4887 results about "Cartography" patented technology

The embodiment of the invention provides a mining area vehicle path planning method and device based on a digital twin map, and belongs to the technical field of vehicle path planning, and the method comprises the steps: obtaining map data and physical scene data of a mining area; creating a dynamic twin map of the mining area based on the map data and the physical scene data; obstacle information is identified and marked based on the dynamic twin map, and obstacles comprise natural obstacles and artificial obstacles; and importing related data of the vehicle into the dynamic twin map, and carrying out obstacle avoidance path planning on the vehicle based on the marked obstacle information to obtain an optimal obstacle avoidance path. According to the scheme, the running efficiency of mining area vehicles can be improved, and collision and obstacle detouring time is shortened.

A system, method, and autonomous vehicle (AV) that generates a row-based world model for perceptive navigation of an AV are described. The system includes a client device, a cloud component, and the AV. The client device receives a map image of a field having a plurality of rows, in which each row includes a plurality of plants. The cloud component then generates a row-based frame of reference, in which each row has an associated frame of reference that includes a distance. A location is determined based on a row number and the distance associated with the row number. The cloud component also associates a semantic instruction with the row-based world model, and the cloud component communicates the row-based world model to the AV.

The invention discloses a laser SLAM (Simultaneous Localization and Mapping) method in a dynamic environment of a well mine, which relates to data processing and comprises the following steps of: performing mapping by utilizing a Cartograph SLAM algorithm; segmenting the PCD point cloud map by adopting an octree-based segmentation method according to a preset movement or deformation frequency threshold value of an object in each region in the well mining environment; adjusting the map resolution of each area in the PCD point cloud map according to the segmentation result; a change detection algorithm is used to carry out change detection on an area with object movement or deformation frequency exceeding a threshold value in the segmented PCD point cloud map, and new point cloud data of the change area is updated to the original PCD point cloud map in an increment mode; carrying out registration and fusion on the PCD point cloud map after local updating and an original PCD point cloud map; the global consistency is improved by aiming at the poor global consistency existing in the laser SLAM mapping of the well industry and mining in the prior art.

An illustrative point cloud segmentation system generates an instance-wise semantic mask for a particular source image of a set of source images that has been used to construct a 3D point cloud representing a scene that includes one or more objects. The point cloud segmentation system maps a set of 3D points from the 3D point cloud to corresponding 2D points of the particular source image, then labels, based on contours defined by the instance-wise semantic mask to demarcate the one or more objects, the mapped set of 3D points in accordance with where the corresponding 2D point for each mapped 3D point is positioned with respect to the contours. Based on the labeling of the mapped 3D points, the point cloud segmentation system produces a segmented 3D point cloud including an instance-wise segmentation of the one or more objects at the scene. Corresponding methods and systems are also disclosed.

An example system may access data of a multidimensional space representing a physical environment and identify interior elements within the multidimensional space using a first machine learning model. The interior element may represent furniture in the physical environment. The system may mask one or more of the interior elements with masks and fill each of the masks with imagery of the physical environment to create an appearance of a defurnished space, the defurnished space having the one or more interior elements appearing as missing from the multidimensional space representing the physical environment. The system may provide all or some of the defurnished space for display.

The invention discloses an unmanned ship positioning method and a berth selection method based on a high-precision semantic map, and the method comprises the steps: carrying out the semantic segmentation of a three-dimensional point cloud of a berth area scene, and extracting angular points and plane points based on the curvature; constructing a local sub-graph according to the historical key frame point cloud, and constructing a semantic weighting-based line-plane matching residual error between the current frame point cloud and the local sub-graph based on the angular points and plane points of the current frame point cloud; constructing a shore surface matching residual error based on fitting prediction between the fitting shore surface of the current frame and the fitting shore surface of the subgraph; and forming a semantic laser odometer factor by the semantic weighting-based line-surface matching residual error and the fitting prediction-based shore surface matching residual error, further constructing an overall factor graph, and optimizing to obtain the optimal global pose of the unmanned ship. According to the method, the structured features are subjected to weighted matching through semantic information and constraint constructed by utilizing a fitting prediction shore surface are jointly added into speedometer optimization, so that real-time high-precision positioning of the unmanned ship can be realized.

The invention relates to the technical field of image processing, in particular to a remote sensing monitoring method and system for cultivated land protection, and the method comprises the following steps: obtaining a remote sensing image sequence of a cultivated land region at a differential time node, extracting pixel gray values of the same land parcel, arranging the pixel gray values according to time, calculating the ratio of a difference value between adjacent frames to an intra-frame gray average value, and obtaining a remote sensing image sequence; and marking positions continuously exceeding the fluctuation reference ratio, and generating a pixel region set. According to the method, a sampling frequency threshold optimization strategy is adopted, abnormal areas are screened in combination with albedo change rate, short-time fluctuation interference is reduced, time sequence consistency is enhanced, a solar incident angle and a terrain slope are introduced to calculate projection influence, a slope surface shielding area is eliminated, and space grid marking and boundary integrity judgment are adopted; the method improves the continuity of cultivated land distribution information, combines time sequence analysis, sampling optimization, physical characteristic compensation and spatial consistency correction, enhances the fine recognition of the dynamic change of cultivated land, and improves the monitoring precision and data reliability.

According to the cultural relic mural three-dimensional digital protection and micro-change detection method, non-contact and damage-free image data acquisition and calculation are performed on the mural collection wall painting by using a digital photogrammetry correlation technology, and an orthographic image DOM and a digital surface model DSM of the mural collection wall painting are obtained. Color management is implemented in the links from data acquisition, calculation to result display, so that the color of the obtained digital orthographic image of the mural is consistent with the color of the actual mural. And carrying out fresco disease change analysis detection on the obtained fresco orthoimage and the digital surface model in a combined manner, obtaining the position and the area of the disease change, identifying the type of the disease, and finally outputting a fresco micro-change detection result extraction image. Scientific detection means and evaluation methods are provided for mural protection, and the efficiency of mural protection work is greatly improved. Meanwhile, detection data are analyzed, the change rule of the mural is mastered, the micro change trend of the mural is predicted, and the mural is effectively protected.

The disclosure discloses a method, an apparatus and a robot for out-of-stock detection of shelf commodity. The method includes: acquiring a plurality of shelf RGB images and a plurality of shelf depth images; executing an out-of-stock area detection step to obtain all vacant areas in the shelf for shelf RGB images and shelf depth images; stitching the shelf RGB images into a complete shelf image, merging and de-duplicating on all vacant areas and detection boxes of commodities, shelf label and layer lattice, and mapping them to the complete shelf image; generating a shed grid area corresponding to each commodity for the complete shelf image, and dividing all vacant areas in the shelf based on the shed grid area; and determining an out-of-stock detection result of each commodity based on the position information and the depth information of the vacant area and the shed grid area.

The invention discloses a dynamic three-dimensional reconstruction method based on incremental updating, which belongs to the field of image data processing, and comprises the following steps: acquiring a forestry scene multi-view image to be reconstructed to form an initial data set D0; constructing an initial three-dimensional scene model of the forestry scene based on D0 and 3DGS methods; monitoring a forestry scene to construct an incremental data set Dt of the tth change; during each incremental learning, the previous incremental model is divided into a change area and a non-change area, and the incremental model of the tth incremental learning is obtained by optimizing the change area and the non-change area and constructing a new scene model and is used for image rendering. According to the method, full-scene reconstruction is avoided, and the accuracy of the three-dimensional model can be ensured while the efficiency of three-dimensional reconstruction in a dynamic environment is remarkably improved. And the scene can be updated only by adding a small number of view angle images, so that the cost and time of data acquisition are greatly reduced, and the method is particularly suitable for large-scale monitoring of forestry and the like and scenes needing long-term monitoring and real-time updating.

In described examples there is a method of matching a design model. In examples, a point cloud representation of a construction site is generated. The point cloud representation is generated using a positioning system that tracks a device such as a headset within the construction site. A building information model is obtained representing at least a portion of the construction site. One or more points in the point cloud representation are compared with the building information model to determine whether the construction site matches the building information model, wherein the point cloud representation is generated dynamically as the device navigates the construction site.

A data processing device includes a plurality of cameras arranged according to a predetermined configuration, where each camera is configured to capture successive images and to detect a current location of one or more elements in the captured images with respect to a coordinate system of the camera, a processor to generate a common camera coordinate system in dependence upon the respective coordinate systems of the plurality of cameras, the common camera coordinate system comprising a set of locations in a three-dimensional space included within the field of view of the plurality of cameras, and a detector to detect a current location of a given element with respect to the common camera coordinate system based on images captured by the plurality of cameras, wherein for each camera, the processor is configured, in response to a detection by the detector that the current location of the given element corresponds to a location in the set of locations of the common camera coordinate system, to generate first data for that location in the set of locations indicative of a mapping between the coordinate system for the camera and the common camera coordinate system, and wherein in response to a detection of a location in the set of locations for which the first data is indicative of the mapping for two or more cameras with respect to the common camera coordinate system, the processor is configured to generate second data for that location indicative of a mapping between the coordinate systems for the two or more cameras.

The invention relates to a method for cooperatively monitoring hydropower station slope deformation through SBAS-InSAR and TLS, and the method comprises the steps: extracting hydropower station slope deformation information through employing a short baseline set technology based on GB-InSAR time sequence radar data; intercepting two groups of point cloud data in the abnormal deformation area; sampling and denoising under the two groups of point clouds; point cloud registration: aligning point clouds in a three-dimensional space to effectively reduce pose errors so as to ensure the consistency of geometric relationships; carrying out point cloud difference and carrying out interpolation on a difference result; and comparing and analyzing the two deformation monitoring results of the landslide abnormal area. According to the embodiment of the invention, comprehensive monitoring with higher precision can be realized through time sequence analysis and multi-dimensional data analysis, the reliability and accuracy of slope disaster early warning are remarkably improved, and the complexity of earth surface movement and deformation modes is revealed.

A method includes: obtaining an original image designated as a background screen; obtaining, based on time information and weather information, a predefined prompt; obtaining display area information of a user interface (UI) object; generating a modified image corresponding to the time information and the weather information by applying the original image, the predefined prompt, and the display area information as input data to a generative model that generates the modified image, wherein in the modified image, the predefined prompt is reflected in a display area where the UI object is displayed in accordance with a first degree, and the predefined prompt is reflected in a remaining area other than the display area in accordance with a second degree that is different from the first degree; and displaying the modified image and the UI object as the background screen by overlaying the UI object on the modified image.

Systems and methods for generating a 3D effect for a video stream are provided. A first video stream from a first client device of a first participant of a virtual meeting and a second video stream from a second client device of a second participant of the virtual meeting is identified. A background and a foreground layer of the first video stream is determined. A first and a second eye position of the second participant of the second video stream are determined. A presentation position of the background layer relative to the foreground layer is determined based on movement between the first and the second eye position of the second participant of the second video stream. A UI presenting the first video stream reflecting the determined presentation position of the background layer relative to the foreground layer is provided for display on the second client device.

A system for providing an extended reality environment that incorporates real-world event data into 3D models representative of the real-world event within the extended reality environment includes an XR processing unit configured to dynamically download 3D hole models of the course, receive real-world live shot data of shots taken on the holes during a golf tournament; map the shot data to the 3D hole models; and cause a spatial computing device to render hole views and immersive views comprising the mapped shot data rendered directly on the 3D hole models in real-time in an interactive extended reality environment. The mapped shot data includes shot trails representative of the shots taken on the golf course, The hole views and immersive views may utilize the same source file.

The invention relates to the technical field of remote sensing landform enhancement, and discloses a remote sensing landform enhancement algorithm, which comprises the following steps of: S101, preprocessing an existing high-resolution DEM (Digital Elevation Model) image and an existing low-resolution DEM image to construct a training data set; step S102, constructing a double-domain multi-scale attention fusion super-resolution network; step S103, training the double-domain multi-scale attention fusion super-resolution network through the training data set; according to the method, the edge information and spatial continuity characteristics of the DEM image are comprehensively considered, the high-frequency and low-frequency information of the DEM image is extracted through Haar wavelet transform by utilizing the characteristic that a wavelet domain is highly sensitive to the high-frequency and low-frequency information, and spatial domain feature information obtained by a parallel branch convolutional layer is combined, so that the high-frequency and low-frequency information of the DEM image is obtained. Through fusion of the two, the network has a feature enhancement function in a shallow layer, and meanwhile, the importance relationship between different depth feature maps is obtained through the multi-scale attention fusion module group, so that the precision of DEM super-resolution reconstruction is improved.

The invention discloses an event camera-assisted multi-modal vehicle-mounted three-dimensional occupancy prediction method, which comprises the following steps of: acquiring continuously acquired laser radar point cloud data, images, events and IMU measurement data, performing synchronization of spatial dimensions and time dimensions on various data, and determining the occupancy of a vehicle-mounted vehicle on the basis of the synchronized laser radar point cloud data and IMU measurement data, so as to predict the occupancy of the vehicle-mounted vehicle-mounted vehicle-mounted occupancy of the vehicle-mounted vehicle-mounted vehicle-mounted vehicle-mounted vehicle-mounted vehicle-mounted image. Dense point cloud data is obtained; performing space-time alignment on the dense point cloud, the image and the event, performing semantic annotation on the image after the space-time alignment, performing dynamic and static separation on the dense point cloud after the space-time alignment through cross-modal semantic projection, and performing static scene optimization and dynamic scene fusion to obtain an occupied voxel true value; training an occupancy prediction model based on the laser radar point cloud data and the occupied voxel truth value; and acquiring laser radar point cloud data and image data of a scene and event data in image exposure time, and realizing three-dimensional occupation prediction by using the trained occupation prediction model.

Systems and methods for view synthesis and three-dimensional reconstruction can learn an environment by utilizing a plurality of images of an environment and depth data. The use of depth data can be helpful when the quantity of images and different angles may be limited. For example, large outdoor environments can be difficult to learn due to the size, the varying image exposures, and the limited variance in view direction changes. The systems and methods can leverage a plurality of panoramic images and corresponding lidar data to accurately learn a large outdoor environment to then generate view synthesis outputs and three-dimensional reconstruction outputs. Training may include the use of an exposure correction network to address lighting exposure differences between training images.

Disclosed are techniques for localization of an object. For example, a device can generate, based on sensor data obtained from sensor(s) associated with an object, a predicted map comprising predicted nodes associated with a predicted location of the object within an environment. The device can receive a high definition (HD) map comprising HD nodes associated with a HD location of the object within the environment. The device can further match the predicted nodes with the HD nodes to determine pair(s) of matched nodes between the predicted map and the HD map. The device can determine, based on a comparison between nodes in each pair of the pair(s) of matched nodes, a respective node score for each pair of the pair(s) of matched nodes. The device can determine, based on the respective node score for each pair of the pair(s) of matched nodes, a location of the object within the environment.

The invention relates to the technical field of ocean exploration, in particular to a non-buried submarine pipeline identification method and system based on multi-beam point clouds, and the method comprises the steps: obtaining three-dimensional point cloud data of a submarine pipeline region, and carrying out the preprocessing of the obtained point cloud data; performing neighborhood construction based on the preprocessed point cloud data, setting a mask attention mechanism according to a neighborhood structure, performing geometric coding and feature enhancement by using an output result of the mask attention mechanism, performing decoding operation on the enhanced features, recovering feature representation of the complete point cloud, and performing suspended span height calculation and verification based on a segmentation result. According to the method, a decoding strategy combining reverse distance weighted interpolation and jump connection is adopted, enhanced features are accurately propagated to the original point cloud, the seabed datum plane is fitted through the reverse distance weighted interpolation, and the high-risk suspended cross area can be rapidly positioned.

The invention discloses a virtual scene construction method based on point cloud data, a medium and equipment. The method comprises the following steps: constructing an initial virtual scene based on original point cloud data; constructing a depth map corresponding to the point cloud data in the initial virtual scene, and determining a shielding relationship among the objects in the initial virtual scene based on the depth map; performing physics-based rendering on an object in the initial virtual scene, and processing the rendered initial virtual scene by adopting global illumination; and integrating the occlusion relationship into the rendered initial virtual scene to generate a new virtual scene. Through the above mode, the method can improve the trueness of building the virtual scene through the radar point cloud data.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: obtaining, for a conversion between a current video block of a video and a bitstream of the video, a value for a metric of a blending region in a direction, the blending region being comprised in a target region associated with the current video block, the value being determined from a plurality of predetermined values for the metric, values for samples of the blending region being determined based on values for samples of a first part of the target region and values for samples of a second part of the target region; and performing the conversion based on the value for the metric.

The invention relates to a three-dimensional live-action model generation method for city updating. The method comprises the following steps: collecting multi-source city image information, and obtaining semantic data according to the multi-source city image information; performing city modeling visualization according to the multi-source city image information to generate a city model; and matching the semantic data with the city model to obtain a three-dimensional real scene model containing the semantic data. According to the method, city information can be obtained more comprehensively by performing total factor accurate collection through a multi-source three-dimensional monitoring technology, the accuracy of a real scene model is improved, semantic data is matched with a city model, a three-dimensional real scene model containing the semantic data is obtained, multiple materials and textures of the model are combined, and the accuracy of the real scene model is improved. And the model is lightened, so that the purpose of reducing the resource loading time is achieved.

The invention, which relates to the technical field of smart text travel, discloses a historical scenic spot-oriented generative smart text travel guide system comprising a historical scenic spot image acquisition module, a pre-training large model module, an image-text understanding module, a poem generation module and an output display module. The invention further discloses a historical scenic spot-oriented generation type smart text travel guide method. The method comprises the following steps of: acquiring an image; extracting feature information; constructing a large-model language knowledge structure, and understanding the cultural connotation of the historical scenic spot; comprehensive information about historical scenic spots is acquired; generating poems conforming to the cultural background and the poem rhythm of the historical scenic area; and displaying the generated poems to tourists through the AR technology. According to the invention, by integrating image acquisition and processing, a large language model, multi-modal data fusion and augmented reality technologies, immersive and personalized guide experience rich in historical culture connotation can be provided for tourists in historical scenic spots, and the visiting experience of the tourists is improved.

Systems and methods are configured to receive one or more photographs depicting a plurality of cuttings, to identify one or more individual cuttings of the plurality of cuttings depicted in the one or more photographs, to extract morphological, color, texture, grain size, and grain distribution data from each individual cutting of the one or more individual cuttings, to perform lithological classification of the one or more individual cuttings at a plurality of hierarchical levels based at least in part on the extracted morphological, color, texture, grain size, and grain distribution data or based at least in part on features directly extracted from the one or more individual cuttings that represent the morphological, color, texture, grain size, and grain distribution data, and to present a consolidated results summary of the lithological classification of the one or more individual cuttings at the plurality of hierarchical levels via the analysis and control system.

The invention discloses a cultural relic handicraft three-dimensional reconstruction method based on multiple views and deep learning, and relates to the technical field of cultural relic handicraft three-dimensional reconstruction methods, and the method comprises the steps: carrying out the image collection of cultural relic handicrafts through employing a multi-view shooting method, and obtaining a structured image data set; performing feature matching and camera parameter estimation on the obtained multi-view image set by adopting a sparse reconstruction method to obtain camera internal parameters, external parameters and sparse point cloud data; performing fine processing on the obtained sparse point cloud data by adopting a dense reconstruction method to obtain a 3D dense point cloud model with a texture map; analyzing the obtained 3D dense point cloud model by adopting a depth map generation method to obtain a depth map and a rendering image corresponding to each image; image acquisition is performed on the cultural relic handicraft by adopting a multi-view shooting method, so that information such as geometric shapes and texture details of the cultural relic handicraft is comprehensively captured, and the problem of information loss caused by a single view angle is effectively solved.

Techniques for enabling a communication device to determine a geographic location, corresponding to its own geographic location, by computing a polygon-shaped geographical region that surrounds the geographic location of the communication device are disclosed. To compute the polygon-shaped geographical region, a processor at the communication device selects a set of location information corresponding to received geographical coordinates of multiple location-enabled devices. The polygon-shaped geographical region is determined such that each of the plurality of location-enabled devices is located along a perimeter of the polygon-shaped geographical region and such that the communication device is located within the polygon-shaped geographical region.

Techniques for estimating a ground profile, including a ground height (e.g., elevation) and ground roll, for an environment associated with a vehicle are described herein. Sensor data associated with a vehicle may be used to determine sensor data points associated with a path of the vehicle. Based on an upper bound slope and lower bound slope, such as a maximum and / or minimum rate of ground roll change, a region (e.g., search window) may be determined. Based on the region, a subset of sensor data points included in the region may be determined. The subset of sensor data points may be used to determine a representative height and representative roll. In some instances, the representative height and representative roll may be determined using a Hough transform and the subset of sensor data points. A ground profile may be determined based on the representative height and representative roll.