390 results about "Visual navigation" patented technology

The invention relates to the technical field of flight control, in particular to a fixed-wing unmanned aerial vehicle attitude correction and precise flight control method fused with visual navigation, which comprises the following steps: extracting ORB feature points and constructing a dynamic feature point cloud; simultaneously acquiring angular velocity and acceleration data of the inertial measurement unit; performing tight coupling fusion processing on the dynamic feature point cloud and the inertial measurement data to generate real-time pose data; inputting the real-time pose data into an attitude correction parameter calculation module, and generating an attitude correction parameter through a dynamic weight distribution algorithm in combination with a preset flight path planning model and GPS positioning data; and a control surface deflection instruction and an accelerator control instruction are generated through the multi-constraint optimization model, and unmanned aerial vehicle flight attitude closed-loop correction is completed. According to the method, aerodynamic parameters such as the air speed, the wind field lateral disturbance and the attack angle are introduced to serve as coupling input, the control surface deflection amount and limiting conditions are dynamically adjusted, and self-adaptive modeling and control over the nonlinear flight characteristics are achieved.

The invention relates to the technical field of unmanned aerial vehicle perception, and discloses a visual navigation method and system of a six-rotor full-drive unmanned aerial vehicle, a terminal and a storage medium. A navigation framework integrating dynamic visual perception and full-drive control characteristics is constructed, and a visual encoder is adopted to extract multi-scale environment characteristics; according to the method, time sequence information is processed in combination with a long-short-term memory network, efficient feature extraction and motion trail prediction of an image sequence under high-speed rotation and violent attitude change of the unmanned aerial vehicle are achieved, the problem of feature tracking loss of a traditional algorithm in a high-dynamic scene is solved, global correlation modeling of environment features is enhanced through an attention mechanism, and the tracking accuracy of the unmanned aerial vehicle is improved. According to the six-rotor-wing full-drive unmanned aerial vehicle navigation method, the navigation precision and robustness of the unmanned aerial vehicle in a complex dynamic environment are remarkably improved by combining control instruction generation of dynamics constraints, high-precision inertial navigation compensation is not needed, and an efficient and reliable navigation solution is provided for application of the six-rotor-wing full-drive unmanned aerial vehicle in emergency rescue, high-speed inspection and other scenes.

The invention relates to the technical field of visual navigation, in particular to a robot navigation method based on vision, which comprises the following steps: acquiring a grayscale image to extract dynamic point locations, screening boundary features to reconstruct a feature set, calculating gradient change to generate candidate guide points, converting coordinates to construct a path track set, and outputting a navigation planning path. According to the method, rapid elimination of static backgrounds is realized through difference threshold selection, dynamic region extraction accuracy is effectively improved, combined screening of boundary line feature point frequency and average feature density is combined, key feature stability is enhanced, and the identification capability of image local structure mutation is enhanced by using a variance change rate of a gradient amplitude sequence. Robustness of path guide point extraction is improved through judgment of an abnormal section, dynamic construction of a path track is realized by means of continuous time integration of image coordinates, path identification precision and spatial positioning continuity are optimized, and continuity, precision and real-time performance of navigation path planning are improved.

The invention relates to the field of agricultural robots, and discloses an agricultural spraying robot system with visual navigation, and the system comprises a multi-mode sensing module, a dynamic environment modeling module, a time sequence prediction module, a multi-target path planning module, a spraying control module, and a hardware acceleration unit. The method comprises the steps that a dynamic semantic map is constructed through multi-modal perception, agricultural semantics and an attention mechanism are fused to predict an obstacle trajectory, a safe path is generated in combination with multi-objective optimization, the spraying amount is adjusted based on crop density, and real-time operation is ensured by means of hardware acceleration. According to the method, a dynamic semantic map is constructed through multi-modal perception, agricultural semantics is combined to enhance trajectory prediction, a safe path is generated by utilizing multi-objective optimization, the spraying amount is adaptively adjusted according to the crop density, and real-time control is realized by adopting heterogeneous hardware acceleration; the defects of a traditional agricultural robot in the aspects of navigation precision, obstacle avoidance safety, resource utilization and response speed are overcome, and the intelligent level of complex farmland operation is improved.

The invention provides an unmanned aerial vehicle multi-mode navigation positioning method, device and equipment, and the method comprises the steps: obtaining GPS, inertia and visual navigation mode positioning data through determining a target positioning precision index, and carrying out the evaluation to generate a multi-mode navigation state set; performing performance competition on each navigation mode based on the state set, establishing an inter-mode negotiation mechanism, and determining a mode priority sequence; establishing a navigation mode credibility matrix in combination with the priority sequence, and establishing an adaptive weight distribution sequence; analyzing a weight change trend and mode switching resource overhead, carrying out navigation mode dynamic screening through switching cost quantitative evaluation, and determining a dominant navigation mode and an auxiliary navigation mode; and performing frequency characteristic analysis on the dominant and auxiliary modes, determining a frequency domain fusion strategy based on a frequency coherence coefficient, generating a fusion positioning track, and outputting a high-precision positioning result through precision evaluation to realize intelligent multi-mode navigation positioning.

The invention provides a visual navigation method and system based on an improved optical flow method, and relates to the technical field of computer vision and inertial navigation. A collaborative optimization framework of an inertial navigation system and visual feature navigation is constructed, carrier motion state parameters are obtained through dead reckoning of an inertial measurement unit, variance parameters of pose variation are calculated, and the layering depth of a pyramid LK optical flow method is dynamically adjusted based on the variance parameters. When the inertial navigation output variance exceeds a preset threshold value, the number of layers of an image pyramid is increased to cope with large-range motion, and otherwise, calculation levels are reduced to improve real-time performance. A dual-period optical flow / feature matching tracking mechanism is designed, improved sub-pixel-level optical flow tracking is adopted in a short period, the calculation complexity is reduced while the positioning precision is guaranteed, feature matching is switched to in a long period, accumulative errors are eliminated, and a period parameter N is determined by inertial navigation precision. According to the method, the calculation complexity can be reduced, and a better effect can be achieved on a low-calculation-performance platform.

The invention provides a real-time three-dimensional imaging system in a thoracic surgery, and relates to the field of thoracic surgery auxiliary equipment, and the system comprises a multi-modal image collection module which is used for collecting multi-source data of tissues and organs in a thoracic cavity; the data core processor is used for processing and fusing the multi-source data collected by the multi-mode image acquisition module; the three-dimensional reconstruction module is used for generating a dynamically updated submillimeter-level precision three-dimensional model according to the processed and fused multi-source data and marking a dangerous structure and a safety boundary; and the augmented reality navigation module is used for superposing the three-dimensional model on an operation visual field through a holographic head-mounted display, and realizing real-time three-dimensional visual navigation in combination with instrument tracking and a multi-stage early warning mechanism. According to the invention, radiation-free and high-precision real-time three-dimensional visual navigation can be provided, and the precision and safety in the thoracic surgery can be ensured.

The invention provides a target perception and avoidance decision-making method in visual navigation of intelligent equipment, and belongs to the technical field of intelligent decision-making. The method comprises the following steps: constructing a space-time diagram neural network, introducing semantic tag codes to define nodes and edges of the space-time diagram neural network, and predicting the trajectory of each obstacle through a regression network; an environment semantic label is defined, the environment semantic label and the predicted obstacle trajectory are coupled, and a semantic enhanced risk field is generated; and constructing a semantic road network map based on the risk field of semantic enhancement, generating a global path point sequence, and calling a dynamic step RRT * algorithm to avoid obstacles by taking the current global path point as a temporary target. According to the method, the risks of different areas can be evaluated more accurately, the risk value is adjusted according to the type and the position of the obstacle, the safety and the reliability in a complex environment are improved, and decision errors caused by environment changes are reduced.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle navigation method and system based on computer vision, and the unmanned aerial vehicle navigation method based on computer vision comprises the following steps: collecting a current visual field image of an airborne camera of an unmanned aerial vehicle, and carrying out adaptive image enhancement; performing scene semantic understanding; semantic perception feature extraction is carried out; feature matching and outer point elimination are carried out; floor identification is carried out, and multi-sensor fusion positioning is carried out; plane segmentation, structural constraint extraction and map normalization are carried out; performing visual navigation evaluation, judging a navigation mode according to an evaluation result, and generating an exploration control instruction quantity based on the navigation mode; generating a cross-floor path sequence, optimizing a track in real time, and performing control fusion in combination with a navigation mode identifier and an exploration control vector; according to the method, the navigation problem of the unmanned aerial vehicle in complex scenes such as smoke interference, multi-story buildings and illumination dramatic change in a GPS denial environment is solved, and high-precision autonomous navigation is realized.

The invention discloses a target aircraft flight trajectory correction method based on multi-source fusion navigation, and relates to the technical field of trajectory correction. According to the method, multi-source navigation information such as AI visual navigation, inertial measurement and barometric altitude can be fully fused, short-time disturbance and long-time deviation are effectively distinguished through conjoint analysis of short-time envelope and trend envelope, and the sensitivity and reliability of trajectory anomaly detection are remarkably improved; by setting a direction consistency screening and grading weight suppression mechanism, misjudgment caused by air pressure fluctuation or instantaneous noise in low-altitude flight can be suppressed, so that the false alarm rate is reduced; the method has the advantages that the method is simple and easy to implement, an envelope difference accumulated evidence and a threshold linkage triggering mechanism are utilized, early warning of trajectory deviation is achieved, the timeliness of abnormal response in a flight task is improved, stable and accurate trajectory correction can be dynamically generated in a changeable flight environment, and robustness and convergence of the correction process are guaranteed.

The invention provides a multi-arm drill jumbo self-adaptive drilling track planning method based on visual navigation, which relates to the technical field of visual identification, and comprises the following steps: acquiring image data, identifying a working surface change area, generating a drilling grid with topological optimization, determining a target drilling position set and a drilling direction set, and performing drilling grouping. Generating a space-time conflict constraint for each group of drill holes, and iteratively optimizing to determine a multi-arm collaborative operation sequence; in the process of executing the multi-arm operation sequence, a structured light projection technology and a visual phase shift method are adopted to obtain a deformation field of the surface of a rock mass, rock mass response characteristics including elastic deformation, plastic deformation and abnormal deformation information are established, and according to the rock mass response characteristics, a progressive B-spline track adjustment algorithm is adopted to adjust the drilling track in real time.

The invention discloses a robot arm control method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a pre-training dynamic model and a real visual depth map of a real robot arm visual angle, and the pre-training dynamic model is used for completing a specified task; determining a joint control value according to the real vision depth map and a pre-training kinetic model; hybrid action control is carried out based on the joint control value, and remote target navigation is carried out on the response action of the real robot arm through a visual navigation model; and the controlled simulation target image and the actual image are obtained, feature matching and closed-loop estimation are carried out based on the simulation target image and the actual image, and pose error compensation is carried out on the action of the real robot arm. The motion is observed and deduced through a real visual depth map, then real and simulated mixed motion control is carried out to reduce a visual and dynamic gap, and finally pose error compensation is carried out. And the pose error of the arm is reduced, and a start pose guarantee is provided for downstream control.

The invention is applicable to the technical field of manufacturing execution systems, and provides a material positioning method and system, the method is applied to a manufacturing execution system with an AR operation terminal, and the method comprises the steps: obtaining a signal of an intelligent electronic tag configured on a material unit through a multi-mode positioning base station network deployed in a material region, real-time position information of the material units is obtained through calculation; constructing and maintaining a digital map which is synchronous with the physical storage environment in real time, and updating the real-time position information of the material unit to the digital map; when a material demand from a manufacturing execution system is received, real-time position information of a needed target material unit is determined in the digital map according to the material demand, and a navigation path to the target material unit is planned; based on the real-time position information of the target material unit and the planned navigation path, visual navigation guidance is provided for an operator through the AR operation terminal. The problem that materials cannot be quickly and accurately positioned and found in the existing production process is solved.

The invention discloses a diffusion model and explicit constraint guidance fused visual navigation method and system. The method comprises the following steps: performing feature extraction on visual observation data to obtain target observation semantic features; performing feature extraction on the navigation target data to obtain target image features; performing feature fusion on the target observation semantic feature and the target image feature to generate a condition vector; inputting the condition vectors into a condition diffusion model for diffusion training to generate a plurality of initial candidate paths; constructing an explicit constraint based on the initial image data, and performing path optimization on each initial candidate path according to the explicit constraint to obtain a plurality of candidate optimization paths; the explicit constraints comprise collision constraints and target distance constraints; and determining a target navigation path from each candidate optimization path according to a path selection strategy. According to the method, the problems of insufficient path safety and poor dynamic adaptability can be solved, the safety and accessibility of the path are remarkably improved, the path navigation efficiency is further improved, and the method can be widely applied to the technical field of computers.

The invention discloses a visual navigation image processing method based on Riemannian manifold learning, and relates to the field of navigation visual image processing. The visual navigation image processing method based on Riemannian manifold learning comprises the following steps: carrying out data preprocessing on image data obtained by an acquisition system such as a camera probe and the like to obtain preprocessed image data, and inputting the preprocessed image data into a deep Riemannian network model for learning, the deep Riemannian network model comprises a classification algorithm based on multi-manifold joint representation and multi-kernel metric learning, a classification algorithm based on a lightweight SPD manifold neural network, a classification algorithm based on SPD manifold depth metric learning, and a classification algorithm based on a deep SPD manifold neural network. According to the method, the deep Riemannian network model capable of effectively overcoming the problem of information degradation is researched from the objective function and the network architecture, and the method has important significance for realizing robust representation and accurate classification of image set data.

The invention relates to the technical field of unmanned aerial vehicle navigation, in particular to an unmanned aerial vehicle navigation method based on plane visibility, and the unmanned aerial vehicle navigation method based on plane visibility comprises the following steps: collecting environment image data through an airborne camera; extracting scene feature points and clustering and grouping the feature points to form a plurality of plane surfaces; a local coordinate system with the position of the unmanned aerial vehicle as the center is established, and plane visibility is judged based on position correlation and direction correlation; a guard node is selected for topological path search, an undirected connection graph is constructed, and paths with different topological characteristics are searched; performing trajectory optimization on the searched path; the yaw angle is planned according to the visible surface distribution characteristics, a control instruction is output, and the unmanned aerial vehicle navigation method based on plane visibility provided by the embodiment of the invention can greatly improve the reliability of visual navigation.

The invention discloses a cotton field robot fusion navigation path generation method, and relates to the field of agricultural robot positioning navigation, and the method comprises the steps: 1, installing an RTK positioning module on a cotton field robot for position navigation; a deep learning visual camera is installed in front of the cotton field robot and used for recognizing and keeping lane lines, and visual navigation is achieved; a walking device, a turning device and an in-situ turning device are installed on the cotton field robot and used for achieving the control effect of walking, turning and in-situ turning. 2, constructing a visual navigation model based on semantic segmentation, and deploying the visual navigation model to an automatic driving system of the cotton field robot; step 3, dotting and marking land parcel information by the RTK dotting device, and importing the land parcel information into an automatic driving system of the cotton field robot; and step 4, starting the cotton field robot to walk and work in the sown cotton field, generating a fusion navigation path, and synchronously recording RTK position track coordinates. According to the invention, an operation path can be automatically planned on a sown standard cotton field.

The invention discloses a multi-agent collaborative visual navigation reasoning enhancement method and system, and the method comprises the steps: an inference agent analyzes input data, and generates structured output containing a reasoning process and an initial answer; independently analyzing the input data by the evaluator agent, generating an evaluation answer and reviewing the output of the inference agent; comparing whether the initial answer is consistent with the evaluation answer, and if yes, outputting a final answer; if not, calling a bifurcation feedback agent, and generating a core bifurcation point abstract and a visual evidence verification plan; and calling a final decision maker agent to directionally retrieve the video evidence according to the visual evidence verification plan, and outputting a final answer. Four roles of an inference person, an evaluator, a divergence feedback person and a final decision maker are introduced, a complete'generation-evaluation-feedback-optimization 'decision link is constructed, and the cognition and inference accuracy in a complex scene is remarkably improved.

The present discloses a lightweight attention mechanism distance estimation method for assisting visual navigation of a vehicle at a container terminal. Firstly, using a depth monocular camera calibrated with the imaging parameters of the planar checkerboard tool to collect RGB-Depth image pairs in the working scenario of the automatic guided vehicle. Secondly, performing depth completion and manual annotation processing on the collected depth image. Thirdly, inputting image pairs into a lightweight monocular metric depth estimation framework which uses an improved lightweight attention mechanism Squeeze Former as the token mixer for training. Finally, fusing the results of relative depth estimation and absolute depth estimation to obtain a prediction of an actual distance between an object in the RGB image and the camera in the real world. The method and model provided by the present invention feature simple equipment, low cost, high timeliness of prediction and accurate results.

The invention relates to the technical field of medical body robots, in particular to a visual navigation method and device for a medical body robot and a medium, and the method comprises the following steps: S1, obtaining a unified feature vector; s2, constructing an initial model; s3, optimizing the initial model; and S4, completing visual navigation. The invention mainly solves the technical problems of real-time visual navigation and multi-task adaptability of the medical intelligent robot in a complex medical scene, especially fusion and processing of multi-modal data in a resource-limited environment. By optimizing the neural architecture and adopting delay control and multi-modal data alignment technologies, the real-time performance and accuracy of the robot in an operation environment are improved, and it is ensured that the robot can process different tasks and adapt to environment changes in a dynamic scene.

The invention relates to the technical field of visual navigation, in particular to a blind person navigation path planning method combining visual SLAM and semantic segmentation. The method comprises the following steps: acquiring an environment image and pose data; performing front-end tracking by using the pose data, and determining a key frame sequence; generating a depth map based on the environment image, and identifying a passable area according to the depth map; constructing a three-dimensional point cloud map according to the key frame sequence; semantic segmentation is carried out according to the three-dimensional point cloud map, and obstacle type labels are recorded; according to the obstacle type label, carrying out safety level layering on the passable area, and determining a path cost weight; performing global path planning based on the path cost weight, and generating a semantic enhancement navigation trajectory; and driving real-time voice guidance by using the semantic enhancement navigation trajectory, and calculating a path execution deviation corresponding to the real-time voice guidance. According to the method, obstacle types are distinguished through semantic segmentation based on a visual navigation technology, a semantic enhancement path is generated, semantic hierarchical navigation is realized, and the intelligence of path decision is improved.

The invention discloses a multi-module collaborative navigation system for an intelligent robot, which relates to the field of robot navigation and comprises a multi-mode perception decision module, a semantic visual navigation module, a path memory fusion module and a rolling predictive control module which are electrically connected in sequence and work cooperatively. The multi-modal perception decision module performs weighted fusion on heterogeneous information, outputs scene representation and decision, and provides data support; the semantic visual navigation module analyzes the natural language instruction and combines visual data to complete path planning; the path memory fusion module carries out backtracking correction in an abnormal environment of semantic ambiguity and perception mismatch; and the rolling predictive control module realizes trajectory tracking and dynamic obstacle avoidance. According to the system, dependence on a prior map is reduced, instruction performability and dynamic obstacle avoidance capability are improved, collision risks are reduced, navigation real-time performance, stability and robustness are enhanced, and the system is compatible with multiple chassis and is suitable for complex dynamic scenes.

The invention relates to the technical field of unmanned aerial vehicle navigation control, and discloses an unmanned aerial vehicle visual navigation and positioning method, system and related equipment, and the method comprises the following steps: obtaining an image in real time; performing joint correction on the current image, and determining a corrected image; high-dimensional semantic features in the corrected image are obtained, and a candidate area is rapidly and roughly matched from the reference database; performing cross-modal comparison on the depth local feature of the corrected image and the depth local feature of each candidate region to determine a target region; and determining current positioning information of the unmanned aerial vehicle based on the target area and the attitude data of the unmanned aerial vehicle, and performing visual navigation based on the current positioning information. According to the method, the matching range is narrowed through joint correction and rapid rough matching, secondary matching is performed on the narrowed range through cross-modal matching, so that the positioning precision is improved, and finally, the matched target area is combined with the flight data (attitude data) of the unmanned aerial vehicle, so that the positioning precision is further improved.

The invention discloses a mechanical dog indoor autonomous navigation method based on a pulse neural network, and provides an indoor autonomous navigation system capable of increasing endurance and reducing calculation complexity for visual navigation. Comprises: preparing a data set; establishing a pulse neural network obstacle avoidance network model, and training the network model; establishing a pulse neural network self-planning path network model, and training the network model; taking the corrected picture as the input of a pulse neural network self-planning path network model, running the network model, processing the output of the pulse neural network self-planning path network model into a control instruction of a mechanical dog, and transmitting the control instruction to the mechanical dog through a port; the obstacle image data acquired by the RGB-D camera is called as the input of the spiking neural network obstacle avoidance network model, the spiking neural network obstacle avoidance network model is operated, the output of the spiking neural network obstacle avoidance network model is processed into an obstacle avoidance instruction of a mechanical dog, and the obstacle avoidance instruction is transmitted to the mechanical dog through a port; and repeatedly executing the steps 4-5, and continuously planning a path to complete a navigation task.

The invention discloses a multi-mode feedback control method and system for a femtosecond laser stone crusher, and relates to the technical field of medical instruments. The method comprises the following steps: constructing spectral component analysis, collecting spectral signals, and executing target chemical component identification; ultrasonic position detection: acquiring an ultrasonic echo signal through a transducer, and calculating a spatial distance; performing vision-assisted positioning, acquiring image data through an endoscope, and generating space image information; according to the spectrum identification result, the distance parameter and the space image information, multi-source data synchronization processing is completed; generating a laser parameter control instruction according to the fusion result; and executing a real-time feedback regulation and control process, obtaining feedback information, and completing control parameter updating and closed-loop response. Through fusion of multi-modal information such as spectrum identification, ultrasonic distance measurement and visual navigation, real-time identification of stone types, intelligent matching of laser parameters and accurate control of spatial positioning are realized, and the accuracy, safety and intuition of lithotripsy operation are remarkably improved.

The invention discloses a transcranial stimulation magnetic therapy coil navigation method, a transcranial stimulation magnetic therapy coil navigation system, a storage medium and equipment. The method comprises the following steps: acquiring three-dimensional structure information of the head of a target object, magnetic resonance imaging (MRI) data of an individual target object, or three-dimensional model data obtained by matching from a general MRI template library; real-time surface geometric information of the head of the target object is collected through an infrared optical tracking system; performing registration fusion on the three-dimensional structure information and the real-time surface geometric information, and generating an individualized three-dimensional brain model synchronized with a real space coordinate system on a navigation interface; the spatial position and posture of the optical marker fixed on the magnetic therapy coil are tracked in real time through an infrared optical tracking system and correspondingly mapped to the individualized three-dimensional brain model, and visual navigation of the magnetic therapy coil relative to the brain anatomical structure is achieved. Through the individualized three-dimensional navigation and real-time visualization technology, the problems that a traditional TMS is low in positioning precision, invisible in operation and poor in treatment repeatability are solved.

The invention discloses an unmanned aerial vehicle high-altitude cleaning path precise planning method and system based on visual navigation, and the method comprises the following steps: constructing an initial three-dimensional grid model of a to-be-cleaned building, dividing cleaning regions according to the initial three-dimensional grid model, and setting priorities; the unmanned aerial vehicle collects visual data of a cleaning area through a multi-mode visual sensor, wherein the visual data comprise surface texture, depth information and a stain distribution thermodynamic diagram; performing local correction on the initial three-dimensional grid model based on the real-time visual data to generate a high-precision dynamic three-dimensional grid model; generating a cleaning path by adopting an adaptive grid traversal algorithm according to the priority of the cleaning area and the coverage range of the unmanned aerial vehicle cleaning head; obstacles in the path are detected in real time through a visual feature matching algorithm, and when the obstacles are detected, path re-planning is carried out to avoid the obstacles until all the areas are cleaned. According to the method, the modeling precision and the dynamic adaptability are improved, and the intelligence and the pertinence of path planning are realized.

The invention discloses a rotor unmanned aerial vehicle visual navigation method and system for an unknown forest region, and the method comprises the steps: firstly, combining a depth camera with a Sobel gradient filtering mechanism, and effectively eliminating interference information; secondly, pixel-level segmentation and three-dimensional positioning of the trunk are realized by segmenting a cascade model of a convolutional network and a residual network, visual information is accurately mapped to a world coordinate system, and the problem of positioning drift caused by similar textures in a forest scene is solved; based on starting point-target point-terminal point minimum jerk trajectory planning, a smooth trajectory is generated through polynomial optimization and quadratic programming, flight jitter is inhibited fundamentally, and the mechanical loss is reduced while the image acquisition quality is guaranteed; and finally, through real-time target point updating and trajectory dynamic correction of the depth camera, a sensing-planning-execution closed loop is formed, so that the unmanned aerial vehicle has dynamic obstacle avoidance and path online optimization capabilities, autonomous crossing is finally realized, and navigation safety and stability in a complex environment are significantly improved.

The invention provides an inertia / satellite / vision adaptive integrated navigation method and system. The method comprises the following steps: carrying out inertia measurement and navigation calculation to obtain inertia data; calculating satellite navigation observed quantity according to the satellite navigation data and the inertial data, and constructing a satellite observation model; calculating visual navigation observed quantity according to the visual navigation data and the inertial data, and constructing a visual observation model; aiming at a satellite and a visual observation model, respectively adopting an innovation-based adaptive covariance estimation method to obtain satellite and visual observation noise covariance updated values; constructing a satellite and visual navigation health degree, and calculating a satellite and visual fusion weight according to the satellite and visual navigation health degree; controlling updating of satellite and visual navigation observed quantity according to the satellite and visual fusion weight; and calculating a weighted equivalent observation matrix and a noise covariance, and carrying out filtering estimation. According to the method, an adaptive noise estimation and sensor health degree evaluation mechanism is introduced, dynamic weighted fusion of multi-sensor data is realized, and the robustness and precision of a navigation system in complex environments such as satellite signal lock losing and visual feature missing are improved.

The invention is suitable for the technical field of artificial intelligence, and provides a robot visual navigation optimization system based on edge computing, the system comprises a visual sensor module, an edge computing unit, a navigation algorithm module, a communication module and a robot executor, the visual sensor module is used for collecting high-definition images or video streams, and the edge computing unit is used for processing the high-definition images or video streams; the edge calculation unit is responsible for locally executing a visual navigation algorithm, the navigation algorithm module is deployed on the edge calculation unit, and the navigation algorithm module is used for executing a visual odometer, synchronous positioning and map construction, environment perception, obstacle detection, local path planning and motion control; and the communication module is used for carrying out model updating, task scheduling and non-real-time data uploading with a cloud. Complex visual data processing and navigation decision are sunk to the robot body or near-end edge equipment, so that the navigation precision and the response speed of the robot in complex and diversified environments are remarkably improved, and the energy consumption efficiency is optimized.