20 results about "Semantic grid" patented technology

The invention discloses a high-precision laser positioning unpacking path planning system, and belongs to the technical field of robot automatic control and industrial automation. The system comprises a data synchronization module used for multi-sensor hardware synchronization and data alignment; the high-precision positioning module is used for outputting a precise pose based on environment skeleton characteristics and sliding window optimization; the semantic map construction module is used for fusing vision and laser data to generate a dynamic semantic grid map; the global path planning module is used for planning a smooth path in the skeleton channel by utilizing a mixed potential field improved A * algorithm; the motion control module is used for realizing closed-loop motion control and safety monitoring through model prediction and tracking; and the operation execution module is used for finishing millimeter-level precise stopping of an operation point by adopting visual servo and triggering unpacking operation. According to the method, the positioning robustness under dynamic shielding is improved through the environmental skeleton features, the safety and the high efficiency of the path are ensured by utilizing semantic understanding and intelligent planning, and the full-process automation from navigation to precise operation is realized.

A method performed by an apparatus for controlling autonomous driving of a vehicle is introduced. The method may comprise generating, based on a segmentation model processing point cloud data, a first semantic grid map, generating, based on an object detection model, a second semantic grid map, adjusting a probability regarding whether occupancy exists for an element included in each grid of the first semantic grid map and the second semantic grid map, and generating a fused grid map by determining, as a representative label, at least one label corresponding to a highest value among final probabilities of the at least one label, wherein the final probabilities are determined based on whether the at least one label matches the element, outputting, based on the fused grid map, a signal, and controlling, based on the signal, autonomous driving of the vehicle.

The invention discloses a multi-unmanned aerial vehicle low-altitude service scheduling result generation method and device, equipment and a medium, and relates to the technical field of unmanned aerial vehicle scheduling, and the method comprises the steps: constructing a low-altitude service semantic grid, converting the service demand characteristics of each region of a city into a region semantic vector, and mapping an unmanned aerial vehicle task to the grid to generate a task semantic vector; a semantic enhancement mechanism is utilized to calculate the semantic association strength of a task and a region, a multi-dimensional fairness index system is constructed, and a dynamic fairness preference weight is generated through self-adaptive correction. And finally, task allocation and path planning are performed based on the dynamic weight and the semantic association strength, so that the dynamic balance service efficiency and fairness of multi-unmanned aerial vehicle scheduling are improved, regional service deviation is dynamically corrected, the service accessibility of vulnerable regions and crowds is ensured, and the method is adaptive to complex urban environments and abnormal scenes.

The application provides a semantic grid map optimization method and system based on multi-task road surface information, and belongs to the technical field of automatic driving navigation. The method comprises the following steps: processing a road environment image to obtain lane line segmentation results and drivable area segmentation results; establishing a coordinate projection relationship from an OCC space to a pixel coordinate system based on a rotation and translation matrix between sensors; projecting surface layer grid center points of a semantic grid map to the pixel coordinate system based on the coordinate projection relationship, and supplementing lane line information according to a projection result; performing semantic correction on a drivable area based on the coordinate projection relationship and the drivable area segmentation results; performing instance extraction and semantic association processing on unknown obstacles; and outputting an optimized three-dimensional semantic grid map. Based on the method, the application further provides a semantic grid map optimization system based on multi-task road surface information. The application improves the quality and practicability of a semantic map without significantly increasing the burden of a system.

The application discloses an indoor distribution cable path intelligent generation method combined with building environment characteristics, and comprises the following steps: constructing a semantic grid map capable of distinguishing different building function areas by performing layer identification, semantic annotation and space discretization on an input building plan design map; based on the semantic grid map, different basic passing generation values are given to different types of areas, and a dynamic engineering constraint function comprising a corner turning penalty and a wall sticking reward rule is defined; path search of an improved A* algorithm is performed, in an engineering constraint cost field fused with the basic passing generation value and the dynamic engineering constraint function, a heuristic search algorithm with a dynamic penalty term is used to calculate and generate a candidate cable path meeting engineering specifications; virtual convergence points are determined through node clustering, the path search algorithm is applied hierarchically, a backbone path limited in a public area is generated, branch paths entering rooms are generated, and a complete indoor distribution system cable path topology is generated by merging.

This invention relates to the fields of deep space exploration engineering planning and artificial intelligence technology, specifically disclosing a method for selecting a lunar south pole research station based on a multimodal large model. The method includes: constructing a site selection index toolkit containing various evaluation tools; acquiring and fusing multi-source data from the lunar south pole to construct a unified multimodal semantic grid and comprehensive cost model; encoding environmental features and mission objectives into cue vectors using an encoder; calculating the similarity between the cue vectors and tool capabilities based on the multimodal large model, dynamically filtering matching toolchains and generating adaptive weight parameters; iteratively optimizing the site selection evaluation results through a closed-loop optimization mechanism of "evaluation-feedback-adjustment" until convergence conditions are met; and finally outputting the optimal or near-optimal site selection scheme, supporting dynamic relocation under data and mission updates. This invention achieves intelligent fusion of multi-source data and multi-objective adaptive decision-making in complex environments, significantly improving the scientific rigor, objectivity, and efficiency of lunar research station site selection.

The present disclosure provides a point cloud noise identification method, device and unmanned vehicle, comprising: performing feature identification on the point cloud data of the current frame to obtain semantic point cloud; performing rasterization processing on the semantic point cloud, and removing the grid unit representing the dynamic target category according to the category to which each point in the semantic point cloud belongs, to determine the target semantic grid data of the current frame; fusing the target semantic grid data of the current frame with the target semantic grid data of the historical frame to generate terrain data; and checking the point cloud data of the current frame according to the terrain data to determine the noise of the point cloud data of the current frame.

This application relates to a semantic grid map construction method, system, and medium with automatic obstacle removal function, belonging to the field of lawnmower technology. The semantic grid map construction method includes: acquiring original point cloud data and original image data of the lawn according to the target projection resolution, and converting them to the same view plane; temporally synchronizing the original point cloud data and original image data to obtain a three-dimensional point cloud and a two-dimensional image, and transmitting them to a data queue; spatially transforming the three-dimensional point cloud, and constructing fused image data by combining object semantic labels with the two-dimensional image in a layered fusion mechanism; generating a lawn grid based on the fused image data, and marking obstacle nodes using a ray tracing mechanism to obtain a lawn grid map; accurately distinguishing obstacle types (static / dynamic, passable / impassable) through category information; and dynamically clearing grids in obstacle-free areas through a ray tracing mechanism, reducing false positive obstacles caused by limited detection field of view.

The invention relates to a ground unmanned platform intelligent sensing method for a field scene, and belongs to the field of automatic driving. According to the invention, a dynamic obstacle sensing flow based on geometric information is constructed through fusion of a laser radar and a millimeter-wave radar; utilizing visual semantic segmentation to construct a static environment taking semantic information as a main part and a special obstacle perception flow; and finally, through an innovative semantic raster map fusion mechanism, unifying the geometric raster map and the semantic raster map, and providing an environment model with both geometric accuracy and semantic richness for planning control. According to the invention, through laser radar-millimeter wave radar fusion and the SORT adaptive algorithm, the tracking accuracy of the dynamic obstacle is improved, the tracking delay is reduced, and the problem of changeable motion modes of the field target can be effectively solved. According to the method, the visual sensor data is collected, semantic segmentation and laser radar correction are performed, the misjudgment rate of special targets such as grasses and water pits is reduced, and the sensing reliability in a complex environment is guaranteed.

This invention discloses a method and system for analyzing the navigation behavior of ships in waterways based on big data, relating to the field of ship technology. This invention collects dynamic ship data and static waterway data, constructs an environmental semantic grid, and maps the dynamic data to generate semantic trajectory sequences. Based on the sequences, it calculates the basic spatiotemporal correlation value, extracts trajectory direction entropy using a local minimum spanning tree, and constructs a ship behavior feature vector. Based on physical limits, it constructs a dynamic pseudo-label dataset and uses an evolutionary algorithm based on a weighted ROC convex hull guidance strategy to iteratively optimize the parameters of the nonlinear classification decision function. Using the optimal parameter set, it constructs a decision function to identify abnormal ship behavior, calculates risk potential energy, and generates chain reaction warnings based on the risk transmission coefficient. This invention effectively integrates environmental semantics and entropy features, solving the problems of scarce abnormal samples and complex nonlinear feature identification without manual annotation, and achieving proactive and precise prevention and control of waterway collision risks.

The application discloses a transformer partial discharge positioning method based on a partial discharge wave speed adaptive compensation. The method comprises the following steps: converting a three-dimensional semantic grid model of a transformer into a three-dimensional semantic voxel matrix; determining a target sound speed tensor matrix corresponding to the three-dimensional semantic voxel matrix; determining a time-of-arrival strategy template of each voxel in the target sound speed tensor matrix according to a semantic label identified by the three-dimensional semantic voxel matrix; generating a plurality of three-dimensional time field matrices according to the respective corresponding time-of-arrival strategy templates based on the target sound speed tensor matrix; and in the case of detecting a partial discharge signal of the transformer, determining a partial discharge positioning result of the transformer based on the plurality of three-dimensional time field matrices and observed time difference data. The application solves the technical problem of low transformer partial discharge positioning accuracy in related technologies due to the neglect of the anisotropic propagation characteristics of sound waves of the transformer winding structure.

The invention discloses a water transportation channel ship navigation behavior analysis method and system based on big data, and relates to the technical field of ships. Ship dynamic data and channel static data are collected, an environment semantic grid is constructed, and the dynamic data are mapped to generate a semantic trajectory sequence; calculating a basic space-time correlation value based on the sequence, extracting a trajectory direction entropy by using a local minimum spanning tree, and constructing a ship behavior feature vector; constructing a dynamic pseudo-label data set based on physical limits, and performing iterative optimization on parameters of the nonlinear classification judgment function by adopting an evolutionary algorithm based on a weighted ROC convex hull guide strategy; according to the method, environmental semantics and entropy value features are effectively fused, the problems of abnormal sample scarcity and complex nonlinear feature recognition under the condition that manual labeling is not needed are solved, and the method is suitable for ship abnormal behavior recognition. And active and accurate prevention and control of the water area collision risk are realized.

A method performed by an apparatus for controlling automatic driving of a vehicle is provided. The method comprises the following steps: generating a first semantic grid map based on a segmentation model for processing point cloud data; generating a second semantic grid map based on the object detection model; adjusting the probability of whether elements included in each grid of the first semantic grid map and the second semantic grid map are occupied or not; a predefined label is put into the transformed point cloud; and generating a fused raster map by determining one or more tags corresponding to a highest value in final probabilities of at least one of the predefined tags as representative tags, where the final probabilities are determined based on whether the at least one tag matches the element; outputting a signal based on the fused grid map; and controlling automatic driving of the vehicle based on the signal.

The application discloses a high-precision laser positioning unpacking path planning system, and belongs to the technical field of robot automatic control and industrial automation. The system comprises a data synchronization module, a high-precision positioning module, a semantic map construction module, a global path planning module and a motion control module. The data synchronization module is used for multi-sensor hardware synchronization and data alignment; the high-precision positioning module is based on environmental skeleton features and a sliding window to optimize output accurate pose; the semantic map construction module fuses visual and laser data to generate a dynamic semantic grid map; the global path planning module utilizes a hybrid potential field to improve an A* algorithm to plan a smooth path in a skeleton channel; the motion control module realizes closed-loop motion control and safety monitoring through model predictive tracking; and the work execution module adopts visual servoing to complete millimeter-level accurate parking at a work point and trigger unpacking work. The application improves the positioning robustness under dynamic occlusion through environmental skeleton features, ensures the safety and efficiency of the path through semantic understanding and intelligent planning, and realizes full-process automation from navigation to accurate work.

The application discloses a target prediction method based on an irregular semantic grid and a multi-modal network, and belongs to the technical field of intelligent mining of space-time big data. First, the IAIGS is used to adaptively divide the DEM and optical remote sensing data within the region R, so as to obtain a series of irregular semantic grids as basic calculation units. Then, the DEM / optical remote sensing slices and trajectory segments corresponding to the irregular semantic grid regions are calculated respectively. Next, the ISG-VAE-encoder is used to perform semantic vector coding on the DEM / optical remote sensing slices of each irregular semantic grid. Finally, the MFN-RSS-ST model is trained by using the trajectory segments, the semantic vector coding and other data, and the irregular semantic grid output by the trained MFN-RSS-ST model is used as the prediction result of the target position at the next moment. According to the application, the optical and DEM remote sensing data and the trajectory data are fused, so that the position distribution of the target at the future moment can be predicted without relying on road network information.

This invention provides a localization method and system based on semantic object size chains. The method includes: acquiring a dataset; constructing a semantic object size chain to establish a two-dimensional semantic grid map; initializing a particle set; updating the particle set, calculating robot motion, performing coordinate transformation, predicting the position of each particle in the particle set to obtain a predicted particle set; calculating the weight value of each particle, extracting environmental semantics, identifying objects, performing foreground / background segmentation and coordinate transformation mapping, determining the corresponding position of the object in the semantic object size chain, using the information of each object in the current frame to inversely solve the robot's position, updating the weight value of the obtained estimated position value set; resampling the particles, continuously updating in a loop until the particles converge, and completing the localization. Compared with mainstream relocalization algorithms, this invention has higher stability, provides richer and more reliable features for robot localization and navigation, and thus can complete more advanced and complex tasks.

The invention discloses an intelligent driving decision-making method and system based on a dynamic four-dimensional space-time semantic grid and multi-modal risk verification, a vehicle and a computer readable storage medium, and relates to the technical field of intelligent driving. A self-adaptive dynamic four-dimensional space-time semantic grid is constructed, an inter-frame increment updating strategy remarkably reduces calculation power occupation, and low-delay operation is achieved; a normalized risk index is generated through multi-dimensional weighting, quintuple binding mapping is solidified through HSM hardware, and non-traffic-signal-lamp differential color matching is adopted for risk identification; a software-level double-decision path is constructed by a single chip, the calculation power of the bottom path is less than or equal to 10%, abnormal takeover meets the vehicle gauge-level real-time requirement, and the cost is reduced by more than 60% compared with hardware redundancy; adaptation of multiple computing power intervals and hardware-level isolation of intelligent driving and cockpit computing power are achieved, and no laser radar or high-precision map dependence exists; 256-bit SHA-256 risk fingerprints are written into the EDR according to an L3 legal evidence obtaining format, and cloud verification is supported. According to the method, efficient calculation power utilization, software redundancy cost reduction and hardware-level credible decision making are achieved, the commercial and social values are remarkable, and the method conforms to the national intelligent network connection automobile industry strategy.

The invention discloses a self-adaptive illumination fusion four-wheeler autonomous navigation system and method based on ROS. The system comprises a hardware platform, a sensing layer, a decision-making layer and an execution layer, wherein the hardware platform comprises a four-wheel drive vehicle body and various sensors; the sensing layer executes time synchronization for data acquired by a sensor, and calculates a dynamic fusion weight of each kind of sensing data according to illumination and environment so as to obtain a fusion pose; the decision-making layer updates a semantic grid map by adding light adaptive loopback detection and satellite positioning constraints based on the fusion pose, and obtains an optimized prediction trajectory; and the execution layer realizes navigation of a four-wheel drive vehicle body according to the optimized prediction trajectory, monitors a trajectory tracking error in real time, and feeds back the trajectory tracking error to the decision-making layer for local re-planning if the trajectory tracking error deviates from a set threshold value. According to the method, the inspection path can be optimized according to the environment dynamic state and the task requirement, and the method has the advantages of high robustness, wide adaptability and high efficiency.

A point grid network is a neural network that can model graph-structured data. The point grid network receives a graph-structured data sample, which may be a graph representation of an object. The point grid network uses an assignment matrix to transform the graph representation into a grid representation of the object. The assignment matrix defines whether graph nodes in the graph representation is to be assigned to grid elements in the grid structure. The grid representation is a tensor that can be processed through convolutional operations or other types of tensor operations. The point grid network can perform convolution on the grid representation and one or more filters to generate a grid-structured feature map. Values in the filter (s) and values in the assignment matrix are determined through training the point grid network. The point grid network may further determine a condition of the object based on the grid-structured feature map.

The invention belongs to the technical field of mobile robot positioning and navigation, and discloses a mobile robot global positioning method for an indoor scene, which comprises the following steps: firstly, creating a semantic laser radar point cloud by fusing a laser radar point cloud with a visual camera, and constructing a semantic grid map Mapsemic, a semantic position map Mapposition and a semantic region map Maparea; then, on the basis of a semantic position map Map and a semantic region map Map, by means of semantic laser radar point cloud, a distance search algorithm and a region search algorithm are adopted for pose screening, and a particle set of poses to be selected is obtained; and finally, for the to-be-selected particle set, adopting a random extraction and likelihood value screening mode to obtain a sample particle set, carrying out iterative calculation on each particle in the sample particle set by adopting a gradient positioning algorithm to carry out positioning, and taking a positioning pose with the maximum likelihood value in all positioning results as a global positioning pose.