2344 results about "Ir camera" patented technology

The embodiment of the invention provides a scene space model adaptive modeling method based on multi-source data fusion. Complete scene information is constructed by fusing a video sequence, point cloud data and IMU (Inertial Measurement Unit) data. A deep learning network is adopted to carry out scene analysis, a scene complexity score is calculated based on a scale coefficient, a shielding degree and a geometric deformation coefficient, and intelligent matching of a modeling strategy is realized. Meanwhile, a scene dynamic area is predicted by using an optical flow algorithm, a geometric deformation field and a camera pose change, the modeling quality is monitored through reconstruction errors, and self-adaptive switching of a modeling algorithm is realized. The method effectively solves the problems of unstable precision and poor adaptability of a traditional modeling scheme in a complex dynamic scene, and improves the quality and efficiency of three-dimensional scene reconstruction.

The invention discloses a historical block scene three-dimensional reconstruction method and system based on Gaussian sputtering, and the method comprises the steps: collecting a historical block video sequence through a lightweight panorama camera, extracting a multi-frame panorama, and generating an image data set through a projection converter; monocular depth and normal estimation is carried out through a pre-training visual model, and a prior depth and normal graph data set of a historical block scene is constructed; sparse reconstruction is carried out on the multi-view image data set based on the SfM technology, initial point cloud and camera pose information are acquired, and a Gaussian ellipsoid is optimized in combination with prior depth and normal information; dynamically calculating the geometric width estimation value of the street, and guiding and adjusting the adaptive density of the Gaussian ellipsoids of the vertical surfaces on the two sides; rendering the optimized Gaussian ellipsoid through an improved rasterization renderer; and designing a multi-modal loss function and a regularization mechanism to optimize reconstruction and rendering results. According to the method, high-fidelity three-dimensional reconstruction of the historical block scene is realized, and the adaptability of the Gaussian sputtering method to the complex block scene is enhanced.

The invention provides a train degradation operation control method and system based on active perception, and the method comprises the steps: obtaining a high-safety visual distance and signal machine information through an intelligent perception system after a train enters an ABL mode; a speed curve is calculated based on the train operation condition, the signal machine information and the high-safety visual distance, and the train is controlled; wherein the intelligent sensing system is composed of vehicle-mounted equipment and trackside equipment; the vehicle-mounted equipment comprises a sensing host, a radar sensor and a camera; the trackside equipment comprises a coding signboard; the camera is used for collecting image data; the radar sensor is used for detecting point cloud data; and the sensing host is used for determining a high-safety visual distance and signal machine information according to the image data and the point cloud data. According to the method, in the transformation process of the urban rail transit signal system, when the signal equipment beside the rail does not meet the transformation condition, the train degradation operation control can be carried out.

Systems and methods for generating a 3D model from a single input image are described. Embodiments are configured to obtain an input image and camera view information corresponding to the input image; encode the input image to obtain 2D features comprising a plurality of 2D tokens corresponding to patches of the input image; decode the 2D features based on the camera view information to obtain 3D features comprising a plurality of 3D tokens corresponding to regions of a 3D representation; and generate a 3D model of the input image based on the 3D features.

An object and camera localization system and localization method for mapping of the real world. The mapping can be done in real-time or near real-time to the detection of the real objects by a camera device which is used to capture one or more images of an object. The localization method can be used to generate an object label of the object and a bounding box of the object in the image. The localization method can be used to generate anchor points in real world coordinates of the real 3D space of the object, a cuboid of the object, and a centroid of the cuboid. A virtual 3D map can be generated which includes the location and pose of the real object in the real-world coordinates.

The invention relates to the technical field of synchronous positioning and map construction, and discloses a multi-sensor fusion SLAM method and system based on data quality comprehensive evaluation, and the method comprises the steps: carrying out the multi-dimensional quality evaluation of laser radar point cloud data and camera image data, and enabling the evaluation indexes to comprise the scene degradation degree, the view field integrity and the intensity information entropy of a point cloud, according to the point cloud data and the definition, self similarity and natural quality of the image, calculating the comprehensive weight of each index by adopting a subjective and objective weight fusion method, and dynamically selecting a laser radar pose, a visual sensor pose or an IMU integral pose increment as an initial value according to the comprehensive quality score of the point cloud data and the image data; and matching optimization of the point cloud and the map and global pose map optimization are carried out. According to the method, the sensor data quality is evaluated in real time, and the fusion strategy is dynamically adjusted, so that the positioning precision and robustness of the SLAM system in a complex environment are remarkably improved, and meanwhile, the calculation complexity is reduced.

The invention discloses an inspection robot and an inspection method thereof, and relates to the technical field of robots, and the inspection robot comprises a remote control center which is in communication connection with an environment perception and mapping module, an intelligent movement and obstacle avoidance control module, and an intelligent analysis and user interaction module; the environment sensing and mapping module integrates a laser radar, an ultrasonic sensor and an RGB-D camera, and realizes multi-dimensional environment data acquisition; the intelligent movement and obstacle avoidance control module adopts an SLAM algorithm to position and map, adopts an RRT algorithm to plan a path, adopts a CNN algorithm to recognize an obstacle, and adjusts movement through a PID controller and a kinematic model to realize intelligent obstacle avoidance; the intelligent analysis and user interaction module reduces delay through edge calculation, provides real-time data processing and a graphical user interface and displays the state and operation guide of the robot. On the whole, by integrating various sensors and advanced algorithms, efficient and intelligent inspection operation is realized, and the inspection efficiency and safety are improved.

The invention provides a moving target capturing method and system based on AI multi-view vision, and relates to the technical field of data processing.The method comprises the steps that camera parameters in all vision systems are calibrated, and the camera parameters comprise a camera internal parameter matrix and a camera external parameter matrix; extracting 2D feature points of the moving target through each calibrated visual system; reconstructing each 2D feature point by combining a multi-view geometric reconstruction algorithm and a least square method according to the calibrated camera parameters to obtain 3D feature points; constraints are added to the 3D feature points, and the constraints comprise spatial constraints, direction vector constraints and time sequence constraints; reconstructing the 3D feature points added with the constraints through a multi-view geometric analysis algorithm to obtain three-dimensional motion information of the moving target; and outputting the three-dimensional motion information of the moving target to complete the capture of the moving target. And non-contact high-precision capture of the moving target can be realized.

The invention discloses a three-dimensional scene reconstruction method, electronic equipment, a storage medium and a program product, and relates to the technical field of data processing, and the method comprises the steps: inputting each frame of multi-view image into a depth model, and obtaining a prediction depth map of each frame of multi-view image; for any target frame image in each frame of multi-view image, optimizing a local 3D Gaussian model and camera attitude information of the target frame image based on the predicted depth map of the target frame image and the depth information of the previous frame image to obtain an updated local 3D Gaussian model and updated camera attitude information of the target frame image; and optimizing the global 3D Gaussian model to obtain an updated global 3D Gaussian model based on the updated local 3D Gaussian model and the updated camera attitude information of each frame of multi-view image, and performing three-dimensional scene reconstruction based on the updated global 3D Gaussian model. According to the invention, the accuracy of a view synthesis result based on three-dimensional scene reconstruction is improved.

An electronic door system includes a door frame, a door hingedly coupled to the door frame, an electronic locking mechanism coupled to the door, one or more lights coupled to the door frame, a camera system including at least two of a camera, a proximity or motion sensor, or a doorbell, and a wiring harness extending through the door frame and the door. The wiring harness is coupled to the electronic locking mechanism, the one or more lights, and the camera system. The wiring harness is configured to electrically couple to an external power source.

The invention relates to the technical field of motion capture data processing, and discloses a robot reinforcement learning model construction method based on optical inertial fusion whole body motion capture, and the method comprises the steps: measuring and recording a plurality of key parameters of a target human body; carrying out primary calibration and recording on all inertial sensors worn on a target human body, and calibrating all cameras arranged around at the same time; in the process that the target human body executes the specified action, synchronously acquiring action sampling data of the target human body; the action sampling data comprises motion data corresponding to the inertial sensor and camera image data corresponding to the camera; the motion data and the camera image data are processed, fused and optimized, and optimized joint posture quaternary data and human body motion sequence data are obtained; and inputting the human body action sequence data into the reinforcement learning model of the humanoid robot, and configuring the preset reward function to perform reinforcement learning training, so that the learning efficiency of the robot and the accuracy of action execution can be improved.

The invention discloses a porcelain bushing power transformation equipment surface modeling method based on an unmanned aerial vehicle and light three-dimensional Gaussian, and the method comprises the steps: determining an unmanned aerial vehicle inspection task route, shooting porcelain bushing power transformation equipment through the unmanned aerial vehicle in a multi-view manner, obtaining an equipment image set, carrying out the noise reduction processing of the equipment image set, and carrying out the camera pose estimation. Obtaining surface depth information of the equipment image set based on an SDF function; dense point clouds are generated based on the equipment images and the corresponding camera pose information, three-dimensional Gaussian is constructed according to the dense point clouds, and rendered images are obtained; compressing the three-dimensional Gaussian through a light Gaussian method; establishing a neural implicit surface reconstruction model; constructing a composite loss function, and training and optimizing the reconstruction model based on the composite loss function; according to the method, the lightweight Gaussian compression technology is introduced into the implicit surface reconstruction method, efficient compression and rapid rendering of the scene are achieved, the storage requirement is remarkably reduced, and the modeling precision of the equipment surface and the capturing capacity of complex surface details are improved.

A sensor driving device according to an embodiment includes a fixed part; a first moving part disposed inside the fixed part and spaced apart from the fixed part; a second moving part disposed inside the first moving part and spaced apart from the first moving part, and including an image sensor; a first driving part that moves the first moving part relative to the fixed part; and a second driving part that moves the second moving part relative to the first moving part, wherein the first driving part is provided to move the first moving part based on a first driving force corresponding to a first driving method, and the second driving part is provided to move the second moving part based on a second driving force of a second driving method different from the first driving method.

The invention provides an infrared scene reconstruction and rendering method and device based on three-dimensional Gaussian splashing, and the method comprises the steps: carrying out the processing of preprocessed infrared image data through employing an SfM algorithm, obtaining a sparse three-dimensional point cloud and a camera pose, and setting a Gaussian ellipsoid at the center position of the sparse three-dimensional point cloud, so as to construct a three-dimensional Gaussian point cloud model; inputting the camera pose into a Gaussian microrasterized renderer to render the three-dimensional Gaussian point cloud model to obtain a two-dimensional image of a corresponding view angle; the two-dimensional image and the infrared image data of the camera pose are compared through a loss function, the model is trained, a trained three-dimensional Gaussian point cloud model is obtained, and the loss function comprises a temperature gradient field, the projection position of a Gaussian ellipsoid in a pixel plane under the current camera visual angle and the main axis projection direction; and processing the trained three-dimensional Gaussian point cloud model by using a three-dimensional graphic engine and a 3D Gaussian rasterization renderer to obtain an infrared simulation image. And the authenticity of reconstruction and rendering of the infrared scene is relatively high.

The invention provides a space three-dimensional target detection method, device and equipment and a storage medium, and aims to unify an infrared image and point cloud data in a BEV space and solve the problem of difference between data structure representation of different sensors and a space coordinate system; point cloud depth supervision and camera parameter priori are introduced into a camera branch to enhance the depth estimation capability of the camera branch, different branches are fused, and fused features and different branches are detected, so that the accuracy of subsequent target detection is improved.

An apparatus comprises an interface and a processor. The interface may be configured to receive pixel data representing a first field of view of a camera and four-dimensional (4D) point cloud data representing a second field of view of a 4D imaging radar sensor. The first field of view of the camera and the second field of view of the 4D imaging radar sensor generally overlap. The processor may be configured to process the pixel data and the 4D point cloud data arranged as video frames and perform an extrinsic parameter calibration for the 4D imaging radar sensor and the camera based on adaptive projection error. The extrinsic parameter calibration may comprise applying a pose calibration process to the video frames. The pose calibration process may use (i) a respective translate vector and a respective rotation matrix for each of the 4D imaging radar sensor and the camera and (ii) a radar corner reflector to obtain configuration parameters for the first field of view of the camera and the second field of view of the 4D imaging radar sensor. The pose calibration process generally comprises capturing data for a plurality of positions of the radar corner reflector between a near-distance value and a long-distance value, and modifying a scale of a projection error of the radar corner reflector based on a size of the radar corner reflector in an image captured by the camera.

A method, for determining a dynamic occupancy grid, includes: obtaining radar measurement data from at least one radar sensor of an apparatus; obtaining camera-derived data based on at least one image obtained by at least one camera of the apparatus; and determining the dynamic occupancy grid based on the radar measurement data and the camera-derived data.

The invention relates to the technical field of concrete production, in particular to an aggregate volume calculation method and device based on visual image detection. The invention discloses an aggregate volume calculation method based on visual image detection. The method comprises the following steps: S1, image acquisition; s2, image preprocessing; s3, acquiring three-dimensional data of the aggregate: aligning the depth image with the RGB image to acquire the three-dimensional data of the aggregate; and S4, volume calculation. According to the method, the aggregate image data acquired by the depth camera and the RGB camera are acquired, the volume of the aggregate is calculated based on the improved Maskrcnn network, the aggregate images can be quickly and continuously acquired and processed, and compared with a traditional manual measurement and physical screening method, the speed of aggregate volume calculation and the overall working efficiency are greatly improved, and the method is suitable for popularization and application. By utilizing a high-precision image processing technology and an advanced algorithm, the outline of the aggregate can be identified more accurately, the volume of the aggregate can be calculated more accurately, personal errors are reduced, and the accuracy of a measurement result is improved.

The invention discloses a moving target positioning method and system considering a camera pitch angle, and relates to the technical field of vehicle positioning. The method comprises the following steps: obtaining a vehicle track of a moving target in a video to be detected and performing coordinate conversion; positioning correction is conducted on the vehicle track through an error correction method, final vehicle positioning is obtained, and the positioning correction process comprises the steps that the camera height and the camera pitch angle are obtained, the relation among the camera height, the camera pitch angle and the coordinate conversion error is constructed, and the coordinate conversion error is calculated; and calculating the actual position of the vehicle target based on the coordinate conversion error to obtain the coordinates of vehicle positioning. According to the invention, the theoretical relationship between the positioning error and the installation height and angle of the camera is deduced by using the imaging principle of the optical lens of the camera and the projection geometrical relationship, so that accurate error correction is carried out, and high-precision positioning of the moving target is further realized.

The present application discloses a displacement sensing apparatus and displacement sensing method for a building structure, and a storage medium. The displacement sensing apparatus comprises: a fixing module comprising a fixing base and a fixing support, wherein the fixing base is configured to be coupled with a monitoring point for a structure under measurement to form a whole structure; and a projection module and a camera module, wherein the camera module is arranged in front of the projection module by means of the fixing support at an angle perpendicular to the field of view, and is configured to collect a laser image formed on the projection module by a laser emission module which is provided at a fixed point.

The invention provides a robot indoor navigation method and system, and the method comprises the steps: obtaining an RGB image frame sequence collected by a camera, and estimating a camera pose and a sparse three-dimensional landmark point of an image frame through a depth block visual odometer; based on the camera pose and the sparse three-dimensional landmark point, dense geometric enhancement is carried out on the image frame through a deep neural network of a transformer architecture to obtain dense geometric prior information; training a neural radiation field according to the initial camera pose, the dense geometric prior information and the image frame data, and performing joint optimization on neural radiation field parameters and the camera pose based on luminosity consistency, geometric consistency and camera pose constraints; based on image frames collected in real time, novel view synthesis is carried out through the optimized nerve radiation field, and robot navigation is carried out based on a novel view. According to the scheme, accurate estimation of the camera pose and high-quality reconstruction of the three-dimensional scene can be realized, the computing resource overhead is low, and the real-time performance can be guaranteed.

The invention discloses silicon optical wafer testing equipment and an optical fiber pose adjusting mechanism, method and system thereof. The optical fiber pose adjusting mechanism comprises a camera assembly and a deflection optical path assembly. The probe station is used for bearing a silicon optical wafer; an optical fiber coupling module; when the driving assembly drives the silicon optical wafer to be located in the imaging view field of the camera assembly, the camera assembly is adjusted to enable the coordinate system to be parallel to the cutting channel of the silicon optical wafer; when the driving assembly drives the deflection optical path assembly to move into an imaging view field of the camera assembly, the camera assembly collects lateral images of the end part of the optical fiber in at least two different directions through the deflection optical path assembly; and the optical fiber coupling module is used for performing pose adjustment on the end part of the optical fiber according to the lateral image and the direction of the coordinate system of the camera assembly, so that the end part of the optical fiber is preliminarily aligned with the silicon optical wafer. The operation difficulty and the equipment structure of alignment coupling between the optical fiber end part and the silicon optical waveguide are simplified, and the equipment cost is reduced.

The present invention relates to a mobile phone protective case with a rotating support, where the protective case includes a main body and a camera support assembly, the main body is provided with an accommodation cavity for accommodating a mobile phone, the camera support assembly is removably installed at a camera hole, the camera support assembly includes an installation base and a support, the support is rotationally connected to the installation base through a hinge, the support is configured to protect a camera of the mobile phone when closed, and the support is configured to support the mobile phone when opened.

The invention discloses a turntable-based dual confocal image scanning microscope and an imaging method, and relates to the technical field of optical elements, systems, instruments and imaging. The imaging light source assembly, the optical fiber transmission assembly, the light source collimation assembly, the structured light generation assembly, the turntable assembly, the microscopic amplification assembly and the camera detection assembly are arranged along a light path; the structured light generation module realizes structured light illumination on a sample surface by loading a specific mask sequence, super-resolution imaging is realized, pinholes in the turntable assembly are equidistantly arranged according to an Archimedes spiral, and the turntable assembly rotates at a high speed to realize multi-point parallel scanning and de-scanning; the detection module performs detection by using an area array detector, so that the resolution improvement of double diffraction limit is realized; according to the dynamic pinhole array-pixel redistribution method, super-resolution reconstruction is carried out based on an image scanning microscopic system principle, and Stokes shift, optical aberration and other non-ideal conditions can be effectively corrected.

Disclosed in the present invention is a photometric stereo-based device for rapidly detecting surface features of an object, capable of rapidly acquiring a plurality of images meeting the requirements of a photometric stereo algorithm. The device comprises a light emitting assembly, an acquisition assembly located above the light emitting assembly, and a data processing module; the acquisition assembly comprises a light path system and a camera group; the light path system comprises: a beam splitting prism group used for splitting reflected light from the surface of an object into a plurality of beams of light and respectively outputting the plurality of beams of light in different preset directions to form images to be processed, and a plurality of bandpass filters correspondingly arranged on the output sides of the plurality of beams of light used for forming said images, wherein the central wavelengths of the bandpass filters are respectively configured to be different wavebands of the reflected light from the surface of the object; the camera group comprises a plurality of cameras which are arranged behind the bandpass filters in a one-to-one correspondence mode and acquire filtered light beams; and the data processing module receives image information sent by the cameras, and performs compensation and photometric stereo reconstruction on the image information, so as to obtain surface features of the object. Further disclosed is a method implemented on the basis of the device.

The invention discloses an underwater environment three-dimensional reconstruction method, device and equipment and a storage medium, and relates to the technical field of underwater robots, and the underwater environment three-dimensional reconstruction method comprises the steps: transmitting laser to a target object, and collecting a laser reflection image; extracting pixel coordinates of laser stripes from the laser reflection image; calculating a three-dimensional space coordinate of the laser point based on the pixel coordinate and a camera internal reference matrix; generating a control instruction according to the depth information of the three-dimensional space coordinates, and sending the control instruction to an underwater vehicle to enable the underwater vehicle to execute equidistant diameter circular motion and collect and feed back three-dimensional point cloud data under different poses; and fusing the three-dimensional point cloud data under different poses to obtain a target three-dimensional point cloud model, and completing three-dimensional reconstruction. According to the invention, three-dimensional reconstruction of the underwater environment can be efficiently and accurately realized at low cost, and powerful technical support is provided for underwater exploration and scientific research.

Monitoring and control system and method for additive manufacturing an in-situ macro-field thermography that uses an IR camera or near-IR camera to prioritize the data captured from an entire print layer, processes data streams layer-by-layer, and provides thermal history of previously printed layers as feedback to the control system. The system leverages one or more different mathematical thermophysical-based (MTB) models to compute a thermally related specification in order to optimize the printing sequence of islands or stripes layer-by-layer. The thermal feedback from the thermography system calibrates or updates the thermal model layer-by-layer. The system and / or method may provide real-time optimization of scanning topology in chessboard and stripe printing strategies. The method may be implemented by one or more computer programs controlling one or more computer controllers of an additive manufacturing system, such as a 3D printer implementing a metal powder bed fusion additive manufacturing process.

The invention provides an infrared / visible light fusion all-time autonomous positioning method and system guided by semantic information in a dynamic scene, and the method comprises the steps: transmitting a visible light image and an infrared image into a trained fusion model guided by the semantic information, and obtaining a fusion image; performing feature point extraction on the fused image; fusing the fused image with the IMU data, and eliminating dynamic feature points of the fused image based on a dual-threshold elimination method of the IMU and an RGB-D camera; estimating the pose of the camera by using the remaining feature points; and optimizing the estimated camera pose. Compared with the prior art, the method has the advantages that on the basis of a traditional visual odometer, the generative adversarial network guided by semantic information is used for fusing the infrared and visible light images, and the dual-threshold dynamic target feature point elimination strategy based on the IMU and the RGB-D camera is used, so that the influence of a dynamic object on the visual positioning precision is effectively avoided, and the all-day and all-time positioning accuracy is improved. And especially, the accuracy of self-positioning in a night environment is improved.

The invention discloses a to-be-detected object surface image acquisition device, an image selection method and a defect detection method. The to-be-detected object surface image acquisition device comprises a projector, a diffusion plate, a reflector and a camera. The projector is suitable for projecting stripes; the diffusion plate is suitable for diffusing fringes projected by the projector to the surface of the to-be-measured object; the reflector moves between a first position and a second position relative to the to-be-measured object, and at the first position, the reflector shields a light path from the projector to the diffusion plate. And at the second position, the reflecting mirror avoids a light path from the projector to the diffusion plate. The camera is suitable for receiving a signal reflected by the surface of the to-be-measured object after the diffusion plate enters the surface of the to-be-measured object so as to obtain a phase deflection technology (PMD) image, or receiving a signal diffused by the surface of the to-be-measured object after the reflection mirror enters the surface of the to-be-measured object so as to obtain a fringe projection profile measurement (FPP) image. A phase deflection technology and a fringe projection profile measurement technology are fused in one set of defect detection system, rapid switching of two three-dimensional measurement defect detection technologies is realized for products with complex materials and different surface roughness, and the defect detection system is ensured to be high in efficiency, high in detection capability, wide in applicability and high in detection rate.

The invention discloses a narrow space spraying trajectory planning method based on reinforcement learning, and the method comprises the steps: obtaining environment data through a LiDAR camera and a TOF camera, and obtaining a LiDAR point cloud and a TOF point cloud; carrying out registration fusion on the LiDAR point cloud and the TOF point cloud, and carrying out normal vector unification processing on each point of the fused point cloud; reconstructing by introducing a Poisson equation improved by shielding perception weight, and eliminating noise through Laplacian smoothing processing to obtain a target point cloud set; designing a state space, an action space and a reward function of reinforcement learning according to the planning of the spraying path trajectory, and determining a spraying path trajectory planning strategy; and based on the target point cloud set, adopting a spraying path trajectory planning strategy to carry out spraying trajectory planning. Sensor data fusion is realized through an improved iterative nearest point algorithm, and a shielding area is repaired in cooperation with a Poisson reconstruction algorithm, so that accurate correspondence between input parameters and output film thickness can be realized, and the control precision of the spraying thickness is remarkably improved.