295 results about "Simultaneous localization and mapping" patented technology

The invention relates to the field of measurement, in particular to a subway tunnel secondary lining section limit measurement method based on SLAM mobile scanning, which comprises the following steps: step 1, tunnel point cloud acquisition and target clustering point cloud acquisition; step 2, CPIII control point coordinate extraction; step 3, point cloud coordinate conversion; 4, performing point cloud processing, and removing noisy points and non-secondary-lining target point cloud data in the tunnel secondary-lining point cloud; and step 5, performing three-dimensional limit deviation analysis, performing spatial comparative analysis on the point cloud under the construction coordinate system and the subway secondary lining tunnel design three-dimensional model by adopting a voxelization deviation mapping algorithm, extracting a continuous deviation region through voxel clustering, and generating a three-dimensional deviation thermodynamic diagram. According to the method, the two-lining tunnel point cloud obtained by SLAM mobile scanning is accurately converted into a construction coordinate system by combining CPIII high-precision control points in the tunnel, rapid, visual and three-dimensional contrastive analysis with design of a three-dimensional model can be achieved, and visual and reliable data support is provided for line and slope adjustment, track laying and defect improvement.

A method for simultaneous localization and mapping underwater. A robot is equipped with an IMU inertial unit and a sonar unit. When the robot submerges underwater, a buoy connected to the robot floats on the water surface, and the buoy moves in coordination with the movement of the robot. The position and observed velocity of the buoy are obtained by a shore-based lidar. During motion estimation in a SLAM algorithm, when an angular velocity is below a preset angular velocity threshold, the observed velocity of the buoy is decomposed into x-axis velocity and y-axis velocity, and updated as the two-dimensional operating velocity of the robot. The polar coordinates of an obstacle in a map under the coordinate system of the robot are associated with the polar coordinates of sonar data of a current frame by using the Mahalanobis distance. The polar coordinates of a new obstacle are converted to the world coordinate system and added to the map. The present invention has the advantages of not relying on underwater visibility, not requiring pre-installed devices, and possessing good generalization and stability.

The invention relates to the technical field of wind power equipment detection, in particular to a wind turbine generator blade fracture damage detection method applying an inspection robot, which comprises the following steps of: S1, constructing a blade three-dimensional point cloud model and positioning the position of the robot in real time through a laser radar and a visual SLAM (Simultaneous Localization and Mapping) module carried on a robot body; s2, generating a spiral detection path covering the surface of the blade based on a path planning algorithm optimized by the genetic algorithm; s3, synchronously collecting blade surface data by adopting a multi-mode sensor array, wherein the blade surface data comprise pulse thermal imaging data, ultrasonic guided wave signals and high-resolution visible light images; s4, performing feature fusion on the acquired data through a deep convolutional neural network; through multi-modal sensor cooperative detection, deep learning feature fusion and digital twinborn evaluation technologies, full-process automatic detection from damage identification to life prediction is realized, the detection precision and efficiency are significantly improved, and reliable guarantee is provided for safe operation of the wind turbine generator.

The invention relates to a semantic simultaneous localization and mapping method and system based on Gaussian splashing. The method comprises the following steps: firstly, collecting a frame of RGB-D image, modeling a scene into a 3D semantic Gaussian field containing a plurality of 3D semantic gausses according to the RGB-D image, and rendering the 3D semantic gausses by using a tile rasterization technology to obtain 2D image plane gausses; rendering results of RGB color, depth and semantic features are extracted from the 2D image plane in a Gaussian mode, and the semantic features are decoded into semantic tags; constructing a mapping and tracking loss function by using an RGB color rendering result, a depth rendering result, a semantic tag and a truth value, and jointly optimizing a camera pose and a semantic Gaussian field based on a tracking stage and a mapping stage; and repeating the steps for each new frame of RGB-D image to complete the construction of the incremental semantic Gaussian map. Compared with the prior art, the method has the advantages of realizing robust camera tracking, real-time high-quality rendering, accurate 3D semantic reconstruction and the like.

The invention discloses a dynamic semantic SLAM (Simultaneous Localization and Mapping)-driven robot full life cycle navigation method, which comprises the following steps: synchronously acquiring multi-modal data through a multi-modal sensor array, and carrying out joint coding based on a cross-modal contrast learning framework to obtain embedded features. A dynamic three-dimensional Gaussian radiation field is constructed, a scene is represented as a parameterized Gaussian kernel set, and mixed Gaussian field representation is obtained through micro rendering and joint training optimization. A Gaussian mixture field is divided into a global static layer and a local dynamic layer, retention constraints are applied to the static layer, and an incremental online updating strategy is adopted for the dynamic layer. Based on the optimized Gaussian field parameters and the multi-modal data, a factor graph containing Gaussian rendering residual factors and inertia pre-integration factors is constructed and solved, the corrected robot pose is obtained, and the space occupancy probability is generated. Operation control parameters are adjusted on line according to the pose and the occupancy probability through a strategy network, and autonomous navigation is achieved in combination with path planning and trajectory tracking.

In a dynamic environment, a visual SLAM (Simultaneous Localization and Mapping) system often causes the problems of large positioning error and inaccurate map construction due to dynamic target interference. In order to improve the robustness and precision of the system, the invention provides a dynamic scene SLAM optimization method based on improved YOLOv11 and geometric consistency constraint. Firstly, ORB features in a scene are extracted, and meanwhile a prior dynamic object and feature points on the prior dynamic object are removed through a YOLOv11 semantic segmentation model; secondly, eliminating feature points on the potential dynamic object by utilizing geometric consistency constraint, and recovering a background shielded by the dynamic object through a semantic perception Gaussian filter; and finally, selecting a high-quality key frame and applying the key frame to loopback detection and global optimization, constructing a basic Gaussian graph through a group of determined poses and point clouds, and finally fusing repair frame information to realize new view rendering and three-dimensional scene optimization.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle group instant localization and map construction optimization method, which comprises the following steps: S1, multi-modal data acquisition and preprocessing, S2, local sub-map construction and federated feature extraction, S3, dynamic scene perception and sub-map optimization, S4, federated feature matching and relocalization, and S5, map fusion and optimization. Through multi-modal sensor fusion, distributed federated cooperation and dynamic scene perception technologies, high-precision positioning and efficient mapping of the unmanned aerial vehicle group in a complex environment are realized, the accuracy of environment perception is guaranteed through time synchronization and noise reduction preprocessing of multi-modal data, the data transmission amount is reduced through local sub-map construction and federated feature extraction, and the real-time performance of the unmanned aerial vehicle group is improved. Dynamic target identification and area marking improve the adaptability of the map to environment change, so that the positioning precision of the system in strong light, haze, dynamic interference and other scenes is improved.

The invention relates to the technical field of computer vision, and particularly provides a tunnel video stream three-dimensional modeling system based on binocular stereo matching and SLAM (Simultaneous Localization and Mapping), which comprises a multi-modal image acquisition unit, a binocular infrared camera and an RGB (Red, Green and Blue) camera are configured to synchronously acquire an infrared image pair, an RGB image pair and inertial measurement unit data of a tunnel environment, the multi-source data is uploaded to the cloud processing platform in real time through the wireless transmission module; based on a dynamic switching mechanism of environmental perception, performing adaptive fusion pose estimation of a feature point method and a direct method on input multi-modal image data; the stereo matching and dense reconstruction unit is used for calculating a disparity map by adopting an improved self-adaptive window stereo matching algorithm, generating a three-dimensional point cloud in combination with the pose information output by the pose estimation unit, and constructing a global consistency point cloud model of the tunnel scene through a time sequence point cloud registration and splicing algorithm; and a multi-algorithm target detection and semantic fusion unit. The method can meet the requirements of precision and robustness of tunnel reconstruction.

A method for motion synchronization-based head pose estimation, by a head mounted display (HMD) device and an HMD device for performing the same are provided. The method includes receiving, by the HMD device, motion data from a plurality of motion sensors of the HMD device, receiving, by the HMD device, a plurality of image frames from at least one simultaneous localization and mapping SLAM camera of the HMD device, estimating, by the HMD device, a plurality of motion parameters of head movements of a user from the plurality of image frames received from memory, generating, by the HMD device, a filtered subset of the motion data received from the plurality of motion sensors based on the plurality of motion parameters of the head movements, synchronizing, by the HMD device, the plurality of image frames received from the memory and filtered subset of motion data, and estimating, by the HMD device, the head pose based on the synchronized plurality of image frames and motion data.

The invention discloses a narrow environment high-precision laser inertial navigation and SLAM (Simultaneous Localization and Mapping) method based on adaptive parameters. According to the method, firstly, radar and IMU data are collected and preprocessed to form a unified input data stream; obtaining a pose prediction result by using the data stream, constructing an observation model, and obtaining a laser observation residual error; calculating a characteristic value proportion in the matching process of the point cloud and the sub-map, and judging a geometric degradation scene; when degradation is detected, adaptively adjusting a voxel filtering radius and a loopback detection threshold value; fusing the pose prediction result and the laser observation residual error by adopting a Kalman filtering model to obtain an optimal pose estimation result, and carrying out loopback detection and back-end map optimization under a dynamic threshold value to correct accumulated drift and update a voxel map; and finally outputting a continuous pose track and a three-dimensional voxel map. According to the method, the robustness and the positioning precision of the laser inertial navigation fusion SLAM in narrow scenes such as pipe galleries, tunnels and chemical plants are effectively improved.

The invention provides a synchronous positioning and mapping method for underwater pipeline detection, and belongs to the technical field of underwater robot navigation and positioning. In order to solve the problems of limited illumination, strong echo interference, high structural repeatability and the like in a seawater pipeline environment, the invention provides a factor graph optimization-based tight coupling pose estimation framework fusing a two-dimensional imaging sonar, a Doppler velocity meter DVL and an inertial measurement unit IMU. Through joint constraint and optimization of multi-source sensing information, the method can realize high-precision positioning and continuous mapping of the ROV in an environment with extremely low visibility and a complex pipeline structure, and effectively improves autonomy and data reliability of an underwater inspection process. The method is clear in structure, easy and convenient to implement, high in calculation efficiency, capable of stably running on a conventional ROS platform and suitable for various types of underwater pipeline detection and inspection tasks.

The invention provides an unmanned vehicle local high-precision positioning mapping system and method based on reinforcement learning polarization normal deambiguity and depth information fusion, and the method comprises the steps: synchronously obtaining a polarization image and a depth image of a target scene in the driving process of an unmanned vehicle in a low-texture and low-illumination scene; constructing the polarization normal candidate, the depth normal, the neighborhood surface curvature and the depth gradient information into a state vector, inputting the state vector into a reinforcement learning agent based on Dueling DQN, and obtaining the depth gradient according to a reward function fusing the included angle error of the polarization normal and the depth normal, the neighborhood normal smoothness loss and the consistency error of the polarization normal and the depth gradient direction. Outputting an optimal polarization normal selection action to obtain an unambiguous surface normal; polarization estimation depths are generated, and confidence coefficients are calculated respectively; according to the depth confidence coefficient and the polarization confidence coefficient, pixel-level weighted fusion is carried out, a fused depth map is obtained, synchronous positioning and mapping are carried out on the fused depth map and the synchronous RGB image, and a point cloud map of the target scene and the moving track of the unmanned vehicle are output.

The invention discloses an SLAM dynamic point semantic filtering method based on DDMA-SAM, and belongs to the technical field of synchronous positioning and mapping. A decoupling distillation mechanism is introduced, an image encoder in an original SAM model is subjected to lightweight optimization, a DDMA-SAM semantic network integrating a multi-scale aggregation detection module and an efficient mask decoding module is constructed, and the SLAM dynamic point semantic filtering method based on DDMA-SAM is obtained. And the segmentation performance is improved while the model parameters are greatly compressed. Based on the semantic network, providing a semantic prior and geometric consistency combined-driven double filtering strategy; based on a semantic mask and a confidence threshold, carrying out preliminary dynamic point identification; in combination with the epipolar geometric constraint and the triangulation reprojection error of random sampling consistency estimation, fine elimination of dynamic feature points is realized, and only static points are reserved to participate in camera pose estimation. According to the method, the real-time performance of the system is kept, and meanwhile, the mapping quality and the track stability in a dynamic scene are effectively improved.

The invention discloses a handheld forest region sample plot calibration surveying and mapping device and method, and relates to the technical field of forest resource investigation, and the device comprises a laser radar, a visual camera and a global navigation satellite system / inertial orientation positioning navigation system integrated navigation unit; when a user carries the device to move, the processing unit constructs a three-dimensional point cloud map and calculates a six-degree-of-freedom pose; when the signal of the global navigation satellite system is unavailable, calculating a current position by combining the three-dimensional point cloud map, the six-degree-of-freedom pose and inertial data; and the processing unit calculates parameters of the stumpage and the candidate area and generates a sample plot calibration surveying and mapping report. According to the invention, the device achieves the synchronous capturing of the characteristics of the trunk base and the canopy at a handheld height, an operator only needs to click and select an initial position on a touch screen, and the system combines laser instant positioning and map construction with a global navigation satellite system / inertial orientation positioning navigation system for tight coupling positioning. And automatically generating sample plot boundaries and ecological parameters conforming to regulations.

Embodiments of the present disclosure provide an image rendering method and apparatus, an electronic device, and a storage medium. The method includes: determining whether a received current frame is a key frame based on a key frame group to be updated located by a simultaneous localization and mapping system, the key frame group to be updated including at least one key frame to be applied; in response to determining that the received current frame is a key frame, updating the key frame group to be updated according to a preset frame number and the current frame to obtain an updated key frame group to be updated; and optimizing a key frame to be applied in the updated key frame group to be updated, and updating a relative pose of the key frame to be applied, so as to perform image rendering based on an updated relative pose.

The invention belongs to the technical field of mining engineering, discloses a mining roadway non-uniform three-dimensional deformation grading calculation and analysis method, and aims to solve the problems that a traditional point cloud comparison method is low in precision and large in error in a roadway high-deformation and non-uniform-deformation environment, and traditional monitoring wiring is complex and cannot achieve global coverage. According to the technical key points, in a GNSS-free underground environment, a portable three-dimensional laser scanning device integrating SLAM synchronous positioning and a map construction technology is adopted to collect two-stage roadway point cloud data, a centimeter-level precision three-dimensional model is constructed through a pose map optimization algorithm, a two-stage deformation hierarchical optimization algorithm is designed, and a two-stage deformation hierarchical optimization algorithm is designed. In the first stage, a deformation core point is selected based on super voxel segmentation, rough calculation is performed in a cylindrical projection space, and a key deformation area is rapidly identified, and in the second stage, a refined displacement amount is calculated for a point whose displacement exceeds a threshold value through geometric constraint and a centroid method, and the data quality is improved in combination with point cloud denoising, so that high-precision continuous monitoring of the full section of the roadway is realized.

The invention provides an SLAM (Simultaneous Localization and Mapping) optimization method and equipment based on point cloud registration and adaptive resolution, and a medium, and relates to the technical field of AGV (Automatic Guided Vehicle) positioning and mapping, and the method comprises the following steps: (1) inputting and preprocessing sensor data; (2) front-end scanning and matching: based on the preprocessed data, carrying out point cloud registration through a GICP algorithm, updating the pose of the current frame and constructing a local subgraph; (3) global SLAM: receiving sensor data, poses and subgraph data from the front-end scanning and matching step, and carrying out loopback detection and global optimization; wherein a self-adaptive resolution switching strategy based on environment complexity is adopted in the loopback detection process; according to the GICP algorithm, the surface of the point cloud is modeled as Gaussian distribution, local features are described through a covariance matrix, and a target error function constructed based on the mahalanobis distance is optimized to estimate pose transformation. According to the method, the local matching precision and robustness are improved by replacing ICP with GICP, and the global calculation efficiency is adjusted and optimized in combination with the dynamic resolution.

In one or more embodiments, instructions that, when executed by a processor, cause the processor to: locate, based on a sensor fusion of a second camera and a second sensor, a first compute device in a map of a 3D scene to define a first device location; calculate, based on the map of the 3D scene and a first sensor, a relative pose of a second compute device with respect to a first compute device location; determine, based on the relative pose, a region of overlap between a FOV of the first camera and a FOV of the second camera; identify, based on the region of overlap, an occluded portion of the second FOV; and send a signal to cause the display to project a plurality of image frames within the second FOV and to reproject the visible portion of the first FOV.

The invention discloses an SLAM (Simultaneous Localization and Mapping) optimization mapping method and device combining geometric verification and constraint of a reflector, relates to the technical field of industrial-grade mobile robot navigation, and solves the problem that in the prior art, a continuous constraint mechanism cannot be established in a mapping process to correct accumulative errors in motion. The method comprises the following steps: acquiring an original laser radar point cloud containing a reflector, screening out reflection points with the distance smaller than a preset distance, and carrying out clustering and quintuple geometric verification on the screened points by adopting a density clustering algorithm, so as to obtain a multi-dimensional geometric verification mechanism based on PCA (Principal Component Analysis); local coordinates of the center of the reflector are calculated and converted into global coordinates, the reflector is used as a stable Landmark depth to be fused into an SLAM back-end graph optimization framework, and optimization nodes including a timestamp, a robot pose, a reflector ID and an observation pose are constructed based on a global coordinate system to participate in graph optimization; and the matching problem caused by insufficient natural characteristics or environment change in a long corridor scene can be solved in a targeted manner.

The invention discloses a data acquisition intelligent interaction robot, relates to the technical field of intelligent interaction, is suitable for a museum, realizes autonomous navigation by integrating a laser radar and a visual SLAM (Simultaneous Localization and Mapping) technology, and performs real-time perception and early warning on audience behaviors by fusing a depth camera and a posture recognition model. The system has a multi-modal interaction capability, supports voice recognition, semantic understanding, touch interaction and knowledge retrieval, automatically triggers an exhibit explanation event according to audience behaviors, realizes aligned broadcast in combination with a navigation fine tuning function, and improves immersion and interactivity of explanation; the robot can dynamically generate behavior data such as interaction logs, audience staying data and explanation content calling frequency, structured processing is conducted through the behavior data caching module, the behavior data are periodically synchronized to the background of the museum through the communication module, and data closed loop and system optimization are achieved; according to the robot, the convenience, safety and personalized service level of exhibit information acquisition are improved, and the robot has good practical value and popularization prospect.

The invention discloses a map construction method of a three-dimensional Gaussian SLAM (Simultaneous Localization and Mapping) algorithm based on depth information fusion, which comprises the following steps of: firstly, adopting a dynamic point cloud downsampling method based on depth information, so that the defect that structural information is easy to lose due to the fact that point cloud spatial distribution and geometric characteristics caused by the depth information are not considered in traditional random downsampling can be overcome; the sampling rate is dynamically adjusted through depth information, a key geometric structure is reserved while data redundancy is reduced, good geometric priori is provided for generation of Gaussian point clouds, and the generated Gaussian point clouds are more fit with the surface of an object; moreover, according to the method, the optimization efficiency in the map reconstruction process is improved through the perspective principle of depth information adaptive point size and simulation of human eye observation, and the geometric error of luminosity rendering of the reconstructed map is smaller; in addition, the depth information is optimized by utilizing multi-frame depth fusion, so that the geometric position generated by the Gaussian point cloud is more accurate, and the subsequent optimization time is reduced.

The invention relates to the technical field of unmanned aerial vehicle cable intelligent routing inspection, and discloses a GPS-free unmanned aerial vehicle cable autonomous routing inspection method based on laser radar SLAM, and the method comprises the steps: completing the initialization setting of a system through carrying a laser radar, an inertial measurement unit and an auxiliary sensor; starting a laser radar SLAM (Simultaneous Localization and Mapping) algorithm in an environment without GPS (Global Positioning System) signals to carry out real-time positioning and map construction, and generating an environment point cloud map; planning an autonomous inspection path and optimizing a flight path based on the point cloud map and the preset geographic information of the cable line; controlling the unmanned aerial vehicle to fly along the path, and updating the position in real time by using an SLAM algorithm to compensate the positioning error; synchronously acquiring laser point cloud data, visible light images and infrared thermal imaging data of the cable; identifying a cable structure and an abnormal state through multi-modal data fusion processing; and finally generating an inspection report containing the health condition and the abnormal position of the cable and transmitting the inspection report to a monitoring center. The continuity and coverage integrity of the routing inspection mileage are improved.

The invention relates to the technical field of SLAM (Simultaneous Localization and Mapping), and provides a dynamic SLAM method and system based on static semantic anchor points, comprising the steps of static structure semantic anchor point generation, anchor point-based robust tracking and multi-sensor tight coupling fusion, back-end optimization and static semantic map construction. By actively selecting high-stability static structure features and combining lightweight semantic segmentation and space-time consistency verification, the problems of poor dynamic elimination effect and high calculation overhead in the prior art are solved. According to the method, the positioning robustness and precision in a dynamic scene are improved, the calculation overhead is reduced, the static map rich in semantics is generated, and the universality is high.

The invention provides a visual inertia simultaneous localization and mapping navigation method based on a ground mobile robot, which comprises the following steps: carrying out joint calibration on camera parameters and IMU parameters of the ground mobile robot to obtain a joint optimization pose, and carrying out IMU initialization through IMU offset calibration and a fixed scale factor to obtain a key frame; adopting an intelligent key frame selection strategy based on a common view, performing common view frame screening through an adaptive threshold adjustment mechanism, and creating a point cloud map; rasterizing the point cloud map to obtain a binary map; global poses are published through a multi-stage coordinate system conversion system, and navigation map publishing is completed in combination with a binary map. The problem that a traditional SLAM system and a robot navigation framework are difficult to integrate is solved, the mapping efficiency and the navigation real-time performance are remarkably improved while the positioning precision is guaranteed, and a more stable and reliable technical support is provided for autonomous navigation of a ground mobile robot in a complex environment.

Disclosed is a device in which asynchronous factor transfer is employed for enabling real-time visual-inertial simultaneous localization and mapping (VI-SLAM) in the device. The device includes visual sensors and inertial sensors to capture image data and inertial data respectively. The device includes a first processor that stores a first subproblem for short-range tracking of the device and a second processor that stores a second subproblem for long-range tracking. The subproblems are represented by factor graphs that may be updated for optimizing the subproblems. The asynchronous factor transfer allows achieving tight coupling between the subproblems such that there is bidirectional information exchange. The exchange allows overcoming inconsistencies between the subproblems and facilitates collaboration between to incrementally determine solutions of a global bundle adjustment problem and open new possibilities for real-time VI-SLAM.

An example method collects first data comprising first surface points within an environment by a sensor associated with a processing system. The method further determines an estimated position of the processing system by analyzing the first data using a simultaneous localization and mapping algorithm and identifies a first set of surface features from the first data. The method further collects second data comprising second surface points within the environment by a three-dimensional (3D) coordinate measuring device associated with the processing system and identifies a second set of surface features from the second data. The method further matches the first set of surface features to the second set of surface features and refines the estimated position of the processing system to generate a refined position of the processing system. The method further displays an augmented reality representation of the second data based at least in part on the refined position.

The invention discloses an SLAM (Simultaneous Localization and Mapping) method and system based on visual inertial guidance and laser cascade registration, and belongs to the technical field of environmental perception, and the method comprises the following steps: acquiring multi-source data, predicting a pose by adopting an IMU (Inertial Measurement Unit) pre-integration method, taking the pose as an initial value, and outputting a robot visual-inertial pose by combining a camera image and utilizing a visual-inertial odometer technology; constructing a visual inertia factor; taking the vision-inertia pose as an initial value of registration, performing multi-stage registration on the input laser radar point cloud frame, outputting a laser estimation pose, and constructing a laser factor; visual loopback detection and laser loopback detection are respectively carried out, an effective closed loop is determined according to the space-time consistency of the two kinds of loopback detection, loopback factors are constructed, a factor graph is constructed, graph optimization is executed, and globally consistent tracks and map points are obtained. According to the method, the real-time performance is maintained, the registration convergence is improved, and the method is suitable for positioning and mapping in a complex degraded scene.

A system and method for mapping vehicle parking includes receiving, by a global navigation satellite system (GNSS) receiver, located within a vehicle, a GNSS signal, where based on the GNSS signal, a position of the vehicle is determined. One or more vehicle movement sensors are configured to track, upon entering into a parking structure with a subsequent loss of reception of the GNSS signal, a movement of the vehicle within the parking structure. An optical sensor, in the vehicle, identifies vehicle location information within the parking structure where one or more sensors within the vehicle map, based on the tracking and identifying, use simultaneous localization and mapping (SLAM), to generate parking space mapping data. Upon cessation of movement of the vehicle, a parking state of the vehicle is initiated where a determination is made of a parking space position of the parked vehicle within the parking structure.

The invention provides a dynamic environment SLAM method and device based on instance segmentation, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring a first environment image from image acquisition equipment, dividing image grid blocks and determining corresponding gradient information; determining texture division areas corresponding to the first environment image based on the gradient information; and determining a geometric feature point set and a contour feature point set of the first environment image according to each texture division region and a preset bimodal feature extraction strategy. And generating a dynamic feature filtering mask of the first environment image when determining that the dynamic feature points exist in the first environment image based on the plurality of second environment images. And based on the geometric feature point set, the contour feature point set and the dynamic feature filtering mask, determining a matching point pair set corresponding to the first environment image, determining current pose information according to the matching point pair set, and outputting the current pose information to a downstream task. Through the method, robust positioning is realized in a dynamic and weak texture environment.

The invention belongs to the field of multi-sensor fusion systems, and discloses a multi-sensor fusion system simultaneous positioning and mapping method and a related device, and the method comprises the steps: obtaining an environment image, a laser point cloud and IMU data; scene semantic information is extracted through a multi-modal large model; dynamically generating a weight matrix of vision and laser radar relative to an IMU (Inertial Measurement Unit) by utilizing a vision and point cloud condition deep neural network in combination with scene information and IMU observation; and performing local and global optimization by combining each sensor speedometer factor, an IMU pre-integration factor and the weight matrix, and finally outputting robot state estimation and map point coordinates. Through semantic understanding and a self-adaptive weight mechanism driven by a conditional deep neural network model, a fusion strategy can be intelligently adjusted before environment change or sensor degradation, the robustness, precision and consistency of a system in an unstructured scene are improved, and the problems of positioning drift and map failure caused by fixed weight in a traditional method are solved.