170 results about "Navigation error" patented technology

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

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

The invention provides a vehicle cluster tunnel access positioning method based on intelligent cooperation and a vehicle-mounted terminal, relates to the technical field of intelligent driving control, and solves the problems of positioning interruption, inertial navigation error accumulation and insufficient cooperative positioning reliability caused by satellite signal failure of a vehicle cluster in a tunnel scene. The method comprises the following steps: judging whether each vehicle enters or exits from a tunnel area, when part of vehicles enter a tunnel, dynamically selecting a reference node from vehicles with effective satellite signals outside the tunnel, and broadcasting original observation values of the satellite signals; establishing a relative position constraint model of a vehicle cluster, acquiring relative position data of adjacent vehicles in real time among the vehicles, and performing dynamic weighted fusion on observation values, IMU data and relative position data of the vehicles with effective satellite signals by adopting a combined filtering algorithm; the satellite signal failure vehicle performs positioning calculation by combining the broadcast observation value with the IMU data and the relative position constraint model; and pre-capturing satellite signal loop parameters when driving out of the tunnel.

The invention provides an oral implant navigation method and system and a storage medium, after an optical locator is fixedly arranged on an implant mobile phone, three-dimensional data of a rotating shaft of the implant mobile phone, the tail end of the rotating shaft and a spherical drill on an optical three-dimensional coordinate system of the optical locator are obtained, and a registration pit is used as a medium to obtain a three-dimensional coordinate system of the implant mobile phone; and positioning three-dimensional data of the three-dimensional coordinate system in the optical three-dimensional coordinate system, registering the three-dimensional data of the pits on the CT three-dimensional coordinate system to complete registration, calculating the pose of the tip of the drill point and the pose of the ideal path under the positioning three-dimensional coordinate system, and performing real-time navigation on the planted mobile phone according to the difference between the tip of the drill point and the ideal path. An optical positioning instrument is fixedly arranged on the planting mobile phone, and while the planting mobile phone is used for drilling operation, image data of the optical identification points are collected through the optical positioning instrument for real-time navigation; the optical positioning instrument and the planting mobile phone are integrally connected, so that the process of positioning the planting mobile phone in real time can be omitted, and navigation errors are effectively reduced.

The invention discloses a deep and far sea unmanned aerial vehicle monitoring and management platform based on multi-sensor fusion, and relates to the technical field of unmanned aerial vehicle marine monitoring. The intelligent analysis module is used for receiving the fusion data after time-space alignment and the real-time ocean current data, optimizing the moving path of the unmanned aerial vehicle according to the received data, evaluating the resource development possibility, obtaining an analysis result and transmitting the analysis result; an anti-interference communication module; an energy management module; and dynamically adjusting an energy distribution strategy. The module combines the task priority weight and the environment energy consumption weight, optimizes the energy use efficiency, prolongs the endurance time of the unmanned aerial vehicle, and ensures the smooth completion of the task. Particularly in a complex marine environment, navigation errors are corrected in real time through a quantum navigation error compensation model, the positioning precision is improved, and the moving path of the unmanned aerial vehicle is optimized in combination with real-time ocean current data and an ocean flow field cost function.

The invention discloses a method and system for calibrating an inertial sensor by a Doppler log, and relates to the technical field of underwater integrated navigation, and the method comprises the following steps: obtaining an IMU pre-integration factor and a DVL speed residual factor according to IMU observation data and DVL observation data; the method comprises the following steps: constructing a factor graph structure model by taking prior information of initial states of a gyroscope and an accelerometer as prior factors, taking the prior factors, an IMU pre-integration factor and a DVL speed residual factor as factor nodes, taking state variables of the gyroscope and the accelerometer as variable nodes and taking a relationship between the factor nodes and the variable nodes as edges; and correcting the state variables by using the factor graph structure model to obtain theoretical outputs of the gyroscope and the accelerometer after inertial navigation error compensation. Observed DVL data are used for carrying out online calibration on IMU errors, an underwater integrated navigation problem is modeled as nonlinear least square optimization, global optimal estimation of all-time state quantities is achieved, and the divergence speed of inertial navigation position errors is restrained.

The invention discloses a factor graph underwater integrated navigation method based on adaptive windows and factors. The factor graph underwater integrated navigation method is suitable for an underwater integrated navigation system consisting of an inertial measurement unit (IMU), a Doppler velocimeter (DVL) and an ultra-short baseline positioning system (USBL). The method comprises the following steps: 1, constructing DVL and USBL information into factor nodes, constructing IMU pre-integration factors through pre-integration, and constructing a factor graph model; 2, improving smooth estimation of the system through a sliding window, dynamically adjusting the factor node weight by using a weighting function, and correcting a navigation error; and 4, clustering the navigation data residual error sequence by adopting a DBSCAN algorithm, analyzing a clustering result, adjusting the size of a window, and performing marginalization operation on historical information to reserve prior. According to the method, through adaptive weight adjustment of sensor factor nodes and dynamic optimization of a sliding window, the interference of sensor abnormal data in an underwater complex environment is effectively inhibited, the balance between positioning precision and real-time performance is realized, and the method is suitable for the high-precision navigation requirement of an autonomous underwater vehicle.

The invention discloses a construction site intelligent navigation traffic control system, and relates to the field of construction site traffic control, and the system comprises a data collection module which is used for collecting construction site traffic environment data in real time through employing an Internet of Things sensor, an unmanned plane, a high-definition camera and laser radar equipment; comprising vehicle access flow, motion trails of construction equipment, construction area division and road conditions; according to the invention, the map precision can be improved, navigation errors caused by map errors can be effectively avoided, dependence on a high-performance server is reduced, the overall deployment cost is reduced, popularization and application in a construction site environment with limited resources are facilitated, efficient storage and transmission are realized, and the influence of emergency situations on traffic control is effectively reduced; the intelligent level of traffic signal regulation and control can be improved, traffic resource waste or congestion caused by traditional fixed-time-length signals is avoided, the uncertainty of the decision making process is reduced, the traffic accident rate is effectively reduced, and the overall traffic operation efficiency of a construction site is improved.

The invention discloses a hinged wheel type pipeline robot positioning method based on multi-sensor fusion ESKF, which takes the ESKF as a core, predicts the nominal state of an IMU through a double-subsample INS mechanical arrangement method, introduces an adaptive slip detection method based on IMU kinematics consistency constraint to perform reliability verification on multi-encoder observation, and finally determines the position of the hinged wheel type pipeline robot. Speed observation fusion is realized; meanwhile, heading updating of the magnetometer is triggered through magnetic field stability detection, and long-term drift is restrained; a pseudo measurement constraint model is established based on pipeline environment characteristics, and accumulative errors are systematically inhibited; and after feedback compensation is carried out on IMU errors and navigation errors through ESKF measurement updating, position, speed and attitude navigation data are output in real time. And finally, performing global posterior optimization on the real-time filtering trajectory by adopting an RTS reverse smoothing algorithm, thereby effectively solving the problems of low positioning precision and insufficient adaptability of a multi-section hinge wheel type pipeline robot structure in a complex underground pipe network.

The invention relates to a disassembly-free online self-calibration double-shaft laser gyroscope strapdown inertial navigation implementation method, which designs a modulation working mode based on double-shaft rotation, according to the design, a double-shaft rotating mechanism is additionally arranged on an inertial navigation unit, and the inertial navigation unit respectively rotates around three gyroscope sensitive shafts by utilizing an outer horizontal shaft and an inner azimuth shaft; exciting a navigation error generated by a scale factor error and an installation error of the laser gyroscope; sensitive shafts of the three accelerometers are enabled to point to the sky respectively, and navigation errors generated by scale factor errors, installation errors and zero positions of the accelerometers are excited; and then obtaining the navigation errors by taking external reference information as a benchmark, establishing an error relation model between the navigation errors and parameters needing to be calibrated, and completing estimation of each calibration parameter by adopting a Kalman filtering algorithm, so as to realize disassembly-free online self-calibration of the double-shaft laser gyroscope strapdown inertial navigation.

The invention discloses an inertial navigation error compensation method and system based on gravity assistance, and belongs to the field of navigation. The inertial navigation error compensation method is suitable for long-time indoor inspection of the robot. Firstly, channel state information, odometer data and inertial measurement data of a robot are obtained; calculating an initial attitude through angular velocity integration, and calibrating a pitch angle and a roll angle by using the separated gravity vector; calculating a motion increment based on odometer data, and performing coordinate conversion in combination with the calibration attitude to obtain navigation position information; inputting the channel state information into a neural network to obtain a channel fingerprint, and matching target position information; and finally, calculating the cross covariance between the navigation position and the target position to obtain a state correction value, and compensating the navigation position and course angle error. According to the invention, when inertial navigation error compensation is carried out on the robot for long-time indoor inspection, the compensation accuracy and effect can be improved.

The invention discloses a vision and inertial navigation integrated navigation method, system and equipment in a weak vision environment and a medium, and belongs to the technical field of navigation, by fusing an inertial measurement unit and landmark focal plane projection data, the navigation precision of the system under the adverse conditions of low texture, dynamic shielding and the like is remarkably improved, and the navigation continuity can be maintained; according to the method, an indirect Kalman filter is used for carrying out feedback correction on initial navigation parameters, noise interference and inertial navigation accumulative errors are effectively inhibited, navigation errors are remarkably reduced for state estimation, and the robustness and adaptability of the system are enhanced; the method does not depend on complete visual resolving, landmark projection coordinates are directly used, dependence on visual information integrity is reduced, and the system can stably operate even if only a small number of landmarks or a single landmark is visible.

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 relates to the technical field of electric power facility monitoring, and discloses an electric power line detection system based on a three-dimensional inertial gyroscope and deep learning, which comprises a data acquisition module, a segmented preprocessing module, a trajectory resolving module, a trajectory correction module, a deviation analysis module and a risk early warning module. According to the application, the electromagnetic shielding and common-mode noise suppression technology is adopted to overcome high-voltage electromagnetic field interference, and the data acquisition quality is ensured; precise adaptation of different line features is realized through segmented adaptive preprocessing; inertial navigation error accumulation is effectively inhibited by utilizing topological constraint and multi-source data fusion; the problem of deviation identification under the small sample condition is solved by combining an attention mechanism and transfer learning; and finally, intelligent risk assessment and early warning are realized through deep reinforcement learning, so that whole-process intelligent processing from original data to risk assessment is realized, and the precision and efficiency of power line detection are improved.

The invention provides an intelligent hospital guide system and method based on AR vision and voice navigation. The intelligent hospital guide system comprises a user interaction layer, a technical support layer, a data and background layer, a third-party service layer and a hardware dependence layer. According to the intelligent hospital guide system based on AR vision and voice navigation, accurate navigation is achieved, a barrier-free road is guided, through the advanced AR vision navigation technology, the APP provides an accurate and errorless navigation path for a patient, the possibility of navigation errors is greatly reduced, virtuality and reality are seamlessly fused, and the intelligent hospital guide system based on AR vision and voice navigation has the advantages of being high in practicability and the like. The patient can walk on a familiar map in a hospital, and each step is full of confidence; the voice navigation function brings unprecedented convenience to the user, and especially for the visual impairment, the old or the users with inconvenience in hands, the service is as an intimate guide to lead the visual impairment, the old or the users with inconvenience in hands to easily shuttle to all corners of a hospital and enjoy barrier-free medical experience.

The invention provides a polarization / inertia / vision intelligent navigation method based on model error learning, which belongs to the field of navigation and comprises the following steps: modeling a polarization / inertia / vision integrated navigation system, and establishing a system state equation by taking an inertial navigation error and a camera pose error as system state quantities; establishing a system measurement equation based on the information of the polarization sensor and the visual sensor; a neural network learning modeling error is constructed by considering uncertainty modeling errors of a system state equation and a measurement equation caused by system parameter errors and environmental interference, and the precision of a polarization / inertia / vision integrated navigation system model is improved; the multi-state constraint Kalman filtering method of the embedded neural network is established for solving the problems that in an actual environment, sensor noise statistical characteristics are unknown and time-varying due to environmental factors. According to the invention, the navigation precision of the polarization / inertia / vision integrated navigation system in a complex environment can be improved.

The invention provides a distributed inertia-based relative navigation method based on a data link, and relates to the technical field of navigation. Firstly, a relative navigation error state equation based on speed error reconstruction is adopted, and a specific force term of high-frequency change is eliminated, so that the degree of dependence on inertial navigation data sharing between nodes is reduced; secondly, by designing and calculating an efficient information fusion method, the calculation complexity of the whole method is reduced; and finally, designing equivalent GNSS measurement for rapidly diffusing high-precision absolute navigation information. According to the invention, two core functions of accurate estimation of the relative state of the cluster node and rapid coverage of absolute information can be realized; through a simulation experiment, high-precision group inertial navigation error state propagation can be realized by sharing own inertial navigation data by cluster nodes at second-level intervals.

The invention particularly relates to an inertial navigation / radar / satellite navigation three-combination emitter integrated navigation method based on Lie group, which comprises the following steps: responding to a navigation control instruction, and configuring a navigation system of an aircraft to enter an integrated navigation state; constructing a special European group based on the attitude features, the speed features and the position features, and establishing a motion description model based on the description of the special European group; acquiring navigation information of an inertial navigation system, and resolving the navigation information by using an error model under a special European group to obtain a state matrix; obtaining current measurement information; wherein the measurement information comprises satellite navigation subsystem measurement information and / or radar navigation subsystem measurement information; based on the measurement information, establishing an observation vector and a measurement equation of a corresponding subsystem under a special European group; resolving the state matrix, the measurement equation and the observation vector by using a Kalman filter to obtain navigation error estimation data; and performing feedback correction on the navigation error based on the navigation error estimation data.

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 invention relates to a positioning method and system based on BDS and INS mutual feedback closed-loop correction, and the method comprises the steps: continuously collecting Beidou satellite positioning data for multiple times based on a Beidou satellite navigation system BDS, and determining an inertial navigation initial position through weighted average; in combination with inertial navigation system (INS) and Beidou satellite positioning (BDS) data, updating to obtain a high-precision position through combined filtering model dynamic error correction and vector average compensation, and taking the high-precision position as a navigation starting point; in the effective area of the Beidou satellite, an inertial navigation system (INS) navigates from a navigation starting point; in a Beidou satellite failure area, inertial navigation error compensation is performed based on an error vector of a position output by an inertial navigation system (INS) at a failure moment and a final high-precision position before the failure moment. Compared with the prior art, the method has the advantages that error accumulation of an inertial navigation system can be avoided, and high positioning precision can be kept when Beidou satellite signals are lost.

The invention discloses an inertial navigation error magnitude prediction method based on a neural network, and belongs to the technical field of inertial navigation. The method comprises the following steps: constructing a feedforward BP neural network introducing time input, taking a standard deviation of an inertial navigation error factor and a carrier motion state as input, and taking an east, north and day three-direction position error standard deviation as output; adopting a Monte Carlo method to generate a high-fidelity training data set based on the plurality of reference trajectories; and a Levenberg-Marquardt algorithm is used to train the network, and accurate fitting of a complex error relation is realized. According to the method, the inertial navigation position error magnitude can be rapidly predicted on line, key error information is provided for adaptive Kalman filtering, and the method has high engineering application value.

The application provides a carrier motion excitation micro-inertial navigation error parameter identification and prediction method, which effectively combines micro-inertial navigation use data and calibration data, compensates device errors according to use environment and motion conditions, fully utilizes historical data, improves micro-inertial navigation key error parameter compensation precision, and further improves micro-inertial navigation performance; meanwhile, the application also introduces the influence of motion excitation on error parameter compensation precision, avoids data pollution of calibration results caused by failure to excite errors; finally, the learning and prediction method of micro-inertial navigation error parameter change trend provided by the application can reduce device errors caused by use life growth, and prolong the service life of the micro-inertial navigation.

The invention discloses a low-altitude aircraft navigation error compensation system and method based on digital twinning, and relates to the field of unmanned aerial vehicle navigation, the system is applied to an unmanned aerial vehicle which flies according to the same predetermined route to execute visual inspection every time, and the system comprises a pickup module used for receiving image data collected by a camera carried by the unmanned aerial vehicle in real time, and sending the image data to a communication module; picking up a key image in the image data; the labeling module is used for labeling key points in the key images picked up by the picking module and distances from the key points to the unmanned aerial vehicle position information at the key image source time; according to the invention, during the flight of the unmanned aerial vehicle, key images can be intelligently picked up and position information can be marked, the yaw degree of the unmanned aerial vehicle can be accurately judged by analyzing the corresponding key images in different periods, whether the unmanned aerial vehicle needs to be coordinated or not can be decided through unique image analysis logic, and then the flight of the unmanned aerial vehicle can be regulated and controlled.

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 application provides a space domain deviation processing device, method and storage medium, and belongs to the technical field of radio positioning and navigation. The space domain deviation processing device comprises: an acquisition unit that acquires inertial measurement data and space domain observation data; a modeling unit that constructs an augmented error state vector comprising a carrier navigation error state and a space domain deviation state; a reference construction unit that recursively constructs a nominal pose of the carrier based on the inertial measurement data and updates an error covariance matrix; a residual calculation unit that constructs a residual based on the nominal pose, the space domain deviation state and an observation model; and an updating unit that corrects and updates the space domain deviation state according to the residual and the error covariance matrix. The space domain deviation processing device provided by the application can reduce the influence of the space domain deviation state on the carrier pose state, reduce the navigation drift problem caused by antenna installation deviation or multipath interference, and improve the positioning and pose stability.

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 invention relates to a biaxial rotation inertial navigation error modulation parameter selection method based on an error propagation equation, and belongs to the technical field of inertial navigation, and the method comprises the following steps: S1, constructing a biaxial rotation inertial navigation error propagation equation considering an error modulation effect; s2, setting a transposition sequence of the biaxial rotation inertial navigation; s3, based on the error propagation equation constructed in the step S1 and the transposition sequence set in the step S2, obtaining navigation errors at different rotation speeds and navigation errors at different rotation stopping time; and S4, determining the optimal rotation speed and rotation stop time based on the navigation errors at different rotation speeds and the navigation errors at different rotation stop time obtained in the step S3. The method improves the navigation precision of double-shaft rotation inertial navigation, is simple to operate, low in time consumption, low in cost, good in selection effect, high in navigation precision and good in practicability, and solves the problems of long time consumption, high cost, poor selection effect and low navigation precision of the existing empirical method.

The invention relates to the technical field of route navigation, in particular to an underground space AR (Augmented Reality) navigation system based on a visual identification feature point technology, which comprises a spatial modeling and data processing module, an identification system management module, a client application module, a real-time positioning module, a navigation engine module and a dynamic data management module. According to the method, aiming at underground multi-layer space design, a centimeter-level three-dimensional base drawing is pre-constructed by utilizing a completed BIM / CAD drawing, a three-dimensional anchor point is formed through an anti-shielding code mark pasted on the surface of a fixed structure such as a beam column, accurate distinguishing and positioning between an upper floor and a lower floor can be realized under a complete off-line condition, and when traditional GPS and Bluetooth signals fail, the positioning accuracy of the upper floor and the lower floor can be improved. The system can still complete cross or triangular positioning by visually identifying the anchor points, and the problem of navigation errors caused by unclear upstairs and downstairs division is solved.