211 results about "Stereo cameras" patented technology

A modeling method for three-dimensional real-time measurement reconstruction, comprising: on the basis of a three-dimensional camera, a laser radar, and an infrared sensor device, generating a multi-source original data set; performing data preprocessing; performing feature extraction and data fusion; performing three-dimensional space construction; by means of a deep machine learning algorithm, performing real-time dynamic adjustment and optimization on a model; and, by adopting an advanced image processing algorithm, generating a high-precision three-dimensional model.

A pest control system is disclosed comprising an optical, computational, and monitoring subsystem, optionally mounted on a mobile platform. The optical system may include a neutralizing laser or multi-wavelength light source, discovery and detail cameras (optionally stereo), a beam-steering mechanism, tunable focus, and optional thermal or depth sensors. The processor, such as a GPU or FPGA, identifies insect or biological targets, adjusts laser focus by depth, and controls beam activation. A monitoring system verifies safety by detecting humans or other non-target entities using environmental and thermal cameras; if detected, laser firing is inhibited. The mobile platform may use wheels, propellers, tracks, or cables, with GPS and data links for remote control. A visible light pre-flash may induce a blink reflex before firing. In some embodiments, a scouting drone transmits target coordinates to the neutralization unit, enabling coordinated, efficient, and safe laser-based pest control.

The invention belongs to the technical field of automatic assembly, and discloses an automatic assembly system and method for a large nut of a water turbine ball valve, and the system comprises a mechanical arm, an end effector and an intelligent nut tightening control system. The end effector integrates an electromagnet adsorption mechanism, a servo drive rotating mechanism, a floating compensation mechanism and a visual recognition mechanism, nuts of different specifications are grabbed in a self-adaptive mode through an electromagnet, submillimeter-level accurate positioning is achieved through a laser galvanometer stereo camera, and the assembling posture is automatically adjusted in combination with the floating compensation mechanism. The intelligent control system adopts a staged tightening strategy, performs real-time monitoring through a torque sensor, and has a dual protection mechanism. The hydraulic turbine ball valve large nut assembling machine achieves full-automatic assembling of hydraulic turbine ball valve large nuts, has the advantages of being high in assembling efficiency, high in positioning precision, high in adaptability, reliable in quality and the like, solves the problems that manual assembling is large in labor intensity, many in potential safety hazards, poor in quality consistency and the like, and remarkably improves the assembling quality and efficiency of key parts of a hydropower station.

A multi-modal stereo vision system includes one or more stereo vision units. Each stereo vision unit includes a plurality of stereo camera pairs. Each image pair includes a first image and a second image. The plurality of stereo camera pairs can capture multi-modal image data.

An object detection system for a vehicle that estimates a location of a target object located in an environment surrounding the vehicle includes an ultra-wide band (UWB) sensor network including three or more anchors mounted to the vehicle in wireless communication with a tag mounted to the target object, a non-stereo camera system that captures image data representing the target object located in the environment surrounding the vehicle, and one or more controllers in electronic communication with the UWB sensor network and the non-stereo camera system. The one or more controllers includes one or more processors that execute instructions to fuse together a camera-based location of the target object and a UWB-based location of the target object by a Bayesian filter to estimate the location of the target object.

The invention provides a macrobrachium rosenbergii quality grading system based on machine vision. The macrobrachium rosenbergii quality grading system based on machine vision comprises a prawn body placing platform used for placing macrobrachium rosenbergii to be tested; the three-dimensional acquisition module comprises a structured light projection device, a binocular stereo camera and a flight time depth sensor; and the three-dimensional reconstruction module is used for constructing the data of the three-dimensional acquisition module into a complete three-dimensional point cloud model through a data fusion algorithm and a point cloud registration algorithm. According to the macrobrachium rosenbergii quality grading system based on machine vision, the overall scheme has robustness and expandability, the system is suitable for various large-scale farms and grading scenes, and the intelligent level of macrobrachium rosenbergii quality control is greatly improved.

An electronic device displays a widget dashboard user interface in a three-dimensional environment, displays a representation of a physical tool to as guidance for indicating a location of the physical tool relative to a location associated with video feed, displays suggestions for changing a pose of a camera to a predetermined pose based on image data detected while the camera previously had the predetermined pose, displays a live camera feed and image data and scrubs through the image data in accordance with changes to a pose of the camera relative to a physical object, displays live stereoscopic camera feed with special effects, detects and responds to inputs for virtually annotating portions of objects, and / or displays models of objects and detects and responds to input for rotating and / or viewing the model from different depth positions within the model.

Disclosed herein is a method for estimating a depth of an image. The method may include acquiring a first depth image based on left and right images collected from a stereo camera, estimating a second depth image based on any one of the left and right images, generating a depth estimation model based on first depth values within a depth range recognizable by the stereo camera in the first depth image and on second depth values of the second depth image matching the first depth values, and inputting third depth values of a range intended to be estimated in the second depth image to the depth estimation model, thereby estimating fourth depth values of the first depth image matching the third depth values.

A computer that includes a processor and a memory, the memory including instructions executable by the processor to select first side stereo images of first and second pairs of stereo images acquired at first and second time steps, respectively, and mask first and second first side stereo images, determine point features in masked first and second first side stereo images and determine line features in masked first and second first side stereo images. Matching point features in the masked first and second first side stereo images can be determined using a first attention / graph neural network (attn / GNN) based on keypoints determined based on the line features. Matching line features in the masked first and second first side stereo images can be determined using a second attn / GNN based on keypoints determined based on the line features. Three-dimensional (3D) locations in a scene can be determined by determining stereo disparity based on the matched point features included in the first first side stereo image and point features determined in a first second side stereo image of first and second stereo pairs of images and a 3D stereo camera pose can be determined by determining a perspective-n-point and line algorithm on the 3D locations.

A method for adjusting a depth of a stereoscopic image includes the following. A first disparity correction amount is obtained according to a distance between centers of two lenses of a stereoscopic camera and an interpupillary distance (IPD) of a first observer. A second disparity correction amount is obtained according to a difference between image distances of a stereoscopic display and the stereoscopic camera at zero disparity. A third disparity correction amount is obtained according to the IPD of the first observer and an IPD of a second observer. A stereoscopic image of an object photographed by the stereoscopic camera which has been corrected by the first disparity correction amount, the second disparity correction amount, and the third disparity correction amount is transmitted to the stereoscopic display. A device for adjusting a depth of a stereoscopic image is also provided.

The invention belongs to the technical field of navigation and positioning, particularly relates to a GNSS / visual loose coupling positioning method fusing environmental semantics, and solves the technical problem that a traditional method is insufficient in positioning precision when facing an urban sheltered environment and a high-dynamic scene. According to the method, a GNSS receiver obtains position coordinates of a carrier through a double-difference measurement model of the GNSS receiver, and continuous image frames captured by a stereo camera are transmitted to a VO module for executing pose estimation of the carrier; carrier position coordinates of a GNSS and carrier pose information of a VO module are combined by adopting a loose coupling model, then a shielding factor is calculated according to environmental semantic information and a local mapping module, an adaptive KF algorithm is introduced in combination with the shielding factor, and under the constraint of the shielding factor, the adaptive KF algorithm can calculate an optimal pose estimation value, so that the optimal pose estimation value is obtained. The value is fed back to the GNSS to correct the position coordinate of the carrier, and finally the corrected position coordinate is output as a positioning result.

The present disclosure is a data positioning apparatus including a stereo camera which acquires visual data corresponding to 2D and 3D, an inertial measurement unit which acquires inertia data corresponding to acceleration and angular velocity data, and a computing module which receives data acquired from the stereo camera and the inertial measurement unit and performs computation of a visual SLAM engine.

The invention relates to a method for determining the position and / or orientation of tools of construction machines in a construction area, comprising the steps of: attaching at least one stereo camera to at least one construction machine or to a movable carrier, such that the stereo camera can detect at least one detection region within the construction area, preferably a working region of the construction machine, providing at least one reference point, the position of which is known relative to a predefined 3D world coordinate system and which can be recognised in the images of the stereo camera, in the detection region of the at least one stereo camera, calibrating the at least one stereo camera on the basis of the detection of the at least one reference point and triangulation in order to ascertain a transformation between the 3D world coordinate system and a 2D image coordinate system of the at least one stereo camera, preparing a virtual digital 3D terrain model of the construction area by means of digital image processing, in particular stereoscopy, and image recognition on the basis of the detection of the construction area by means of the at least one stereo camera, and representing predetermined working positions and detected actual tool positions and / or actual tool inclinations of the tool of the at least one construction machine in the virtual digital 3D terrain model.

A work information projection system estimates a position in a workplace. The work information projections system includes a camera (13), a stereo camera (14), and a controller. The camera (13) is arranged facing in a first direction and acquires first environment information which is information about an object arranged in and around the first direction. The stereo camera (14) is arranged facing in a second direction and acquires second environment information which is information about an object arranged in and around the second direction. The controller estimates a position in a first coordinate system based on the first environment information when it determines that the second environment information becomes unacquirable.

Systems and methods related to visual guidance for improving autonomous navigation with row following corrections in stereo camera systems are disclosed herein. A visual positioning system may implement a method for navigating a vehicle. The method may comprise: receiving navigation coordinates defining a route comprising a straight path; detecting detected lines that are parallel to the straight path based on images received from at least one camera on the vehicle; accessing ground truth data; deriving vehicle coordinates from the ground truth data; determining a centerline in vehicle coordinates based on the detected lines; determining corrected vehicle coordinates based on the centerline in vehicle coordinates and the vehicle coordinates from the ground truth data; and generating control signals to navigate the vehicle along the route using the corrected vehicle coordinates. The visual positioning system may maintain localization accuracy and provide drift correction in the presence of poor GPS or radio localization.

The invention provides a visual identification positioning method, device and equipment for a robot, and the method comprises the steps: collecting a plurality of frames of left and right target images through a stereo camera, carrying out the dual feature detection of the images, extracting point features and line features, and forming complementary feature information; matching optimization processing is carried out on the point-line features, and a high-quality feature matching result is obtained; key frame selection is carried out based on a point-line fusion feature matching result, and redundancy calculation is reduced; and carrying out three-dimensional reconstruction on point-line features in the key frame, and carrying out fusion optimization with inertial measurement unit data to obtain accurate robot position and attitude information. Through a point-line feature fusion mode, the limitation of purely depending on point features in a texture missing environment is effectively solved, the precision and robustness of robot vision positioning are remarkably improved, and the method is suitable for robot autonomous navigation in texture missing and rugged terrain environments.

An image processing device 20 includes a communication unit 21 and a controller 22 (processor). The communication unit 21 acquires a standard image and a reference image captured by a stereo camera 10. The controller 22 calculates a disparity based on the standard image and the reference image. The controller 22 calculates, at each pixel position, a cost value representing a degree of difference for each pixel position between the standard image and each of multiple images obtained by displacing the reference image by multiple different deviation amounts in a direction corresponding to a baseline length direction of the stereo camera 10. The processor aggregates the cost values using dynamic programming and calculates a disparity for each pixel position based on the aggregated cost values for each different deviation amount. The multiple different deviation amounts are discrete values at uneven intervals.

The present disclosure relates to user interfaces for augmented reality experiences including live feeds from one or more external sensors. An electronic device displays a widget dashboard user interface in a three-dimensional environment, displays a representation of a physical tool as guidance for indicating a location of the physical tool relative to a location associated with a video feed, displaying a suggestion for changing the pose of the camera to a predetermined pose based on image data detected when the camera has previously the predetermined pose, displaying a live camera feed and the image data and browsing the image data according to the change in the pose of the camera with respect to the physical object, a live stereo camera feed with a special effect is displayed, input for virtually annotating portions of an object is detected and responsive to, and / or a model of the object is displayed, and input for rotating and / or viewing the model from different depth positions within the model is detected and responsive to.

System and method for a computer vision technique for contactless measurement of vehicle toe are disclosed herein. The method includes capturing with a stereo camera an image containing a vehicle's wheel, segmenting the image, determining a suitable circular component of the wheel and computing the angle of the wheel relative to the camera and, subsequently, to the body of the vehicle. The method includes repeating the process for a plurality of wheels and computing the toe angle of the vehicle based on the obtained results.

To provide an electronic apparatus, a program, and an information processing method capable of accurately measuring a position of a pedestrian.SOLUTION: An electronic device (10) includes a disparity data processing unit (11) that generates disparity data based on image data acquired from a stereo camera, a skeleton detection unit (12) that detects a skeleton of a person included in the disparity data, a center-of-gravity recognition unit (13) that recognizes a center of gravity of the person based on the skeleton of the person, an ROI setting unit (14) that sets an ROI including the center of gravity of the person for the disparity data, and a conversion unit (15) that converts a position of the center of gravity of the person into a position in a sensor coordinate system based on the disparity data in the ROI.SELECTED DRAWING: Figure 1

An autonomy computing system and a method of an autonomous vehicle for rectifying stereo images includes a memory storing computer executable instructions and a processor coupled to the memory, the processor, upon execution of the computer executable instructions, configured to: receive an image pair captured using respective cameras in the stereo camera pair; predict a rotation matrix between the first image and the second image by: extracting a first feature map and a second feature map; applying positional feature enhancement on the feature maps to derive a pair of enhanced feature maps; computing a correlation volume across the enhanced feature maps; determining a set of likely matches between the enhanced feature maps; computing a predicted relative pose; and computing the rotation matrix. The system and method further include calibrating the stereo camera pair to rectify the first image and the second image based on the rotation matrix.

The application provides a tunnel vehicle positioning and trajectory tracking method, device and system, the method comprising: acquiring lane marking corner point coordinates in the tunnel; determining the coordinates and direction cosine matrix of the stereo camera in the tunnel based on the lane marking corner point coordinates; wherein a plurality of groups of stereo cameras are installed at equal intervals on the tunnel vault center line, and each group of stereo cameras comprises a main camera and an auxiliary camera located on both sides of the tunnel vault center line; determining the monitoring field of view of the stereo camera based on the coordinates and direction cosine matrix of the stereo camera in the tunnel; acquiring images captured by the stereo camera in the monitoring field of view, inputting each frame of image into a trained Swin Transformer model, and obtaining a vehicle detection result; and determining the position and motion trajectory of the vehicle based on the vehicle detection results of different frames of images. The application can solve the technical problem of low accuracy of tunnel vehicle positioning and trajectory tracking results.

A method and a system are disclosed for maintaining accuracy of a photogrammetry system comprising a stereo pair of cameras and characterized by calibration parameters determined at initialization, the system for tracking one of a touch probe and a 3D sensor, the method comprising in use, continuously detecting a presence of a reduced number of 3D target points comprising at least one pair of 3D target points selected in a group comprising at least two 3D target points; measuring image position data associated with the at least one pair of 3D target points of the reduced number of 3D target points; computing at least one updated calibration parameter using the measured image position data and corresponding reference distance data associated with the at least one pair of 3D target points of the reduced number of 3D target points; and updating at least one calibration parameter of the photogrammetry system.

The invention relates to patient occupancy detection. An apparatus is provided for processing data collected from an area within a facility for transmission to a cloud-based processing system for processing at the cloud-based processing system. The device comprises an input unit, a point cloud data generator and an output unit. The input unit is configured to receive data collected from an area in a health care institution, the data to be sent to a cloud-based processing system. The acquired data includes one or more of video data, image data, and ranging data. The point cloud data generator is configured to process the collected data to generate point cloud data that can be used to provide point cloud rendering of the region in the health care institution. The output unit is configured to communicate with a network to transmit the generated point cloud data and the corresponding metadata to the cloud-based processing system.

This disclosure relates to the calibration and tuning of stereo cameras. A disclosed method includes measuring an operating temperature of a subject camera, applying the operating temperature to a subject camera function to produce an output, and tuning the subject camera at the operating temperature using the output. The subject camera functions can be derived using a calibration method. The calibration method can include obtaining reference camera measurement data at a set of temperatures and deriving a reference function for the reference camera using the data. The reference function uses temperature as an input. The calibration method can also include obtaining subject camera measurement data from the subject camera and deriving the subject camera function for the subject camera using the reference function for the reference camera and the subject camera measurement data.

A robotic surgical navigation system is disclosed. An example system includes a stereoscopic camera and a robotic arm having an end-effector connected to the camera. The system also includes a navigation computer that determines a first transformation between the stereoscopic camera and a target surgical site, a second transformation between the end-effector and a robotic base of the robotic arm, and a third transformation between the robotic base and the target surgical site. The navigation computer calculates a fourth transformation using the first, second, and third transformations. The fourth transformation represents a transformation between the end-effector and the stereoscopic camera. The navigation computer uses the transformations to determine coordinates for a view vector of the stereoscopic camera that are in a coordinate system of the robotic arm, thereby enabling movement of the robotic arm based on commands provided by an operator in relation to the view vector of the camera.

An adjustable stereoscopic camera system includes a first camera (210) mounted to a first mounting plate (220); and a second camera (250) mounted to a second mounting plate (260). The first mounting plate (220) is movable along a y axis in an x, y, z coordinate system and rotatable in α and γ directions about the x and z axes where α, β, and γ indicate directions of rotation about the x, y, and z axes respectively.

To provide a stereo camera capable of measuring the distance to an object using only images from two cameras positioned to capture images in different directions.SOLUTION: A stereo camera according to one aspect of the present disclosure includes: two cameras positioned to capture images in different directions and having a common overlapping area in the captured images; a normal image conversion unit that converts each of the captured images from the two cameras into a normal image; an image matching unit that extracts the overlapping area from the two converted images that have been normalized by the normal image conversion unit; a parallax extraction unit that extracts the parallax of an object captured in the overlapping area; and a distance measurement unit that measures the distance to the object by triangulation from the parallax. The normal vectors of the coordinate planes of the two converted images are parallel, and perpendicular to a base line connecting the optical centers of the lenses of the two cameras.SELECTED DRAWING: Figure 2

To highly accurately measure cloud heights of clouds in the whole sky.SOLUTION: A cloud measurement system comprises: a stereo camera 30 that can change zoom magnification in an imaging direction and optically; a picture reception unit 204 for acquiring the imaged picture; a cloud edge detection unit 207 that recognizes a cloud displayed on the picture, detects a boundary of the cloud, and acquires an edge region including the boundary from a plurality of sub regions in the picture; a cloud imaging order setting unit 209 that on the basis of the edge region, sets target sub regions to be imaged while changing the zoom magnification of the camera and their imaging order; a camera control unit 210 that controls the stereo camera 30 so as to perform enlargement imaging on the target sub regions according to the set imaging order to acquire a stereo picture for each target sub region, a stereo vision distance measurement unit 202 for acquiring a distance to a cloud displayed on the stereo picture using a parallax, and a cloud bottom height estimation unit 203 for acquiring a cloud bottom height of the cloud on the basis of the acquired distance.SELECTED DRAWING: Figure 1