80 results about "Real time mapping" patented technology

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention belongs to the technical field of data processing, and particularly discloses a dynamic path planning method and system for an inspection robot, and the method comprises the steps: collecting field data through a sensor, processing a laser scanning point cloud and a camera image through a positioning mapping algorithm, and fusing multi-source information to generate a real-time map. Obtaining a three-dimensional environment model containing a dynamic obstacle position; according to the three-dimensional environment model, obstacle features are extracted, a deep learning model is adopted to identify moving personnel and randomly placed goods, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; according to the invention, the problem of inaccurate dynamic obstacle identification and obstacle avoidance in a complex scene in the prior art is solved.

The invention provides a map building and path planning method based on multi-modal SLAM, a robot system collects data through a multi-modal sensor, a data fusion module fuses the collected data by using an AI model, a map building module carries out real-time map building according to the fused data, and a path planning module carries out path planning according to the fused data. The global path planning module performs global path planning through an AI model, the actual path planning module outputs a local path by using the AI model, and the robot system dynamically adjusts and outputs an execution path according to the local path in combination with the latest data of the multi-modal sensor. The motion execution module controls the robot to move to a task position according to the output execution path, monitors the execution condition of the path in real time and feeds back the execution condition to the positioning and posture estimation module and the path planning module, and the positioning and posture estimation module updates the current positioning and adjusts the posture of the robot according to feedback data. And the path planning module finely adjusts the global path and the local path according to feedback data to improve the path planning quality.

The invention discloses an unmanned aerial vehicle routing inspection path planning method and system for a power grid line, and particularly relates to the technical field of power grid routing inspection, and the method comprises the steps: 1, dynamically fusing a multi-source sensor and prior data, and constructing and updating a comprehensive threat field with a time attenuation mechanism and a passability map in real time; 2, decomposing an inspection line into subtask sections, and generating a reference route of each section by combining static safety cost; 3, on the basis of a real-time map in the flight of the unmanned aerial vehicle, a local optimal track is solved online through rolling window optimization of adaptive weight adjustment; and step 4, realizing multi-machine collaboration and automatic handover of blocked tasks through a block chain type distributed task state account book and an intelligent contract rule. According to the invention, the real-time avoidance capability, the environmental adaptability and the task continuity of the unmanned aerial vehicle to dynamic obstacles are remarkably enhanced, and the safety and the efficiency of power grid inspection operation are effectively guaranteed.

The invention belongs to the technical field of data processing, and particularly discloses an intelligent path planning and control system for an automatic driving carrying robot, and the system is operated through the following method: collecting environment data through a multi-mode sensor, and processing a laser radar point cloud and a camera image; fusing multi-source information to generate a real-time map, and obtaining a three-dimensional environment model containing dynamic obstacle positions; according to the three-dimensional environment model, obstacle features are extracted, an improved neural network model is adopted to identify a plurality of current obstacles, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; the objective of the invention is to solve the problem that accurate sensing, real-time decision making and efficient obstacle avoidance are difficult to realize in a complex dynamic environment in the prior art.

The invention provides a belt conveyor intelligent inspection robot and a control method thereof, and relates to the field of mechanical automation. The intelligent inspection robot for the belt conveyor comprises a robot body and a position sensor, the position sensor is arranged on the robot body, and the robot body carries a central processor developed based on a robot operating system of an ROS2 framework. The position information of the corresponding robot is obtained after the position information is processed by the central processor, and meanwhile, mapping is performed to obtain corresponding real-time map information, so that autonomous navigation of the robot can be performed after the position information of the robot and the corresponding real-time map information are obtained, obstacles are avoided, and the navigation efficiency of the robot is improved. And meanwhile, the current position of the robot can be obtained in real time, and the corresponding robot is set to move to the corresponding position of the belt conveyor for inspection, so that the practicability is higher than that in the prior art.

The application provides an underground inspection path planning method for an inspection robot, and relates to the technical field of robot path planning.The method comprises the following steps: acquiring basic map data, robot basic data and an inspection plan; acquiring real-time map data and robot real-time data; determining an inspection node; determining a selectable inspection path; determining an inspection consumption coefficient; determining a preset inspection path according to the robot basic data, the real-time map data, the inspection consumption coefficient and the basic map data; determining whether the preset inspection path needs to be changed according to the basic map data, the robot basic data and the robot real-time data; in the case that it is determined that the preset inspection path needs to be changed, determining a changed inspection path according to the robot basic data, the basic map data and the robot real-time data, and performing inspection according to the changed inspection path. According to the application, the rationality of underground inspection path planning can be improved.

The invention discloses a port operation vehicle scheduling method and system based on map data, and particularly relates to the technical field of scheduling optimization. Real-time map state information of a port channel network is generated by collecting real-time map data of the port channel network and analyzing the real-time occupation state of a port channel road section. And analyzing the local change trend of the occupation state of the port channel road section and the priority change of the vehicle task based on the real-time map state information, outputting dynamically updated vehicle task priority information and local area map data updating information, identifying a priority propagation abnormal area, and generating priority inversion risk early warning information. By identifying the vehicle path conflict points causing the traffic jam, generating the path conflict resolution strategy information, adjusting the vehicle path planning and executing the vehicle scheduling, the collaboration, responsiveness and traffic efficiency of the operation vehicle scheduling in the port environment are effectively improved.

The invention discloses an underground parking lot intelligent dynamic parking guidance and management system, and relates to the technical field of intelligent traffic, and the system comprises the steps: building a parking lot space map according to the obtained target underground parking lot space distribution information, carrying out the real-time monitoring through a sensor network, and obtaining parking space use data, a parking lot real-time map is generated based on the parking space use data, vehicle information is acquired through entrance intelligent identification equipment, a target parking space is determined based on matching analysis of the vehicle information and the real-time map, and a parking guide route is obtained based on the parking lot real-time map and parking space planning analysis by adopting a route planning algorithm. And the vehicle is guided to the parking space through the guiding route, and payment management is carried out. The technical problem that the parking efficiency is low due to the fact that an underground parking lot lacks rapid and accurate vehicle guiding means and efficient parking space resource management capacity is solved, and the technical effects of rapidly and accurately guiding vehicles, efficiently managing parking space resources and improving the parking efficiency are achieved.

The invention belongs to the technical field of information, specifically, multiple sensor data are fused through multi-source information fusion and a deep learning algorithm, a specific fusion network and an optimization algorithm are applied, recognition errors and positioning deviation are greatly reduced, grabbing errors are reduced, and the cargo damage rate is remarkably reduced. For dynamic carrying path planning, a traditional preset map algorithm is easy to collide and is long in delay; a real-time map is constructed through reinforcement learning and a real-time environment perception algorithm, training is carried out according to a reward function, the collision frequency is sharply reduced, the carrying time is shortened, and energy consumption is reduced. When the storage space is utilized, the traditional random storage efficiency is low; the space optimization and intelligent storage strategy algorithm utilizes a genetic algorithm for modeling, the storage space utilization rate is improved, the goods searching time is shortened, and the damage rate is reduced.

The embodiment of the invention discloses a mapping method and device, a robot and a storage medium. The method comprises the steps that a ToF laser radar is used for obtaining a radar point cloud corresponding to the surrounding environment of the robot in real time to obtain a current frame radar point cloud; calculating a normal vector of each radar point in the current frame of radar point cloud, and performing feature point extraction on the current frame of radar point cloud based on the normal vector of each radar point to obtain a current frame of feature point cloud; based on a difference value between the current frame feature point cloud and the historical key frame feature point cloud, judging whether the current frame feature point cloud is a key frame feature point cloud, and when the current frame feature point cloud is the key frame feature point cloud, determining a current accurate pose of the robot based on the current frame feature point cloud; and constructing a real-time map of the surrounding environment of the robot based on the current accurate pose and the current frame of radar point cloud. According to the embodiment of the invention, mapping is carried out on the environment around the robot with relatively low cost while the required mapping precision, real-time performance, stability and environmental adaptability are ensured.

The invention discloses a working condition prediction and energy management method and device based on multi-modal information fusion, equipment and a storage medium, and relates to the technical field of working condition prediction. The multi-modal information fusion-based working condition prediction and energy management method comprises the steps of receiving three-dimensional point cloud data acquired by a laser radar in real time and two-dimensional image data acquired by a camera in real time; extracting and fusing features in the three-dimensional point cloud data and the two-dimensional image data to generate a fused feature vector; off-line map data are obtained, the off-line map data are updated in real time according to the fusion feature vector, and real-time map data are obtained; and obtaining historical speed data of the vehicle, obtaining a predicted vehicle speed according to the fusion feature vector and the historical speed data of the vehicle, and performing energy management according to the real-time map data and the predicted vehicle speed. The accuracy of working condition prediction can be improved.

The invention discloses a well mining parking trajectory planning method based on Chebyshev distance determination, and the method comprises a map information vectorization module which is used for converting real-time map information into a combination form of a plurality of line segments; the SAT collision detection module is used for predicting the feasibility of a path when a child node is expanded; the Chebyshev distance judgment module is used for detecting the minimum distance between the planned path and the road boundary; the heuristic function module is used for exploring the optimal path of the parking track; and the RS curve linking module is used for linking the parking switching point and the target parking point. According to the method, the space utilization rate and the operation efficiency in a narrow scene are effectively improved, a planned path can be tried, adjusted and planned according to sensor data in the absence of GPS positioning correction, a vehicle is far away from a tunnel wall and obstacles as far as possible on the premise that parking planning succeeds, error allowance is provided for a subsequent chassis control module, and the parking efficiency is improved. And the collision risk is effectively reduced.

The invention provides a control method for continuous operation of an unmanned container truck in a magnetic storm environment, and the method comprises the steps: 1, carrying out the initialization of a sensor on the unmanned container truck after the unmanned container truck is started, building an initial map coordinate, and obtaining the magnetic field intensity and air pressure value of a current environment; 2, a sensor collects data in real time and preprocesses the data; 3, triggering a magnetic storm mode according to the magnetic field intensity of the current environment, and converting the unmanned container truck into fusion positioning; step 4, in a magnetic storm mode, fusing the preprocessed data by adopting extended Kalman filtering and a parameter adaptive adjustment algorithm to obtain attitude and position estimation; step 5, constructing a real-time map according to attitude and position estimation, marking a static target, identifying and tracking a dynamic target, and updating a motion track of the dynamic target; and step 6, based on the constructed real-time map, searching an optimal path for the unmanned container truck to run.

This invention discloses a local SLAM construction method, a global SLAM construction method, and a construction device. In this embodiment, a cloud map and a real-time map are compared, and an object classification model is used to obtain the object classification of the current scene. Based on the object classification, prediction updates are performed, and a sub-map is obtained based on the prediction updates. The updated sub-maps obtained by the local SLAM construction method are then fused to obtain the global map. By classifying objects into dynamic and static objects, and predicting the motion of dynamic objects, a sub-map is obtained based on the prediction results, enabling real-time identification of dynamic objects.

The invention belongs to the technical field of unmanned aerial vehicle path planning, and particularly relates to a limited space unmanned aerial vehicle autonomous homeward voyage method based on real-time mapping and path evaluation. The method comprises the steps that when the unmanned aerial vehicle flies in a limited space without a GPS, real-time positioning is carried out through a laser radar SLAM technology, a three-dimensional grid map is constructed synchronously, and meanwhile a flight path is recorded in real time in a key point mode. When the electric quantity is insufficient or communication is lost to trigger return flight, the system generates two candidate paths in parallel, one path is a historical path which is backtracked in a reverse order, and the other path is a new path which is searched on a real-time map by using an A * algorithm. Then, the two paths are evaluated through a comprehensive scoring function, and scoring standards comprise path smoothness, path safety and path length; and finally, selecting a path with a relatively high score as an execution path, and performing safe return by combining a local obstacle avoidance algorithm. According to the invention, the limitation of pure path backtracking is overcome, and safer, more efficient and more intelligent autonomous homeward voyage is realized.

Systems and methods for real-time map data coverage optimization are disclosed. A system includes one or more processors configured to determine a real-time map data collection coverage in an interested region, wherein real-time map data are collected by one or more consumer vehicles at locations of the consumer vehicles, determine whether one or more uncovered locations in the interested region are not covered by the locations of the one or more consumer vehicles, in response to determining that the one or more uncovered locations in the interested region are not covered by the locations of the one or more consumer vehicles, generate a route comprising the one or more uncovered locations based on a map of the interested region, and send an instruction to cause operation of a data-collecting vehicle to follow the route for real-time map data collection.

The invention relates to the technical field of robot navigation and obstacle avoidance, in particular to a method for efficiently generating an Euclidean symbol distance field (ESDF) map based on laser radar point cloud clustering. Firstly, down-sampling processing and ground segmentation elimination are carried out on an original point cloud; extracting and combining obstacle point cloud clusters by adopting an Euclidean clustering algorithm, and eliminating dynamic objects at the same time; then, a sparse voxel hash table is initialized according to a set voxel resolution, and obstacle point cloud mapping is marked as occupied voxels; and finally, locally updating the distance value of the newly occupied voxels or the state-changed voxels by adopting an incremental breadth-first search algorithm. According to the method, for a dynamic complex scene, the number of voxels participating in Euclidean symbol distance field calculation can be remarkably reduced, consumption of a memory and calculation resources is reduced, local increment updating and a sliding window mechanism are supported, high-frequency and low-delay real-time ESDF map construction can be achieved on an embedded or edge platform, and the real-time ESDF map construction efficiency is improved. And the method has good engineering practical value and popularization prospect.

A method and apparatus evaluates a map for autonomous driving. The method acquires pixel points of lane lines of a road in a static map; acquires pixel points of lane lines of the road in a real-time map; forms first fitted curves of the lane lines in the static map by fitting the acquired pixel points of the lane lines in the static map respectively; forms second fitted curves of the lane lines in the real-time map by fitting the acquired pixel points of the lane lines in the real-time map respectively; calculates fitted-curve-similarities between the first fitted curves and the corresponding second fitted curves; and evaluates the consistency of the static map and the real-time map based on the fitted-curve-similarities.

The invention relates to an intelligent multi-scene sickbed carrying and transferring vehicle which is characterized in that a placement platform for placing a stretcher is supported on a vehicle frame, and a shifting mechanism for moving the stretcher is arranged on the vehicle frame above the placement platform; the stretcher is connected to the shifting mechanism through the positioning belt, the shifting mechanism drives the stretcher to move and position in the horizontal direction and the vertical direction, and in the moving process of the stretcher, the gravity center supporting structure on the frame is matched to adjust the gravity center of the whole vehicle. A platform lifting structure used for adjusting the height of the placement platform and a control system are further installed on the portion, below the placement platform, of the vehicle frame, the control system conducts real-time SLAM map construction and dynamic obstacle avoidance on the whole vehicle movement by collecting two-dimensional point cloud data and sends a movement signal to the roller driving device, the roller driving device drives the rollers to run, and the roller driving device drives the placement platform to move. The moving modes of any angle such as translation and in-situ turning of the moving vehicle in the moving process are achieved, and the trafficability in a narrow space and the flexibility in multiple scenes are improved.

This application provides a real-time trajectory data processing method, apparatus, and system. The method includes: acquiring trajectory data of a moving object and performing denoising processing on the trajectory data to obtain valid trajectory point data; acquiring M candidate road network points of the current trajectory point from the valid trajectory point data; where M is a positive integer; determining a variable sliding window corresponding to the current trajectory point; wherein the length of the variable sliding window is K, the variable sliding window includes N first transition probabilities of the previous trajectory point and a first candidate road network point combination corresponding to each first transition probability, each first candidate road network point combination including K historical candidate road network points, where N and K are positive integers; and performing map matching processing on the M candidate road network points based on the variable sliding window to obtain the road network points of the current trajectory point. This achieves high-precision and low-latency real-time map matching of trajectory data.

The invention relates to the technical field of unmanned ship navigation control, in particular to a multi-source information fusion intelligent navigation control system suitable for an unmanned self-propelled ship, and the system comprises a data collection module which is used for collecting data in a surrounding range with the self-propelled ship as an original point; the data acquisition module is used for acquiring data of the self-propelled ship, the data processing module is used for fusing the data acquired by the data acquisition module and drawing real-time map data with the self-propelled ship as an original point, and the attitude adjusting unit is used for adjusting the navigation attitude of the self-propelled ship according to the real-time map data. And cloud management side end calculation of data is realized through the data processing module and the attitude adjustment unit, and the demand of high-precision attitude control on the computing power of the ship is greatly reduced.

The invention relates to a computer-implemented method of photo or video shooting with a digital device comprising at least one optical device, based on providing recommendations for professional framing. The method comprises stages of: receiving a data stream through the optical device on the digital device; processing the data stream using machine learning techniques using models trained on a plurality of images including at least composition information to recommend the best shot; shooting in automatic, semi-automatic, tracking shooting and guided shooting modes; wherein, in automatic mode, the shooting process is initiated by tapping on any area of the screen; in semi-automatic or tracking mode, shooting is carried out by combining the frame capture area and the three-dimensional frame of the shot; if in the automatic, semi-automatic and tracking modes there is no possibility to create a professionally composed shot, then the guiding shooting mode is turned on, in which three-dimensional shooting points are generated into an interactive real-time map for each user individually, with each point containing interactive information for obtaining a professionally composed shot, while the map is a 2D map or an AR map and / or a mixed reality map; the mixed reality view displays three-dimensional shooting points on a connected mixed reality relay device and wirelessly transmits guiding signals of graphic output to the projection screens of the mixed reality devices, directing the user to successful shooting angles and advising on how and in what position to hold the shooting device while the algorithm takes a shot; sending the shots obtained during shooting, in at least one of the modes, to the buffer of pre-saved shooting results, where rapid post-processing is carried out and the resulting shooting result is displayed on the screen; further interacting with the results are recorded and processed using reinforcement learning methods to further improve shooting processes and results. The invention provides professionally composed photo / video shots without subsequent post-processing.

A low-altitude 3D radio map reconstruction method based on scene guidance and sparse measurement is proposed. This method constructs and trains a reconstruction network offline, comprising a sparse data converter module, a radio map data feature extraction module, a multi-scale convolutional pyramid module, and a layer-by-layer radio map reconstruction module. In the online phase, the trained reconstruction network performs real-time map reconstruction. This invention utilizes a deep neural network architecture to fuse environmental scene understanding and sparse measurement data, achieving high-precision, physically consistent 3D reference signal received power (RSRP) field reconstruction, significantly reducing measurement overhead and improving reconstruction quality. By fusing environmental building distribution with sparse RSRP measurements, capturing environmental patterns using feature pyramids, and maintaining vertical dependence through a layered reconstruction strategy, high-precision 3D radio map reconstruction is achieved.

The invention provides an online high-precision map construction method and a model construction method thereof, and the model construction method comprises the steps: sequentially constructing a backbone network, a view conversion network, a hierarchical query embedded network and a model optimization network based on a MapTr model architecture, and obtaining an initial model; training the initial model to obtain a map construction model; and carrying out model integration on the plurality of map construction models to obtain an online high-precision map construction model. According to the method, efficient and accurate high-precision map construction can be realized through a stronger backbone network, an effective view conversion and data enhancement technology and a model integration strategy. The method has excellent performance in the aspect of multi-category semantic information extraction, and is suitable for real-time map construction tasks in an automatic driving system.

The application discloses a map change detection method and device, equipment and a storage medium. The map change detection method comprises the following steps: controlling a movable device to move and record a point cloud based on an acquired prior grid map, and obtaining a real-time grid map; determining an area that is overlapped in the prior grid map and the real-time grid map as a target prior map, and determining an area that is overlapped in the real-time grid map and the prior grid map as a target real-time map; and determining a map change rate of the target real-time map relative to the target prior map based on a grid difference comparison result between the target prior map and the target real-time map. The above scheme can accurately determine whether the current map has changed.

The present invention belongs to the technical field of artificial intelligence, and specifically relates to a positioning method, device, equipment and medium for driverless mining trucks. The positioning method for driverless mining trucks of the present invention first obtains multiple front road images and infrared images within a preset distance range of the front road environment during the driving of the mining truck; after obtaining the front road images, the deep learning algorithm SuperPoint is used to extract the feature key points of each image, and then SuperGlue is used to match the key points of adjacent images, so as to obtain the conditions for pose estimation of the on-vehicle camera. The infrared sensing is used to correct the shadow misjudgment of the real-time map, and finally the real-time position change of the mining truck is obtained, which can correct the GNSS positioning and be used as an alternative positioning means in places with poor signals in mountainous areas.

The invention belongs to the technical field of robot navigation, and relates to a precise docking motion control method and system based on a local real-time map, and the method comprises the following steps: 1, obtaining the position and angle of a current point odometer of a mobile charging robot, and then obtaining the position and angle of a target point; 2, judging whether the construction of a local map coordinate system is completed, if the construction of the local map coordinate system is not completed, constructing a local region coordinate system, and if the construction of the local region coordinate system is completed, turning to step 3; 3, converting the current point to a local map; 4, calculating the position and angle deviation of the current point and the target point under the local coordinates; 5, calculating an angular velocity and a linear velocity according to the position and the angular deviation; and 6, releasing the angular velocity and the linear velocity. The method has the effect of constructing the local map according to the target point detected by the starting point and the current odometer data under the condition of not depending on the existing map and not changing the map characteristics.

The embodiment of the invention relates to the technical field of automatic driving, and discloses a strip mine map construction and updating method and device, electronic equipment and a storage medium, and the method comprises the steps: constructing a strip mine map comprising a static map, a road driving area map, a loading area map, a dumping site unloading area map and an obstacle map, performing real-time map updating on the road driving area map, the loading area map, the dumping site unloading area map and the obstacle map; wherein the dumping site unloading area map comprises a retaining wall curve, the retaining wall curve is obtained by processing a comprehensive data matrix through a 3D artificial intelligence model, the comprehensive data matrix is obtained by combining a plurality of data matrixes, and each data matrix comprises coarse curves of a plurality of retaining walls; the coarse curve of each retaining wall is determined based on a frame of sensor data collected during the movement of the driverless truck. According to the mode, a real-time strip mine high-precision map is provided so as to guide, plan and decide operation of various mining area automatic driving vehicles.

An autonomous driving control method receives a HD map and extracts a HD road boundary and a HD lane line based on the HD map. A real-time map displaying a road driving environment of a vehicle is generated using sensor information detected by a sensor mounted on the vehicle. A real-time road boundary and a real-time lane are extracted based on the generated real-time map. The HD road boundary is compared to and analyzed from the real-time road boundary, and the HD lane line is compared and analyzed to the real-time lane. It is determined whether the HD map has changed based on the analyzed result value.