394 results about "Simultaneous localization and mapping" patented technology

The invention discloses a multi-target scene visual SLAM (Simultaneous Localization and Mapping) method fusing target semantics and Gaussian splashing. The method comprises the steps that input data are preprocessed, two-dimensional space masks and semantic information corresponding to the two-dimensional space masks are extracted, global semantic identifiers are distributed to the two-dimensional space masks corresponding to a background and each target, and a target semantic segmentation map is obtained; reading first frame RGBD data as a current frame, acquiring a target semantic segmentation map corresponding to the current frame, and initializing camera pose parameters; respectively establishing an initial target Gaussian splashing model for the background and each target; adopting a Gaussian splash algorithm to render all the target Gaussian splash models to a next frame, and generating an RGB rendering image, a depth rendering image and a target semantic rendering image of the next frame; and designing a loss function between the next frame of rendered image and the corresponding real image, optimizing the pose of the camera, and updating the parameters of the target Gaussian splash model. According to the invention, simultaneous positioning and map construction of multiple target scenes are realized.

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 belongs to the technical field of path planning of unmanned equipment, and relates to a medical rescue unmanned aerial vehicle and robot dog cooperative linkage method, which comprises the following steps: integrating LiDAR point cloud, visual images, IMU inertial data and compensated UWB positioning data into a multi-modal data stream subjected to space-time alignment through time alignment and space alignment technologies; respectively extracting LiDAR and visual feature poses through parallel SLAM (Simultaneous Localization and Mapping) calculation, and carrying out graph optimization and joint optimization by utilizing a GTSAM library, so as to construct a globally optimized pose and a global semantic map; the path planning algorithm provides dynamic channel routing for the unmanned aerial vehicle and the robot dog based on a global semantic map, the feature fusion weight is adjusted by considering the environment illumination intensity and the point cloud density, the reverse iris control mechanism detects whether the robot dog enters a shielding area through a UWB signal intensity attenuation value, the unmanned aerial vehicle is switched to a UWB signal follower, and the UWB signal follower is switched to the unmanned aerial vehicle. And through combination with a Kalman filter, penetration positioning in a shielding area is realized, so that stable execution of a rescue task in a complex environment is ensured.

An external rotation 3D lidar device and a simultaneous localization and mapping (SLAM) method comprises a 3D hybrid solid-state lidar device that is driven to rotate by an external motor. The device significantly improves the horizontal field of view of the lidar and can be mounted on a ground robot to comprehensively improve its 360-degree environment sensing capabilities. Error-state Kalman filtering and pose graph optimization are combined and the overall framework is divided into two parts: front-end odometry and back-end loop-closure optimization. Therefore, high-frequency odometry that meets the requirements of the robot can be output in real time and cumulative errors can be eliminated through the back-end loop-closure optimization.

The invention discloses a reinforcement learning adaptive multi-mode SLAM (Simultaneous Localization and Mapping) method based on 4D Gaussian splashing, and belongs to the field of simultaneous localization and map construction. An overall solution of further combining 4D GS and reinforcement learning on the basis of an existing multi-sensor fusion SLAM system is provided, a sensor mode is autonomously selected according to real-time environment information through reinforcement learning, and meanwhile, map updating and optimization are performed by utilizing the 4D GS and are integrated into the SLAM system. Therefore, the purposes of improving the working efficiency and the real-time performance of the whole real-time multi-mode SLAM system and saving resources are achieved. Comprising the following steps: step (1), multi-source sensor input and data preprocessing; (2) selecting a sensor combination type; (3) carrying out multi-source sensor data fusion processing; and step (4), updating and optimizing the map.

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.

The invention belongs to the technical field of simultaneous localization and mapping in the aspect of computer vision, and particularly relates to a dynamic simultaneous localization and mapping (SLAM) method based on 3D Gaussian, which comprises the following steps of: aligning an RGB image captured by a sensor with a depth image, and processing the RGB image by using a semantic segmentation algorithm to obtain a depth image; setting a semantic tag for a pixel point where the semantic target is located; for the RGB image, an ORB feature point and a SuperPoint feature point are extracted by using a FAST algorithm and a SuperPoint algorithm respectively; feature points with semantic tags are recognized and removed, and then pose estimation of the current frame is optimized in combination with the depth image; projecting a semantic target of a historical frame into a current frame image through pose transformation, recognizing a dynamic object according to the similarity of the semantic target of the historical frame and the semantic target of the current frame and the intersection-union ratio of a semantic target mask, and respectively constructing a dynamic map and a static map; and further optimizing the static map by using a 3DGS technology to obtain a dense map finally represented by 3D Gaussian.

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.

A dynamic vision SLAM (Simultaneous Localization and Mapping) method based on an extended Bayesian model belongs to the technical field of autonomous robot navigation and computer vision crossing, and mainly comprises the following steps: performing prior dynamic object recognition on a current frame image by using an improved PWt-YOLO network, and outputting a binary mask and semantic probability distribution; oRB feature extraction is carried out by using a hierarchical quadtree algorithm and combining an adaptive threshold value; establishing an extended Bayesian probability model, fusing semantic prior, optical flow residual and epipolar geometric constraints of two-dimensional Gaussian distribution constructed based on dynamic object boundaries, realizing time sequence transmission of dynamic feature probabilities through a Markov chain, and executing feature point filtering according to joint dynamic probabilities; and robust pose estimation is realized through RANSAC-PnP, a sliding window and the like. According to the method, the problem of SLAM system positioning drift in a dynamic environment can be effectively solved by constructing a multi-modal fusion dynamic feature discrimination system.

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.

Disclosed in the present invention is a LiDAR odometer method using a piecewise linear continuous time trajectory. The present invention comprises the following steps: first, using a time window to divide an initial LiDAR point cloud time sequence so as to obtain LiDAR point cloud sub-sequences under a plurality of time windows, and constructing an initial local point cloud map; and then, on the basis of the initial local point cloud map, optimizing LiDAR poses of the LiDAR point cloud sub-sequences under the time windows to obtain an overall continuous time trajectory and a final local point cloud map. The present invention achieves high-precision motion estimation for LiDARs, has high robustness and real-time performance, is applicable to the fields such as robot navigation, simultaneous localization and mapping, automated driving, and provides a relatively simple and efficient solution that can effectively handle the characteristics of LiDARs as streaming sensors.

The provided is a LiDAR odometry using a piecewise linear continuous-time trajectory. The LiDAR odometry includes the following steps: dividing an initial LiDAR point cloud time sequence through time windows to obtain LiDAR point cloud subsequences in the multiple time windows, and constructing an initial local point cloud map; and optimizing, based on the initial local point cloud map, a LiDAR pose for the LiDAR point cloud subsequence in each time window to obtain an overall continuous-time trajectory and a final local point cloud map. The provided achieves high-precision motion estimation of the LiDAR, with high robustness and real-time performance, and is suitable for fields such as robot navigation, simultaneous localization and mapping, and autonomous driving. The provided gives a simple and efficient solution that effectively utilizes the characteristics of the LiDAR as a streaming sensor.

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 relates to an air-ground collaborative exploration method in an unknown environment based on three-dimensional Gaussian splashing, and belongs to the field of air-ground robot collaborative exploration in a dangerous environment. The method comprises the following steps: S1, acquiring image data of a to-be-explored area by using an unmanned aerial vehicle, describing a complex scene and a differentiable rendering technology by using three-dimensional Gaussian distribution, accurately estimating a camera pose of the unmanned aerial vehicle and reconstructing a three-dimensional environment in real time by combining a simultaneous localization and mapping (SLAM) system, traversing a space and marking a series of key waypoints; step S2, the ground robot realizes accurate exploration of a local three-dimensional environment by using waypoints provided by the unmanned aerial vehicle and combining a collaborative mechanism of global planning and local planning; and S3, dynamically updating the path plan through the robot in the exploration process, and constructing a fine three-dimensional map of the exploration area. According to the invention, the air-ground robot can collaboratively explore the unknown environment and establish the environment map.

The invention discloses a synchronous localization and mapping method based on PID real-time correction fusion vision, and relates to the technical field of automatic control and environmental perception, and the method comprises the steps: collecting the environmental data of a robot in real time, and carrying out the preprocessing; the direction angle of each feature point is extracted and calculated based on the environment data, and path information is calculated; a state space and an action space are defined based on the path information, and a control signal of the robot is adjusted through an SAC-PID controller; and performing visual SLAM by using ORB-SLAM to output a global consistent map. According to the method, the robot path is intelligently optimized and adjusted and dynamically corrected through the SAC-PID controller combined with reinforcement learning, so that the precision and stability of path tracking are improved, a global consistent map is constructed and optimized through the ORB-SLAM technology, the navigation ability of the robot in a complex dynamic environment is further improved, and the navigation efficiency of the robot in a complex dynamic environment is improved. The self-adaptability of the controller is enhanced, the environment perception technology is optimized, and the defects of insufficient control precision and poor map consistency in the prior art are overcome.

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.

A system for enhancing animation media production that yields animated media or scenes that seamlessly blend with real-world environments. The system comprises a computing device having at least one processor and a memory in communication with the processor configured to store instructions that are executable by the processor. The computing device is in communication with a server through a network. The system uses neural radiance field (NeRF) system to provide depth maps. The system uses simultaneous localization and mapping system to monitor and map the environment in a 3D model of a scene in real-time environments. The system uses distributed AI agents, which ensures animated characters and elements can instantly adapt to dynamic changes in the environment, thereby eliminating post-production corrections when unexpected changes occur during filming. The system computes accurate lighting conditions and perspectives of the animated elements.

The invention relates to the technical field of positioning of planar robots, in particular to a wheel type odometer and monocular vision fused SLAM positioning system with three driven omnidirectional wheel sensors. According to the method, firstly, internal reference calibration is conducted on a sensor, then the pose of a vehicle body is calculated according to data of a wheel type odometer sensor, joint initialization of a wheel type odometer and a monocular camera is conducted after timestamp synchronization is completed, and the pose of the vehicle body is calculated through vision-wheel type odometer tight coupling nonlinear sliding window optimization. A dynamic plane constraint self-adaptive method based on local geometric features and meta learning is added, a mixed strategy based on motion component decomposition and residual entropy dynamic adjustment is designed, back-end optimization is carried out, and whether a loop exists or not is detected to optimize a track and reduce errors.

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 discloses a blogger live video AR glasses scenery labeling method and labeling system, and the method specifically comprises the steps: dynamically adjusting a knowledge graph retrieval strategy of a tourism live scene through a hot topic set and an emotion classification result, and obtaining a dynamic retrieval result; the method comprises the following steps: acquiring blogger head posture data based on an IMU (Inertial Measurement Unit) of AR (Augmented Reality) glasses, and constructing a 3D spatial topological map of a tourism live scene through an SLAM (Simultaneous Localization and Mapping) algorithm Spatial registration is carried out on the dynamic retrieval result and the 3D spatial topological map, and a scene enhancement labeling layer is generated through a NeRF algorithm; and displaying the video stream and the scene enhancement annotation layer in an overlapping manner, and dynamically adjusting the annotation visibility by adopting a self-adaptive transparency algorithm. According to the method, the video stream and the interaction data stream are collected in real time, dynamic annotation of the scene content of the AR glasses is achieved, live broadcast scene changes and audience interaction requirements can be responded in time, and the real-time performance and accuracy of live broadcast annotation are 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 discloses a building model reconstruction method and system based on a quadruped robot and machine vision, and the method comprises the steps: generating a global map of a to-be-reconstructed region through a synchronous positioning and map construction technology based on the quadruped robot carrying a laser scanner; and a target scanning point is determined by combining three-dimensional ray tracing and an A * path search algorithm, and global path planning is carried out. Based on a high-precision sensor carried by the quadruped robot, accurate positioning and obstacle avoidance of the quadruped robot in a complex environment are realized through adaptive Monte Carlo positioning and an improved dynamic window method algorithm. And after the robot moves to the target scanning point, the robot stops and starts the laser scanner, and high-precision point cloud data acquisition is carried out until all sampling is completed. After the collected point cloud data is preprocessed, a three-dimensional building model is generated through point cloud registration and Poisson surface reconstruction technologies. According to the invention, comprehensive, efficient and accurate three-dimensional reconstruction in a complex building environment can be realized.

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.