842 results about "Local map" patented technology

The invention belongs to the technical field of positioning and navigation, and discloses a high-reliability positioning and navigation method for an air-ground cooperative unmanned system. According to the method, a negative obstacle is accurately extracted by using an RANSAC algorithm and a region growing method, a two-dimensional grid map is generated through three-dimensional point cloud re-projection, a negative obstacle region is marked on the map, and a prior map is provided for an unmanned vehicle. The unmanned vehicle constructs a local map in combination with a sensor of the unmanned vehicle, and the local map is matched with a prior map through an ICP algorithm to accurately position a global pose. According to a priori map and real-time sensor data, a grid cost map containing static obstacles, dynamic obstacles and negative obstacles is generated, a TSCNN predicts dynamic obstacle tracks according to time sequence data, map cost values are updated, an A * algorithm carries out optimal path planning based on the updated dynamic cost map, and the optimal path planning is carried out based on the updated dynamic cost map. And dynamic obstacles and negative obstacles are avoided in real time. According to the invention, efficient, accurate and safe navigation and obstacle avoidance can be realized in a dynamic environment and a complex terrain.

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.

Approaches presented herein provide for the identification of differences between local map data, for a region of a physical environment, and observation or perception data generated by one or more machines or other such sources. In at least one embodiment, sensors on an ego machine can capture sensor data for a region in which the ego machine is located, and a language model on the ego machine can compare this sensor data, or perception data generated using the sensor data, against the local map data. The language model can generate a tokenized description of identified differences, in a domain-specific language. The tokenized description can be transmitted to a map management service that can compare these differences against differences identified by other machines, for example, to determine whether to update and redistribute at least a portion of the map data.

The invention relates to a dynamic environment semantic SLAM method based on monocular vision and LiDAR fusion, and belongs to the technical field of robots. The method comprises the following steps: 1) performing instance segmentation on point clouds to obtain unknown motion state point clouds, pure static road sign point clouds and ground attribute point clouds, identifying semi-static and dynamic road sign point clouds through initial pose estimation and Kalman filtering, and then performing accurate pose estimation; 2) segmenting and enhancing the image instance, extracting SuperPoint feature points, projecting a dynamic road sign to the segmented image so as to distinguish static and non-static points, tracking static features by adopting a plurality of tracking strategies, and optimizing the pose by utilizing a local map; 3) carrying out heterogeneous sensor attitude fusion; 4) outputting an optimized track by combining two loopback detection results; and 5) constructing a global pure static semantic map. According to the method, the dynamic environment influence is considered, and the positioning precision and the mapping quality of the system in the dynamic complex environment are improved.

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 provides a visual localization prior map construction method and device based on a laser radar. The method comprises the following steps: acquiring foundation laser radar point cloud data and mobile laser radar image data of a localization area; constructing a local map of the ground-based laser radar point cloud data, and matching the frame of the mobile laser radar image data with the local map to obtain the current pose estimation of the laser radar; global positioning is carried out on the laser radar according to the key frame in the current pose estimation, an optimization factor graph is constructed based on a global positioning result, and pose constraint is carried out on the initial pose estimation of the laser radar; and based on the final pose estimation of the laser radar after pose constraint, constructing a priori map database for visual localization in the localization area. Through the method and the device, the defects of failure and poor robustness of a prior map positioning method in a complex scene with serious GNSS positioning error or even failure in the prior art are solved.

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 provides an unmanned fire extinguishing robot navigation method and device based on a 4D millimeter wave radar, and solves the technical problem that in the prior art, effective data cannot be accurately captured for positioning navigation, so that fire cannot be accurately extinguished. The method comprises the following steps: acquiring an initial attitude angle, a local map and an optimal path of a robot, and navigating the robot according to the optimal path; the method comprises the following steps: acquiring feature point clouds, preprocessing the feature point clouds and a local map to obtain a preprocessed feature point cloud set and a preprocessed local map, performing point cloud matching based on a variant ICP algorithm to obtain pose change parameters, and superposing the pose change parameters with an initial attitude angle of a robot to form an incremental odometer. The preliminarily estimated real-time motion state of the robot is obtained and corrected to obtain the high-precision robot positioning and motion state, and the process is cycled until the robot reaches the fire source position, and then water spraying and fire extinguishing are conducted. The method can be widely applied to the technical field of robot navigation.

The invention discloses a multi-mode SLAM method, system and device, a medium and a program product, and the method comprises the steps: segmenting a current image frame through employing a lightweight semantic segmentation network, obtaining a semantic mask of the current image frame, carrying out the correction, and projecting the corrected semantic mask to a three-dimensional point cloud, and obtaining a point cloud image of the current image frame under a radar coordinate system; performing point cloud segmentation to obtain a plurality of point cloud sets with different semantic classification tags; predicting a saliency mask of the current image frame, extracting visual feature points of the current image frame, and obtaining depth values of the visual feature points; and finally, tracking the visual feature points and constructing a local map. And meanwhile, a Scan-Context descriptor and a similar distance are utilized to carry out loopback detection and global BA on the point cloud set, and a global map is constructed. According to the invention, various sensor data are comprehensively utilized, the perception capability of environment characteristics is improved, and the problems of insufficient perception and reduced positioning precision in a complex environment are effectively solved.

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 an AGV natural scene positioning method and system based on a 3D laser radar, and relates to the technical field of automatic guided vehicle control. The method comprises the following steps: constructing matching sub-graph information, and providing a key reference for subsequent positioning; a visual sensor is used for capturing multiple image data, IMU data and odometer data of the current pose of the AGV are collected in real time at the same time, and the motion state of the AGV is accurately analyzed according to the IMU data and the odometer data. A 3D laser radar obtains scene point cloud data to construct a local map, the local map is registered with a matched sub-graph, initial pose estimation is obtained, finally, the initial pose, a motion state and image data are integrated, final pose information of the AGV is determined through complex operation, multi-source data cooperatively exerts force, and the positioning accuracy and reliability of the AGV in a natural scene are comprehensively improved.

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 method for realizing BEV local map real-time perception by fusing SD Map, which comprises the following steps: encoding a navigation map to obtain a broken line sequence of the navigation map, and processing the broken line sequence of the navigation map by using a Transform encoder to obtain features of the navigation map; extracting features from the vehicle-mounted sensor data by using a map encoder, and converting the features into uniform BEV features; and based on a preset local map feature fusion network, fusing the features of the navigation map with the BEV features by using a multi-head cross attention mechanism to obtain fused BEV features, inputting the fused BEV features into a preset lane topology model decoder, and predicting a lane center line, traffic elements and an affinity matrix associated with the lane center line and the traffic elements. Therefore, the problems that in the prior art, a high-precision map is high in cost and difficult to maintain, and local map generation stability is poor are solved, and robustness and accuracy of a sensing system are improved.

The invention relates to a contrast longitudinal federal map learning method based on adaptive multi-kernel clustering, and the method comprises the following steps: S1, initializing a global model: initializing a global model gG through a server, and distributing the global model gG to all K clients; s2, local training: each client performs local training on the graph learning model by using the graph data held by the client, and model update is generated; and S3, parameter uploading: the K clients send the local graph learning model update theta k to the server. And S4, model aggregation: the server sends the aggregated model update theta global back to the clients, and the server aggregates the model parameter update of the K clients through weighted average to construct a global model. According to the method, multi-source graph knowledge can be integrated in a privacy-protected longitudinal federation environment, and the model is trained under the condition of no label based on a comparative learning mechanism. And self-adaptive multi-core graph clustering is integrated, so that the performance of the model is improved.

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 multi-unmanned-aerial-vehicle cooperative SLAM system based on common-view area detection. According to the system, firstly, an adaptive threshold value and a feature homogenization method are introduced to improve a feature extraction algorithm to extract image features; secondly, on the basis of a feature point screening algorithm fusing YOLO and geometric constraints, the influence of motion exterior points is reduced; then, an improved feature point screening mechanism is provided for the problem of feature mismatching existing in the unmanned aerial vehicle image; thirdly, on the basis of local positioning and mapping of the unmanned aerial vehicle, a common-view area detection algorithm based on a visual word bag and relative pose calculation are fused into a local map; and finally, performing global optimization by adopting an improved L-M solution algorithm to improve pose estimation, and generating a high-precision global map. According to the method, the multi-unmanned aerial vehicle cooperative SLAM system is improved, so that the interference of movement obstacles in an unknown environment is reduced, the mapping and positioning precision is improved, and better real-time performance and robustness are achieved.

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.

The invention discloses an unmanned aerial vehicle cluster trajectory planning method and system under unreliable communication, and the method comprises the steps: firstly building and updating a three-dimensional map based on an airborne sensor of an unmanned aerial vehicle, then building a local map based on the data of the sensor at the current moment, and recognizing an idle area and an occupied area; generating a track for avoiding an obstacle by combining time and space constraints; the track and environment information are shared through communication, and collision is avoided through coordination; an MPC module is used to predict a trajectory when trajectory information is lacked, and collision check is performed. And in the feasible trajectory execution process, environment changes are coped with through timing collision detection, and safe navigation is ensured. According to the method, collision of the unmanned aerial vehicles due to absence of cluster real-time trajectory information for a long time is avoided by optimizing a communication network and an MPC prediction trajectory; and an MPC module is added to generate a prediction track when the interaction track information is lost so as to check whether the newly generated track is collided or not.

The invention discloses a heaven and earth integrated multi-domain fused automobile high-level automatic driving system, which realizes more comprehensive and non-blind area sensing and positioning of an automatic driving automobile through multi-domain information fusion of a satellite platform, a cloud platform, an automobile end platform and the like, and promotes comprehensive upgrading of comprehensive efficiency of an intelligent traffic system. Based on cloud error broadcasting and a high and low orbit satellite fusion enhancement technology, the problem of automobile communication loss caused by the fact that single ground communication is limited by base station coverage is solved; based on a cloud reinforcement learning architecture, the communication network stability of the space-ground integrated system is improved through adaptive switching of a communication network; a road semantic map and a decision control experience pool are established based on vehicle high-precision trajectory data collected by a platform, and the limitation of the perception ability of a bicycle is made up through cloud federal learning scene experience sharing; the vehicle end dynamically updates a cloud road semantic map through local map matching; and information support is provided for realizing a chassis multi-domain fusion technology based on high-precision positioning.

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 discloses a three-dimensional point cloud map construction method, system and device and a medium. The method comprises the following steps: acquiring a color image, a depth image and inertial measurement unit data; constructing an image pyramid according to the color image, and extracting feature points; performing dynamic target detection and feature point screening on the image pyramid through target detection model detection; according to the feature points and inertial measurement unit data, performing preliminary estimation and refined optimization on the pose; performing thread tracking and key frame judgment according to the distribution and poses of the feature points to obtain a key frame, and updating a local map; carrying out loopback detection on the current frame, and optimizing a global map; and performing point cloud generation, point cloud splicing and point cloud optimization according to the color image and the depth image corresponding to the key frame to obtain a three-dimensional point cloud map. By screening the dynamic feature points and fusing the IMU data, high-precision indoor positioning and map modeling are realized, and the method can be widely applied to the technical field of intelligent positioning and mapping.

The invention relates to the technical field of automatic agricultural equipment, in particular to an orchard plant protection robot cluster operation system which comprises a central scheduling module, a cluster communication module, an environment modeling module and an updating module. The central scheduling module communicates with a robot through a communication technology, and the cluster communication module constructs a robot cluster communication network by using a communication network configuration strategy; the environment modeling module evaluates the complexity of a local environment through a sensor assembly, and generates a local map containing orchard terrain, vegetation density and obstacle information. The updating module splices the local maps to generate an operation map, updates a communication network according to the environmental complexity, and optimizes task allocation and path planning of the robot cluster; the system has the functions of task scheduling, communication network optimization and map updating, and efficient collaborative operation of a robot cluster in a complex orchard environment is achieved.

The invention relates to a multi-robot unknown environment collaborative exploration method based on a dynamic layering and zoning strategy, which comprises the steps that a plurality of robots are distributed in different areas of an unknown environment for collaborative exploration, each robot constructs a local map in real time through an SLAM algorithm, and a global map is constructed based on information sharing among the robots; based on the exploration area in the global map, segmenting the two-dimensional exploration area corresponding to the global map into a plurality of sub-areas through a dynamic layering and partitioning strategy to obtain a layering and partitioning result; wherein the layering and partitioning result is a sub-region set when the two-dimensional exploration region reaches the optimal hierarchy; based on the starting point position of each robot, distributing the layering and partitioning results among the robots, and balancing the task load between any two adjacent robots in the multiple robots to obtain a task unit set of each robot; and a minimum coverage path is planned on the optimal level based on the task unit set of each robot, and collaborative exploration is carried out based on the minimum coverage path.

The invention provides a map updating method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps of performing prediction processing based on historical change data of a global map and current environment data through a first convolutional neural network model to obtain a first prediction result of map elements in the global map; performing first fusion processing on the global map and the part of the local map at the plurality of collection times to obtain a fusion result of the plurality of collection times, and performing prediction processing based on the fusion result of the plurality of collection times through a recurrent neural network model to obtain a second prediction result of map elements in the global map; wherein the local map is determined according to the vehicle perception data of the vehicle driving on the global map; and performing map updating processing on the global map according to the first prediction result and the second prediction result of the map elements in the global map. According to the method, the timeliness and accuracy of map updating can be remarkably improved.

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.