57 results about "Pose tracking" patented technology

A composite pose-estimation algorithm includes a video-object segmentation sub-algorithm (311) configured to determine a mask of a visual object in an image, and an object-pose tracking sub-algorithm (312) configured to track a pose of a visual object over multiple depth-video frames, wherein the pose-estimation algorithm is configured to input a depth video, from which frames are extracted and fed to the video-object segmentation sub-algorithm, which determines respective object masks to be used by the object-pose tracking sub-algorithm alongside the depth video. A method of tracking a pose of a physical object comprises: obtaining a depth video depicting a physical object in a plurality of poses from an input interface (330); forming a storable data item representing the physical object by applying the pose-estimation algorithm to the depth video; and tracking the physical object or a copy thereof using an instance of the pose-estimation algorithm which has been initialized by means of the storable data item.

The invention relates to a human body pose tracking method. The method comprises the following steps: establishing a world coordinate system by using an SLAM (Simultaneous Localization and Mapping) system of head-mounted equipment, and acquiring an absolute pose of the head of a user in the world coordinate system; creating an individualized motion model, and obtaining individualized motion model parameters; acquiring real-time pose data of a distributed IMU node, wherein the distributed IMU node comprises a plurality of IMU units distributed on user limbs; and creating a factor graph optimization model, and determining the human body pose of the user by taking the absolute pose of the head as a root node and combining the individualized motion model parameters and the real-time pose data. The method is high in tracking precision, and can avoid the reduction of the tracking precision of the human body pose caused by integral drift. The invention further provides a human body pose tracking device, a robot control method and motion capture gloves.

The invention relates to the technical field of engineering construction measurement and control, and discloses a calibration method for positioning and attitude determination of a heading machine, which comprises the following steps: constructing a data acquisition module, a route making module, a real-time dynamic monitoring module, an execution module, a feedback module and an optimization module; the data acquisition module is used for completing comprehensive acquisition of multi-source data of the heading machine, a route, an environment and an obstacle, the route making module is used for fusing the multi-source data to carry out path planning and error compensation, and the real-time dynamic monitoring module is used for realizing heading machine pose tracking and emergency correction through visual marking points and a laser scanner. The execution module is used for converting an optimal tunneling path into a driving motor control instruction, the feedback module is used for collecting deviation data of an actual operation pose and a planned path in real time and returning the deviation data, the optimization module realizes long-term self-adaptive optimization of the system, and finally, precision, safety and high efficiency of the tunneling operation of the tunneling machine in a complex underground environment are realized.

The invention discloses a target attitude tracking method based on an enhanced Mama attention network, belongs to the technical field of computer vision, and aims to solve the problems that precision and efficiency are difficult to balance, dynamic scene adaptability is poor and semantic and spatial synchronization cannot be considered in an existing target attitude tracking method. Through deep collaboration of feature extraction and preliminary aggregation, time sequence dependence modeling based on Mama, feature optimization of attention enhancement, four-branch multi-scale adaptive attention mask generation, dynamic feature fusion and enhancement, and attitude output and assessment multi-link technical innovation, a multi-link attitude estimation method is provided. The three core problems that precision and efficiency are difficult to balance, dynamic complex scene adaptability is poor and semantic space cannot be synchronized in an existing target attitude tracking method are solved in a targeted mode, and a better solution is provided for attitude tracking requirements of industrial and other actual scenes.

Systems and techniques are provided for determining a pose. A process can include obtaining inertial measurement unit (IMU) data from an IMU associated with a device. The IMU data can be used to determine a propagated state associated with a state estimation engine, wherein the propagated state includes an initial orientation estimate corresponding to a pose of the device. The state estimation engine can comprise an Extended Kalman Filter (EKF). A predicted orientation measurement can be generated using a first machine learning network to process the IMU data and the initial orientation estimate included in the propagated state associated with the state estimation engine. An updated state associated with the state estimation engine can be determined based on using the predicted orientation measurement to update the propagated state. A device pose estimate can be determined based on the updated state associated with the state estimation engine.

An XR system is provided that calibrates display parameters. The XR system captures pose data of the XR system relative to a real-world environment using a pose tracking component. Video data of the real-world environment is captured using one or more cameras of the XR system. One or more reference features in the real-world environment are identified using the video data and pose data. The XR system causes display of one or more virtual objects aligned with the reference features using display parameters. A user interface is displayed to allow a user to adjust the display parameters and XR system receives adjustments to the display parameters from the user via the interface and updates the parameters accordingly. An updated pose of the XR system is captured using the pose tracking component and the virtual objects are then re-displayed using the updated display parameters and updated pose.

Methods provide computer-navigation assisted total hip arthroplasty (THA) procedures. Such procedures include the use of a navigated instrument, e.g., a navigated reamer construct or a navigated inserter construct to prepare the acetabulum and insert an acetabular shell, a camera tracking system adapted to track a pose of the navigated inserter or reamer construct relative to the patient in use, and a computer platform adapted to receive pose tracking information and generate and display navigational guidance for the user.

This invention belongs to the field of on-orbit non-cooperative spacecraft pose tracking technology, specifically involving an online adaptive method to compensate for domain offset in on-orbit non-cooperative spacecraft pose tracking. Step 1: Represent the target spacecraft as 11 three-dimensional key points; extract the key point bounding boxes after projection, and crop to obtain the input RoI; train an asymmetric encoder-decoder network to regress the key point heatmap; Step 2: Design a dynamic memory library M based on a first-in-first-out queue; Step 3: The student model receives complete samples from M, and the teacher model receives masked samples from M; consistency constraints are implemented by minimizing the weighted MSE loss between the student and teacher predicted heatmaps; Step 4: Calculate the average value of the [CLS] token features of all training images as the source domain global class prototype, and align the source domain prototype with the newly calculated class prototypes of the teacher and student models. This invention makes it possible to achieve high-precision, highly robust, and highly practical real-time online tracking of non-cooperative spacecraft pose.

The application relates to the technical field of camera pose tracking, and discloses a low-cost camera six-degree-of-freedom tracking method, which comprises the following steps: acquiring three-dimensional coordinates of a Pivot in a virtual scene global coordinate system, a body coordinate system offset vector of the Pivot to a camera CMOS optical center, and an installation angle deviation matrix of an IMU relative to a camera body; and based on sensing data collected by the IMU, a pose rotation matrix in an IMU coordinate system is solved, and after installation angle deviation compensation and Pan zero point offset compensation are performed, the technical problem that in the prior art, when only relying on a low-cost inertial measurement unit (IMU), position integral error accumulation cannot be avoided, and after discarding visual collection and a high-precision mechanical encoder, it is difficult to balance hardware low cost, environmental adaptability and pose output stability is solved. The application provides a low-cost camera six-degree-of-freedom tracking method.

The invention discloses a fabric-like sewing type cavity self-healing endoscope scene reconstruction method. The method comprises the following steps: carrying out initialization modeling on a two-dimensional Gaussian scene; tracking the pose of the camera; gaussian extension and key frame sampling strategy; a mapping mapping and hole perception completion module; light beam adjustment optimization of fusion smooth constraint is carried out; according to the method, firstly, continuous and dense representation of an endoscope scene is realized through two-dimensional Gaussian scene modeling and a Gaussian extension mechanism; 2, a cavity sensing and complementing mechanism is adopted, so that the mapping process has a fabric-like sewing type self-healing capability; thirdly, light beam adjustment optimization of normal smooth constraint is introduced, and the phenomena of local geometric roughness and Gaussian scene discontinuity are relieved; the overall frame is designed in a modularized mode and can be seamlessly integrated with an existing two-dimensional Gaussian mapping and light beam adjustment system; and 5, the method is suitable for a complex tubular organ scene. According to the method, topological perception and self-healing complementation of the mapping process can be realized in a complex tubular organ scene, and the continuity, stability and geometric quality of an endoscope three-dimensional reconstruction result are improved.

In some aspects, a gesture tracking device may receive availability information from a sensor system including a plurality of sensors based on a current operating condition associated with the plurality of sensors. The attitude tracking device may select a set of sensor modalities associated with the sensor system based on the availability information. The gesture tracking device may select a gesture tracking model based on the selected set of sensor modalities and one or more key performance indicator (KPI) requirements related to a current context associated with a gesture tracking configuration of the client application. The attitude tracking device may estimate an attitude associated with the object using an attitude tracking model based on sensor inputs associated with one or more sensors selected from the plurality of sensors. Numerous other aspects are described.

An XR system is provided that calibrates display parameters. The XR system captures pose data of the XR system relative to a real-world environment using a pose tracking component. Video data of the real-world environment is captured using one or more cameras of the XR system. One or more reference features in the real-world environment are identified using the video data and pose data. The XR system causes display of one or more virtual objects aligned with the reference features using display parameters. A user interface is displayed to allow a user to adjust the display parameters and XR system receives adjustments to the display parameters from the user via the interface and updates the parameters accordingly. An updated pose of the XR system is captured using the pose tracking component and the virtual objects are then re-displayed using the updated display parameters and updated pose.

This application relates to the field of pose measurement, and more particularly to a pose tracking measurement method, apparatus, device, and storage medium. This application sets a reflector as a tracking point on the object being measured, establishes a target based on the reflector, and calculates the unit vector of the spatial plane normal vector; obtains a rotation matrix based on the relationship before and after rotation of the target coordinate system, and obtains the target tracking vector based on the rotation matrix and the unit vector; performs relationship matching or fitting on the target coordinate system according to the measurement requirements of the object being measured; and tracks the reflector position based on the target tracking vector and the result of the relationship matching or fitting to perform pose tracking measurement. This solves the practical measurement problems of existing technologies, such as limited pose measurement range, high measurement accuracy requirements, and complex construction of relationships between measured objects.

Techniques and systems are provided for pose prediction. For instance, a process can include combining image features detected from an obtained image with estimated image features to generate combined features; generating temporally encoded features by temporally encoding the combined features; combining detected motion tracking information with estimated motion tracking information to generate combined motion tracking information; generating temporally encoded motion tracking information by temporally encoding the combined motion tracking information; generating spatially encoded multi-modal information by spatially encoding the temporally encoded features and the temporally encoded motion tracking information; and predicting a body pose by regressing the spatially encoded multi-modal information.

The embodiment of the invention provides a high-precision spatial registration method and system based on a total ankle skeleton, and belongs to the technical field of medical image processing, and the method comprises the steps: obtaining tibia and talus three-dimensional data through CT or MRI scanning, and constructing and storing a high-precision skeleton simulation model; positioning trackers containing asymmetrically distributed passive infrared reflection balls are fixed to the tibia and the talus respectively, based on the positioning trackers, double cameras of an optical tracker are used for shooting the reflection balls, three-dimensional coordinates of mark points are obtained through parallax calculation, the real-time space posture of the bone is fitted, a registration matrix is updated, and based on a preoperative model and the tracking posture, the three-dimensional coordinates of the mark points are obtained. A talus coordinate system and a tibia coordinate system are constructed respectively, coarse registration is carried out through coarse registration points based on the coordinate systems, then fine registration points are collected through a probe, fine registration is carried out through a weighted iteration nearest point algorithm, and space registration is completed. According to the invention, through multi-modal data fusion, dynamic pose tracking and a weighted iteration nearest point algorithm, full-ankle bone submillimeter-level high-precision space registration is realized.

The application discloses a robot operation target 6D pose tracking method based on time sequence point cloud fusion. First, a point cloud video sequence of a robot operation scene is acquired and an operation target is segmented from the point cloud video sequence, then, dense corresponding relations among time sequence observation point clouds are established in a reverse prediction mode, and the dense corresponding relations are taken as a fusion criterion to perform point-by-point feature fusion on the time sequence observation point clouds, on the basis of the point-by-point feature fusion, a transformation pose between adjacent frames is predicted from the time sequence fused features in a confidence degree regression mode, finally, the transformation pose between the adjacent frames is continuously regressed to realize continuous pose tracking of the operation target. The application tracks a 6D pose of a robot operation target from a three-dimensional point cloud video sequence based on a deep learning technology, in view of irregularity and disorder of three-dimensional point cloud data, time sequence correlation information among observation point cloud data is fully utilized and effective feature fusion is performed, accurate and stable tracking is realized, and the application has good engineering practical value.

The invention discloses a virtual-real interaction multi-prop matching tracking method and system based on multiple sensors, and the method comprises the steps: S1, receiving the image data, obtained by a multi-view camera module, of infrared light-emitting points on a plurality of prop bodies, calculating the coordinates of each infrared light-emitting point in a three-dimensional space, and calculating the coordinates of each infrared light-emitting point; obtaining a first movement track set; s2, receiving IMU attitude data sent by an inertial measurement unit on each prop body, and estimating an IMU track of an infrared light-emitting point on each prop body through pre-integration and coordinate transformation to obtain a second motion track set; s3, aligning and normalizing the tracks, and calculating the similarity score of each track through a dynamic time warping algorithm to obtain a similarity score matrix; s4, inputting the similarity score matrix into a Hungary algorithm for optimal matching to obtain an identity binding result; and S5, motion prediction is carried out on the prop body whose identity is successfully bound according to the first motion track, and continuous pose tracking is realized.

The application discloses a pose correction method and device, a chip, equipment and a storage medium, and relates to the technical field of positioning. The method comprises the following steps: determining a pose drift amount based on first sensor data of a first device and second sensor data of a second device, wherein the pose drift amount is used to represent a drift condition of a pose of the first device relative to the second device; and performing pose correction based on the pose drift amount to obtain corrected first sensor data and / or second sensor data. The embodiment scheme of the application improves the accuracy of the device pose tracking result without increasing the cost of additional hardware.

The invention relates to a whole body attitude estimation method and system based on a downward fisheye and an enhanced EgoPoseFormer model, and the method enlarges the human body capture range through the layout of the bottom visual angle of a head-mounted display, compensates geometric nonlinearity in a feature extraction stage through distortion perception convolution, cooperates with an attention mechanism of an embedded pose compensation item, and achieves the estimation of the whole body attitude. And the feature space consistency is maintained when the camera dynamically shakes. Meanwhile, in combination with cross-frame time sequence gating and a multi-modal filtering algorithm, inertial data is utilized to perform kinematics correction on a visual predicted value, and smooth track output is realized in a shielding or rapid motion scene. According to the scheme, the computing power overhead is reduced through model quantification and operator fusion, and low-delay real-time whole body attitude tracking is realized in a mobile terminal chip environment.

The invention discloses a medical bed motion tracking system and method based on trinocular vision. The system adopts a non-equidistant compact three-camera layout, the left and middle base line is about 30cm, the left and right base lines are about 40cm, and all-angle dead-angle-free medical bed pose capture is realized by matching with medical spherical reflective mark points. Aiming at the shielding problem in a clinical environment, a dual anti-shielding strategy is adopted, trinocular redundancy characteristics are physically utilized, and binocular tracking is automatically switched when a monocular is shielded; a marker ball template is introduced in algorithm to match and repair damaged features. In a stereo matching link, an epipolar line and antipolar line dual verification mechanism is provided for a one-to-many mismatching problem, mismatching is eliminated through bidirectional projection consistency, uniqueness and accuracy of a matched triple are ensured, and high-precision three-dimensional reconstruction and pose tracking are further realized.

The invention discloses a 3D-SLAM (Three Dimensional-Simultaneous Localization and Mapping) method and system for a deep ground emergency rescue scene. The method comprises the following steps: S1, constructing scene representation based on a Gaussian splash rendering technology; s2, initializing a sub-map according to the input frame; s3, calculating a key frame, and adding the 3D Gaussian to the sub-map; s4, performing pose tracking to enable the rendering result of the current frame to be matched with the depth and color of the real image; s5, calculating the geometric and color loss of the sub-map, optimizing the 3D Gaussian parameters in combination with the pose objective function, and finally obtaining a rendering result; the system comprises a data acquisition module, an image preprocessing module, a modeling and optimizing module and a system control and scheduling module. According to the method, the camera attitude can be accurately estimated, the positioning precision is improved, and a high-quality and vivid real world scene is rendered.

This invention provides a robotic arm pose tracking control method for moving targets, relating to the control of autonomous moving bodies in three-dimensional space and the field of robotics. The control method includes: establishing the second-order pose dynamics equation of the target rigid body according to the Newton-Euler method; establishing the second-order pose dynamics equation of the robotic arm end effector according to the Lagrange equation; obtaining the relative pose dynamics equation based on the second-order pose dynamics equations of the target rigid body and the robotic arm end effector; and designing a robust adaptive controller for the relative pose dynamics equation, enabling the robotic arm end effector to complete the pose tracking control task of the moving target rigid body.

The invention discloses an attitude tracking control method for a spherical coaxial parallel mechanism, and relates to the technical field of robots. The method comprises the following steps: establishing an active-passive joint angle constraint equation of the parallel mechanism; performing physical simulation and fitting to obtain an approximation function of the passive joint angle relative to the active joint angle, taking the approximation function as an initial value, and performing numerical solution on the constraint equation to obtain an accurate passive joint angle; calculating the attitude of the movable platform through the joint angle and constructing a speed Jacobian matrix; and finally, designing a movable platform angular velocity control law according to the attitude error, obtaining an active joint control velocity through velocity Jacobian matrix mapping, and realizing tracking of an expected attitude.

The application discloses a trajectory tracking method and device, equipment and computer readable storage medium, belong to intelligent industrial robot technical field. The application obtains a first pose tracking error when the first time automatic guided vehicle runs on the path; according to the first pose tracking error, a controller is selected, the controller includes proportional-integral-derivative controller and or iterative learning controller, wherein the first pose tracking error is input to the proportional-integral-derivative controller to output a first control quantity, and the first pose tracking error is input to the iterative learning controller to output a first correction control quantity; based on the first control quantity and the first correction control quantity, a first final control quantity is output, and the automatic guided vehicle is controlled to run based on the first final control quantity. The beneficial effects of meeting the trajectory tracking accuracy and robustness are realized, and the learning convergence speed is greatly improved.

A pose tracking method and system, a mobile device, an electronic device and a storage medium. The pose tracking method includes: acquiring an image of a mobile device whereon is disposed with a light emitting unit for emitting signal light; based on the image, extracting, as a reference feature, a light spot feature corresponding to the light emitting unit on the image, and extracting a two-dimensional feature point corresponding to a three-dimensional feature point of the mobile device in the image; obtaining an initialization pose of the mobile device based on the two-dimensional feature point; and based on the initialization pose and the at least two reference features, optimizing the initialization pose for fine-tuning the initialization pose, to make the light spot feature corresponding to the initialization pose coincide with the reference feature. According to the embodiments of the present invention, the precision of pose tracking is improved, and the number of light emitting units required is reduced, such that a structure is simplified, and power consumption is reduced.

The application provides a three-dimensional rotation control method and system for radio frequency immunity test of an electric energy meter, which comprises the relative conversion relationship between a calibration camera and a three-dimensional turntable coordinate system, and establishes a 3D reference feature library of key points on the surface of the electric energy meter; in the test process, feature points are extracted from real-time monitoring images and matched with the reference library, and reliable 2D-3D point pairs are screened out by using a RANSAC algorithm; six degrees of freedom poses of the electric energy meter in the camera coordinate system are solved based on an EPnP algorithm, and then are mapped to the turntable coordinate system through coordinate transformation to obtain actual poses; the current actual poses and target test poses are respectively converted into the start and end angles of the axes of the turntable, and trajectory planning is performed in the joint space; the instantaneous poses are solved through real-time visual feedback, the pose deviation from the theoretical trajectory is calculated, and the angle compensation of each axis is iteratively solved by using a Gauss-Newton method, so that the control command is corrected, closed-loop pose tracking is realized, and the electric energy meter is stably brought to the predetermined test angle.

This invention discloses a deep learning-based method for endoscopic ultrasound probe recognition, posture tracking, and fusion display, belonging to the field of medical image processing technology. The method involves: acquiring endoscopic video frames as input to a posture detection model, outputting the probe frame and the coordinates and confidence scores of key points such as BlackCenter, Hole, and Top; after threshold filtering, calculating the center position and orientation angle based on the Hole-Top direction vector, and using confidence scores to drive adaptive hierarchical filtering to stabilize the angle and scaling; when there is occlusion or insufficient key points, calling position memory to maintain continuous tracking. The fusion end employs layered overlay, coordinate mapping, and fan-shaped mask clipping, combined with asynchronous inference and frame queue dropping to ensure real-time smoothness; it also provides automatic mask parameter estimation and calibration. This invention improves positioning and posture accuracy and anti-occlusion robustness, significantly suppresses jitter, reduces manual calibration costs, and enhances the consistency of fused display and the reliability of surgical navigation.

The application provides a method for synthetic data-driven target 6D pose tracking, and relates to the technical field of 6D pose estimation. The application proposes a high-robustness rigid body target 6D pose real-time tracking network. In the data preprocessing stage, through the enhancement processing of synthetic data, the sample diversity is enhanced, and the data acquisition cost is reduced. The key space-time information of the target object is extracted in the continuous frame to estimate the pose, and the residual sampling filtering module is designed to improve the robustness of the network to occlusion, and the target jitter problem caused by the change of the viewing angle is reduced in the long-term tracking process. The network proposed by the application can better train the real-time and accurate tracking of the target 6D pose through the synthetic data, and the network convergence speed is faster. The experimental results show that the method proposed by the application is higher than the similar related methods in the tracking accuracy and efficiency, and the effectiveness of the method is verified.

The invention relates to a direct projection augmented reality surgical navigation system and method based on machine learning, and the system comprises a surgical planning module which is used for processing medical image data of a target patient and generating a three-dimensional planning model; the image acquisition module is used for acquiring real-time image information of an operation area; the posture tracking module is used for carrying out posture registration based on machine learning so as to track the posture data of the target patient in the operation area and generate posture information; and the augmented reality projection module is used for adjusting the three-dimensional planning model and directly projecting the three-dimensional planning model to the surgical area to generate real-time surgical navigation information. Therefore, the problems that in the related technology, due to the fact that dependence on entity marking points is high, and the installation and calibration process is tedious and prone to interference, the marking points become surgical obstacles or the marking points move, precision is affected, due to the fact that navigation information is displayed through an external displayer, hand-eye spaces of doctors are inconsistent in the surgery process, the structure is complex, cost is high, and time and labor are wasted are solved. And the real-time requirement of a fine operation is difficult to meet.

Systems and techniques are provided for determining a pose. A process can include obtaining inertial measurement unit (IMU) data from an IMU associated with a device. The IMU data can be used to determine a propagated state associated with a state estimation engine, wherein the propagated state includes an initial orientation estimate corresponding to a pose of the device. The state estimation engine can comprise an Extended Kalman Filter (EKF). A predicted orientation measurement can be generated using a first machine learning network to process the IMU data and the initial orientation estimate included in the propagated state associated with the state estimation engine. An updated state associated with the state estimation engine can be determined based on using the predicted orientation measurement to update the propagated state. A device pose estimate can be determined based on the updated state associated with the state estimation engine.