45 results about "Pose estimation algorithm" patented technology

A method for pose and gait classification and motion prediction using spatio-temporal relationships between body joints includes capturing a sequence of images or video frames of a subject; applying a neural network-based pose estimation algorithm to the sequence of images or video frames to detect landmark positions of anatomical joints; constructing a spatio-temporal graph from the detected landmark positions of the one or more anatomical joints, wherein nodes of the spatio-temporal graph correspond to the anatomical joints and the landmark positions, spatial edges of the spatio-temporal graph represent anatomical connections between the anatomical joints within a single image or frame, and temporal edges connect the one or more anatomical joints across successive images or frames of the sequence of images or video frames; and inputting the constructed spatio-temporal graph into a spatio-temporal graph convolutional network (ST-GCN) to classify pose and gait patterns and predict motion or stability states.

The invention relates to the technical field of human motion prediction, and discloses an action standardization evaluation method and system based on a human motion prediction algorithm. Specifically, a historical patient motion image sequence and a corresponding real future patient motion image sequence are respectively input into a preset human body posture estimation algorithm to obtain a historical patient motion posture and a real future patient motion posture, a wearable device is not needed, the convenience and the universality are improved, and the cost is reduced. And inputting the historical patient movement postures into a pre-trained human body movement prediction model to obtain a plurality of predicted future patient movement postures. And performing average joint error on each frame of the predicted future patient movement posture and each frame of the real future patient movement posture, and judging whether the patient movement accords with the specification or not according to the error. The motion posture of each patient is predicted through the human body motion prediction model and compared with the real future motion posture of the patient, and accurate capture and personalized evaluation of the motion of the patient are achieved.

The invention provides a portrait animation generation method and system based on a reversible encoder and dynamic optimization, and relates to the technical field of video portrait reconstruction, high-precision portrait animation generation is realized through multi-module collaborative innovation, multi-scale appearance features are extracted by using a VAE encoder, identity vectors extracted by ArcFace and global appearance embedding of CLIP are combined, and the portrait animation generation method and system based on the reversible encoder and dynamic optimization are realized. Constructing a multi-modal characteristic system; a high-precision attitude estimation algorithm is adopted to extract fine joint points, and an error between a generated attitude and an original attitude is calibrated in real time through a dynamic optimization strategy, so that the attitude driving accuracy is remarkably improved. A reversible residual block is designed to realize lossless two-way transmission of features, and the problem of information compression of a traditional encoder is avoided; a high-frequency detail injection mechanism is introduced, high-frequency features such as facial textures are extracted through a pre-training model and fused with shallow features, an attention mechanism guided by identity features is introduced based on the generation process of a diffusion model, and the consistency of character identities in the generation process is ensured.

The invention discloses a camera pose estimation algorithm in a dynamic scene, and the algorithm comprises the following steps: S1, inputting a video frame into an optical flow estimation neural network (CNN), and obtaining a dense optical flow of adjacent image frames; s2, inputting the dense optical flow and the video frame into a motion segmentation network, obtaining a binary mask, and obtaining a motion segmentation result by using the mask thresholding optical flow; s3, inputting a motion segmentation result into a Transform module, and fully extracting context information; s4, inputting the context information extracted in the step S3 into a residual neural network, and performing feature extraction; and S5, calculating the content in the step S4 by using a full-connection neural network to obtain a camera pose. Through the algorithm, static and dynamic areas can be accurately segmented in a complex dynamic environment, and the contextual information of the image is fully utilized to realize the accurate estimation of the six-degree-of-freedom pose of the camera.

The invention relates to the technical field of traffic safety facility identification, in particular to a traffic safety facility statistical survey method based on deep learning. The method comprises the following steps: acquiring a video frame image and synchronously acquiring GPS and IMU data of camera equipment; constructing a corrected pose transformation matrix through a fusion pose estimation algorithm, generating a three-dimensional point cloud by combining monocular depth estimation and multi-frame depth map fusion, and extracting geometric attribute information of traffic safety facilities; identifying the traffic safety facilities in the image by using a YOLO algorithm, extracting the types and positions of the facilities, and associating the types and positions with three-dimensional attributes to generate structured data; performing grade judgment on the facility state based on a fuzzy reasoning method; and performing segmented statistics and classified summarization according to the road stake numbers, and outputting a statistical report. The method can realize facility three-dimensional perception, state intelligent evaluation and classified statistics, and is suitable for intelligent highway operation and maintenance and digital traffic facility management.

The present invention relates to the technical field of traffic safety facility recognition, and specifically provides a statistical survey method for traffic safety facilities based on deep learning. The present invention includes: collecting video frame images and synchronously obtaining GPS and IMU data of the camera device; constructing a corrected pose transformation matrix through a fusion pose estimation algorithm, generating a three-dimensional point cloud by combining monocular depth estimation and multi-frame depth map fusion, and extracting geometric attribute information of traffic safety facilities; using the YOLO algorithm to identify traffic safety facilities in the image, extracting the facility category and location, and associating them with three-dimensional attributes to generate structured data; judging the facility status level based on a fuzzy inference method; performing segmented statistics and classified summaries according to road mileage, and outputting a statistical report. The present invention can realize three-dimensional perception of facilities, intelligent evaluation of status, and classified statistics, and is applicable to the operation and maintenance of intelligent roads and the management of digital traffic facilities.

The invention discloses an intelligent recognition method and system for grabbing scattered parts, and relates to the technical field of disordered grabbing. In order to solve the problems of high data labeling cost, low occlusion scene pose precision, difficulty in cross-domain generalization and the like in the prior art, a full-process closed loop of'simulation data synthesis-multi-modal segmentation-pose estimation-semi-supervised training 'is constructed: a field adaptive simulation data set is generated through PyBullet + PyRender, and a MaskNet algorithm is obtained based on ViT improved YOLO11 to realize multi-modal instance segmentation; a MaskPosenet two-stage pose estimation algorithm is designed, and cross-domain generalization is realized in combination with pseudo-label semi-supervised learning of a cosine scheduling threshold. According to the method, the data cost is greatly reduced, the complex scene capturing precision and generalization ability are improved, and the method is adaptive to industrial scenes such as logistics sorting and flexible manufacturing and has extremely high application value.

The invention relates to the field of computer vision and intelligent manufacturing, in particular to a vehicle charging port image marking method and device, and the method comprises the steps: obtaining a vehicle charging port image, and carrying out the local frame selection processing; sequentially carrying out graying processing, contrast-limited adaptive histogram equalization processing and median filtering processing; carrying out edge detection by using a Canny edge detection algorithm; adopting a Hough circle detection algorithm to generate an initial position parameter of the circular hole; or, adopting a pre-trained convolutional neural network semantic segmentation algorithm to carry out pixel-level prediction, and extracting corresponding edge points as initial position parameters of the circular holes; if it is judged that the precision of the initial position parameter does not meet the preset labeling requirement, click operation is executed, and a correction point set is formed; and based on the correction point set and the edge points corresponding to the initial position parameters, fitting an ellipse to obtain a marking result of the charging port circular hole. The method can improve the labeling efficiency, guarantees the labeling precision and stability, and is adaptive to subsequent deep learning segmentation and pose estimation algorithms.

The invention relates to the technical field of warehouse logistics equipment, and discloses a monocular camera-based pallet pose estimation method, a storage medium and related equipment. The method comprises the following steps: acquiring a fork entrance surface image of a pallet through a monocular camera; performing feature matching on the fork entrance surface image and a pre-stored template image to obtain a pallet area in the fork entrance surface image; the pallet area is preprocessed; detecting the edge in the pallet area through an edge detection algorithm; detecting angular points in the pallet area through angular points, and screening projection angular points corresponding to preset control angular points of the fork entrance surface of the pallet; obtaining projection coordinates of the projection angular points in a first coordinate system of the monocular camera through a sub-pixel angular point detection algorithm; and according to the 3D world coordinates and the projection coordinates of the control angular points, solving attitude parameters and position parameters of the pallet in the first coordinate system through a pose estimation algorithm. According to the method, the calculation amount of pallet pose estimation can be reduced, and the pose estimation response speed is increased.

The present invention discloses a hydraulic pump parts tracking and registration method based on joint perception of model and image. The method comprises collecting a photo stream of the hydraulic pump parts, obtaining a point cloud model from the photo stream, denoising the point cloud model and optimizing the point cloud density before storing it in a database as the original point cloud; using a calibrated camera to collect continuous frame images of the target being tracked in the hydraulic pump assembly environment in real time, and constructing a two-stage neighborhood hierarchical search mechanism and a three-factor feature weighted fusion based on a fast point feature histogram algorithm to extract feature points of the original point cloud in the database. In terms of time performance, the present invention can reduce processor computing costs and achieve real-time processing; and in terms of registration accuracy, through the effective fusion of point cloud features and visual features, combined with the nested loop mechanism of the pose estimation algorithm, the overall operating efficiency of the algorithm is improved, thereby enhancing the accuracy and robustness of the overall matching.

This invention discloses a fault-tolerant method for quay crane pose estimation based on multi-sensor fusion, belonging to the technical field of port automation equipment. When a quay crane loses connection due to a network failure, the system automatically detects the pose loss by comparing and analyzing pre-registered quay crane IDs and placement order information with real-time received data. Upon detecting a missing quay crane position, the system immediately reports an error and records the disconnection information. Different intelligent inference strategies are employed based on the quay crane's position order within the area: for quay cranes in the middle position, the median value of the adjacent quay cranes is used as the initial pose; for quay cranes at the edge position, the approximate pose is inferred based on the positions of adjacent quay cranes and the overall trend. This invention employs a point cloud matching-based pose estimation algorithm, achieving high-precision relative pose estimation through multi-sensor fusion of LiDAR and visual cameras, significantly improving the fault tolerance and operational continuity of the port automation system.

The two-stage object six-degree-of-freedom pose estimation method based on a monocular camera solves the problem of how to achieve high-precision and high-efficiency pose estimation of weak-texture industrial parts, and belongs to the technical field of monocular camera pose estimation. The present invention is a two-stage pose estimation method from coarse to fine. The coarse pose estimation network roughly estimates the six-degree-of-freedom pose value based on the fusion of mask features and 2D position features. The coarse pose estimation stage provides the initial value for precise pose estimation, avoiding the optimization process of the precise pose estimation algorithm from falling into the local optimal value. The precise pose estimation network introduces a dynamic snake-shaped convolution layer to construct an object contour feature extraction network, improves the quality of contour feature generation, and filters out background contours. Through the differentiable Gauss-Newton optimization method, iterative optimization is performed at multiple scales to minimize the reprojection error between the projection contour and the object contour, thereby achieving accurate pose estimation.

The invention provides a laser radar position identification method based on point cloud structure features. Comprising the following steps: S1, acquiring three-dimensional point cloud data acquired by a laser radar, and generating a bird's-eye view BEV image reflecting point cloud spatial distribution characteristics; s2, extracting two-dimensional key points from the bird's-eye view BEV image, and dividing the corresponding three-dimensional key points into point features, line features and surface features; s3, constructing a global hash descriptor reflecting the overall structural characteristics of the point cloud and a local probability descriptor reflecting the local structural characteristics; s4, performing rapid candidate retrieval based on the global hash descriptor to obtain a candidate frame set for position identification; and S5, carrying out relative pose solving on the candidate frame and the current frame by adopting a robust pose estimation algorithm, and determining a final position identification result. According to the invention, through BEV image conversion, double descriptor cooperation and multi-dimensional innovation of a two-stage strategy, core bottlenecks of efficiency and robustness imbalance and incomplete feature representation in laser radar position identification are systematically solved.

A human pose estimation system 100 for a motor vehicle 130, the system includes an imaging device 102 and a processor 104, the processor further comprises a base model 110 operable to execute a pose estimation algorithm 112. The pose estimation algorithm is operable to determine a pose of the at least one vehicle occupant 132, 132’ contained in the at least one video image frame 120 captured by the imaging device. The processor further comprises a visibility predictor 114 operable to execute a visibility prediction algorithm 116 in parallel with the pose estimation algorithm. The visibility algorithm is operable to determine a visibility state of at least one keypoint relative to the pose of the at least one vehicle occupant contained in the at least one video image frame captured by the imaging device. Preferably, the processor is operable to generate a value of total loss function of the human pose estimation system and input the total loss function generated to an artificial neural network 106 to train the artificial neural network.

The invention discloses a three-dimensional attitude estimation method based on data expansion and multi-view positioning, and relates to the field of computer vision, and the method comprises the steps: recognizing a two-dimensional bounding box of a human body in different view angle image frames through a human body detector in an input module; the positioning module adopts a dual space technology to process the bounding boxes, and obtains a linear relation between a two-dimensional human body and a three-dimensional human body by fitting an ellipse, so as to solve position correlation information of the same human body on different image frames; the detection module analyzes the associated information by using an optimized two-dimensional human body posture estimation algorithm so as to obtain an accurate two-dimensional human body posture; the fusion module preprocesses and encodes the two-dimensional postures to generate key point tokens, and integrates the key point tokens from different views to predict the three-dimensional posture of the human body in a world coordinate system. According to the invention, the method achieves the efficient estimation from a two-dimensional human body posture to a precise three-dimensional human body posture through the technical processing of the image frames of different view scenes and dual space.

The invention relates to a rigid body target pose measurement method fusing multi-view semantic key points, and the method comprises the following steps: constructing a sample set, and training a target detection network and a semantic key point positioning network based on the sample set; detecting all target objects in the multiple views based on the trained target detection network, obtaining bounding boxes of the target objects and generating a clipped image set; performing key point prediction on the target object in the image set based on a semantic key point positioning network; key points under different views are matched based on multi-view set constraints, and a multi-view semantic key point set is formed; and inputting the obtained multi-view semantic key point set into a three-stage asymptotic pose estimation algorithm to solve the pose of the target object. According to the method, the semantic key points of multiple views are fused, so that the performance bottleneck in a special / complex scene in a traditional scheme can be effectively solved, and the accurate measurement of the position and the attitude of the rigid body target is realized.

The invention provides an object attitude estimation method and system based on three-dimensional feature mapping and visual angle aggregation, and belongs to the technical field of attitude estimation, and the method is applied to a system comprising an image processing module, a three-dimensional feature mapping module, a visual angle aggregation module and an attitude estimation module. The method specifically comprises the steps of performing feature extraction on a template image and a target image, performing three-dimensional space mapping on template image features, determining template three-dimensional features and view angle information, determining the view angle information of each target image feature and the corresponding template three-dimensional feature through feature matching, and determining the target image feature according to the view angle information. And obtaining a matching relation between target image features and template three-dimensional features through view angle aggregation, selecting an attitude estimation algorithm to solve an alternative rough attitude estimation result, and performing iterative optimization by taking the alternative rough attitude estimation result as an initial value of an attitude optimization network to obtain an object attitude estimation result. According to the method, the error transmission risk in attitude estimation can be effectively reduced, the accuracy is guaranteed, the calculation complexity can be reduced, and the estimation efficiency is improved.

In view of the problems of poor real-time performance and low recognition rate of existing head pose estimation algorithms in complex scenes, the application provides a new lightweight head pose estimation method. The method has a multi-level output structure, uses three different types of branch networks to extract features from the input image, and each branch has three stages, and each stage only needs to refine the features of the previous stage. The features extracted by each branch at the same stage are fused by a feature fusion module to generate a feature map, effectively avoiding the problem of feature loss. The feature extraction module selects the Ghost module as the feature extraction network, uses model compression to reduce the network parameters and computational complexity while ensuring the accuracy of the network; in order to extract more important effective features, an efficient channel attention module ECA-Net is introduced, thereby improving the accuracy of head pose estimation. The method of the application can achieve excellent head pose estimation efficiency and has wide applicability.

The invention provides a mechanical arm control method for tracking a dynamic target in real time, and belongs to the technical field of mechanical arm control, and the mechanical arm control method comprises the steps that a vision measurement system is constructed through a binocular infrared camera and a visible light camera, and a marker image on the dynamic target is captured; based on a visual detection algorithm, two-dimensional code data corresponding to the marker image are extracted for tracking identification detection, and when it is detected that the dynamic target is a to-be-tracked target, real-time position information and real-time attitude information of the to-be-tracked target are calculated through a perspective attitude estimation algorithm; performing fusion processing on low-frequency vision measurement data constructed by the real-time position information and the real-time attitude information and the high-frequency motion signal based on an inter-frame difference method to obtain real-time pose data; and the visual servo controller controls the mechanical arm to move according to a motion optimal solution obtained based on the mechanical arm kinematics model. According to the method, real-time tracking of the dynamic target is achieved, and the method has high operation grabbing efficiency and is suitable for various task scenes.

Images of a plurality of objects in a bin from multiple views are received from two image sensors. An initial pose estimation for an object is generated using a pose estimation algorithm and an image of the images. A depth map for the object is generated using a stereo matching algorithm and the images. A depth for the object is rendered using the initial pose estimation for the object and camera intrinsics of the two image sensors. A value that represents an accuracy of the depth of the object in the bin is determined based on comparing the depth map for the object and the rendered depth for the object. A robot arm is instructed to grasp the object based on comparing the value with a first threshold, the robot arm being a proximal distance from the two image sensors, the plurality of objects, and the bin.

This invention discloses a three-dimensional pose estimation method based on data augmentation and multi-view localization, which relates to the field of computer vision. The method includes: using a human body detector in an input module to identify the two-dimensional bounding boxes of human bodies in image frames with different viewpoints; a localization module using dual space technology to process these bounding boxes, obtaining a linear relationship between two-dimensional and three-dimensional human bodies by fitting ellipses, thereby solving the position association information of the same human body in different image frames; a detection module using an optimized two-dimensional human pose estimation algorithm to analyze this association information to obtain accurate two-dimensional human pose; and a fusion module preprocessing and encoding these two-dimensional poses to generate key point tokens, and integrating the key point tokens from different views to predict the three-dimensional pose of the human body in the world coordinate system. By processing image frames of scenes with different viewpoints and using dual space technology, the present invention achieves efficient estimation of human pose from two-dimensional to accurate three-dimensional human pose.

This invention discloses a machine vision-based method, system, device, and medium for monitoring learning attention, specifically relating to the field of object pose determination in image analysis. It addresses the perspective distortion problem caused by changes in the distance between the learner's face and the camera in existing technologies. The method involves acquiring continuous video sequences using a monocular camera, detecting the learner's facial region, and extracting the two-dimensional coordinates of facial feature points. Based on these two-dimensional coordinates, topological persistence features are analyzed to determine the distance change state. When a distance change occurs, the motion trajectory of the facial feature points is constructed, and the curvature distribution is calculated. Curvature entropy features are obtained through information entropy analysis. Consistency judgment is performed in conjunction with the topological persistence features, and the estimated distance change is adaptively corrected. Finally, the projection model parameters of the three-dimensional head pose estimation algorithm are dynamically adjusted based on the distance change, and the head pose angle is calculated by combining the facial feature point coordinates, thus achieving accurate judgment of the learner's attention state.

A millimeter-wave radar personnel positioning method based on cross-supervised learning uses a binocular camera to collect depth point cloud data, and applies a human pose estimation algorithm to extract the spatial coordinate data of personnel targets from the depth point cloud data; uses the spatial coordinate data as labels and the echo data collected by the millimeter-wave radar as inputs to achieve cross-supervised learning training of the Hourgalss neural network; uses the millimeter-wave radar to collect radar echo data, inputs the radar echo data into the trained Hourglass neural network to predict the personnel coordinates based on the Hourglass neural network, then uses the AOA positioning algorithm to process the radar signals to calculate the personnel coordinates based on the AOA algorithm, and finally fuses the two methods to calculate the personnel coordinates to obtain the final personnel positioning result. The present invention can greatly improve the personnel positioning accuracy, thereby achieving the effect of having both the high-angle positioning accuracy of a commercial binocular camera and the high-distance positioning accuracy of a millimeter-wave radar only using the millimeter-wave radar.

The invention relates to a skeleton point-based shielding scene behavior recognition method and system, and the method comprises the steps: firstly extracting time sequence skeleton point data through a human body detection and posture estimation algorithm, and constructing five types of structured inputs through a mask module; then, the multi-stream GCN learns local spatio-temporal features of different body areas respectively; in the fusion process, the model can adaptively reinforce the feature contribution of a region which is highly related to the current action or is not shielded, and meanwhile, the interference caused by a weak correlation region and a missing part is effectively inhibited. Meanwhile, three types of regularization items of diversity, sparsity and consistency are designed, so that the weights are kept different among the types, are kept compressed on branches and are kept stable in the types. The scheme provided by the invention is obviously superior to the existing method in complex scenes such as random shielding, arm shielding, leg shielding and the like, has high robustness and strong generalization ability, and is suitable for the fields of intelligent monitoring, human-computer interaction, edge calculation behavior analysis and the like.

The invention discloses a skeleton action recognition method based on a partition space-time collaborative graph convolutional network, and relates to the technical field of computer vision and pattern recognition, and the method comprises the steps: firstly obtaining human skeleton sequence data through a depth sensor or a posture estimation algorithm, and carrying out the preprocessing; then, constructing a partition association graph, dividing part partitions based on a human body natural structure, and establishing in-part stable topology and cross-part semantic topology; then introducing a part dynamic collaborative convolution (PDC-GC) module, and cooperatively extracting global semantic and local dynamic features of the action through part association modeling and dynamic neighborhood convolution; introducing a space-time sub-domain coding (STSE) module, capturing multi-scale space-time dependence by dividing four sub-domains, and reducing calculation complexity in combination with sparse coding; and finally, constructing a partitioned space-time collaborative graph convolutional network (PC-GCN) network, fusing a PDC-GC module, an STSE module and multi-level features, and realizing high-precision skeleton action recognition.

This invention discloses a method and system for deep learning-based human behavior recognition using ZYNQ, comprising the following steps: 1) acquiring a video dataset of human behavior; 2) performing data preprocessing, processing the human estimation information collected from the video dataset in step 1) using a human pose estimation algorithm, and dividing the collected data into a training set and a test set; 3) constructing a human behavior recognition model, using a designed deep learning neural network to extract and process features from the dataset in step 2), thereby training the model; 4) setting training parameters and training the designed deep learning network; 5) according to actual needs, using ZYNQ as the core platform, transferring the trained model, and finally deploying it to achieve online human behavior recognition. This invention is beneficial for recognizing complex, long-term reasoning behaviors in video scenarios and has practical application value.

The present application relates to the field of stage lighting, and specifically discloses a method, device and medium for realizing intelligent stage performance, which comprises the following steps: establishing a pose prediction model for predicting the pose data of performers after a first time length by using a target detection algorithm, a target tracking algorithm, a human pose estimation algorithm and a time series prediction model; establishing and training a lighting effect model for calculating stage lamp control data from the pose data of performers by using a machine learning algorithm and a lighting parameter formula; and calculating and obtaining stage lamp prediction control data according to the real-time pose data of performers obtained by a sensor node, so as to control the stage lamp. The present application realizes intelligent control of the stage lamp, realizes real-time monitoring and tracking of the performers, and ensures that the light and shadow can accurately follow the motion changes of the performers by predicting the trajectories of the performers in advance.

This application provides a method, apparatus, computer device, and storage medium for pose estimation of a mobile robot. The method includes: acquiring a target image through a vision system; estimating the pose information of a target object from the target image using a preset pose estimation algorithm; determining the orientation information of the target object, the distance between the mobile robot and the target object, and the pose category of the target object based on the pose information; searching a preset prior knowledge base for the target observation angle of the vision system associated with the pose category; determining the target observation position of the mobile robot based on the current position of the mobile robot, the target observation angle of the vision system, the orientation information of the target object, and the distance between the mobile robot and the target object; controlling the mobile robot to move to the target observation position, and simultaneously executing the step of acquiring the target image through the vision system. This method can improve the accuracy of pose estimation.

The application relates to a lightweight robust face alignment method and system based on multi-task learning, which comprises the following steps: S1, collecting face images, and performing translation, scaling and rotation preprocessing on the face images to obtain a training set; S2, obtaining face key points L of the face images in the training set; using a pose estimation algorithm to mark 3D head pose Euler angles Theta of the face images in the training set, and converting the 3D head pose Euler angles Theta into a head pose rotation matrix R Θ ; S3, inputting the training set into a face alignment network to output face key point prediction values P and head pose rotation matrix prediction values R Φ , and respectively constructing a head pose loss function, a face key point loss function and a total loss function for training the face alignment network. The method provided by the application simultaneously aligns face key points and head poses using a lightweight model, and uses a head pose alignment task to assist the positioning of the face key points, thereby enhancing the robustness of the lightweight model to large-pose faces.

The application relates to the technical field of intelligent unmanned cluster systems, and discloses a cooperative positioning method for an unmanned cluster in a denial environment, which considers any pair of neighboring individuals except for both of which having no relative measurement capability, constructs observation equations among relative measurement information of both parties, displacement measurement information of both parties' odometers and relative poses of both parties, introduces a data-driven adaptive estimation technology, breaks through the deficiency of traditional relative pose estimation algorithms of unmanned systems that require robots to keep sustained excitation relative motion, thereby causing the control performance to be degraded, designs a distributed pose coupling estimator to realize cooperative estimation of relative poses of a leader individual by any individual in the cluster, realizes cooperative pose estimation of the unmanned cluster system under a weakly connected measurement topological condition, and solves the problem that existing cooperative positioning methods have poor scalability due to high requirements on measurement topologies.