16178results about "Navigational calculation instruments" patented technology

The invention provides an AI algorithm-based scenic spot car intelligent path establishing and planning method and system, and relates to the technical field of path planning, and the method comprises the steps: collecting scenic spot road geographic information data, and constructing a weighted road network model; collecting real-time positioning of the trolley and sensor data; using the reinforcement learning model to generate a real-time optimal path planning strategy; calculating an initial recommendation path; road condition changes are monitored, and a path is dynamically adjusted; and outputting the updated optimal path scheme and carrying out navigation. According to the method, intelligent and real-time path planning of the vehicles in the scenic area can be realized.

The invention relates to a multi-robot collaborative navigation method and system based on a visual language large model, and the method comprises the steps: carrying out the analysis according to a task instruction inputted by a user, a semantic map constructed by each robot, and a large language model, and then distributing a subtask instruction to the robot; each robot acquires an environment image in real time, analyzes and predicts the next action by using the visual language navigation large model according to the distributed subtask instruction, and executes the corresponding action and updates the state; an operator monitors the state of the robot and checks the task progress in real time through the man-machine interaction interface, and adjusts the task or deals with abnormity in the execution process when needed, and the robot dynamically adjusts an execution strategy according to feedback information, optimizes the task and completes navigation. Compared with the prior art, the multi-modal data is deeply fused, the robot is endowed with higher environment perception and task understanding ability, the cooperation efficiency of the multi-robot system is improved, and the dynamic environment adaptability is enhanced.

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

The invention provides an automatic walking and positioning method and system for a signal positioning and measuring robot in a complex environment, and relates to the field of technical positioning and measuring robots, and the method comprises the steps: obtaining an indoor three-dimensional map and multi-mode perception data, carrying out the semantic segmentation through a graph neural network, and achieving the environment matching through the combination of a point cloud registration technology; and generating robot motion track planning data. Multi-dimensional signal features are collected through a phased-array antenna system, and a signal feature distribution diagram is constructed in combination with a robot pose and an environment electromagnetic propagation model. A partitioning strategy and a multi-constraint optimization algorithm are adopted to calculate three-dimensional coordinates of a signal source, and finally, the signal source is accurately positioned through a depth calibration network.

The invention discloses an unmanned aerial vehicle route automatic planning method for electric power inspection, and relates to the technical field of unmanned aerial vehicle inspection. Comprising the following steps: starting an unmanned aerial vehicle, carrying out environment perception initialization, calculating the total cruise mileage, evaluating the interference risk, carrying out real-time obstacle avoidance, dynamically optimizing an inspection route, carrying out energy monitoring management, generating a return flight strategy, recording an inspection task and carrying out adaptive learning. Through dynamic electromagnetic interference modeling, multi-modal fusion perception, self-adaptive risk decision and cloud collaborative learning, the problem of insufficient adaptability of a traditional electric power inspection unmanned aerial vehicle in a complex electromagnetic environment and a dynamic obstacle scene is solved, the safety and the inspection efficiency are improved, the robustness is enhanced, and the method is suitable for popularization and application. Intelligent upgrading is carried out through continuous learning and multi-machine cooperation, the overall operation and maintenance cost of the system is reduced, a high-reliability and full-automatic inspection solution is provided for intelligent power grid construction, and the industrial application value is remarkable.

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 discloses an underwater robot autonomous obstacle avoidance and path planning system based on a multi-modal sensor, particularly relates to the field of underwater robot navigation, solves the problems of multi-modal sensing and path planning in a complex environment, and unifies the feature space of a multi-source heterogeneous sensor based on a physical field correction mode of an acousto-optic fluctuation rule. Fusion deviation caused by physical property difference of sonar and visual data is overcome, and obstacle characterization precision and dynamic environment adaptability are remarkably improved; a dynamic confidence decision-making mechanism is combined with a path curvature-moment constraint model, the environmental perception credibility and the robot motion performance are synchronously fused into a path generation process, the feasibility of motion control is ensured while the geometric collision risk is avoided, and the contradiction between the safety and the performability in path planning is solved; the global path optimization eliminates the hidden danger of local path sudden change through iterative smoothing under double constraints of physical field and dynamics, and forms an optimal navigation scheme considering obstacle avoidance efficiency, energy consumption and motion stability.

The invention discloses a logistics robot path planning method based on multi-modal perception, and relates to the technical field of robot path planning. Laser radar, visual camera and IMU data are fused, and environment state feature vectors are generated through multi-modal data synchronization and space-time alignment; the method comprises the following steps: analyzing environmental semantics by using models such as PointPill and YOLOv8, extracting dynamic characteristics, and identifying obstacles; constructing a space-time risk field, searching a path by a space-time algorithm, converting path points into a continuous trajectory, and optimizing the continuous trajectory; deviation is evaluated in real time, dynamic re-planning is triggered, and multi-robot cooperation and environment semantic understanding are included. Through multi-mode perception fusion, hierarchical planning, multi-target optimization and a cooperation mechanism, the obstacle detection accuracy and the obstacle avoidance success rate are improved, the path planning time is shortened, the energy consumption is reduced, the multi-robot conflict is reduced, the task efficiency is improved, the environment semantic understanding and task adaptive ability is enhanced, and the method is suitable for scenes such as intelligent storage and the like and has wide application prospects. The automation level is improved.

The invention discloses a rescue robot path planning method and system under industrial vision assistance, and relates to the field related to industrial vision, and the method comprises the steps: collecting three-dimensional space data of a rescue environment in real time, generating a dynamic environment point cloud data set, and constructing a three-dimensional semantic map of a rescue area; thermal imaging data updated in real time are called, path analysis is carried out in combination with the three-dimensional semantic map, and a path planning strategy set is obtained; and predicting the motion track of the dynamic obstacle based on the local dynamic obstacle avoidance strategy, optimizing the global path planning strategy according to obstacle prediction track data, and generating a motion control instruction of the rescue robot. The technical problem of poor real-time performance and adaptability of path planning caused by insufficient perception of environment dynamic information in path planning of an existing rescue robot is solved, the strong perception capability depending on industrial vision is achieved, the environment dynamic information is accurately captured in real time, and the real-time performance of path planning is improved. And the real-time response speed of path planning and the adaptability to a dynamic environment are improved.

The invention provides a terrain surveying method and device based on unmanned aerial vehicle cooperation and electronic equipment, and relates to the technical field of engineering surveying and complex terrain surveying and mapping. The method comprises the following steps: determining a first surveying flight route of an unmanned aerial vehicle in combination with ground elevation data of a to-be-surveyed area, controlling the unmanned aerial vehicle to collect initial area topographic data, identifying and classifying the initial area topographic data, and determining a weak texture false topographic area; re-planning a second survey flight route of the unmanned aerial vehicle according to the weak texture false terrain area, starting the millimeter wave radar, and scanning and collecting supplementary terrain survey data at different angles; fusing the supplementary topographic survey data at different angles to obtain local topographic survey data; and splicing and fusing the initial regional topographic data and the local topographic survey data to obtain real topographic survey data. According to the technical scheme, the measuring precision and the surveying efficiency of the topographic surveying data of the complex topographic region can be improved, and the reliability and the stability of the real topographic surveying data are improved.

The invention provides a cast workpiece three-coordinate structured light rapid measurement system, and relates to the technical field of industrial automatic measurement, and the system comprises a calibration parameter module which is used for calibrating a standard ball, building an initial conversion relation between a structured light coordinate system and a three-coordinate measuring machine world coordinate system, and collecting temperature, humidity and vibration data in real time; dynamically updating the conversion relation to form calibration parameters; and the measurement path planning module is used for dividing the casting workpiece into a plurality of measurement areas based on the calibration parameters, planning a motion path of the three-coordinate measuring machine and driving the motion platform to move to each measurement position. According to the invention, full-process automation from calibration, measurement to modeling analysis of the casting workpiece and the advantage of comprehensive quality analysis are realized, and a reliable three-dimensional measurement and quality control solution is provided for the casting industry.

The invention relates to the technical field of building information models, augmented reality, synchronous localization and map construction, and provides a deep learning-based SLAM-BIM augmented reality cooperative localization method and system, and the method comprises the steps: introducing a Transform time sequence feature extractor and a geometric relation graph, evaluating a dynamic distribution weight through combining with the confidence, achieving the cross-modal closed-loop detection, and obtaining an SLAM-BIM augmented reality cooperative localization result. A lightweight semantic segmentation network and a feature fusion module are utilized, a dense map with consistent geometric semantics is constructed, a space-time error propagation equation is constructed, online calibration is realized by means of BIM scale prior, an incremental fusion algorithm is designed, a global pose is optimized in combination with AR interaction, and the system fuses SLAM visual trajectory features and BIM semantic geometric features through a deep learning technology. According to the method, the problems that traditional SLAM accumulative errors are large and the BIM fusion precision is low are solved, robust positioning and map construction in a complex scene are achieved, the cooperation precision and real-time performance of SLAM and BIM are improved, and the method is suitable for AR scenes such as building construction and operation and maintenance.

The invention relates to the technical field of digital twinning, and discloses an AGV path optimization method and system based on digital twinning, and the method comprises the steps: carrying out the data standardization processing of AGV real-time coordinate data, environment obstacle distribution data and cargo storage position data, and constructing a storage environment state matrix; aGV task time parameters are calculated, and a task evaluation matrix containing task timeliness indexes and task priority coefficients is obtained; performing multi-target parameter calculation on the AGV motion energy consumption coefficient and the task weight coefficient, and establishing an AGV path optimization target function; performing path planning analysis by adopting a path exchange operator, a path insertion operator and a path inversion operator to generate an AGV initial path set; according to the AGV optimal path planning method and system, the flexibility and the optimization effect of path planning are improved, and the convergence speed and the quality of path optimization are remarkably improved.

The invention belongs to the technical field of structure monitoring systems, and discloses a structure monitoring system and method for civil engineering, and the method comprises the steps that a data collection module collects structure data; the cruise monitoring mechanism collects a high-definition image, an infrared image and laser point cloud data; the environment data acquisition module acquires environment data; the image analysis module identifies abnormal conditions; the infrared data analysis module detects internal defects; the laser data analysis module accurately measures the deformation of the structure; the comprehensive analysis module performs comprehensive evaluation; the risk prediction module fuses multi-source data and predicts potential problems and risks; when an abnormal condition or a potential problem risk is monitored, the alarm module gives an alarm in time. According to the invention, through combination of sensor monitoring and unmanned aerial vehicle cruise monitoring, the structure of civil engineering is monitored in an omnibearing manner; the comprehensive analysis module is used for integrating the multi-source monitoring data to carry out comprehensive evaluation and abnormity judgment; and the risk prediction module fuses monitoring results of multiple modules and predicts potential problems and risks.

The invention belongs to the technical field of intelligent navigation, and discloses a navigation obstacle avoidance method and system in a low-confidence and feature similar environment. The method comprises the following steps: acquiring multi-modal sensor data of obstacles in a detection area in a preset low-confidence environment by using a sensor; preprocessing the data of the multi-modal sensor; introducing a generative adversarial network to carry out image enhancement processing on the denoised image; performing cross-domain fusion on the data by using a converter architecture; obtaining an obstacle position, an obstacle type and a confidence degree distribution value based on a deep neural network, and constructing and obtaining an obstacle confidence degree map; dynamically planning an obstacle avoidance path by combining a deep reinforcement learning algorithm based on an obstacle confidence map obtained in real time, and performing auxiliary planning of the obstacle avoidance path based on an MPC model; the method can effectively cope with high challenge scenes such as tunnels and is widely applied to the fields of industrial automation, intelligent transportation and the like.

The invention relates to the field of unmanned ship path planning, in particular to an autonomous water quality monitoring unmanned ship navigation control method and system based on multimode communication. The method comprises the following steps: collecting regional water quality monitoring parameters based on a shipborne water quality monitoring sensor, carrying out space pollution distribution excavation, and constructing a regional pollution concentration distribution diagram; obtaining a regional satellite remote sensing map, carrying out water flow distribution dynamic evolution, and constructing a water flow path distribution network; carrying out pollutant migration logic evolution and multi-region gradient diffusion prediction on the water flow path distribution network according to the regional pollution concentration distribution map, and constructing a water quality pollution diffusion prediction situation map; and according to the water quality pollution diffusion prediction situation map, carrying out highest diffusion progressive gradient identification, carrying out optimal navigation monitoring sequence analysis, and constructing an optimal navigation monitoring sequence. According to the invention, through dynamic and real-time unmanned ship water quality cruise path adjustment, the accuracy and efficiency of water quality monitoring are improved.

The invention discloses an intelligent photoelectric theodolite aerial target positioning and tracking system, which relates to the technical field of photoelectric detection, and comprises a multi-mode photoelectric sensor module integrating visible light, infrared thermal imaging, a laser radar and a polarized light sensor and supporting spectrum adaptive switching; the dynamic noise suppression processing module is used for eliminating environmental interference based on a time-space domain hybrid filtering algorithm; the multi-target tracking control module adopts a time-sharing partition scanning strategy and a graph neural network data association algorithm; the anti-interference servo driving module is used for realizing stable tracking under strong disturbance through inertial navigation-visual fusion compensation; and the edge computing platform is used for deploying a lightweight deep learning model to complete target recognition and trajectory prediction. According to the invention, through interdisciplinary collaboration of quantum dot materials, graph neural networks and physical equation constraints, the bottleneck of a single technology is broken through; and through closed-loop optimization of dynamic anti-interference and edge intelligence, full-link enhancement of'perception-decision-execution 'is realized.

The invention relates to the technical field of navigation and positioning, in particular to an intelligent positioning and navigation early warning system and method for ships and boats. The method comprises the following steps: collecting and carrying out sea condition sensing and positioning signal evaluation according to a multi-source sensor synchronous data set to obtain a sensor reliability score table; performing target motion situation analysis according to the sensor reliability score table to obtain a fusion situation vector diagram; performing target motion state judgment according to the fused situation vector diagram to obtain a target motion state judgment result; performing multi-target collision risk assessment according to the target motion state judgment result to obtain a risk degree assessment matrix; performing navigation maneuvering performance constraint according to the risk assessment matrix to obtain a route planning maneuvering constraint rule set; and performing route trajectory search according to the route planning manipulation constraint rule set to obtain a navigation trajectory scheme. According to the invention, through the positioning navigation early warning technology, the positioning precision, the navigation safety and the early warning system practicability of the ship in a complex environment are obviously improved.

The invention relates to the field of unmanned aerial vehicle automation, in particular to a three-dimensional modeling and unmanned aerial vehicle landing area address selection method based on laser point cloud data. The method comprises the steps of collection and dynamic compensation of laser radar point cloud data, multi-scale adaptive registration of three-dimensional point cloud, topographic feature extraction and evaluation, selection of an optimal landing point, path planning based on an improved A algorithm, and real-time environmental data updating and dynamic adjustment mechanism. According to the method, point cloud data is dynamically compensated through Kalman filtering, a 3D-NDT algorithm is utilized to perform high-precision three-dimensional environment modeling, key topographic features are extracted through an RANSAC algorithm to comprehensively evaluate topographic complexity and safety distance, an optimal landing point is selected, an optimal path of an unmanned aerial vehicle is planned in combination with an improved A algorithm, and path safety and flight energy efficiency are ensured. By updating environment data in real time and dynamically adjusting a flight path and a landing point, the unmanned aerial vehicle is adapted to sudden environment changes, and safe and stable autonomous landing of the unmanned aerial vehicle in a complex and changeable environment is ensured.

The invention discloses a land surveying and mapping path planning method based on an unmanned aerial vehicle technology, and the method comprises the steps: enabling an unmanned aerial vehicle to achieve the precise perception and recognition of static and dynamic obstacles in a dynamic environment through a self-adaptive multi-mode perception fusion algorithm; on the basis of multi-modal state estimation and the game theory, modeling and predicting behaviors and future trajectories of the dynamic obstacles; an obstacle avoidance path is generated and optimized in combination with an adaptive fast random tree and deep reinforcement learning; the unmanned aerial vehicle tracks a planned path and deals with dynamic environment changes in real time; the multiple unmanned aerial vehicles realize cluster cooperation through a group self-organizing flight optimization algorithm, and dynamically adjust task allocation and flight strategies; and through incremental environmental perception updating and feedback-based path planning optimization, the perception and decision model is automatically updated after each task is executed. According to the method, the defects of a traditional path planning algorithm in a complex dynamic environment are overcome, and the real-time performance and adaptability of the unmanned aerial vehicle for executing the land surveying and mapping task are greatly improved.

The invention belongs to the technical field of unmanned aerial vehicle intelligent navigation, and discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and the method comprises the steps: calculating the scene adaptation weight of multi-modal data through a dynamic attention mechanism based on a space-time alignment feature package, and carrying out the fusion to generate a multi-modal joint feature matrix; based on the multi-modal joint feature matrix, constructing a three-dimensional topological model of an urban airspace, predicting a dynamic obstacle trajectory in combination with a space-time diagram neural network, and generating a hierarchical navigation instruction set; the unmanned aerial vehicle executes a flight instruction according to the hierarchical navigation instruction set, and generates a flight state monitoring log by collecting data in flight of the unmanned aerial vehicle in real time; according to the method, the multi-modal joint feature matrix is generated through environment parameter driving weight distribution, and the complex scene sensing precision is remarkably improved.

The invention discloses a mobile storage and charging robot remote scheduling and path planning system based on the Internet of Things, and relates to the technical field of robot control. Comprising a path dependency modeling module, a path conflict analysis module, a resource dependency graph construction module, a decoupling rearrangement scheduling module, a time sequence offset evaluation module and a path weight regulation and control module, and obtaining a path node sequence and an access time period of a current to-be-executed task of each mobile storage and charging robot, and generating a task path pre-occupation graph. By constructing the path dependence model, the conflict prediction mechanism and the task decoupling rearrangement strategy, accurate identification and effective intervention of path conflicts and resource deadlocks in the multi-robot scheduling process are realized, the stability of the scheduling system and the task execution continuity are improved, efficient completion of energy supply is ensured, and the scheduling efficiency is improved. And the operation efficiency and safety of the system are obviously optimized.

The invention discloses a landslide hidden danger point displacement monitoring system applying multi-source data fusion, and the system comprises a multi-source sensing unit which comprises a spaceborne radar sensing module which is used for obtaining large-range ground surface deformation data through a synthetic aperture radar; the earth surface deformation sensing module is used for monitoring earth surface displacement in real time through a double-frequency GNSS receiver and an optical fiber grating sensor; the underground stress sensing module is used for acquiring the stress change of an underground rock-soil body through a micro-seismic monitoring array and a distributed optical fiber sensor; and the edge calculation unit comprises a dynamic weight fusion module which is used for dynamically distributing the weight of the multi-source sensor according to the geological state and generating high-precision fusion displacement field data. According to the invention, through multi-source data fusion, model parameter real-time updating and a closed-loop optimization mechanism, bottlenecks of a traditional monitoring system in aspects of data quality, model precision and response efficiency are broken through, and data support is provided for accurate early warning and intelligent emergency of landslide disasters.

The invention provides a robot obstacle avoidance and navigation method based on multi-modal fusion and a visual language model, and the method comprises the steps: collecting different modal data in real time through a plurality of sensors, and carrying out time synchronization processing and normalization processing; extracting features of different modal data and fusing the features through an intermediate layer; and inputting the fused multi-modal data into a visual language model, generating a semantic map of the environment by utilizing semantic segmentation of the model and a target detection result, generating an action strategy in combination with a natural language instruction and a visual analysis result, and converting the generated action strategy into a control signal which can be executed by the robot to realize closed-loop control. According to the method, the visual language model and the multi-modal sensor fusion technology are combined, and the perception, decision making and real-time response capabilities of the mobile robot in a complex dynamic environment are improved.

The invention relates to the field of water quality pollution traceability, in particular to a water quality pollution traceability analysis method and system based on an intelligent unmanned ship. The method comprises the following steps: acquiring a multi-mode network monitoring parameter of a real-time position based on a multi-mode communication module, performing a dynamic multi-mode network intelligent switching decision, and constructing an intelligent multi-mode network switching strategy; real-time water area multi-dimensional water quality monitoring parameters of the intelligent unmanned ship are obtained according to the multi-dimensional water quality sensor, abnormal pollution parameter change detection is carried out, and abnormal change pollution parameters are marked; performing reverse water quality spatial change analysis and gradient descent iterative traceability calculation on the abnormal change pollution parameters to generate a potential pollution diffusion path; and performing intelligent traceability cruise according to the potential pollution diffusion path, and tracking and positioning the polluted water source to obtain a polluted water source point. According to the invention, efficient and accurate water quality pollution traceability is realized.

The invention relates to the technical field of building appearance defect detection, in particular to a building appearance defect detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the building information of a target building, and generating a hierarchical scanning path and a three-dimensional obstacle avoidance flight path, a visible light image, an infrared thermodynamic diagram and laser radar point cloud information are collected for space-time alignment processing, and an attention mechanism neural network is used for extracting multi-scale features to generate a detection report containing defect three-dimensional coordinates, damage levels and safety risk assessment. The method achieves the purpose of efficiently and accurately detecting the building appearance defects, can adapt to complex building structures and environmental conditions, supports defect trend prediction and maintenance decision, and remarkably improves the building safety management efficiency.

The invention provides a data center inspection robot monitoring analysis method and system based on machine vision, and relates to the technical field of inspection robots, and the method comprises the steps: obtaining a video stream and environment parameter data collected by an inspection robot, and carrying out the detection and recognition of an abnormal state through a deep learning target; and constructing a multi-modal data fusion analysis model to form a knowledge graph to generate a root cause analysis result, and planning an inspection path based on priority scores. According to the invention, intelligentization and precision of data center monitoring are realized, and inspection efficiency and fault diagnosis accuracy are improved.

The invention provides a robot path planning method and system based on fusion of an improved ant colony algorithm and DWA, and the method comprises the steps: building a grid map of the movement of a robot, and generating a global path of the robot in the grid map through employing the ant colony algorithm; transmitting the global path to a DWA dynamic window model to generate a dynamic window; adding unknown static and dynamic obstacles into the grid map, and setting a local target point by using a local target point setting method based on the global path; based on the local target point and the robot movement information, robot speed sampling is carried out in the dynamic window, and a simulation track of robot movement is obtained; evaluating the simulation track by using an obstacle avoidance evaluation function, and if the robot reaches a target point, obtaining an optimal path; and if the robot does not arrive at the target point, regenerating a simulation track for iterative calculation until the robot arrives at the target point. The improved algorithm can effectively improve the performance and adaptability of robot path planning in different environments.

The invention discloses an AR navigation system and method based on a visual language model, and the method comprises the steps: firstly carrying out video data collection, and constructing a memory database; secondly, querying a navigation target most related to a natural language request of a user in the constructed memory database to obtain a current AR equipment pose and a target pose; and then solving the shortest path from the current pose to the target pose by using the current AR equipment pose and the target pose according to the point cloud map, and optimizing the path direction to finally obtain an optimized path. And finally, guiding a user to move along the planned path through view superposition path indication and voice prompt in AR equipment by utilizing the optimized path, and updating the point cloud map and the memory database. According to the invention, high-precision real-time positioning and sparse point cloud map construction can be realized only by camera input in indoor and outdoor complex environments with low GPS precision, accurate navigation is carried out, and the flexibility and intelligent level of navigation are improved.

The invention relates to a multi-sensor cross-scene dynamic preferential fusion positioning and mapping method, and the method comprises the steps: obtaining the data of a plurality of sensors, and completing the unification of the time-space relation of the data of the plurality of sensors; processing the data, carrying out loopback detection on image key frame data acquired by a camera, constructing to obtain an I MU pre-integration factor, a visual inertial odometer factor, a laser radar odometer factor, a GPS inertial odometer factor, a UWB factor, a GNSS factor and a loopback detection factor, and adding the factors into a factor graph for optimization; a global positioning pose and a map are obtained; and optimizing the multi-sensor data fusion strategy based on a deep fuzzy neural network. According to the multi-sensor cross-scene dynamic preferential fusion positioning and mapping method provided by the invention, high-precision positioning and navigation of an agricultural robot in different scenes are realized through real-time fusion of various sensor data, and the problems of scene dependence and insufficient precision of an existing single sensor scheme are solved.