63 results about "Navigation error" patented technology

The invention relates to the technical field of robot navigation, and discloses a visual positioning method, self-moving equipment and a storage medium, and the method comprises the steps: obtaining a plurality of frames of environment images containing a time sequence relation; analyzing the multi-frame environment image to obtain an anomaly recognition result in the environment image; extracting effective features based on the multi-frame environment image to obtain a first effective feature set; screening the first effective feature set according to an anomaly recognition result, and removing false features corresponding to the image anomaly to obtain a second effective feature set after the anomaly is removed; and calculating pose information of the self-moving device based on the second effective feature set. According to the technical scheme, the accuracy and robustness of visual positioning are improved, and navigation errors caused by image abnormity are reduced.

The invention discloses a waypoint prediction method fusing instruction landmark features in visual language navigation, and belongs to the crossing field of artificial intelligence and computer vision. The method is realized through the following steps: firstly, extracting landmarks and co-occurrence landmarks thereof from a natural language instruction by utilizing a large language model, and generating a candidate landmark sequence; then, the occurrence probability of the candidate landmarks in actual observation is calculated based on a visual large model CLIP, and noise interference is dynamically corrected and suppressed through a learnable co-occurrence scoring module; secondly, fusing the corrected landmark features with the depth visual features, inputting a double-layer Transform model to model a spatial relationship, generating a waypoint probability heat map, and outputting adjacent waypoints through non-maximum suppression (NMS); and finally, topological mapping is carried out on the predicted waypoints, an optimal path is obtained through cross-modal path planning, and an intelligent agent is guided to execute low-level actions. The method does not need to depend on a predefined environment map or manually annotate data, navigation errors in a continuous environment are remarkably reduced, the generalization ability of unseen scenes is improved, and the method is suitable for open world navigation scenes such as robot navigation and automatic driving.

The invention discloses a self-adaptive integrated navigation method and system based on satellite differential signals, and belongs to the field of integrated navigation, and the method comprises the following steps: carrying out state prediction based on the output of an inertial navigation system; a deviation function is established through sliding window fitting, and effectiveness evaluation and preventive correction are carried out on differential satellite navigation system signals; dynamically adjusting a measurement noise matrix according to the positioning mode and performing residual error verification; carrying out measurement updating by utilizing the deviation passing the verification to obtain an estimated value containing the error of the inertial device so as to carry out closed-loop feedback correction on navigation parameters; and when the satellite signal fails, compensating the inertial navigation system by using the device error parameter in the estimated value and realizing pure inertial recursion. According to the method, the data quality can be improved from the source, the self-adaptive robustness of the system in a complex environment is enhanced, inertial navigation error divergence is continuously inhibited, and the navigation endurance precision under the extreme condition of satellite signal failure is guaranteed.

The application provides a spacecraft autonomous navigation method and electronic equipment. The spacecraft autonomous navigation method comprises the following steps: determining technical indexes of an optical sensor according to navigation requirements, and determining a configuration of the optical sensor according to orbit information of the spacecraft; collecting a star map by using the optical sensor with the determined technical indexes and configuration, processing the star map, identifying a refraction star and extracting a refraction angle; and using a Kalman filtering algorithm to perform information fusion on information output by an inertial navigation system and a refraction apparent height calculated based on the refraction angle, so as to correct navigation errors and obtain corrected navigation information. The application corrects drift errors of the inertial navigation by astronomical navigation, maintains continuous navigation information by using the inertial navigation when the astronomical system is unavailable, and achieves the purpose of improving navigation accuracy.

The invention discloses an airborne bistatic radar platform error compensation and target positioning method and system. The method comprises the following steps: constructing a measurement model for a bistatic radar system; the measurement model comprises a platform position motion model, a target position motion model, an observation equation of a platform to a target and a navigation error model of the platform; based on the measurement model, performing linear fitting on each platform position measurement and target position measurement by using a linear fitting model, estimating navigation error parameters of each platform according to fitting results, and correcting platform motion parameters according to estimated values of the navigation error parameters; and based on the corrected platform motion parameters, constructing a linearized target positioning model in combination with multi-platform observation data, and estimating the motion parameters of the target by using a weighted least square method to realize target positioning. Under the condition that no navigation source exists, the positioning precision of the airborne platform is improved, and then the positioning precision of the bistatic airborne radar on the non-cooperative target is improved.

This invention discloses an adaptive inertial navigation solution method and system based on motion feature recognition, comprising: extracting multidimensional features reflecting the motion state of the carrier based on angular velocity and specific force data collected by the inertial measurement unit, and constructing a feature matrix; performing dimensionality reduction processing on the feature matrix to obtain low-dimensional feature vectors; performing cluster analysis based on a clustering algorithm that initializes cluster centers using prior samples to identify the motion state of the carrier; and adaptively selecting an appropriate combined navigation filtering strategy for navigation solution. This invention effectively suppresses the accumulation of inertial navigation errors, improving the autonomous navigation accuracy and long-term stability of the system in scenarios of satellite signal rejection or visual failure; it can finely distinguish different motion modes such as hovering, uniform motion, and transitional states, avoiding the shortcomings of traditional threshold methods that are susceptible to noise interference and have poor adaptability; by introducing continuous frame mode voting and physical anti-jump mechanisms, it further eliminates single-frame misjudgments, ensuring the continuity and robustness of state recognition.

The present application relates to the technical field of inertial navigation, and provides an inertial navigation error correction method, system and device based on reverse sequence navigation solution, which comprises the following steps: recording original data measured by an inertial navigation system from starting to the end of alignment and into navigation time; recording gyro drift and platform initial misalignment angle estimated by an alignment filter at the end of alignment and into navigation time; performing navigation solution on the original data based on time reverse sequence, estimating gyro drift error according to reverse sequence navigation result, and performing secondary compensation on gyro drift and platform initial misalignment angle error; and performing navigation solution based on compensated platform misalignment angle and gyro drift from navigation time. The present application performs reverse sequence navigation solution based on alignment parameters and data recorded in the alignment stage after the end of alignment, and realizes secondary estimation and compensation of drift and misalignment angle error after the alignment stage based on the solution result, so as to obtain higher navigation precision.

The invention provides an inertial navigation error correction method and device based on photoelectric equipment image target detection, and belongs to the technical field of photoelectricity, and the method comprises the following steps: obtaining a photoelectric image of a to-be-detected target based on a pre-correction attitude angle of photoelectric equipment; performing target detection on the photoelectric image to obtain a target deviation angle; obtaining a horizontal coordinate system deviation angle based on the target deviation angle and the image rotation angle; obtaining an inertial navigation correction angle of the photoelectric equipment based on the horizontal coordinate system deviation angle and the tracking turntable direction; and performing inertial navigation error correction on the photoelectric equipment based on the inertial navigation correction angle. According to the inertial navigation error correction method and device based on photoelectric equipment image target detection, the inertial navigation error can be effectively reduced, and the pointing precision of photoelectric equipment is improved.

The application discloses a heterogeneous flight cluster inertial navigation error cooperative correction method based on pseudo observation, which is composed of multiple unmanned aerial vehicles (UAVs) and the UAVs observe each other, and comprises the following steps: S1, acquiring inertial navigation positions of the UAVs at a current time and inertial navigation position estimation noises; S2, selecting a reference anchor point, and obtaining relative distance observation, relative azimuth observation and relative pitch angle observation among the UAVs in the cluster; S3, establishing an observation equation of the heterogeneous cluster UAVs according to the observation data obtained in the step S2; S4, constructing a pseudo observation equation according to whether the observation equation is sufficient; S5, classifying according to the rank of an observation matrix, and solving the observation equation; and S6, updating the inertial navigation position, attitude and speed of each UAV. The inertial navigation position information after correction is combined with the inertial navigation information to correct and update the navigation position, attitude and speed, and better overall positioning precision of the cluster can be obtained when the available observation quantity is insufficient.

The application relates to a Lie group strapdown inertial navigation error model based on a carrier system quaternion attitude error, which comprises that the quaternion attitude error can be divided into a navigation system quaternion attitude error and a carrier system quaternion attitude error, the application first deduces an attitude error model based on the carrier system quaternion, then on the basis, a Lie group strapdown inertial navigation error model based on the carrier system quaternion attitude error is proposed and deduced, the model has obvious advantages in the aspects of combined navigation positioning precision, error convergence speed and filter robustness, and the advantages are more prominent especially under the condition of large misalignment angle. Through experiment comparison of the advantages and disadvantages of four models, the application improves the deficiency of the existing literature on the combined navigation model under the condition of large misalignment angle, and the model can complete high-precision nonlinear combined navigation in a short time under the condition of large misalignment, and meets the requirement of high-precision combined navigation.

The application discloses a kind of unmanned aerial vehicle flight control method and system, for solving the protection level failure caused by navigation error non-gaussian distribution and the problem of frequent false trigger of flight control of unmanned aerial vehicle in complex electromagnetic environment of city low altitude. Method includes obtaining bottom sensor original observation value, using robust filtering algorithm to suppress multipath interference and output residual error;Non-gaussian error boundary is constructed based on set theory geometric envelope algorithm, and the rigidity quantitative index of navigation reliability is deduced by protection level;Dynamic mapping operation risk alarm limit, and introduce time anti-shake tolerance window to filter transient signal burr;When reliability is continuously broken down, trigger state machine to execute stepwise autonomous degradation. The application breaks the limitation of traditional gaussian hypothesis, and considers the absolute safety and task continuity of flight.

This invention discloses a neural network-based inertial navigation optimization method for tunneling machines. The method includes: collecting multi-source sensor data from the tunneling machine during the tunneling process to obtain feature vectors; inputting the feature vectors into a model to output a high-precision pose error prediction value and its corresponding motor parameters; using the pose error prediction value to compensate the original pose information calculated by the inertial navigation system in real time to obtain optimized precise pose information and its corresponding motor parameters; calculating the deviation of the tunneling machine relative to the design axis based on the precise pose information and generating corresponding motor control commands. This method can alleviate the problem of large inertial navigation errors under complex working conditions, significantly improve the accuracy and efficiency of tunneling machine axis tracking, and reduce the risk of motor overload and equipment wear, achieving energy-saving and efficiency-enhancing control effects.

The application provides a rocket initial azimuth alignment correction method, comprising the following steps: S1, collecting satellite navigation data, inertial navigation data and standard trajectory speed at at least one preset time after the rocket takes off; S2, obtaining effective navigation data through preprocessing; S3, obtaining a first percentage according to each effective navigation data and a preset number, and judging whether the first percentage is greater than a first standard percentage; if yes, obtaining first fitting data; if no, setting the inertial navigation error to zero; S4, obtaining the inertial navigation error according to the first fitting data, the preset time and the collection time; S5, obtaining corrected attitude quaternion according to the inertial navigation error, the standard trajectory speed, initial state data and real-time attitude quaternion, and replacing the real-time attitude quaternion with the corrected attitude quaternion. The beneficial effect is that the real-time attitude quaternion is intelligently corrected to eliminate the azimuth error, and the rocket can smoothly enter the orbit.

The present application relates to the field of artificial intelligence automatic driving technology, in particular to a kind of general navigation method suitable for multiple motion model vehicle, medium and equipment, method includes the following steps: establishing observation equation, updating correction navigation error state, obtain updated estimation error state vector;Take the navigation component of updated estimation error state vector and update correction recursive nominal navigation state, obtain updated recursive nominal navigation state.The present application solves the problem that all wheels exist swing angle when full-wheel steering motion vehicle in rotation and translation motion in prior art, violates the important assumption of IMU / Wheel rigid connection, the present application proposes general, accurate wheel speed meter full parameter observation equation suitable for Ackermann and arbitrary shaft full-wheel steering multiple motion model, in addition, based on inertia unit auxiliary single wheel measurement, a complete, unified integrated navigation filter is established to realize multiple model adaptive fusion navigation state estimation.

The invention discloses a high-precision multi-center relative navigation method for a GNSS intermittent denial environment. The method belongs to the technical field of unmanned aerial vehicle cluster relative navigation and cooperative positioning. Aiming at the problems that the diffusion efficiency of absolute navigation information is low and the relative position estimation is easy to suddenly change when a node obtains high-precision GNSS measurement again in the existing multi-center relative navigation method in a GNSS intermittent denial environment, a unified global error state model is constructed under a multi-center relative navigation framework; inertial navigation errors, clock errors and multi-source measurement information are fused, rapid diffusion of absolute navigation information in a cluster is achieved by constructing equivalent GNSS measurement, and an absolute measurement part updating mechanism based on matrix transformation is adopted. The method is suitable for high-precision collaborative navigation application of formation flight, collaborative detection, target tracking, emergency search and rescue and the like of an unmanned aerial vehicle cluster in a GNSS intermittent rejection or strong interference environment.

This invention relates to the field of vehicle positioning and navigation technology, and provides a fusion positioning method that takes into account vehicle dynamics and nonholonomic constraints. The method includes the following steps: reading information provided by a GPS receiver, IMU sensor, vehicle CAN bus, etc.; establishing a coordinate system based on the read information; calculating position, velocity, and attitude information using an INS kinematic model based on information provided by the IMU sensor and GPS receiver (or UWB); establishing state equations and observation equations, estimating navigation error values ​​and IMU sensor bias values ​​using an error state Kalman filter, and using the results to correct the INS kinematic model; establishing a trajectory estimation model based on vehicle dynamics, establishing state equations and observation equations, and estimating position and heading angle information using an extended Kalman filter. This method can improve the accuracy of navigation and positioning when GNSS signals are interfered with or interrupted, effectively improving positioning accuracy and extending positioning time.

The application relates to a kind of inertial navigation error accurate correction methods of comprehensive utilization underwater multiple navigation information, with Doppler log velocity information as reference information, the longitude and latitude error of inertial navigation latitude oscillation error is obtained when two adjacent peaks, based on the longitude and latitude error of two time points, the inertial navigation is implemented single point correction, and the longitude divergence coefficient of inertial navigation is calculated simultaneously.After single point correction, the inertial navigation output compensates the longitude divergence error and is used as the guide information of carrier navigation, the carrier is guided to the area of the fluctuation of marine physical field, and the matching position is obtained by feature matching positioning.Taking the matching position as the starting point, the Doppler log velocity is pushed, and the inertial navigation output is corrected by using the position information, so that the inertial navigation position error can be autonomously and accurately calibrated in underwater scenes where accurate external position information such as satellite navigation cannot be obtained, and the oscillation term in the inertial navigation error can be effectively inhibited in subsequent time, and the information quality of inertial navigation is comprehensively improved.

Methods and systems are provided for planning locations to surface an unmanned underwater vehicle (UUV) to reset inertial navigation errors by obtaining a GPS fix. A spatial point process model for historical maritime traffic is used to quantify surfacing risk throughout an operational area. Accumulated navigation uncertainty for each candidate surfacing point of a feasible path is modeled and penalized. This allows autonomy to balance the tradeoff between surfacing risk, navigation performance and pathlength. The planning method results in minimizing the path length the UUV travels and the number of times the UUV surfaces, while satisfying a defined constraint on maximum allowable risk.

The application discloses a single-antenna abnormality detection and correction method based on Doppler velocity measurement, and relates to the technical field of integrated navigation, and has the technical scheme as follows: original observation data of GNSS is acquired, and the original observation data at least includes Doppler velocity measurement information; carrier velocity information at the current moment is solved based on the Doppler velocity measurement information; attitude information of the current carrier is acquired; whether the carrier is in an abnormal motion state is judged based on the carrier velocity information and the attitude information; an NHC model is constructed; according to the judgment result, the constraint strategy of the NHC model is adjusted, the measurement information is constructed based on the adjusted constraint strategy, the integrated navigation filter used for estimating the navigation state of the carrier is observed and updated by using the measurement information, and the navigation error of the carrier is corrected. The application judges the abnormal state by using the Doppler velocity measurement information and adaptively adjusts the NHC constraint strategy, so that the precision of the single-antenna integrated navigation under abnormal working conditions is improved without increasing the hardware cost.

The present application belongs to the technical field of medical image auxiliary, and specifically relates to a complex chest trauma surgery auxiliary stereoscopic imaging system and method. The present application realizes accurate assistance to the thymus resection surgery process through integration of multi-modal image data, real-time three-dimensional space capture, dynamic correction and view adjustment driven by risk score, not only improves the accuracy and safety of the surgery, but also significantly reduces the surgery risk and operation difficulty. Through real-time acquisition and processing of multi-modal image data, a high-precision three-dimensional anatomical structure model containing thymus tissue, blood vessel network and nerve distribution can be constructed, providing an intuitive surgery navigation view for doctors. Meanwhile, a dynamic correction technology is adopted, the thymus displacement parameters caused by the respiratory movement of the patient's chest cavity are monitored in real time, and the real-time three-dimensional space data is dynamically adjusted based on the parameters, ensuring the spatial synchronization of the image data and the anatomical structure, and effectively avoiding the navigation error caused by the respiratory movement.

The application provides a self-adaptive inertial navigation error correction method and device, medium and equipment. Inertial sensor data of a target object in a balanced state and a moving state is collected to obtain static inertial sensor data and dynamic inertial sensor data. Based on a static zero bias estimation model, a zero bias error prediction result corresponding to the static inertial sensor data and the dynamic inertial sensor data is calculated. Based on the dynamic zero bias error prediction result and the static zero bias error prediction result, an error factor is calculated to correct the dynamic inertial sensor data. By collecting the inertial sensor data of the target object in the balanced state and the moving state respectively, and using the static zero bias estimation model to calculate the zero bias error prediction in the balanced state and the moving state respectively, the zero bias error prediction in the two states is combined to determine the error factor, and the inertial sensor data is corrected according to the error factor, so as to alleviate the error accumulation problem of the inertial navigation system and improve the long-term accuracy of the inertial navigation system.

The invention discloses an autonomous underwater vehicle, an HOV and AUV cluster collaborative operation navigation positioning method, equipment and a medium, and relates to the field of underwater precise positioning, the method comprises the following steps: controlling a manned submersible to send an underwater sound pulse signal, self position information and a timestamp of a sending moment to a surrounding water area; controlling the autonomous underwater vehicle to receive the underwater sound pulse signal, and determining a measurement equation of the position estimation information of the autonomous underwater vehicle according to the signal propagation time and the underwater sound velocity; acquiring inertial navigation data of the autonomous underwater vehicle, performing joint optimization by using the multi-platform information fusion cooperative positioning model, and correcting an inertial navigation error of the autonomous underwater vehicle; and according to the corrected inertial navigation error, corrected navigation information is output, so that cooperative navigation positioning of the manned submersible and the AUV cluster is realized, and high-precision navigation positioning of the AUV cluster under the observation condition of taking the manned submersible as a single anchor point is realized.

The application relates to the technical field of neural networks, in particular to a pedestrian positioning method based on a CAM, a SARB and an LSTM network, which comprises the following steps: collecting a footstep vibration signal; collecting a pedestrian video image through a camera and extracting a pedestrian track; labeling the footstep vibration signal; expanding data samples by adopting a sliding window segmentation method and labeling each data segment with pedestrian track data labels extracted from a video; constructing a neural network model based on a CAM module, an input module, a SARB module and an LSTM neural network model, training the neural network model by using the expanded data set, and calculating a pedestrian track; and constructing a navigation error evaluation benchmark. The application processes sensor data by constructing a deep neural network model, reduces manual participation, improves positioning accuracy, establishes a grade classification rule of navigation error, and measures positioning accuracy.

This invention discloses a high-precision positioning system based on carrier phase-based satellite-inertial integrated navigation. The system includes two independently operating first Kalman filters and a second Kalman filter. The first Kalman filter is used for estimating the inertial navigation error state variables, with a total dimension of 15. The second Kalman filter is used for estimating satellite navigation parameters, with a total dimension of 3+n. Compared to traditional carrier phase-based satellite-inertial integrated navigation systems that use a single filter with a dimension of 15+n, this invention controls the filter dimension to within 20, thereby reducing the computational load significantly and improving the real-time performance of the satellite-inertial integrated navigation. Furthermore, the covariance matrices of the inertial navigation system and the satellite navigation system are independent of each other, ensuring no impact on the success rate of fixing the satellite navigation ambiguity parameters. By using carrier phase observations with known ambiguity and performing satellite-inertial integrated navigation with the inertial navigation system, the zero bias of the inertial navigation system can be estimated more accurately, resulting in a high-precision and high-reliability navigation solution.

The application discloses a kind of based on space target angle distance auxiliary inertial navigation error fast determination method, by at least two pointing of star camera optical axis is carried out high frequency multiple observation to obtain sufficient effective asynchronous measurement information, to ensure the observability of navigation error parameter;By establishing the optimization model with navigation position error as the estimated parameter, the first round optimization is carried out using nonlinear least square algorithm, and the position error is iteratively estimated;By establishing the optimization model with navigation position error and velocity error as joint estimated parameter, the first round optimization result is used as position error parameter initial value, the carrier position of inertial navigation solution is corrected using the position error estimated by the first round, and the second round optimization is carried out using nonlinear least square algorithm again, the change trend of carrier position in state propagation process can be effectively extracted, so as to effectively estimate the velocity error, and the estimation accuracy of position error is further improved based on the first round optimization.

The inertial navigation error correction method based on indoor walking constraint terrain matching includes the following steps: Step 1. Constructing a standard path database; Step 2. Reconstructing the vehicle's trajectory based on planar two-dimensional coordinates and attitude angles; dividing the trajectory into multiple feature path segments to be calibrated based on its inflection points; Step 3. Replacing the coordinates of the planar inflection points in the trajectory with standard coordinates, where the standard coordinates are the inflection point coordinates in the standard path; Step 4. Performing two-dimensional correction on the trajectory. This invention corrects the cumulative error generated by the inertial navigation system during pedestrian path reconstruction by matching feature points in special terrain with indoor walking constraint paths, eliminating the error in inertial navigation trajectory estimation caused by human gait, and effectively improving the accuracy of inertial navigation positioning. This invention requires no additional hardware equipment, enabling continuous positioning in complex indoor scenarios and reducing the cost of indoor positioning.

The application provides an underwater multi-source navigation positioning method based on seabed oil and gas pipeline detection, establishes an information filtering model of centralized SINS / DVL / USBL combined navigation, and obtains global optimal prediction estimation of navigation error state; then, a SINS / DVL / USBL combined navigation system state model and a SINS / DVL / USBL combined navigation observation model are established respectively; through the improved DS-UKF algorithm, the delayed measurement information is fully utilized to improve the precision of the nonlinear state estimation; finally, through the outlier detection algorithm of the weighted one-class SVM, the noise and jump point problems of the USBL data are effectively solved, and the underwater navigation precision is improved. The application effectively solves the underwater positioning precision problem of the long-time pipeline detection robot, and greatly improves the underwater positioning precision by comprehensively considering factors such as information time delay and information mutation.

The application discloses a satellite navigation data synchronization system and method based on big data, relates to the technical field of satellite navigation data synchronization, and comprises the following steps: acquiring satellite navigation synchronization records of a satellite navigation platform in a current period, and analyzing data abnormal states of the satellite navigation synchronization data; analyzing the type matching degree between a synchronization abnormal type in the synchronization abnormal type data and a navigation error record set, obtaining a target synchronization abnormal type; acquiring synchronization abnormal root cause data corresponding to the target synchronization abnormal type in the satellite navigation platform, evaluating the data fitting degree between a historical contrast abnormal synchronization record corresponding to an abnormal root cause in the synchronization abnormal root cause data and abnormal satellite navigation synchronization data, and obtaining a target abnormal root cause; acquiring a preset abnormal solution of the target abnormal root cause from the satellite navigation platform, sending the abnormal solution to a staff member through the satellite navigation platform, and managing satellite navigation data synchronization.

The invention discloses a polarization / hypersensitivity / inertial navigation tight combination modeling and adaptive fusion method, and belongs to the technical field of bionic polarization integrated navigation. According to the method, polarized light intensity measured by a polarization sensor and sun image coordinates measured by a solar sensor are used as measurement and are subjected to tight combination modeling with an inertial measurement unit, and an expansion state equation including incident light intensity and degree of polarization and a nonlinear measurement equation are established. A self-adaptive fusion strategy is designed based on a dual credibility discrimination function of a polarization degree and a distance between a sun image coordinate and a sun-sensitive principal point coordinate, and a measurement mode of a polarization sensor, a sun-sensitive sensor or a combination of the polarization sensor and the sun-sensitive principal point is dynamically selected. And estimating a state quantity in real time through unscented Kalman filtering, and correcting a navigation error. The original light intensity information measured by the polarization sensor and the original sun image coordinate information measured by the solar sensor are directly used for measurement, so that the nonlinear transfer effect is inhibited. According to the invention, the orientation precision and environmental adaptability of the integrated navigation system in a complex environment are improved.

The invention discloses an inertial navigation parameter real-time estimation and updating method, system, equipment and medium for monitoring sag and windage yaw states of a power transmission line conductor, and belongs to the technical field of three-dimensional state monitoring of power transmission lines, and the inertial navigation parameter real-time estimation and updating method comprises the following steps: collecting inertial observation data of the power transmission line conductor; constructing an error state model, fusing the inertial observation data and the first auxiliary observation data by adopting a first filtering estimator, and performing real-time estimation of an error state; performing closed-loop feedback correction based on the error state; calculating sag parameters and windage yaw parameters of the transmission line conductor according to the navigation state; and performing time sequence analysis, and outputting a safety state monitoring result of the power transmission line conductor. By establishing the error state model, online estimation and real-time compensation of the navigation error and the zero offset of the sensor are realized, the sag and windage yaw parameters of the power transmission line conductor are calculated based on the corrected high-precision navigation, and the power transmission line conductor three-dimensional state real-time monitoring method suitable for the complex environment is formed.