17results about How to "Accurate pose" patented technology

The invention discloses a recovery method, device and equipment of an underwater robot and a storage medium, and relates to the technical field of computers. The method comprises the steps that a mother boat comprises a first mechanical arm and a second mechanical arm, camera arrays are arranged on the first mechanical arm and the second mechanical arm, at least four marks are determined on the robot, the mother boat obtains an image set through the camera arrays, a target image is determined, a first coordinate and a second coordinate are obtained, and the target image is obtained according to the first coordinate and the second coordinate. Determining a third coordinate of the mark in the camera coordinate system, determining a fourth coordinate of the mark in the mother boat coordinate system according to the third coordinate and the rotation angle corresponding to the mark, determining the pose of the robot by using the fourth coordinates corresponding to the at least four marks and the fifth coordinate in the robot coordinate system, and determining the position of the robot according to the pose of the robot. And the robot is recycled. According to the method, the more accurate pose of the underwater robot can be obtained, damage caused by collision with the mother boat is reduced, and the recovery risk is reduced.

The application relates to a bronchoscope control method and device, an automatic registration method and computer equipment. The method comprises the following steps: acquiring a pre-constructed bronchial model, wherein the bronchial model comprises a plurality of registration points; moving the bronchoscope to each registration point according to a preset path, including: collecting real endoscopic images during the moving process; and controlling the movement of the bronchoscope according to the real endoscopic images and virtual endoscopic images corresponding to the current registration point. According to the real endoscopic images and the virtual endoscopic images corresponding to the current registration point, the bronchoscope can be automatically moved to each registration point according to the preset path, an accurate pose can be provided for the bronchial model, and the accuracy of virtual endoscopic rendering is improved.

The application discloses a real-time fine mapping method suitable for an underwater robot, which comprises the following steps: firstly, a 3D Gaussian map is constructed by using a first frame; then, key frame determination is performed on the following image frames; and finally, an incremental map is constructed by using the key frames; the process effectively overcomes the optical interference specific to the underwater environment, filters out the geometric noise caused by suspended particulate matters and the like, and significantly improves the geometric consistency and color authenticity of the map; all frames are tracked in real time, accurate poses are obtained and output, the process effectively overcomes the interference of underwater optical degradation on pose estimation, and ensures the robustness and accuracy of the tracking process.

The present disclosure relates to a method and device for determining camera pose, and an electronic device, and particularly relates to the technical field of visual positioning and tracking. The method comprises: acquiring a target image captured by a camera; determining a plurality of first feature points in voxels; when there is no other feature point in the voxels between a first target feature point and a center point of the camera, performing feature point matching between the first target feature point and a pixel point in the target image, the first target feature point being any one of the plurality of first feature points; and if the first target feature point matches the pixel point in the target image, calculating an actual pose of the camera based on the first target feature point.

The present disclosure provides a pose determination method, a pose determination device, a computer readable storage medium and an electronic device, and relates to the technical field of computer vision. The pose determination method comprises the following steps: determining first two-dimensional feature points on a current frame color image collected by a first camera and matched with a previous frame color image collected by the first camera, determining second two-dimensional feature points on a current frame color image collected by a second camera and matched with a previous frame color image collected by the second camera, converting the second two-dimensional feature points to a first camera coordinate system to obtain third two-dimensional feature points, and determining a pose of the first camera when collecting the current frame color image according to the first two-dimensional feature points, the third two-dimensional feature points, three-dimensional feature points of the previous frame color image collected by the first camera in a world coordinate system, and three-dimensional feature points of the previous frame color image collected by the second camera in the world coordinate system. The present disclosure can improve the positioning accuracy.

The invention provides a continuous time pose estimation method based on a multi-resolution Hash map. The continuous time pose estimation method comprises the following steps: S1, acquiring laser radar data of mobile equipment at the current moment; s2, splitting each frame of laser radar data and converting the split data into global coordinate system laser radar data under a global coordinate system; s3, extracting a key point of each frame of laser radar data in the global coordinate system laser radar data, constructing a point-surface residual error based on a Hash map of all key points of the current frame of laser radar data, constructing a position consistency residual error and a speed consistency residual error of the current frame of laser radar data based on the previous frame of laser radar data, and constructing an optimization problem, solving the pose corresponding to each frame of laser radar data based on the optimization problem; and S4, updating the multi-resolution Hash map based on the pose of each frame of laser radar data, and removing out-of-view data in the multi-resolution Hash map, thereby realizing a rapid, efficient and low-delay continuous time pose estimation method.

The application relates to a visual positioning method, a device and an intelligent vehicle. The method comprises the following steps: acquiring an image including complete ground road signs collected by a camera, segmenting the image including the complete ground road signs to obtain a binary image, extracting corner points of real ground road signs in the binary image, acquiring historical poses of the camera and a ground road sign map, obtaining estimated corner points of the ground road signs according to the historical poses and the ground road sign map, matching the estimated corner points of the ground road signs with the corner points of the real ground road signs, and obtaining a pose of the camera at present. The application aims to solve the positioning drift phenomenon of a visual positioning system of an intelligent vehicle during driving, match the estimated corner points of the ground road signs with the corner points of the real ground road signs, continuously correct the pose deviation of a real camera and an estimated camera, make the obtained pose of the camera at a current time more accurate, and improve the positioning accuracy of the intelligent vehicle.

The invention relates to the technical field of simultaneous localization and map construction, and discloses an SLAM (simultaneous localization and mapping) back-end pose optimization method based on a graph neural network, which comprises the following steps of: extracting key feature points of image streams continuously input into an SLAM system, and recording feature descriptors; projecting the key feature points to a three-dimensional space, obtaining corresponding points of left and right camera images through stereo matching, and triangularizing the corresponding points to obtain three-dimensional coordinates of the key feature points; key frames are selected, key feature points and feature descriptors in the key frames are recorded, and whether the number of the key frames is larger than a preset threshold value or not is judged; through key feature point matching, determining a common-view relationship between the key frames, obtaining a graph topological structure of the key frames, and inputting the graph topological structure into a graph neural network for pose optimization; sorting the key frames in the key frame database according to the size of the timestamps in a positive sequence, and keeping the rear half part of the key frames in the key frames sorted in the positive sequence for the next round of data collection and optimization; according to the method, the key frame pose optimization precision is improved, and the performance of the SLAM system is improved.

The invention belongs to the technical field of stacker control, and particularly discloses a stacker control method and device, electronic equipment and a medium, and the method comprises the steps: enabling a stacker to travel in a working environment in advance, calculating the pose of a binocular camera through a high-precision integrated positioning system, observing a road sign through the binocular camera, and forming a visual point cloud map; acquiring spatial position distribution of to-be-carried materials in the current environment based on the binocular camera, and projecting the spatial position distribution to the visual point cloud map by utilizing spatial position transformation to generate a semantic map; the stacker moves according to the semantic map to arrive at a target position to work; and a to-be-carried material is recognized through the binocular camera, and the to-be-carried material is carried. When the stacking machine works in a complex and huge environment, accurate positioning, accurate moving to a carrying point and accurate carrying can be achieved.

The invention provides an audio playing method and system based on calibration, head-mounted equipment and a storage medium, and relates to the field of audio processing. The calibration-based audio playing method is applied to a processor of the head-mounted device. The head-mounted device is connected with at least one wireless device through wireless communication, and the calibration-based audio playing method comprises the following steps: determining relative azimuth information between the head-mounted device and the wireless device based on the wireless communication connection between the head-mounted device and the wireless device; calibrating a null drift parameter of the IMU based on the relative azimuth information; the zero drift parameter is a parameter influenced by zero drift; acquiring a current calibration pose of the head-mounted device from the IMU after the zero drift parameter is calibrated; and according to the current calibration pose, adjusting orientation features in audio data to be played and sending the orientation features to the loudspeaker for playing. The calibration-based audio playing method is beneficial to improving the three-dimensional effect of audio data playing.

The embodiment of the invention is suitable for the technical field of smart home, and provides a control method and device of a sweeping robot, the sweeping robot and a storage medium, and the method comprises the steps: collecting environment point cloud data in a current direction through a distance measuring sensor of the sweeping robot; environment point cloud data intersection information is determined according to the environment point cloud data and a currently established map block of the sweeping robot; and according to the environment point cloud data intersection information, controlling the sweeping robot to execute a preset action so as to collect environment point cloud data in multiple directions. Through the method, the sweeping robot can be rotated in time, so that the scanning view angle of the distance measuring sensor of the sweeping robot is adjusted, and the quality of the built map blocks of the sweeping robot is optimized.

This invention relates to the field of optical alignment technology, specifically providing a global field-of-view calibration method and device based on a digital model of an alignment system. The method includes: establishing a measured coordinate system and acquiring measured point cloud data; matching the measured point cloud data with ideal point cloud data to determine the direction of the optical axis in the measured coordinate system; constructing a detection optical path and acquiring the mirror model of the standard mirror in the detection optical path and three reference planes on the standard mirror fixture; calculating the normal vector of the standard mirror and the spatial pose relationship between the reference planes and the standard mirror; establishing a field-of-view coordinate system and transforming the spatial pose relationship between the reference planes and the standard mirror to the field-of-view coordinate system; and calculating the field-of-view angle of the normal vector of the standard mirror. This invention reliably transmits the optical surface pose, which cannot be directly measured, to a field-of-view coordinate system established based on the secondary mirror of the camera under test, making the calculation of the optical axis direction and field-of-view angle intuitive and accurate, effectively ensuring the alignment accuracy of asymmetric, off-axis optical systems such as off-axis three-lens reflex cameras.

The present application relates to a kind of based on multi-sensor fusion's heading machine tracking positioning method, belong to heading machine positioning technical field.The method includes: fixedly installing total station in the roadway top behind the heading direction of heading machine, suspending tracking positioning robot in the guide rail above roadway, carrying binocular camera, strapdown inertial navigation system on tracking positioning robot, installing infrared target plane on the body of heading machine;Control tracking positioning robot moves to the best observation distance interval of binocular camera, and obtains the target image of infrared target plane collected by binocular camera;According to target image, calculate camera-carrier rotation matrix and camera-carrier translation matrix;Obtain the attitude of tracking positioning robot relative to navigation coordinate system;According to camera-carrier rotation matrix, camera-carrier translation matrix, the attitude of tracking positioning robot relative to navigation coordinate system and coordinate system conversion relationship determine the pose of heading machine.The present application can continuously track and locate heading machine.

The present application discloses a method, device, equipment and storage medium for recovering an underwater robot, and relates to the field of computer technology. The method comprises: a mother boat comprises a first mechanical arm and a second mechanical arm, a camera array is deployed on the first mechanical arm and the second mechanical arm, at least four marks are determined on the robot, the mother boat obtains an image set through the camera array, determines a target image, obtains a first coordinate and a second coordinate, determines a third coordinate of the mark in the camera coordinate system according to the first coordinate and the second coordinate, determines a fourth coordinate of the mark in the mother boat coordinate system according to the third coordinate and the rotation angle corresponding to the mark, determines the posture of the robot using the fourth coordinate corresponding to at least four marks and the fifth coordinate in the robot coordinate system, and recovers the robot according to the posture of the robot. The method can obtain a more accurate posture of the underwater robot, reduce damage caused by collision with the mother boat, and reduce the risk of recovery.

The invention discloses a medical robot system based on compliant dragging and a control method thereof.The system comprises a control module, a mechanical arm, an end effector and a sensor arranged at the end effector, and the sensor is configured to detect force applied externally and send a detection result to the control module; the control module is provided with a compliant control model, parameters related to the model comprise the detection result of the sensor and the pose error, the speed and the acceleration of the mechanical arm, and the pose error is the deviation between the current actual pose and the expected pose of the mechanical arm; the control module outputs actual control force to the mechanical arm according to the compliance control model so as to dynamically adjust the motion trail of the end effector, and compliance parameters of the compliance control model are adaptively adjusted according to the pose error, the speed and the acceleration of the mechanical arm in the motion trail adjusting process. The medical robot disclosed by the invention can realize self-adaptive control of flexible dragging and accurate pose control on the premise of not depending on an ROS system.

Embodiments of the present application disclose a target object pose determination method and device and electronic equipment. The method performs coarse and fine granularity matching on the geometric features of the scene point cloud and the geometric features of the model point cloud, respectively establishes a first matching result and a second matching result between the scene point cloud and the model point cloud, and then calculates a target pose of the target object based on the first matching result and the second matching result. Through the present application, the technical problem that the poses of all objects in an application scene cannot be accurately estimated and it is difficult to meet the requirements of an application scene with high pose accuracy is solved, and the technical effect of more accurately identifying the pose of an object and improving the adaptability of a complex application scene is achieved.

The application discloses a visual odometer method fusing mid-term semantic information, comprising the following steps: S1. a tracking thread receives a binocular image stream, and delivers a monocular image to a semantic segmentation process; the semantic segmentation process performs semantic segmentation on the monocular image, and delivers a semantic segmentation graph to the tracking thread; S2. the tracking thread constructs a current frame according to the semantic segmentation graph and generates an initial map; local map points are matched with feature points of the current frame; the pose of the current frame is optimized; a key frame is determined; key frame information and the semantic segmentation graph are transmitted to a local mapping thread; S3. the local mapping thread updates map points according to the feature points of the key frame, constructs a mid-term sliding window and updates semantic map points of the mid-term sliding window, establishes a mid-term constraint according to the semantic map points and optimizes the pose of the key frame; S4. the local mapping thread reversely projects the map points to the semantic segmentation graph by using the optimized pose, updates semantic information of the semantic map points, and manages the semantic map points according to the quality of the semantic map points.