51 results about "Local map" patented technology

The invention discloses an AR home experience method in a large scene. On the basis of combination of a natural feature identification-based three-dimensional registration method and a binocular tracking positioning and local mapping method, the camera attitude is estimated by using feature points of a real-time scene and corresponding three-dimensional points thereof under the binocular trackingpositioning and local map construction technology. According to the mode, on-site environment features shot in real time are used as recognition tracking objects, a virtual home model can still be normally positioned and tracked under the condition that no identification graph exists, the problems that an existing AR home experience application is small in use range and poor in stability are solved, and therefore the AR home experience of virtual and real fusion can be met in a wider range and more truly.

This invention discloses a method for reusing obstacle avoidance trajectory parameters based on local environmental geometric and motion constraint features. The method includes the following steps: acquiring a local point cloud to construct a local map; calculating the geometric residuals from points to planes based on the local map to obtain residual statistical features; calculating the plane normal distribution based on the local map to obtain structural orientation features; and constructing an information matrix based on the Jacobian matrix optimized by the direct method pose to obtain motion constraint features; combining the residual statistical features, structural orientation features, and motion constraint features into a local environmental feature descriptor; binding the local environmental feature descriptor with trajectory generation parameters of historical obstacle avoidance trajectories and storing it in a database; calculating the similarity between the current local environmental feature descriptor and the historical feature descriptors in the database, and selecting the corresponding historical trajectory generation parameters based on similarity matching; and using the historical trajectory generation parameters as the initial parameters for the current obstacle avoidance trajectory planning.

The application provides a mapping method, equipment and readable storage medium of an engineering machine, and the technical scheme is as follows: IMU data and IMU odometry are used to correct motion distortion of a current frame laser point cloud, point lines and point surface features are extracted according to the curvature of each point of the current frame laser point cloud, a local map is matched with the current frame laser point cloud, and factor graph optimization is performed on a key frame. The output laser odometry is used to correct IMU zero offset, the IMU pre-integrator is reset, and the IMU odometry is output. When the current frame laser point cloud meets a loop detection condition, a normal distribution transformation algorithm is used to perform loop detection on the current frame laser point cloud, and a global pose is updated, so that the mapping problem in the degradation scene of the engineering machine is solved, and fast mapping and positioning in a large scene are realized.

The application provides a navigation method and device of a mobile robot, electronic equipment and a storage medium, and relates to the technical field of robots. The method comprises the following steps: acquiring a positioning score of the mobile robot; if it is determined that the mobile robot loses positioning according to the positioning score, rotating the head of the mobile robot to construct a local map; acquiring a global map for navigation of the mobile robot, determining a navigation target point according to the global map; determining a target terminal point in the local map according to the navigation target point, and generating a local navigation path by using the target terminal point to perform local navigation. Through the fusion of head motion control and local planning navigation strategy, the robot can flexibly adjust the viewing angle, actively acquire a wider field of view and richer environmental information in a narrow space or an environment with unstable positioning signals, improve the navigation accuracy and robustness of the robot in a complex and variable scene, and effectively cope with challenges such as positioning drift.

An embodiment herein provides a robotic device 100 that enables real-time autonomous navigation in a dynamic environment by determining a navigable space. The robotic device includes at least one image capturing device 102 to capture at least one image or video within a current Field of View (FOV) of the dynamic environment, and a processor 106 that is configured to obtain the point cloud, identify at least one obstacle in the dynamic environment, generate a local map that represents an occupied region and a free region, construct a virtual wall representing an imaginary boundary positioned at a pre-determined distance, project a plurality of rays from an origin of the at least one image capturing device toward the virtual wall at varying angles, determine at least one visible region of the virtual wall, update the local map and enable the robotic device to navigate to a target location.

This invention discloses an online high-precision map building method and system based on positioning fusion, including: acquiring three-axis acceleration data and three-axis gyroscope data through an IMU; obtaining predicted continuous attitude change data based on the changes in velocity and angle; matching and building a local map through LiDAR point cloud data, and acquiring the driving trajectory; matching the driving trajectory with the local map to obtain LiDAR observation pose data; obtaining satellite positioning trajectory data fed back by RTK; obtaining the overlap degree in a unified coordinate system by fitting the predicted continuous attitude change data, LiDAR observation pose data, and satellite positioning trajectory data at the same time, and obtaining the variance value of the relative deviation; judging the positioning accuracy of the predicted continuous attitude change data, LiDAR observation pose data, and satellite positioning trajectory data at the same time based on the obtained variance value of the relative deviation, and obtaining the pose of the sub-map at the same moment.

The application discloses a three-level coordinated production line body intelligent control method and system, and belongs to the technical field of intelligent manufacturing. The method comprises the following steps: establishing a global task planning layer, performing semantic analysis and logical disintegration at a first preset frequency, and generating an atomic action sequence; establishing a local space perception layer, receiving the atomic action sequence at a second preset frequency, constructing a dynamic local map by using sensor data, determining a workpiece pose by point cloud registration, and mapping the atomic action sequence into a continuous motion trajectory flow; establishing a real-time action execution layer, receiving an instantaneous target pose at a third preset frequency, calculating a theoretical driving torque by combining a Lagrange dynamics equation, switching to an impedance control mode at a contact moment, and driving an execution mechanism to complete work; and establishing an asynchronous coordination layer, realizing three-layer asynchronous communication through an industrial network, and controlling the three-layer frequencies to be distributed in a ladder type in an increasing manner. The application has the effects of improving the flexibility, precision and safety of a production line.

This application discloses a large-scale monocular visual SLAM-GS method based on depth prior and subgraph management, comprising: acquiring keyframes of a monocular video stream; acquiring an original depth prior map for the keyframes; aligning the original depth prior map with the current local map to obtain an aligned depth prior map; dynamically dividing the global map into multiple subgraphs and maintaining a bounded set of active subgraphs; when creating a new subgraph, initializing the map elements of the new subgraph using keyframes in the overlapping area with the old subgraph and their aligned depth prior maps; constructing and optimizing a 3D Gaussian map based on the aligned depth prior map within the set of active subgraphs; maintaining a global pose map with subgraphs as nodes, and correcting the global cumulative error by optimizing the relative poses between subgraphs. This invention achieves integrated fusion of monocular visual SLAM and 3D Gaussian modeling in large-scale, long-term scenes.

A method, device and electronic equipment for improving map accuracy, the method comprising: obtaining a local coordinate set and a world coordinate set of a smart vehicle, determining an actual driving track of the smart vehicle based on each local coordinate in the local coordinate set, and determining a standard driving track of the smart vehicle based on each world coordinate point in the world coordinate set, correcting the actual driving track based on the standard driving track to obtain a target driving track corresponding to the actual track, determining vehicle detection information corresponding to each local coordinate point in the local coordinate set, associating each vehicle detection information with a corresponding coordinate point in the target driving track, and determining target vehicle detection information of the smart vehicle. Through the above method, the actual track is corrected based on the standard track, thereby avoiding different scales of the same track in different local maps, and further improving the accuracy of the high-precision map obtained based on the local map.

A method and apparatus are disclosed for a clutter tidying robot utilizing floor segmentation for its mapping and navigation system, whereby a perception module and navigation module transform lidar and image data from lidar sensors and cameras of a robot sensing system using segmentation and pseudo-laserscan or point cloud transformations to generate global and local maps. The robot pose and maps are transmitted to a robot brain that directs an action module to produce robot action commands controlling the operation of a clutter tidying robot using the pose and map data. In this manner multi-stage planning and sophisticated obstacle avoidance techniques may be incorporated into autonomous robot operations.

This invention provides a vehicle tracking method, system, device, and storage medium based on a semi-enclosed scene. The method includes the following steps: acquiring overhead video using several sensors positioned at a high altitude in the semi-enclosed scene; matching the video frames of the overhead video with a high-precision map of the scene to obtain a local map corresponding to the overhead video; performing image recognition on the video frames of the overhead video to obtain at least one vehicle image region and corresponding vehicle information in the overhead video; obtaining the vehicle's position within the local map based on the position of the vehicle image region in the video frame image; and predicting the trajectory of the target vehicle to reach the next sensor based on the trajectory of the target vehicle in the local map corresponding to one sensor, thereby establishing a mapping relationship between vehicle information and the vehicle's motion trajectory in the high-precision map of the semi-enclosed scene. This invention can accurately and continuously track the trajectories of multiple vehicles in a semi-enclosed scene using a small number of cameras, while significantly reducing hardware costs and computing power requirements.

Examples relate to data update requests for data items stored at a data store in a data system. In some examples, real-time data update requests to update data items at the data store via a plurality of data update nodes are received, where each data update node has a respective local map. In response to the real-time data update requests and for each respective data item, a respective data item is assigned to a respective data update node. The respective data item in the data store is updated and respective supplemental data of the respective data item including a historical time stamp indicating a historical generation time of the respective data item is stored in the respective local map of the respective data update node. A copy of the respective supplemental data of the respective data item is stored in a central database.

The present application relates to a multi-machine cooperative mapping method based on hierarchical boundary point detection algorithm and electronic equipment, the method comprises the following steps: constructing a centralized system by multiple mobile robots equipped with laser radar and depth camera, each robot uses Gmapping algorithm for local mapping and positioning; the system uses map fusion technology based on feature point matching, extracts the features of the local map and calculates the transformation matrix to unify the local map of each robot to the global coordinate system; using the cooperative exploration strategy based on the front point, combined with the multi-machine task allocation algorithm, the efficient cooperative coverage of indoor unknown environment is realized. Through the multi-machine centralized cooperative architecture and multi-source sensor fusion technology, the present application solves the problem of low efficiency and limited coverage range of single robot mapping, improves the integrity and real-time performance of mapping in complex indoor environment, and can be widely applied to indoor autonomous navigation robots, intelligent warehouse inspection systems, indoor rescue robots, building intelligent security and other fields.

This invention discloses an intelligent warehouse management system based on integrated hardware and software, belonging to the field of warehouse management technology. Specifically, it includes: First, a node synchronization module deploys and synchronizes multiple mobile sensing nodes. A data acquisition module controls the movement of work units and collects time-stamped point cloud data, sending it to the sensing nodes. A coordinate alignment module sets the receiving node as the master node and converts the point cloud data to its coordinate system to generate a local map. A fragmentation and distribution module divides the map into geographical segments and distributes them to neighboring nodes. A collaborative fusion module receives feedback fragments from neighboring nodes, merges them, and generates a corrected local map. Finally, a path planning module calculates and sends updated paths for work units based on this map. This invention achieves decentralized real-time environmental perception and adaptive path planning in a dynamic warehouse environment, improving the system's robustness and operational continuity in complex and changing scenarios.

An augmented reality system generates computer-mediated reality on a client device. The client device has sensors including a camera configured to capture image data of an environment. The augmented reality system generates a first 3D map of the environment around the client device based on captured image data. The server receives image data captured from a second client device in the environment and generates a second 3D map of the environment. The server links the first and second 3D together in a singular 3D map. The singular 3D map may be a graphical representation of the real world using nodes that represent 3D maps generated by image data captured at client devices and edges that represent transformations between the nodes.

This application provides an image domain visibility analysis method based on the 26-grid method, relating to the fields of image analysis and computer vision. The method includes: accurately stitching local maps of multiple image frames based on the relative positioning relationships between image frames and dynamic target annotation results; converting the stitched map into static raster data; and mapping and saving the static rasters to the independent coordinate system of each image frame. The point cloud in the dynamic annotation results of each frame is also voxelized to generate dynamic rasters. The 26-grid method is combined to complete a comprehensive analysis of the visibility of dynamic targets in the image domain. This invention can be widely applied to image domain visibility analysis scenarios such as autonomous driving environmental perception, drone inspection, and robot navigation, and has high practical value and promising prospects for widespread application.

This application discloses a vehicle cooperative following method, which establishes a wireless communication connection with the leading vehicle to provide a channel for data transmission; then, through this connection, it receives the leading vehicle's periodically transmitted location information, obtaining the dynamic location data source of the leading vehicle; next, it displays the leading vehicle's location information in real time on the local map interface of the following vehicle, enabling the driver of the following vehicle to obtain an intuitive and continuous spatial location perception; finally, based on the real-time location of the leading vehicle and the current location of the following vehicle, it automatically plans and generates a driving route and navigation guidance to the location of the leading vehicle. This method creatively combines direct communication between vehicles, dynamic location sharing, and real-time path planning to form a closed-loop cooperative navigation process, enabling the following vehicle to automatically and accurately follow the leading vehicle, effectively solving the problem of the following vehicle easily getting lost due to obstructed vision and inconvenient communication in convoy travel, and improving the safety and overall stability of convoy travel.

The invention relates to a system and method for locating a vehicle on a map. In one embodiment, a method of locating a vehicle on a map includes: receiving map data having a first road and a second road, where the first road has a first number of lanes and the second road has a second number of lanes; receiving sensor data from one or more sensors of the vehicle; and receiving a vehicle positioning signal. The method further includes generating, from the sensor data, a local map having a number of local map lanes for the vehicle positioning signals; and positioning the vehicle on the first road or the second road based, at least in part, on the number of local map lanes, the number of first lanes, and the number of second lanes for the vehicle positioning signal.

This invention provides a centralized multi-robot cooperative SLAM method based on an FPGA platform. It uses a motion camera to acquire color images and IMU data of the surrounding environment; the color images are input into the FPGA platform of a single robot, converted into grayscale images, and subjected to image pyramid scaling, corner detection, and feature description before being transmitted to the processing system of the single robot; the processing system generates observation information, which in turn generates image frames; keyframes are generated and sent to a central server; the central server performs loop closure detection on the keyframes, performs map fusion on the local maps built by different single robots to obtain a global map, and optimizes the global map; finally, a 3D dense point cloud map is constructed. This method enables cooperative localization and 3D mapping of multiple robots in large-scale and complex environments, solving the problems of limited computing resources and detection range of a single robot in complex scenes, and improving the scale, accuracy, and robustness of map construction.

This application relates to a map construction method. Addressing the technical problem that existing environmental occupancy map construction methods, while aiming to improve query and update efficiency and significantly reduce memory usage, struggle to support real-time, large-scale autonomous navigation tasks in dynamic environments, this application provides a boundary representation-based map construction method and related apparatus. The map employs a global-local map framework. The local map includes the occupancy status of surrounding voxels centered on the robot, while the global map includes the voxel occupancy status in the boundary graph. The voxel occupancy status in the boundary graph is determined by using adjacent voxel pairs to represent the boundary surface layer separating free voxels from adjacent voxels, thus obtaining boundary voxels and constructing the boundary graph. A data structure combining two-dimensional hashing and one-dimensional arrays is used to manage the boundary voxels. Under this data structure, the occupancy status of any voxel is determined through the boundary graph. This application effectively improves query and update efficiency and significantly reduces memory usage.

Disclosed are a pool automatic cleaning apparatus, a server and a terminal for the pool automatic cleaning apparatus, and a method for generating a pool map and a method for processing a pool map. The method for generating a pool map can include: controlling the pool automatic cleaning apparatus to move in a pool to generate local map data about the pool; acquiring global positioning information of a current position through a global navigation satellite system of the pool automatic cleaning apparatus or a base station; transmitting the global positioning information to a server for the pool automatic cleaning apparatus or the base station; receiving satellite map data about the pool, which is acquired by the server based on the global positioning information, from the server; and generating target map data about the pool based on the local map data and the satellite map data.

This application provides a map construction method, apparatus, device, and computer storage medium. The method involves encapsulating collected environmental state information into a first link state data packet according to a preset format, and broadcasting this first link state data packet to a robot network via a flooding mechanism. The robot network includes multiple intelligent robots, among which the first intelligent robot is included. The preset format includes at least a local map state field. The method also receives and parses second link state data packets uploaded by other intelligent robots in the robot network (excluding the first intelligent robot), where the second link state data packets are encapsulated using the preset format. A local global environmental state database is updated based on the first and second link state data packets, and a global map is constructed based on this database. The method provided by this application ensures consistency in global environmental perception and avoids map conflicts.

The application relates to the field of computer vision, in particular to a local map point cloud global alignment method and device, electronic equipment and a storage medium. The method comprises the following steps: performing feature extraction and representation on target area to-be-registered ground local point cloud data and a global remote sensing image, and constructing a cross-modal feature matching model, and obtaining the relative position and direction of the ground local point cloud in the global remote sensing image through matching, and aligning the map data of two modes and different scales to the same geometric space. Thus, through semantic representation of the local ground point cloud data and the large-scale remote sensing image, the relative position and direction of the local point cloud in the global remote sensing image are obtained, the conversion error from the image to the point cloud data is increased in the current image positioning mode, the positioning accuracy is insufficient, and the panoramic image needs to be collected by a special panoramic camera, thereby increasing the economic cost and other problems. The positioning accuracy is improved, and the economic cost is reduced.

The application provides a tunnel three-dimensional map construction method and system based on a multi-source sensor, belongs to the technical field of tunnel robot environment perception and intelligent surveying and mapping, and comprises the following steps: a multi-source heterogeneous sensor parameter adaptive mechanism is used to dynamically adjust multi-source heterogeneous sensor parameters and collect multi-source sensor data; the multi-source sensor data is synchronized and registered, and a fused point cloud is obtained through weighted fusion based on the synchronized and registered sensor data; a semantic segmentation model is used to identify semantic information of the fused point cloud, and a local map is obtained by optimizing a map label according to the semantic information; and a global tunnel three-dimensional map is obtained through fusion based on a plurality of local maps. The application provides a high-precision, strong-robustness and semantic-information-rich three-dimensional environment map for tunnel robot navigation.

The application discloses a kind of motion trajectory prediction methods, devices and media based on dynamic local map, equipment.The features include: obtaining the historical motion trajectory information and map information of target vehicle, by preset feature encoder respectively on the historical motion trajectory information and the map information data extraction, obtain the trajectory feature vector and map feature vector;By preset feature fusioner on the trajectory feature vector and the map feature vector feature fusion obtains map fusion vector, to the map fusion vector and the trajectory feature vector feature fusion obtains trajectory fusion vector;According to trajectory fusion vector, the motion trajectory of target vehicle is predicted, obtains the trajectory prediction result of target vehicle.Accurate prediction of the motion trajectory of target vehicle is realized, the fusion of multiple vectors is considered to improve the prediction accuracy, reduce the influence of other interference factors in road on motion prediction, improve the prediction accuracy.

The application relates to a mobile robot repositioning method and device and a mobile robot, wherein the method comprises the following steps: matching an environment image collected by a visual sensor in a prior map to complete visual repositioning and obtain a rough repositioning pose; when the visual repositioning fails, matching an environment point cloud collected by a laser sensor in the prior map to complete laser repositioning and obtain a rough repositioning pose; and extracting a local map on the prior map according to the rough repositioning pose, and performing point cloud registration on the current environment point cloud and the local map to obtain a precise repositioning pose. The application can guarantee repositioning accuracy and improve repositioning robustness.

The application provides a self-adaptive map-free navigation method and system for uneven terrain, and relates to the field of mobile robots, and the following steps are iteratively performed until the final target point is reached based on the current position of the robot: positioning the current position based on multi-source sensor data of the robot, and constructing a local map; selecting an optimal boundary vertex sequence from the local map according to the current positioning result and the local map, and then obtaining an optimal sub-target point; performing passability analysis and collision detection based on the optimal sub-target point, and generating a path to the sub-target point; and predicting a trajectory in real time according to the current generated path and the local map, and driving the robot to move to the sub-target point along the predicted trajectory; and the application enables the mobile robot to adaptively navigate on uneven terrain under the condition of no prior map, and improves the adaptability and convenience of the autonomous navigation system.

This invention relates to the field of visual SLAM technology, providing a SLAM method and apparatus for indoor environments with weak texture and fast, wide-view conditions. The invention uses a preset extraction model to extract feature points and their descriptors from acquired images; it uses these feature points and descriptors to estimate the initial pose, and uses a preset matching model to perform feature matching for pose optimization during tracking, obtaining keyframes; it then performs coupling matching and / or triangulation feature matching using the preset matching model based on the keyframes to build a local map; finally, it performs coupling matching and / or feature matching using the preset matching model on historical keyframes and the current keyframe to perform loop closure detection on the local map, generating the final map. This invention solves the problems of tracking failure and insufficient positioning accuracy in existing SLAM methods under fast, wide-view, weak-texture, and severe shaking environments.