104 results about "Pose tracking" patented technology

Dense range data obtained at real-time rates is employed to estimate the pose of an articulated figure. In one approach, the range data is used in combination with a model of connected patches. Each patch is the planar convex hull of two circles, and a recursive procedure is carried out to determine an estimate of pose which most closely correlates to the range data. In another aspect of the invention, the dense range data is used in conjunction with image intensity information to improve pose tracking performance. The range information is used to determine the shape of an object, rather than assume a generic model or estimate structure from motion. In this aspect of the invention, a depth constraint equation, which is a counterpart to the classic brightness change constraint equation, is employed. Both constraints are used to jointly solve for motion estimates.

The invention provides an optical measurement system and method for a three-dimensional shape of a large-scale complex curved surface member. The method is based on binocular optical grating projection measurement technologies, point cloud poses of all station positions during multiple station position measurement are obtained via a laser tracker and a corresponding target ball, point cloud obtained via the multiple station position measurement is converted to be under a unified laser tracker coordinate system according to corresponding pose data, and overall merging of point cloud data of a large-scale complex surface-shaped member can be realized; in the system, a six degree-of-freedom robot is used as a carrier for a point cloud space pose tracking unit and a binocular structured light measurement device, single station position measurement precision is ensured via calibration of the binocular structured light measurement device before measurement work starts, and data integrity and measurement efficiency are ensured via measurement route planning. Via the optical measurement system and method, all kinds of large-scale complex surface-shaped members can be accurately measured in non-contact conditions; practical, reliable and complete original three dimensional shape data can be provided for evaluation of all kinds of processing quality.

The invention relates to a robot positioning method with fusion of visual features and IMU information. The invention puts forward a method of fusion of monocular vision with IMU. Visual front-end pose tracking is performed and the post of a robot is estimated by using feature points; an IMU deviation model, an absolute scale and a gravity acceleration direction are estimated by using pure visualinformation; IMU solution is performed to obtain high-precision pose information and thus an initial reference is provided to optimize the search process, and the initial reference, a state quantity and visual navigation information are used for participating in optimization; and a rear end employs a sliding-window-based tightly coupled nonlinear optimization method to realize pose and map optimization. And the computational complexity is fixed while the speedometer is calculated based on a sliding window method, so that the robustness of the algorithm is enhanced.

The invention provides a SLAM method applied to a multi-lens combination panoramic camera, which well realizes the automatic positioning and map construction functions of a vehicle-mounted multi-lenscombination panoramic camera, and achieves a high positioning accuracy of 0.1 m level without resorting to an expensive GPS/IMU integrated navigation system. The method firstly performs camera calibration on a multi-lens combination device, and then establishes a parallel three-thread, wherein the pose tracking thread mainly completes system initialization, feature extraction on a panoramic image,matching and projection, pose solution optimization and selection of key frames; The sparse map construction thread is mainly responsible for establishing sparse map points according to the initial posture and matching points, optimizing the camera posture and map points locally, and eliminating the wrong map points and redundant key frames. The closed-loop correction thread is mainly responsiblefor detecting whether the motion of the camera has returned to the previous position (i.e. Closed-loop), adding closed-loop constraints to correct the posture and global posture and map point optimization (i.e. Global beam adjustment).

The invention discloses a high-rise building external wall cleaning robot comprises a robot body, a cleaning mechanism, an external wall absorbing mechanism, a robot running and obstacle detouring mechanism, a robot supporting system and a GPS position and pose tracking controlling system, wherein the cleaning mechanism and the external wall absorbing mechanism are arranged on the robot body. The high-rise building external wall cleaning robot is characterized in that the robot running and obstacle detouring mechanism is a flying vehicle which is arranged on the upper part of the robot body and is connected with the robot body through a traction cable on the lower part, the lower part of the flying vehicle is provided with a propeller, and the robot supporting system is arranged in the flying vehicle. The invention has simple structure and low cost and is easy to operate and convenient to use and maintain.

In accordance with various embodiments of the invention, a user-borne computer input device is disclosed including a 5D or greater “mouse” or other tool or object containing or consisting of one or more permanent magnetic presenting permanent magnetic dipoles. The position and orientation of the device are determined by magnetic field strength measurements derived from at least two vector-magnetometers fixed in a reference frame connected to the computing device. The system allows a user to interact with the computing device in at least 5 dimensions by manipulating the position and orientation of the device within a measurement volume, which may be a few cubic meters.

A state estimation system that utilizes long-range stereo visual odometry that can degrade to a monocular system at high-altitude, and integrates GPS, Barometer and IMU measurements. The system has two main parts: An EKF that is loosely fused and a long-range visual odometry part. For visual odometry, the system takes the EKF information for robust camera pose tracking, and the visual odometry outputs will be the measurement for EKF state update.

Methods and systems for registering a manipulator assembly and independently positionable surgical table are provided herein. In one aspect, methods include reading a fiducial marker on the surgical table with a sensor associated with the manipulator assembly and localizing the manipulator assembly and surgical table with respect to a common reference frame. Methods may further include translating a 3D configuration of the surgical table to a 2D frame of reference so as to estimate a 3D pose of the surgical table relative the manipulator assembly for use in coordinating movements therebetween.

A camera pose tracking apparatus may track a camera pose based on frames photographed using at least three cameras, may extract and track at least one first feature in multiple-frames, and may track a pose of each camera in each of the multiple-frames based on first features. When the first features are tracked in the multiple-frames, the camera pose tracking apparatus may track each camera pose in each of at least one single-frame based on at least one second feature of each of the at least one single-frame. Each of the at least one second feature may correspond to one of the at least one first feature, and each of the at least one single-frame may be a previous frame of an initial frame of which the number of tracked second features is less than a threshold, among frames consecutive to multiple-frames.

A system and a method for pose tracking, particularly for top-down, online, multi-person pose tracking. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: provide a plurality of sequential frames of a video, the sequential frames comprising at least one keyframe and a plurality of non-keyframes; for each of the non-keyframes: receive a previous inference bounding box of an object inferred from a previous frame; estimate keypoints from the non-keyframe in an area defined by the previous inference bounding box to obtain estimated keypoints; determine object state based on the estimated keypoints, wherein the object state comprise a “tracked” state and a “lost” state; and when the object state is “tracked,” infer an inference bounding box based on the estimated keypoints to process a frame next to the non-keyframe.

This application discloses a repositioning method and apparatus in a camera pose tracking process, a device, and a storage medium, belonging to the field of augmented reality (AR). The method includes: obtaining a current image acquired after an i^th anchor image in a plurality of anchor images; obtaining an initial feature point and an initial pose parameter in the first anchor image in the plurality of anchor images in a case that the current image satisfies a repositioning condition; performing feature point tracking on the current image relative to the first anchor image, to obtain a plurality of matching feature point pairs; filtering the plurality of matching feature point pairs according to a constraint condition, to obtain a filtered matching feature point pair; calculating a pose change amount of a camera from the initial pose parameter to a target pose parameter according to the filtered matching feature point pair; and performing repositioning according to the initial pose parameter and the pose change amount to obtain the target pose parameter of the camera.

Embodiments of this application disclose a method for determining camera pose information of a camera of a mobile terminal. The method includes: obtaining a first image, a second image, and a template image; performing feature point detection on a first feature point of the template image and a second feature point of the second image, to obtain a first homography; determining a first target homography according to a first optical flow feature point in the first image and a second optical flow feature point in the second image, and determining a second homography according to the first target homography and a second target homography; and performing complementary filtering processing on the first homography and the second homography, to obtain camera pose information of the camera. In the embodiments of this application, complementary filtering processing may be performed on two homographies obtained in a camera pose tracking process.

The invention discloses an integrated control method for a tethered robot approaching target pose. The method comprises the steps of: 1) establishing a tethered robot pose dynamic model; 2) designingan interference observer; 3) designing a controller; and 4) distributing control force / torque. According to the integrated control method for the tethered robot approaching target pose, the interference observer is designed to estimate un-modeled deviation and environmental interference, and the integrated control method is designed for target pose tracking. In addition, the pose-orbit couplingcharacteristic of the tethered robot is fully considered, so that the pose of the tethered robot can be integrally controlled by a tether in a target approaching process.

The invention provides an information detection system for rotation center movement of a glenohumeral joint of a human shoulder and belongs to the technical field of detection. The detection system comprises a human sternum position tracking sensor, a human humerus position tracking sensor, a human scapula pose tracking sensor and a data acquisition card, wherein the human sternum position tracking sensor is used for detecting the position of human sternum and provided with four point positions for acquiring human pose information in real time; the human humerus position tracking sensor is used for detecting the position of human humerus; the human scapula pose tracking sensor is used for detecting the pose of human scapula; the data acquisition card is used for collecting the positions ofthe human sternum and the human humerus and the pose of the human scapula; the rotation center movement information of the glenohumeral joint of the shoulder in one cycle is displayed by processing based on upper computer application and data processing subprogram. According to the acquisition system, the problems of data mark point loss of a VICON detection system and blocking of a human camerasystem are solved; the acquisition system is simple, high in adaptive capacity, accurate in data acquisition and good in real-time performance, and can be applied to shoulder function bionics, shoulder function simulation, ergonomics and the like.

The invention provides a double-rigid-body characteristic point saturation fixed time relative pose tracking control method. The method comprises the steps: establishing a pose kinetic equation of a tracker and a target device based on a Newton-Euler method; establishing a relative pose kinetic equation considering model uncertainty according to the established respective pose kinetic equations ofthe two rigid bodies and the relative pose and the relative speed between the two rigid body feature points; designing a fixed time disturbance observer according to the established relative pose kinetic equation; designing a non-linear saturation compensator according to the established fixed-time non-singular terminal sliding variable so as to solve the saturation effect of an actuator; and combining the established fixed-time disturbance observer and the nonlinear saturation compensator to obtain a model-based robust adaptive controller. The invention relates to the field of control of anautonomous moving body in a three-dimensional space and spacecrafts.