484 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.

The invention relates to the technical field of laser SLAM, and particularly discloses a laser SLAM method based on ground segmentation, and the method comprises the steps: obtaining laser radar point cloud data information, carrying out the point cloud preprocessing of the laser radar point cloud data, and obtaining ground point cloud features and non-ground point cloud features; respectively carrying out feature extraction, feature matching and pose estimation on the ground point cloud features and the non-ground point cloud features to obtain a pose nonlinear optimization constraint result; selecting a key frame according to a pose nonlinear optimization constraint result, constructing a local map according to pose information of the key frame, and obtaining a local map construction result; loopback detection is carried out according to the local map construction result, and a loopback detection constraint result is obtained; and performing global pose optimization processing according to a loopback detection constraint result and a local map construction result to obtain a global pose and global map construction result. The laser SLAM method based on ground segmentation provided by the invention can improve the positioning precision and the operation stability.

The invention relates to a distributed trajectory planning method and system for an unmanned aerial vehicle cluster in an obstacle environment. The method comprises the steps of initializing a system, constructing a local Euclidean symbol distance site map, and realizing real-time interaction of state information of the unmanned aerial vehicle. An initial collision-free path is generated using jump point search. And solving an optimal control point through an L-BFGS algorithm through multi-constraint trajectory optimization in combination with a load swing dynamics model, four-rotor dynamics limitation, cluster collision avoidance and environment obstacle avoidance requirements. And a dynamic time redistribution strategy is adopted, and the track time interval is adjusted according to speed and acceleration overrun conditions. The method further comprises an adaptive re-planning mechanism, and local target points are updated in real time and adjacent aircraft collaborative optimization is triggered based on local map boundary detection and quadrotor track safety detection. According to the method, efficient, safe and stable trajectory planning of the hanging load quad-rotor unmanned aerial vehicle cluster in a complex environment is realized, the requirement of autonomously and efficiently completing tasks is met, and the task execution efficiency and safety are improved.

The invention belongs to a synchronous localization and mapping (SLAM) method in the field of robots, and discloses a binocular vision SLAM method based on a deep learning feature extraction and matching algorithm. The system comprises three modules: a front-end tracking module, a local mapping module and a loopback detection module. For each frame of input binocular image, firstly, feature points of the image are extracted, and then frame-to-frame matching is used to track a current image frame and determine whether the current frame is set as a key frame. In the local mapping module, matching from a key frame to a local map is used to obtain a more accurate pose and a global map, and in addition, the loopback detection module is used for inhibiting accumulative errors of a large scene. Experiments on a public data set and a data set collected by a robot platform show that robust, accurate and globally consistent pose estimation and mapping are realized by the method, and quick real-time operation can be realized on an embedded platform at a speed exceeding 10 FPS.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving local vector map construction method based on a standard precise map and fusion perception. The method comprises the following steps: preprocessing standard precise map information, and constructing a fusion sensing network; in the standard and precise map information preprocessing, original standard and precise map data are processed into vectorization expression suitable for model input; the fusion sensing network comprises a feature extraction backbone network, a BEV (aerial view) view conversion network, a multi-mode BEV feature fusion network and a map element decoding module; in the map element decoding module, the processed standard precise map elements are interacted with BEV aerial view features of the sensor; in local map model training, query denoising is introduced as an additional training task, and query sorting selection is introduced to accelerate the model convergence speed. According to the method, higher accuracy, better robustness and higher operation efficiency can be achieved, and the influence of environmental factors is smaller.

The invention provides a basement scene automatic vector map construction method and system, and the method comprises the steps: carrying out the IPM conversion of a plurality of ground-oriented top view images, carrying out the splicing, obtaining a bird's-eye view, carrying out the semantic segmentation of the bird's-eye view, extracting semantic elements, and converting the semantic elements into point cloud data; providing IMU and vehicle wheel speed data, combining with the point cloud data to construct a local map, and correcting to obtain a semantic point cloud map; preprocessing the semantic point cloud map, and clustering the point cloud data in the semantic point cloud map according to the similarity semantic point cloud to obtain clustered point cloud data; and based on the semantic point cloud map, combining the clustering point cloud data, constructing boundaries on two sides of a road, generating a lane center line and a boundary line, obtaining a vector map, embedding parking space information, and performing curve optimization on the center line and the boundary line, so as to obtain a basement scene automatic vector map. Vector representation of the complex environment of the underground garage is achieved, and high-quality basic map data support is provided for applications such as vehicle navigation and path planning.

The invention relates to a dynamic visual map matching autonomous optimization method based on IMU sensing, and belongs to the technical field of positioning and navigation of autonomous mobile equipment. The method comprises the steps of obtaining IMU high-frequency motion data and visual image data, performing noise reduction to obtain carrier short-time attitude change information, performing semantic segmentation on an image to recognize a dynamic target, extracting feature points and performing dynamic target weight evaluation; calculating confidence degree scores of the feature points, and mapping fusion weights of the IMU and the vision through a self-adaptive credibility distribution mechanism; s3, predicting a trajectory and a potential matching area based on carrier short-time attitude change information, incrementally updating a local map, and matching by taking an IMU trajectory as a constraint to obtain an initial result; and detecting the matching validity, reducing the re-matching range by using an IMU short-time track during mismatching, and correcting an initial value to obtain a final result. The map matching precision and stability in a complex dynamic scene are improved, the real-time performance and continuity are balanced, and the problems that a traditional scheme is redundant in calculation power and slow in mismatch recovery are solved.

A detection device, such as an unmanned vehicle, is adapted to traverse a search area and generate sensor data associated with objects that may be present in the search area. The generated sensor data is used by a system including object detection inference models configured to receive the sensor data and output object data, a local object tracker configured to track detected objects in a local map, and a global object tracker configured to track detected objects on a global map. The local object tracker is configured to fuse object detections from the object detection inference models to identify locally tracked objects, and a Kalman filter processes frames of fused object data to resolve duplicates and / or invalid object detections. The global object tracker includes a pose manager, configured to track global objects in the global map and update the pose based on a map optimization process. User-in-the-loop processing includes a user interface for displaying and manual editing of detected object data.

The invention discloses a LiDAR-IMU-camera tight coupling positioning and mapping method and device for a mobile platform, and belongs to the technical field of robot SLAM. Synchronously triggering the color camera, the laser radar and the IMU through hardware, and unifying timestamps; performing compensation and distortion removal on the laser point cloud motion by the IMU data; based on curvature, intensity and density self-adaptive downsampling, local plane fitting errors are used for distributing observation weights; the IMU pre-integration pose is used as an initial value, point-to-surface registration of the weighted point cloud and the local map is carried out, and a laser-IMU tight coupling odometer factor is obtained; the color image and the laser intensity graph are fused into a multi-mode loopback descriptor, and loopback factors are generated through global retrieval and geometric verification; and inputting an odometer, an IMU, a laser loopback factor and a visual loopback factor into an increment factor graph optimizer, jointly solving a global optimal key frame pose, and outputting a dense laser point cloud map and a color visual point cloud map. The method can be operated in real time on an embedded platform, effectively inhibits drifting, and realizes centimeter-level global consistent positioning and mapping.

A man-machine collaborative decision-making planning system and method based on environment adaptive trajectory optimization comprises a man-machine interaction module, a rule-based motion planning module and a learning-based parameter generation module, the man-machine interaction module performs data processing such as environment perception and state estimation according to data collected by an onboard sensor and a remote controller, and the parameter generation module generates parameter parameters according to the data. Input information required by the motion planning and parameter generation module is obtained; the motion planning module performs topological path search, visibility detection and trajectory optimization processing according to the local map and the user instruction information to obtain a planned trajectory of unmanned aerial vehicle tracking control; and the parameter generation module performs environment adaptive strategy processing according to the distribution information of the trajectory in the environment to obtain a speed constraint parameter for adjusting motion planning. According to the method, semantic information and depth information in environmental perception are considered, the speed parameters of the planner are automatically and dynamically adjusted, and the adaptability of the planned trajectory to different scenes is effectively improved, so that the cognitive and operation burden of a pilot in a complex environment is reduced, and the navigation efficiency and the system safety are improved.

The invention relates to the technical field of map construction, and provides a mapping method and device based on fusion of a laser radar and an inertial measurement unit. According to the scheme, motion distortion compensation is carried out on original point cloud data through motion estimation based on inertial data, the compensated point cloud is matched with a local map, and the local map is obtained through a filter; carrying out tight coupling fusion on the inertial data and the point cloud matching result, and outputting the pose information of the sensor at the current moment; according to the pose information of the sensor, the compensated point cloud is added into the dynamically managed local map, and the updated local map is periodically published, so that a complete mapping function from data acquisition, processing and fusion to map construction is realized. And through motion distortion compensation, the inertial data and the point cloud matching result are tightly coupled and fused, and the updated local map is periodically published, so that high mapping precision can still be kept when the mapping area is large.

A method for calculating an optimum route for a first vehicle to travel from a current location to a selected parking space includes receiving map data of a parking zone, the map data including local map data and global map data of the parking zone, sensor data, parking space data indicating availability of parking spaces within the parking zone, and vehicle dynamics and location data, selecting, as the selected parking space, at least one available parking space based on the parking space data, determining a plurality of target positions between the current location and the selected parking space, calculating, in response to the selecting of the selected parking space and based on the plurality of target positions, the optimum route, including calculating trajectories of the first vehicle along an entire route between the current location and the selected parking space, and controlling the first vehicle to travel from the current location to the selected parking space.

The invention provides a mobile robot autonomous navigation method based on pedestrian trajectory prediction obstacle avoidance, which comprises the following steps: firstly, initializing a robot H, and acquiring 3D laser radar information, camera information, local map data and pose information of the robot H; secondly, according to the position information of the robot H, the multi-modal fusion information and the local map data, the dynamic obstacle state and the static obstacle position are obtained, the types of obstacles are distinguished, and passable areas are divided; next, a prediction trajectory is generated by using a progressive learning trajectory prediction network of LSTM + GAN, sub-target points are generated in a passable area in combination with an RRT algorithm, an optimal sub-target point is selected through an evaluation function, and global path planning is performed by using BIRRT; and finally, inputting the predicted trajectory into a DWA algorithm to realize local path planning and real-time obstacle avoidance of the robot. The obstacle avoidance capability and navigation efficiency of the robot are remarkably improved, the detouring distance and time are reduced, and the adaptability and flexibility of the robot in logistics storage and other scenes are enhanced.

The invention discloses a multi-scale map-based rapid relocation method for an unmanned logistics vehicle in a dynamic factory, and the method comprises the following steps: S1, filtering point cloud dynamic elements based on binary semantic segmentation of a deep convolutional neural network, S2, processing static point cloud, and S3, carrying out the rapid relocation of the unmanned logistics vehicle based on global and local map search. According to the method, movable elements in the cloud are filtered based on the deep convolutional neural network, and interference of a dynamic target on subsequent vehicle positioning is eliminated. The method is also combined with a GOD map descriptor to process the static point cloud, so that the subsequent positioning step is clear and rapid. According to the method, local map matching and global map matching are combined, and rapid relocation of the unmanned vehicle is achieved.

The invention discloses a robot automatic mapping method and related equipment in an unknown open environment, and the method comprises the steps: obtaining sensor data, and constructing a local map according to the sensor data; topologizing the local map to obtain a collision-free topological graph of the local map; performing sparse processing on the collision-free topological graph to obtain a sparse topological graph; and inputting the obtained sparse topological graph into a strategy network model based on deep reinforcement learning, and outputting a strategy for controlling the robot to move so as to move the robot. According to the method, the environment mapping problem is modeled into a graph structure, spatial dependency in the environment is modeled through the relation between the nodes, and effective learning of the robot on the relation between different areas in the complex environment is achieved. In addition, the attention mechanism helps the robot to preferentially select the area with the high potential return in the autonomous exploration process by dynamically adjusting the attention weight of each area, and therefore the exploration efficiency is improved. The robot can be widely applied to the technical field of robots.

The invention relates to the technical field of robot control, and discloses a robot scheduling method, device and system. Environment data sent by a robot is received, and the environment data is environment data of an area where the robot is located; according to the environment data, performing map model construction on the area to obtain map information; obtaining local map information, positioning information and a motion path according to the map information; and sending the local map information, the positioning information and the motion path to the robot to support autonomous movement and task execution of the robot in the current area. In this way, the overall operation efficiency and stability of the system are improved.

The invention discloses a detection result determination method and device, a storage medium and a vehicle. The method comprises the steps that image data of an area where a vehicle is located currently are obtained, and the image data are used for representing a multi-angle view of the area; a first aerial view angle feature in the image data is extracted, and the first aerial view angle feature is at least used for representing road information in the image data; determining local map data corresponding to the area from the priori map, and extracting a second bird's-eye view feature in the local map data, the second bird's-eye view feature being at least used for representing road information in the local map data; fusing the first aerial view angle feature and the second aerial view angle feature to obtain a fused feature; and determining a detection result of the lane line in the area based on the fusion feature. The technical problem of low lane line detection efficiency is solved.

An embodiment method of generating a local map for travel control of a mobility includes loading the local map from an entire map stored in a memory, wherein the local map includes at least a portion of the entire map, acquiring a two-dimensional (2D) light detection and ranging (LiDAR) point data from a LiDAR mounted on the mobility, acquiring a three-dimensional (3D) feature point data by receiving a front image of the mobility from a camera mounted on the mobility, reducing a dimension of the 3D feature point data to a 2D feature point data, binding the 2D feature point data to the 2D LiDAR point data, and publishing the 2D feature point data bound to the 2D LiDAR point data on the local map.

The invention discloses an obstacle avoidance trolley control method based on multi-sensor fusion and Kalman filtering, and particularly relates to the field of obstacle avoidance trolley environment adaptation and control. A data acquisition module acquires multi-sensor original data of an obstacle avoidance trolley in different scenes; the Kalman filtering fusion module carries out data fusion; the environment feature recognition module performs feature marking on the fused data; the local map construction module constructs a local map under a two-dimensional coordinate system according to different scenes; the path feasibility evaluation module calculates a traffic safety index of each node in a differentiated manner; calculating the deviation between the actual safety index of each node and the reference value; the obstacle avoidance control module generates a corresponding motor control instruction; according to the method, after the node traffic safety index is calculated, the differential motor instruction is generated, operation delay caused by excessive braking of a traditional fixed control strategy or potential safety hazards caused by insufficient control are avoided, and unnecessary energy consumption waste and fault shutdown cost are reduced.

The invention relates to the technical field of automatic driving positioning, and particularly discloses a laser radar SLAM method, device and system for an automatic driving vehicle, and the method comprises the steps: obtaining the original point cloud data of a laser radar, and carrying out the adaptive roughness evaluation and feature screening of the original point cloud data of the laser radar, and obtaining feature information; performing dynamic outlier detection on the feature information to screen out outlier feature points to obtain effective feature information; attitude estimation is carried out on the effective feature information according to a double-peak geometric primitive constraint mechanism, a six-degree-of-freedom pose is obtained, and the double-peak geometric primitive constraint mechanism is obtained through construction according to the feature information of the current frame and corresponding feature information in a local map; and according to the six-degree-of-freedom poses, local map construction and global map construction are carried out in sequence, and positioning information of the autonomous vehicle is obtained. According to the laser radar SLAM method for the autonomous vehicle provided by the invention, the precision and reliability of laser radar SLAM can be improved.

The invention provides a centralized multi-robot collaborative SLAM (Simultaneous Localization and Mapping) method based on an FPGA (Field Programmable Gate Array) platform. The method comprises the following steps: acquiring a color image and IMU (Inertial Measurement Unit) data of a surrounding environment by using a motion camera The color image is input into an FPGA platform of the single robot and converted into a grey-scale map, image pyramid scaling, corner detection and feature description are carried out, and the image is transmitted to a processing system of the single robot; the processing system generates observation information and further generates an image frame; generating a key frame and sending the key frame to a central server; the central server side carries out loopback detection on the key frame, carries out map fusion on local maps established by different single robots to obtain a global map, and optimizes the global map; and constructing a three-dimensional dense point cloud map. According to the method, cooperative positioning and three-dimensional mapping of multiple robots can be carried out in a large-scale and complex environment scene, the problem that computing resources and a detectable range of a single robot in the complex scene are limited is solved, and the scale, the precision and the robustness of map construction are improved.

The invention discloses a synchronous positioning and mapping method and system based on multi-sensor fusion, a storage medium and electronic equipment. The method comprises the following steps: acquiring IMU data, an image and point cloud data measured by a laser radar; performing pre-integration on the IMU data to obtain real-time pose estimation, and performing laser probability updating and visual probability updating based on the real-time pose estimation to obtain an updated local map; a laser point cloud matching residual error, a visual reprojection residual error, an IMU pre-integration residual error and a marginalization residual error are constructed, a residual error equation is constructed based on the laser point cloud matching residual error, the visual reprojection residual error, the IMU pre-integration residual error and the marginalization residual error, a sliding window is adopted for nonlinear optimization, and robot pose estimation is obtained; and visual loopback detection, laser loopback detection and pose global optimization are carried out. According to the method, the calculation rate, the positioning precision and the reliability can be improved in a complex or feature degradation scene.

The invention provides a robot obstacle avoidance method, a robot obstacle avoidance device and a computer storage medium. The robot obstacle avoidance method comprises the following steps: dividing a current obstacle point cloud from current point cloud data; mapping the current obstacle point cloud to a map coordinate system, and updating an obstacle grid of a global map; acquiring a current obstacle grid of a local map based on the current position of the robot; determining an actual obstacle distance according to the distance between the current obstacle point cloud and the robot; determining a virtual obstacle distance according to the distance between the current obstacle grid and the robot; and making an obstacle avoidance decision for the robot based on the actual obstacle distance and the virtual obstacle distance. When an obstacle avoidance decision is made through the robot obstacle avoidance method, comprehensive evaluation can be carried out based on the actual obstacle distance and the virtual obstacle distance, and the reasonability and accuracy of obstacle avoidance opportunity selection are improved.

The invention provides an unmanned vehicle layered road network path planning method and system based on air-ground cooperation, and belongs to the technical field of unmanned equipment path planning. The method comprises the following steps: S1, carrying out preliminary global planning on a road network according to a satellite map so as to extract an ordered global road point set with an interval of n1; s2, the high-altitude unmanned aerial vehicle goes to all global road points in sequence to obtain a finer local map, two layers of road networks are aligned and fused, part of road network information is supplemented, and the road points are calculated and updated; s3, taking the working range of a sensor carried by the unmanned vehicle as a planning step length, considering dynamic constraint and local dynamic and static obstacle avoidance, performing local planning on the unmanned vehicle to obtain an unmanned vehicle road point, and smoothing to obtain a final expected track of the unmanned vehicle; and S4, moving according to the position of the unmanned vehicle, and continuously iterating the steps S2 and S3 until the unmanned vehicle reaches the target point. The method improves the planning success rate.

The invention relates to a map construction method and device, computer equipment and a readable storage medium. The method comprises the steps that in the moving process of a robot in a pipeline, laser radar point cloud, pipeline environment data, pipeline visual data and motion data collected by different sensors are obtained; under the condition that the current map building moment is reached, the initial pose of the robot is predicted according to the motion data, and a refractive index compensation factor of the pipeline is determined according to the pipeline environment data; determining an initial map point cloud of the target time period according to the initial pose, the refractive index compensation factor, and the laser radar point cloud and pipeline visual data in the target time period; according to a preset curvature threshold and a refractive index compensation factor, correcting the initial map point cloud to obtain a target local map; and updating the initial global map at the previous map construction moment according to the target local map to obtain a target global map. By adopting the method, the accuracy of the constructed map can be improved.

The invention relates to the technical field of sweeping path planning, in particular to a map optimization method and device for a sweeping robot. The method comprises the following steps: collecting a historical cleaning log, performing three-dimensional point cloud reconstruction of a cleaning area, and constructing a normalized cleaning map; when it is recognized that a new round of cleaning task is started, a latest cleaning monitoring video is collected in real time, difference comparison detection is conducted on a normalized cleaning map, and latest position information of movable elements is calculated; local map updating is carried out according to the latest position information, and an incremental optimization map is constructed; and performing full-coverage cleaning planning based on the incremental optimization map, and constructing a full-coverage cleaning path. According to the invention, real-time and dynamic incremental updating of the cleaning map is realized, the cleaning efficiency is improved, and the cleaning path planning is optimized.

The invention discloses a laser radar inertial odometer method based on block-by-block updating, which comprises the following steps: constructing a kinematics equation and an observation equation of a laser radar inertial odometer system based on a block-by-block updating scheme, and performing state propagation on point cloud block laser radar data and inertial measurement data to obtain a prior prediction state; compensating the pose of each point cloud point in the point cloud block, and aligning the point cloud point to the pose of the point cloud block at the last moment; registering the aligned point cloud blocks with a local map, executing nearest neighbor plane search, and then calculating a point-surface residual error; combining the prior state prediction with the posterior state residual error, and performing state iteration updating and iteration convergence to obtain a final result; and then local map updating is carried out. According to the method, the continuous laser radar point cloud data is divided into a plurality of small blocks for batch processing according to the extremely short time interval, so that the time overhead of single update can be remarkably shortened.

The invention discloses a point cloud processing method, device and equipment and a readable storage medium, after an electronic device obtains a current key frame, the current key frame is utilized to update the observation times of map points in a local map, and according to the observation times of the map points in the local map, unstable observation points are determined from historical key frames and deleted. By adopting the scheme, the electronic equipment determines and deletes the unstable observation points in the historical key frame according to the observation times by updating the observation times of the map points in the local map, and only keeps the stable observation points in the historical key frame, so that the high-quality historical key frame is obtained. The environment map is created by using high-quality historical key frames subsequently, ghosting, noise and the like of the environment map are avoided, and the purposes of improving navigation and positioning precision are achieved while the quality of the environment map is improved.

The invention relates to the technical field of robot intelligent control, in particular to an intelligent path planning method and system based on a complex environment. Comprising the steps that through heterogeneous laser radar combination (air-ground global obstacle perception is achieved, a layered map (a static global map and a dynamic local map) is constructed, airspace safety corridor constraints are introduced based on an improved RRT * algorithm, and cooperative obstacle avoidance of double mechanical arms, a three-axis platform and an AGV is ensured. Real-time local path optimization is realized in combination with a dynamic window method (DWA), the path risk is monitored through a safety index (SI), and millisecond-level re-planning or emergency stop is triggered. The method has the advantages that the airspace obstacle detection height reaches 3 m, the path planning success rate is increased to 98.7%, the re-planning response time is smaller than or equal to 180 ms, the collision risk is remarkably reduced, and the method is suitable for autonomous navigation of complex scenes such as electric power inspection and warehouse logistics.

The invention belongs to the technical field of laser SLAM, and particularly relates to a laser SLAM back-end optimization method and device based on lightweight spherical map representation. The method comprises the following steps: generating a local map represented by a spherical map from a local point cloud generated by executing an SLAM task by a laser radar through spherical sampling; and point cloud-map registration is performed on any point cloud frame of the local point cloud at the current moment and the local map at the previous moment as well as any point cloud frame of the local point cloud at the previous moment and the local map at the current moment, and the point cloud-map registration means that the point cloud frame of the local point cloud at the previous moment and the local map at the current moment are adjusted by adjusting the pose of the point cloud frame. Enabling the sum of the distances between each point cloud point in the point cloud frame and the associated plane in the local map to be minimum; and determining the structural similarity between the local maps based on the difference of the structural features of all fitting planes contained in different local maps, performing loopback detection on the local maps by integrating the structural similarity and the Euclidean distance, and integrally and uniformly adjusting the pose of each local map forming a loopback.