environment perception, global map, vehicle state monitoring, risk degree evaluation, trajectory planning, dynamic adjustment and the like, vehicle surrounding environment and vehicle state information can be captured and analyzed in real time, the optimal driving trajectory is calculated and output in combination with global road data, and dynamic adjustment is performed according to real-time change in the driving process, so that the driving efficiency is improved. Therefore, safe and efficient driving of the unmanned intelligent automobile is realized.

The invention relates to a map construction method and device, computer equipment and a readable storage medium. The method comprises the steps that in the moving process of a robot in a pipeline, laser radar point cloud, pipeline environment data, pipeline visual data and motion data collected by different sensors are obtained; under the condition that the current map building moment is reached, the initial pose of the robot is predicted according to the motion data, and a refractive index compensation factor of the pipeline is determined according to the pipeline environment data; determining an initial map point cloud of the target time period according to the initial pose, the refractive index compensation factor, and the laser radar point cloud and pipeline visual data in the target time period; according to a preset curvature threshold and a refractive index compensation factor, correcting the initial map point cloud to obtain a target local map; and updating the initial global map at the previous map construction moment according to the target local map to obtain a target global map. By adopting the method, the accuracy of the constructed map can be improved.

The invention provides a robot autonomous positioning system and method based on a three-dimensional model technology, relates to the field of intelligent autonomous positioning, and realizes rough positioning of a robot by constructing a global map feature descriptor search library and a block index and combining image information. And the point cloud feature information is accurately matched in the corresponding blocks to calculate the position and posture of the robot. According to the method, feature coding is carried out on robot image information, retrieval correspondence is carried out on the robot image information and pre-constructed block indexes, and matching is carried out after related block indexes are extracted, so that the semantic matching degree is intelligently estimated and compared with a preset threshold value, and whether the robot image information is included into one of the block indexes which are roughly positioned globally or not is determined; therefore, the high accuracy of the preliminary positioning stage is ensured, the error accumulation in the subsequent steps is reduced, and the stability of the whole positioning process is enhanced.

The invention discloses a vehicle positioning method based on a reflective column. The problems that in the prior art, system integration is complex, cost is high, mapping dependence is high, robustness is poor, and scene recognition capacity is insufficient are solved. The method comprises the following steps: S1, acquiring point cloud data of a surrounding environment scanned by a laser radar on a vehicle; s2, identifying at least three reflective columns based on reflection intensity information in the point cloud data, and determining a first coordinate of each reflective column in a laser radar coordinate system; step S3, based on the first coordinates of the at least three reflective columns and in combination with pre-stored global layout information of the reflective columns, determining a sub-region where the current vehicle is located; and S4, according to the second coordinate and the first coordinate corresponding to the current sub-region, calculating the pose information of the vehicle in the global map coordinate system through coordinate transformation.

The invention discloses a multi-sensor fusion navigation system and method of an electric power inspection unmanned aerial vehicle and the unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle navigation, the multi-sensor fusion navigation system comprises a laser radar unit and an inertia measurement unit which are coupled to form a laser radar-inertia subsystem, and a vision unit and the inertia measurement unit which are coupled to form a vision-inertia subsystem; the laser radar-inertial subsystem is used for calculating an iterative nearest point error between the current frame and the global map; using the iterative nearest point error as an observed value of an error state iterative Kalman filter to execute a filtering updating process, and constructing and updating a global map; and the vision-inertia subsystem is used for constructing a hybrid reprojection error based on the projection points and the image feature points of the recovery depth, and performing state estimation by taking the hybrid reprojection error as an observation value of an error state iteration Kalman filter. The technical problem that in the prior art, stable and high-precision navigation cannot be achieved in an electric power scene is solved.

The invention relates to the technical field of robot intelligent control, in particular to an intelligent path planning method and system based on a complex environment. Comprising the steps that through heterogeneous laser radar combination (air-ground global obstacle perception is achieved, a layered map (a static global map and a dynamic local map) is constructed, airspace safety corridor constraints are introduced based on an improved RRT * algorithm, and cooperative obstacle avoidance of double mechanical arms, a three-axis platform and an AGV is ensured. Real-time local path optimization is realized in combination with a dynamic window method (DWA), the path risk is monitored through a safety index (SI), and millisecond-level re-planning or emergency stop is triggered. The method has the advantages that the airspace obstacle detection height reaches 3 m, the path planning success rate is increased to 98.7%, the re-planning response time is smaller than or equal to 180 ms, the collision risk is remarkably reduced, and the method is suitable for autonomous navigation of complex scenes such as electric power inspection and warehouse logistics.

The invention discloses a tight coupling laser inertial vision fusion method based on a merged probability voxel map, and relates to the technical field of vision fusion. Comprising the steps of performing display parametric modeling based on radar measurement noise, and constructing a probability voxel plane model; combining the planes which may have a coplanar relationship, and adding the laser radar point clouds in the determined effective voxel planes into a global map; projecting the global map into an image frame to obtain a tracking point, tracking by using an LK optical flow method, and meanwhile, further eliminating an abnormal point by using a random sampling consensus algorithm based on a dynamic Bayesian network; and based on image information acquired by the camera, optimizing and maintaining the global map by minimizing frame-to-frame pixel errors and frame-to-map color errors and updating the global map. According to the method, the performance of the laser inertial visual odometer is improved by combining a framework of efficient coupling radar, IMU and camera information of the probability voxel map.

The invention relates to an intelligent shooting guiding method and device, equipment and a storage medium, and the method comprises the steps: obtaining a target image containing target semantic information of an image and an image depth map based on a deep learning model and a depth estimation model, obtaining a feature point set of the target image, and generating an initial global map based on the feature point set, generating a semantic tag map based on the target semantic information, the image depth map and the initial global map; performing feature point extraction and screening on a target image, calculating semantic weight values of a plurality of obtained first feature points, determining a moving path based on the semantic weight values and a path planning algorithm, and generating a control instruction including a linear velocity and an angular velocity for controlling the shooting equipment to move, and obtaining and obtaining a detection quantity based on environment change in real time; dynamically adjusting the moving path, and obtaining and controlling the shooting equipment to move based on the dynamically adjusted moving path; intelligent shooting guiding can be realized, and adaptability and flexibility of path planning in the shooting guiding process are enhanced.

The invention relates to the technical field of robot positioning and mapping, and discloses a positioning and mapping method and device and a storage medium. The method comprises the following steps: performing motion distortion correction on point cloud data of a current frame collected by a laser radar to obtain corrected point cloud data of the current frame; constructing a local sub-map of the current frame, calculating a degradation probability of a unit direction vector of the local sub-map, correcting a Hessian matrix of an iterative nearest point algorithm according to the degradation probability, and determining a pose estimation result of the corrected point cloud data relative to the local sub-map according to the corrected Hessian matrix; and fusing the pose estimation result and an IMU predicted value through a Kalman filter, calculating to obtain optimal pose estimation, and inserting the corrected point cloud data into a global map based on the optimal pose estimation. The problems of point cloud distortion and scene degradation in a dynamic scene are solved, and high-precision and robust positioning and mapping are realized.