611 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.

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 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 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 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 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.

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 crowd navigation method of a mobile robot based on a heterogeneous neural circuit strategy, which comprises the following steps: constructing a space-time diagram, and converting original local map data acquired by radar scanning into raster map data; constructing a heterogeneous neural circuit strategy network model and a reward function; inputting the time-space diagram and the raster map data into the heterogeneous neural circuit strategy network model, and outputting the action of the robot with the optimal value in the current environment by the heterogeneous neural circuit strategy network model; building a simulation environment of reinforcement learning interaction to train the heterogeneous neural circuit strategy network model, adopting a parallel PPO algorithm to accelerate the training speed of the heterogeneous neural circuit strategy network model until the training is completed, and applying the trained heterogeneous neural circuit strategy network model to an actual scene. The method has the advantages of higher navigation success rate, lower collision risk and stronger environment perception and modeling capability, and shows excellent performance in highly dynamic and crowded complex scenes.

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.

The invention discloses an orchard dense map construction method and system based on an adaptive ORB threshold and point-line fusion, and the method comprises the steps: employing a FAST corner extraction method of an adaptive threshold in a tracking thread of ORB-SLAM3, solving a corresponding threshold according to the average pixel of a current image under different illumination, carrying out the ORB feature extraction of an image, and carrying out the point-line fusion. The number of feature points extracted under different conditions is increased, and the robustness of the algorithm is improved; aiming at obvious line features of branches and branches in the mulberry field environment, adding a line feature extraction method in a tracking thread at the same time, performing point-line fusion feature tracking, and performing BA optimization on key frame map points generated by the tracking thread to complete local map construction; the method comprises the steps of obtaining a key frame and depth information, performing depth optimization on the obtained key frame and depth information to obtain a camera pose, performing local point cloud construction and splicing according to the camera pose, finally performing global BA optimization on an obtained point cloud map, and constructing an orchard three-dimensional dense map to solve the problem of sparse point cloud information of an original algorithm.

The invention provides a training method of a trajectory prediction model and a trajectory prediction method, and relates to the technical field of artificial intelligence such as automatic driving and intelligent transportation. Wherein the initial trajectory prediction model comprises an embedded layer, an encoder and a decoder, and the method comprises the following steps: performing feature extraction on historical trajectory information of a sample vehicle and local map information associated with the historical trajectory information by using the embedded layer to obtain vehicle trajectory features and map features; encoding the vehicle track features and the map features by using an encoder to obtain vehicle track codes and map element codes; decoding the vehicle track code and the map element code by using a decoder to obtain a target prediction track of the target vehicle; and training the initial trajectory prediction model according to the loss between the target prediction trajectory and the real driving trajectory of the target vehicle to obtain a trajectory prediction model. According to the method provided by the invention, the prediction accuracy of the trajectory prediction model and the storage performance of the trajectory prediction model are improved.

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.