2139 results about "Navigation system" patented technology

The invention relates to the technical field of unmanned aerial vehicle navigation and control, and discloses an unmanned aerial vehicle indoor and outdoor seamless navigation method fusing Beidou and visual positioning. Comprising the following steps: S1, synchronously acquiring original observation data of a Beidou satellite navigation system of an unmanned aerial vehicle, an image sequence acquired by a visual sensor and inertial data of an inertial measurement unit; s2, processing the original observation data of the Beidou satellite navigation system to obtain the position of the unmanned aerial vehicle, according to the unmanned aerial vehicle indoor and outdoor seamless navigation method and system fusing Beidou and visual positioning, through a self-adaptive fusion filter module, the fusion weight of the unmanned aerial vehicle and visual positioning is dynamically adjusted according to the Beidou signal quality; smooth transition of indoor and outdoor navigation main sources is achieved, pose jump is effectively avoided, a tight coupling depth fusion algorithm is adopted, the precision and robustness of the system are improved, the continuous and stable flight capacity of the unmanned aerial vehicle in complex indoor and outdoor environments is ensured, and the application scene is expanded.

The invention relates to the technical field of robot navigation, and discloses a safety inspection robot multi-mode navigation system fused with subconscious learning. The system obtains a visual image, a depth distance, an inertial measurement unit and other multi-dimensional perception data of an inspection environment, performs environment feature extraction to generate an environment semantic topological graph, and constructs a dynamic navigation cost map and a preliminary navigation path according to the environment semantic topological graph. During path execution, the system detects abnormal navigation behaviors in real time, extracts the features of the abnormal navigation behaviors and obtains a first correction coefficient. And the system judges whether the implicit environment semantic identifier is identified, and if the implicit environment semantic identifier is identified, the corresponding path key node is taken as a semantic navigation reference point, a final navigation path is generated through fusion, and a time stamp is recorded. And if the time stamp exceeds the preset tolerance, extracting a corresponding second correction coefficient. And the system combines the basic navigation performance evaluation value, the first correction coefficient and the second correction coefficient to output a multi-modal navigation decision result.

The invention discloses an intelligent navigation system of a sweeping robot, which relates to the technical field of household intelligent equipment and comprises a multi-source sensor array, a dynamic environment modeling module, a path planning engine, a multi-mode positioning module, an autonomous decision controller, a cloud collaborative optimization unit and a fault degradation processing module. Omnibearing environment perception is provided through a multi-source sensor array, data are fused in real time through a dynamic environment modeling module, a grid map is updated in an incremental mode, obstacle semantic information is marked, an improved A * algorithm and a dynamic window method are fused through a path planning engine, and an optimized path giving consideration to global efficiency and local dynamic obstacle avoidance is generated. The multi-modal positioning module fuses SLAM resolving and IMU data through tight coupling to achieve centimeter-level high-precision and high-robustness positioning, and the autonomous decision controller intelligently and dynamically adjusts the cleaning priority, the advancing speed and the exception handling strategy according to the path, environment state and pose information, so that the cleaning behavior better meets the requirement.

The invention discloses a method for judging an RTK abnormal value in an automatic driving integrated navigation system, and relates to the technical field of automatic driving high-precision integrated navigation, and the method comprises the steps: collecting RTK observation data, inertial measurement unit data, a wheel speed pulse signal and LiDAR point cloud data, carrying out timestamp alignment and coordinate system unification, and generating a fusion data value; calculating a carrier-to-noise ratio weight signal quality index of the satellite through the fused data value, and generating a signal quality report; and combining the signal quality report, the current satellite geometric accuracy factor and the vehicle motion acceleration, calculating a residual threshold, constructing a coriolis force compensated double-integral prediction model by using inertial measurement unit data, and predicting the position of the vehicle at the current moment. According to the method, multi-dimensional features such as satellite signal quality, vehicle motion state and residual analysis are fused through multi-source decision, and the anomaly detection capability of complex scenes such as urban canyons is effectively improved.

In one implementation, a method for navigating a host vehicle relative to a road segment includes receiving a captured image representative of at least a portion of the road segment; receiving a planned drivable path representation based on one or more map sections associated with the road segment, wherein the drivable path is determined for navigating the host vehicle; generating an overlaid representation of the captured image with the planned drivable path; providing the overlaid representation to a trained network, wherein the trained network is configured to receive the overlaid representation as input and provide, based on the combination of the captured image and the planned drivable path, an output that includes an indication of whether the host vehicle drivable path represents a valid path along the road segment; and causing a navigation system of the host vehicle to determine a navigational change for the host vehicle based on the indication of whether the host vehicle drivable path represents a valid path along the road segment.

To eliminate the influence of station position fluctuations in a satellite navigation system. [Solution] In satellite navigation systems, including the US GPS and Japan's Quasi-Zenith Satellite System, when multiple reference stations are installed to form a wide-area differential correction system (WADGPS), previously, position errors caused by fluctuations in station position would remain in the position information calculated by the user receiver.However, by adding a correction amount to the correction information provided by WADGPS in advance to cancel this, the position errors caused by fluctuations in station position at the user station can be reduced without changing the calculation processing content at the WADGPS user station, thereby improving the accuracy of the position information calculated by the user station.

The invention relates to the technical field of automatic driving, and discloses a dense semantic map navigation system based on multi-sensor fusion and factor graph optimization, which comprises a sensor module, an environment mapping module, a real-time positioning module, a detection and classification module and a path planning control module, a complete technical architecture from environment mapping, real-time positioning, obstacle detection and tracking, trajectory prediction, global path planning and local path planning is covered, and high-precision environment mapping, real-time positioning, obstacle detection and avoidance and path planning are realized by using various sensors such as a laser radar, an IMU (Inertial Measurement Unit) and a binocular camera and a sensing data fusion technology. In combination with deep learning and an optimization algorithm, high-precision autonomous navigation of the unmanned vehicle in a complex dynamic environment is realized, and the robustness and safety of path planning are improved.

The invention discloses a power distribution network fault positioning method and device based on a satellite time service inverter, and belongs to the technical field of power system fault diagnosis, and the method comprises the steps: obtaining a time service signal of a satellite navigation system, decoding the time service signal to generate a unified space-time reference, distributing the unified space-time reference to a plurality of inverter nodes in a power distribution network, and forming a cooperative measurement network; a coding sequence current signal is injected into a power distribution network line through a master control inverter node; synchronously acquiring response voltage traveling wave data by each inverter node, performing cross-correlation analysis on the response voltage traveling wave data and the coded sequence current signal, and extracting propagation time delay of each inverter node; and correcting the reference line wave velocity by using the current line parameters and the current meteorological data, and calculating fault point position data in combination with propagation time delay. According to the method, an inverter cooperative measurement network is constructed by using satellite time service, a coding sequence current signal is actively injected, and a real-time wave velocity is corrected by combining an intelligent model, so that rapid, accurate and high-reliability positioning of a power distribution network fault can be realized.

The invention belongs to the technical field of unmanned aerial vehicles, and provides an omni-directional vision positioning system and method and an unmanned aerial vehicle, the omni-directional vision positioning system is installed on a fuselage of the unmanned aerial vehicle, and the omni-directional vision positioning system comprises an image acquisition device used for acquiring a real-time scene image in an omni-directional range in the flight process of the unmanned aerial vehicle; the inertial measurement sensor is connected with the image acquisition equipment and is used for synchronously acquiring motion state data of the unmanned aerial vehicle; the navigation system is connected with the image acquisition equipment and the inertial measurement sensor and is configured to execute at least one of distortion correction processing and feature extraction processing on the real-time scene image to obtain a processed image; and according to the motion state data and the processed image, determining current pose information of the unmanned aerial vehicle in a space coordinate system constructed by taking a take-off point of the unmanned aerial vehicle as an original point. The positioning precision and robustness of the unmanned aerial vehicle in a complex environment are improved, and the requirement of high-precision autonomous flight is met.

The invention discloses an improved Kalman filtering ocean current estimation method based on inertial information and DVL speed. The method comprises the following steps: S1, system initialization and data acquisition; s2, performing inertial navigation system (INS) resolving, namely performing mechanical arrangement resolving based on IMU data; s3, constructing a fusion filtering state model and an observation model; s4, adaptive Kalman filtering iteration estimation: after discretizing the state equation and the observation equation, executing filtering iteration according to a period; and S5, state feedback correction and ocean current output. According to the improved Kalman filtering ocean current estimation method based on inertial information and DVL speed, a fusion framework of an augmented state model and adaptive Kalman filtering is constructed, and high-precision ocean current estimation and navigation correction are realized through five key processes.

The invention relates to the technical field of blind guiding robot interactive navigation, and particularly discloses a blind guiding robot interactive navigation system combining vision, inertial navigation and voice, which is characterized in that environmental vision, movement and voice data are synchronously acquired through a multi-modal perception data acquisition module, and are analyzed through a situation-user joint state feature extraction module; a user potential intention deduction module is combined to predict a user intention and generate a self-adaptive guide strategy; a self-adaptive guiding path optimization module adjusts a navigation path according to a strategy to ensure safety and high efficiency; finally, the situational multi-mode interaction instruction output module provides voice and tactile feedback, and interaction experience and navigation autonomy are enhanced. According to the method, the travel problem of the visually impaired person in a complex environment is solved, the individuation, context perception capability and safety of blind guiding are improved, and more natural and efficient navigation assistance is brought to the visually impaired person.

The invention relates to three-dimensional traffic navigation, in particular to a three-dimensional traffic navigation system based on space-time grid coding, which comprises a low-altitude path planning module for planning an optimal flight path for an aircraft in a low-altitude airspace based on a four-dimensional space-time grid coding model and adjusting the path in real time in combination with dynamic environmental factors; the airspace conflict detection module is used for carrying out conflict detection on aircrafts in a low-altitude airspace, judging whether conflicts exist or not and finding potential conflicts in time; the path collaborative planning module is used for carrying out collaborative path planning on the multiple aircrafts with conflicts in the low-altitude airspace and solving the problem of conflicts among the multiple aircrafts; the cross-modal traffic coordination module is used for integrating ground and low-altitude traffic systems, realizing seamless connection and efficient coordination of an optimal ground path and an optimal flight path, and providing an integrated three-dimensional traffic navigation scheme; the method can overcome the defects that in the prior art, path planning efficiency is low, airspace conflicts are difficult to accurately detect, and a cross-modal traffic cooperation mechanism is lacked.

The invention relates to an unmanned aerial vehicle autonomous path navigation system based on multi-modal fusion and adaptive optimization and an algorithm thereof. The system comprises a multi-modal sensor module, a data fusion processing module, an adaptive algorithm processing module and a flight control module. The method comprises the following steps: step 1, data acquisition; step 2, data fusion; step 3, algorithm processing; and 4, controlling the flight of the unmanned aerial vehicle. The method has the advantages that multiple sensors such as a visual camera, a laser radar and a millimeter wave radar are integrated through deep fusion of multi-modal sensors, a fusion algorithm based on deep learning and a Transform architecture is adopted, weights are automatically distributed through an attention mechanism, deep data fusion is achieved, and the accuracy and accuracy of data fusion are improved. Compared with a single sensor or a simple data fusion mode, the problem that the single sensor is easily interfered by the environment is effectively solved, and the obstacle recognition accuracy and the environment modeling precision are greatly improved under the complex environments of low illumination, strong smoke, electromagnetic interference and the like.

The invention belongs to the technical field of space intelligence, and relates to a dynamic obstacle intervention-oriented space intelligent scene self-reconstruction navigation system, which comprises a dynamic intervention sensing module, a dynamic obstacle intervention processing module, a dynamic obstacle intervention processing module, a dynamic obstacle intervention processing module, a dynamic obstacle intervention processing module and a dynamic obstacle intervention processing module, the spatial semantic topology reconstruction module is used for automatically updating a spatial semantic map and a topology connection relation according to the dynamic intervention event set; the local topology rapid recombination module is used for carrying out rapid recombination on the intervened and influenced local topology based on a topology incremental learning principle; and the perception and navigation bidirectional consistency optimization module is used for establishing a reverse feedback path between perception and navigation. According to the invention, the real-time response and structure-level self-adaption of the navigation system to environment change can be realized, so that the stability and consistency of navigation decisions can be maintained in changeable scenes. The invention further provides a space intelligent scene self-reconstruction navigation method oriented to dynamic obstacle intervention.

A map generation application and method generates a computer-readable worksite map for managing navigation and travel for a plurality of mobile machines equipped with perception-based localization and navigation systems at a worksite. Survey data and development data associated with the worksite are obtained and used to prepare an unmarked worksite development map including one or more travel / activity areas. Marker positioning factors are obtained and are associated with the one or more travel / activity areas. The application determines assigned marker positions based on the marker positioning factors for the placement of physical markers about the worksite.

The invention relates to the technical field of intelligent driving assistance systems, in particular to a vision-assisted high-precision lane-level navigation system which comprises a visual perception module, a geometric modeling module, a trajectory optimization module, a dynamic obstacle avoidance module and an execution control module. Establishing a lane local coordinate system based on arc length parameterization, and accurately capturing road geometric characteristics; state representation is constructed in a Riemannian metric space, an optimal track is selected by calculating geodesic line deviation measurement, and the generated track is made to better conform to road geometric characteristics; compared with the prior art, the method has the advantages that the lane-level positioning with centimeter-level precision can be realized, the driving track with smoother curvature change can be generated, and the driving comfort can be obviously improved.

The invention discloses an intelligent robot navigation system with a body based on a multi-modal large model, and the system comprises an RGB-D camera module which captures a color image and a depth image of an environment in real time, and carries out the preprocessing of the color image and the depth image, and outputs the preprocessed image; the laser radar SLAM module provides a basis for subsequent coordinate calculation and navigation path planning; the multi-modal VLM module is used for calculating the three-dimensional coordinates of the target location in the map; and the navigation control module controls the robot to move. The beneficial effects of the invention lie in that the system realizes efficient environmental perception and natural language understanding through multi-modal fusion, and can accurately identify a target location, calculate a three-dimensional coordinate and plan a safe path, thereby improving the autonomous navigation precision and reliability of the robot, and being suitable for intelligent movement control in a complex scene.

Surgical robotic systems and navigation systems employ a robotic arm including a plurality of links. A link tracker assembly is located on the surface of one or more of the links to enable the link to be tracked by a localizer. The link tracker assemblies can utilize a plurality of tracker elements that can be removably coupled to the link. A photosensor assembly is located on the surface of one or more of the links to receive signals from the localizer for controlling the link tracker assembly. The robotic arm can be coupled to a base and the systems can be used in collaboration with a base tracker that is coupled to the base. The robotic arm can also include a mounting interface for receiving an end effector and the systems can be used in collaboration with an end effector tracker that is coupled to the end effector.

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 discloses a thoracoscope minimally invasive cardiac surgery navigation system based on multi-modal image fusion, and the system comprises the steps: constructing a 5D heart digital twin containing an anatomical structure, a soft tissue boundary, metabolic activity and electrophysiological information through fusing preoperative CT, MRI, PET and intraoperative ultrasonic images; real-time dynamic registration is realized by using an electrocardio gating driven biomechanical model; superposing the multi-color coding navigation information to the visual field of the thoracoscope through a wavelength selection type spectroscope; a dynamic risk scoring model is constructed based on the distance, the speed and the tissue vulnerability, and three-level grading intervention is triggered; a miniature force feedback actuator is integrated to realize closed-loop control; and pre-operation virtual drilling and post-operation federal learning optimization are supported. The system remarkably improves the surgical precision and safety, and is suitable for high-difficulty heart minimally invasive surgeries such as mitral valve repair and atrial fibrillation ablation.

The invention discloses a UWB navigation system and method based on visual feature fusion, the system comprises a UWB positioning module, a visual perception module, a data fusion processing module and a navigation control module, the UWB positioning module adaptively generates UWB anchor point layout, calculates UWB coordinates in real time, and then obtains UWB positioning data according to fruit tree coverage rate and soil humidity calibration; the visual perception module collects orchard images to recognize fruit trees and obstacles, obtains visual coordinates and corrects the visual coordinates to obtain visual positioning data; the data fusion processing module calculates weights of the UWB positioning data and the visual positioning data, fuses the UWB positioning data and the visual positioning data according to the weights to obtain fusion coordinates, and performs error correction to obtain optimal coordinates; and the navigation control module calculates a target path point according to the optimal coordinate, the obstacle recognition result and the weeding operation path, and controls the weeding robot to run to the target path point. According to the invention, high-precision navigation of the weeding robot in a complex orchard environment is realized.

The embodiment of the invention provides a satellite communication and navigation integrated system, which comprises a baseband subsystem and a processor subsystem, the baseband subsystem comprises a front-end processing module, a low-orbit communication module, a navigation processing module and a decoder module, the front-end processing module is configured to preprocess an acquired satellite signal and output a baseband signal, and the decoder module is configured to decode the baseband signal. The baseband signal comprises a navigation signal and a low-orbit communication signal; the navigation processing module is configured to process the navigation signal to obtain a first target signal; the low-orbit communication module is configured to perform signal processing on the low-orbit communication signal to obtain a second target signal; the decoder module is configured to decode the first target signal and the second target signal to obtain navigation data and communication data; and the processor subsystem is configured to perform resource scheduling on the front-end processing module, the navigation processing module, the low-orbit communication module and the decoder module based on the navigation data and the communication data. According to the invention, the integrated requirement of the navigation chip on navigation and communication can be realized.

The invention relates to the technical field of urban traffic management and carbon emission reduction accounting, and provides a bus travel carbon emission reduction accounting method fused with Beidou spatio-temporal data, which comprises the following steps: acquiring spatio-temporal trajectory data based on a Beidou satellite navigation system, and constructing a passenger-trajectory-station three-dimensional correlation model for passenger flow analysis; constructing a passenger transfer analysis system based on the passenger flow analysis result; constructing a dynamic'perception-prediction-optimization 'dynamic scheduling optimization system for passenger flow detail analysis on the basis of space-time trajectory data and a reinforcement learning algorithm; and constructing a mileage-frequency distribution model and a station efficiency evaluation model based on the spatio-temporal trajectory data, respectively generating a trajectory matching optimization strategy and a differentiated station optimization scheme, and realizing adaptive optimization of the public transportation system under dynamic demand change. Compared with a traditional static data dependence method, the method achieves the dynamic and fine-grained monitoring of the operation process of the public transportation system, and effectively improves the timeliness and integrity of data.

The invention relates to a risk prediction and emergency navigation system based on digital twin and AI video linkage, and belongs to the technical field of public safety and emergency management. The system comprises an AI video processing module, a digital twin module, a risk prediction module and an emergency navigation module, analyzing the target area video data stream, and extracting target dynamic behavior features to generate video feature data; the digital twinborn module fuses the real-time environment parameters and the video feature data, constructs and updates a video linkage twinborn model, and outputs spatial state data; the risk prediction module is combined with a space-time fusion prediction mechanism and a historical risk case library to calculate a risk occurrence probability, a risk level, an influence range and an evolution trend; and the emergency navigation module dynamically plans a path and generates navigation information adaptive to different users and terminals based on the risk prediction result and the spatial state data. According to the method, deep fusion of multi-source data is realized, the risk prediction accuracy and navigation dynamic adaptability are improved, and the method is suitable for various scenes.

The invention discloses an online self-calibration filtering method for inertial navigation, and relates to the technical field of inertial navigation, and the method comprises the following steps: obtaining multi-modal data, and establishing an inertial device error mathematical model and an inertial navigation system error mathematical model; carrying out recursive estimation on the state variables by the extended Kalman filtering model to obtain error parameters of the inertial device and the navigation system; constructing a fuzzy adaptive model based on the error parameters, generating an observation noise correction factor through a fuzzy logic rule, and correcting an observation noise covariance matrix in the extended Kalman filtering model in real time; carrying out online estimation and compensation on error parameters of the inertial navigation system by utilizing an extended Kalman filtering model, and outputting compensation parameters containing zero offset and scale factors in real time; correcting the original data based on the compensation parameters, injecting the original data into the navigation solution, and updating the fuzzy adaptive model according to the compensated error data.

The invention discloses an AGV navigation system used in a storage dynamic environment, relates to the technical field of storage robot control, and is used for solving the problem of poor stable navigation in dynamic storage. According to the method, a closed-loop navigation link of state convergence, confidence evaluation, corridors, setting generation and cooperative gating is constructed, state frames are packaged in unified time, and positioning confidence and availability marks are generated based on the consistency of pose predictive quantity and pose observed quantity, so that credible hierarchical control is realized in shielding, light reflection and personnel forklift passing scenes, and the positioning accuracy is improved. A time sequence occupation constraint set is formed through obstacle tracking, a forbidden area mark is fused, a passable corridor boundary is generated, a speed set value sequence and a curvature set value sequence are generated in a corridor, acceleration change rate constraint and steering change rate constraint are applied, and stable driving control and steering control instructions are output. And the way-giving sequence and the recovery sequence are matched, and the execution state and the occupation state are written back, so that the navigation safety, the stability and the operation continuity are improved.

The invention discloses an autonomous positioning and navigation method and system for a ground mobile robot. The method comprises the following steps: S1, multi-source asynchronous environment perception and initial positioning; s2, carrying out adaptive fusion on multi-source data of time-space association; step S3, global pose optimization under semantic constraint; s4, decision planning driven by the dynamic risk field; and S5, automatically recovering the abnormal state and updating the map. According to the invention, through environmental response type sensor fusion, multi-modal semantic constraint modeling and entropy-driven intelligent decision-making mechanism synergistic effect, industrial pain points of positioning drift, low planning efficiency and insufficient long-term operation reliability in a complex dynamic scene are systematically solved; the centimeter-level positioning precision and the smooth motion trail are kept under the extreme conditions of severe illumination change, dense obstacle disturbance and repeated structure path, the passive operation and maintenance mode that a traditional navigation system depends on manual intervention is thoroughly changed, and a full-autonomous navigation capability closed loop is provided for industrial logistics and commercial service scenes.

On the basis of tight coupling of the SINS and the USBL, a dual-hydrophone differential model is provided, constant errors in USBL measurement are eliminated, and the influence of slant range noise on navigation precision is relieved; a measurement equation of a dual-hydrophone differential model is established, so that the precision and reliability of the integrated navigation system are improved; a robust iterative Kalman filtering algorithm based on approximate least-square estimation is introduced, measurement dimensions are comprehensively evaluated, measurement noise covariance is dynamically adjusted, and the numerical stability and robustness of the algorithm are improved. According to the method, whether the measured data at the current moment is an abnormal value or not is judged according to previous data, the robust weight is reasonably distributed to the measured data, the adverse effects of subsequent steps such as state estimation are reduced through a method of reducing the weight of the abnormal value, and normalization operation is carried out on the measured residual error according to the adverse effects, so that the residual error distribution is more regular, and the accuracy is improved. And the updating result of the measurement noise covariance is further optimized, so that the precision of the whole Kalman filtering algorithm is improved.

In one implementation, a system for generating a map for use in navigating a host vehicle relative to a road segment includes at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to: receive drive information from each of a plurality of harvesting vehicles that traversed the road segment, wherein the drive information received from each of the plurality of harvesting vehicles includes at least one location indicator associated with an actual trajectory traveled by the harvesting vehicle, as the harvesting vehicle traversed the road segment; provide the drive information received from each of the plurality of harvesting vehicles to a trained model, wherein the trained model is configured to receive the drive information as input and output normalized drive information for each of the plurality of harvesting vehicles, wherein the normalized drive information includes the at least one location indicator aligned relative to a predetermined reference location; aggregate the normalized drive information provided for each of the plurality of harvesting vehicles to determine one or more target drivable paths through the road segment; store in the map the one or more target drivable paths; and distribute the map data to at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to the one more mapped target drivable paths.

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.