6839 results about "Position fixing" 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 belongs to the technical field of path planning, and discloses an unmanned driving dynamic path planning method and system based on multi-source data fusion, and the method comprises the steps: collecting an ice and snow pavement friction coefficient, a curve curvature and an obstacle point cloud, constructing a sensor confidence coefficient matrix, generating a fused semantic map, and constructing a dynamic environment semantic model. Outputting a real-time friction coefficient field and a risk thermodynamic map layer; the roadside unit broadcasts coordinates of opposite vehicles in a blind area of a curve to a vehicle end, constructs an ice and snow pavement offset crowdsourcing map, and generates a global-local fusion topology; fusing the real-time friction coefficient field and the global-local fusion topology to generate a smooth trajectory set, and further generating a risk optimal path instruction set; a steering angle and torque instruction is decomposed, positioning drift is compensated in real time, and a normal mode for updating the vehicle positioning state and a degradation mode when the millimeter wave radar fails are constructed; and generating execution logs and health state vectors, and aggregating the execution logs and the health state vectors of multiple vehicles to form closed-loop iterative update.

The invention discloses a fusion optimization method based on Kalman filtering and LSTM cascade. The fusion optimization method comprises three steps of dynamic state estimation, time sequence error modeling and closed-loop fusion optimization. IMU (Inertial Measurement Unit) data is used as input, dynamic state estimation is realized through Kalman filtering, modeling system errors are separated, an LSTM (Long Short Term Memory) network is used for carrying out time sequence modeling on a residual error sequence to capture nonlinear errors, and finally, a corrected state quantity is fed back to a Kalman filtering updating link through a closed-loop feedback mechanism. And collaborative optimization of error compensation and state estimation is realized. According to the method, IMU error accumulation is effectively inhibited through a dynamic-data dual-drive mechanism, the navigation precision and robustness in a complex scene are remarkably improved, and the requirements of high-dynamic applications such as intelligent driving and unmanned aerial vehicle navigation can be met. Finally, the optimized IMU data and the GNSS observation value are fused for integrated navigation, high-precision navigation solution is achieved through Kalman filtering, and indoor and outdoor seamless positioning and the all-attitude control requirement of a high-dynamic carrier are met.

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

The invention discloses a high-precision topographic surveying and mapping system and method based on an unmanned aerial vehicle, and particularly relates to the technical field of unmanned aerial vehicle surveying and mapping, and the method comprises the following steps: carrying out the topographic modeling before a task, recognizing GNSS shielding, and dividing a navigation region; gNSS, INS and visual inertial navigation positioning information are fused in flight, the weight is dynamically adjusted, and historical trajectory constraint is combined; constructing a closed-loop detection mechanism to carry out error feedback and feedforward prediction; confidence is calculated according to image definition, IMU stability and time synchronization deviation, high confidence points are screened for DEM reconstruction, local heavy flight is triggered by low confidence points, and surveying and mapping precision and reliability are improved; according to the method, the autonomous navigation and positioning precision of the unmanned aerial vehicle in a complex environment is improved, the quality control of surveying and mapping data and the DEM reconstruction reliability are enhanced, the closed-loop self-correction and feed-forward error prediction capabilities are provided, and the stability, precision and intelligent level of the system are remarkably improved.

The present application relates to PPP-RTK, RTK, SSR, or like correction data based devices and methods to produce geolocation solutions with improved accuracy. Further, such devices and methods may be further employed in various related utility locator devices, utility locating transmitters, and other utility locating and mapping devices.

The invention discloses an ROV positioning navigation system based on vision inertial navigation fusion and pose correction, which comprises an inertial measurement value and image preprocessing module loaded on an ROV, the inertial measurement value and image preprocessing module is connected with a vision and inertial navigation data fusion module through a wire, and the vision and inertial navigation data fusion module is connected with a pose correction module. The invention also discloses a positioning navigation method of the ROV positioning navigation system based on visual inertial navigation fusion and pose correction. According to the method, the inertial data provided by the IMU and the environment characteristic information captured by the visual sensor are fused, high-precision estimation of the position and attitude of the ROV can be realized, the sensor data are optimally fused by using the MSCKF algorithm, the influence of the error of a single sensor on the positioning result is reduced, and the positioning precision and reliability of underwater operation are remarkably improved.

The invention discloses a vehicle positioning method and system based on UWB and vehicle constraint, and relates to the field of vehicle positioning, and the method comprises the steps: deploying a UWB reference station in a well mining environment, and enabling a vehicle to be provided with a UWB tag; obtaining distance measurement data of at least two UWB reference stations, and detecting and compensating the distance measurement data to obtain a vehicle UWB position; collecting vehicle-mounted IMU (Inertial Measurement Unit) data, and obtaining a vehicle attitude quaternion through attitude calculation; collecting vehicle wheel speed meter data, and correcting the vehicle wheel speed meter data according to vehicle motion constraint conditions; according to the vehicle UWB position, the vehicle attitude quaternion and the corrected vehicle wheel speed meter data, the vehicle position is obtained through an adaptive Kalman filtering fusion algorithm. Aiming at low positioning precision caused by easy accumulation of errors and drifting of a traditional inertial navigation system in a well mine closed space in the prior art, the invention reduces the influence of a multipath effect in the traditional inertial navigation system on the positioning precision.

The invention discloses a navigation matching correction method based on an inspection robot, and the method comprises the steps: building a feature database through the deployment of a physical calibration object and the recognition of a natural feature object, providing a reliable positioning reference for a robot, achieving the coarse positioning through the fusion of visual and laser radar data in a positioning process, and introducing a dynamic credibility evaluation mechanism. The positioning reliability is quantified in real time through an exponential decay model, when the credibility is lower than a threshold value, the system automatically triggers a compensation behavior to re-search features, in the aspect of multi-robot cooperation, secondary positioning correction is achieved through track matching and data fusion, high-confidence-coefficient reference data are screened through a clustering algorithm, the group positioning precision is improved, and the positioning accuracy is improved. Aiming at a key inspection area, multi-angle image matching is adopted to realize fine positioning, a positioning error is dynamically corrected through a sliding window, the continuity and accuracy of robot navigation in a complex environment are remarkably improved through closed-loop correction and self-adaptive optimization, and the method is suitable for intelligent inspection requirements of railway trains.

Three-dimensional outdoor positioning can be considerably enhanced by using various 3D imaging techniques, LiDAR ranging measurements of surrounding landmarks, and Inertial Measurement Units (IMUs) reaching accuracies in the centimetres range. These technologies, in conjunction with global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou, provide autonomous geo positioning. It is crucial to acknowledge the constraints of dead reckoning, which may result in mistakes in positional precision. Integrating modern imaging and correction techniques can significantly improve the accuracy and dependability of outdoor localisation. The amalgamation of various 3D imaging methodologies with LiDAR enhancements improves the comprehension and ranging of the environment, tackling the difficulties presented by GNSS constraints. The integration of IMUs with satellite systems ensures the provision of continuous traveling positioning data, even in instances of obscured or faulty satellite signals. This comprehensive approach improves outdoor location precision and expands the use of these technologies across other domains, such as autonomous navigation, robotics, and geospatial analysis, resulting in more effective and dependable positioning solutions. The hardware system consists of a swarm of sensors, including GNSS, INS, IMU, LiDAR, stereo imaging, and RGB technologies, which are combined to generate three-dimensional colored representations of natural landscapes and urban environments. This system reconstructs topographical features, enabling detailed mapping and analysis. It produces comprehensive three- dimensional sensory data, including geo-location and POI identification, and incorporates advanced image processing techniques, such as edge detection, for precise spatial awareness and environmental understanding. The system is designed with a compact form factor, making it easily integrated into wearable devices and autonomous vehicles. The integration of these technologies forms a sophisticated network of swarm intelligence, crucial for autonomous vehicle positioning and navigation. Particle swarm optimization (PSO) is used to address non-deterministic navigation, with different coupling fusion methods available. The Neuro-fuzzy PSO AI Positioning and Navigation System generates three-dimensional sensory data, providing detailed information about geographical distribution and dead reckoning areas.

The invention discloses a polling path dynamic planning method, medium and equipment, and the method comprises the steps: obtaining environment information in real time, dynamically switching an RTK-GPS mode, a visual inertial navigation mode and a laser SLAM positioning mode, generating the current positioning information of a robot in combination with a global map, and constructing a local environment map; and planning an adaptive second preset inspection path based on the first preset inspection path. Environmental feature information is actively collected in an outdoor environment, a real-time obstacle avoidance path is generated by dynamically updating a local environment map, and the real-time obstacle avoidance path is fused to a second preset inspection path, so that smooth switching and dynamic obstacle avoidance cooperation of a positioning mode in an indoor and outdoor scene are realized; the problem of planning failure caused by multiple positioning source conflicts, local path oscillation and environment sudden change in a complex scene is solved, and the continuity of the inspection process and the path planning robustness are guaranteed.

The invention discloses a modular collaborative layout method for gyroscopes and accelerometers, relates to the technical field of inertial sensing, and solves the problem of accumulative errors of gyroscope drift and accelerometer zero offset drift in a complex geological environment under a mine, and by calculating a drift change rate Cpj and a zero offset change rate Azp in real time and executing error compensation, the gyroscope drift rate Cpj and the accelerometer zero offset change rate Azp are calculated in real time, and the accelerometer drift rate Cpj and the accelerometer zero offset change rate Azp are calculated in real time. The positioning and path tracking precision of the equipment under the condition of no GPS is improved; meanwhile, aiming at external interference factors such as vibration, noise and air flow in the mine, a data fusion strategy is adjusted in real time by calculating a dynamic interference adaptation index Xw and comparing the dynamic interference adaptation index Xw with a threshold value, and the influence of interference on attitude estimation is effectively reduced; besides, according to the scheme, sensor data are dynamically adjusted through a temperature compensation and humidity correction mechanism, the influence of temperature and humidity changes on the gyroscope and the accelerometer is compensated, the stability and precision of the system in the extreme environment are ensured, and the adaptability and reliability of the mine environment are enhanced.

The invention relates to the technical field of calibration test, and particularly discloses a strapdown inertial navigation automatic calibration test method of a temperature control biaxial rate position turntable. Comprising the following steps: S1, multi-dimensional error modeling and layered calibration architecture: constructing a comprehensive error model containing a sensor scale factor error, an installation non-orthogonal error, a zero offset error, a cross axis coupling error and a temperature related error; s2, a wide temperature range self-adaptive compensation mechanism: carrying out heat preservation at a preset temperature point and executing turntable positioning and rate movement; s3, performing automatic data processing and parameter estimation; and S4, performing real-time error correction and dynamic verification: performing real-time error correction based on the estimated parameters, and evaluating navigation performance indexes such as position, speed and attitude precision. Through technical innovation, high-precision, automatic and wide-temperature-range calibration of the strapdown inertial navigation system is realized, and the method is suitable for aerospace, intelligent equipment and other scenes with strict navigation precision requirements, and has remarkable engineering application value and market competitiveness.

The invention discloses a multi-mode adaptive fusion navigation positioning method and system in a satellite navigation denial environment, the system adopts GNSS / IMU / visual sensors, and the method comprises the following steps: receiving data of the three sensors and completing modeling; initializing the three sensors; judging the strength of the GNSS signal by utilizing chi-square test; optimization variables are set, a residual matrix is constructed, and nonlinear optimization is carried out; setting a GNSS trust factor when the GNSS signal is weak or denial, and calculating a weight; and weighting and fusing sensor data, outputting optimal state estimation of the system, and realizing continuous navigation and positioning. According to the method, a self-adaptive weight adjustment mechanism is established, GNSS trust factors are introduced, and the weights of multi-sensor data are adjusted in a self-adaptive manner, so that the positioning difficulty under the conditions that the GNSS is subjected to deception interference, suppression interference, building blocking and the like can be overcome, and continuous navigation positioning of an unmanned system (such as a robot, an unmanned vehicle, an unmanned aerial vehicle, an unmanned ship, an unmanned underwater vehicle and the like) is realized.

The invention discloses an unmanned aerial vehicle navigation positioning method and system based on big data, and the method comprises the steps: obtaining the position data, sensor data and environment data of an unmanned aerial vehicle, constructing a real-time map according to the position data and the sensor data through employing an SLAM algorithm, and carrying out the real-time positioning; carrying out sub-region division on the real-time map, and obtaining a risk score by adopting clustering analysis; obtaining path time between the sub-regions according to the environment data and the risk score, establishing a fitness function according to the risk score and the path time, and establishing a path planning model based on the fitness function; obtaining offset data according to the sensor data, establishing a path compensation strategy according to the offset data, and performing path correction; learning the path compensation strategy and the path planning model to obtain an unmanned aerial vehicle navigation model, and optimizing the unmanned aerial vehicle navigation model through a sine and cosine optimization algorithm. The method is suitable for various unmanned aerial vehicle application scenes, and wide application of the unmanned aerial vehicle technology in a complex environment is promoted.

The invention discloses a safety device for reducing the back-up collision risk in the automatic driving test process, and belongs to the technical field of automatic driving test safety. Aiming at the collision risk problem caused by rapid movement of dynamic obstacles and communication delay when a vehicle backs up in a closed test site, the device obtains millimeter-level position coordinates and course angles through positioning units deployed on a test vehicle and a target object; a C-V2X protocol is utilized to construct a real-time data link to share vehicle position, speed, backing track prediction data and target object outer contour feature point coordinates, potential conflicts are identified based on future minimum spacing calculation and dynamic threshold, and a cooperative collision avoidance path is generated in combination with a distributed optimization algorithm. And the high-risk vehicle preferentially preempts a communication channel to issue deceleration, course adjustment and braking instructions to surrounding vehicles. The device can effectively improve the reversing safety in a multi-vehicle cooperative test scene, and is suitable for collision risk real-time early warning and active intervention of an automatic driving vehicle closed test site.

The invention provides a high-precision perception-driven automatic driving positioning method and system, and relates to the technical field of automatic driving, and the method comprises the steps: collecting vehicle environment data in real time through a multi-mode sensor array, and generating a multi-source perception data set; performing time-space synchronization processing; performing motion distortion correction on the fused sensing data stream to generate dynamic compensation sensing data; inputting a multi-source fusion positioning model, and performing three-dimensional space matching in combination with high-precision map data to obtain real-time positioning coordinates; and performing iterative optimization on the real-time positioning coordinates through an adaptive error correction module to generate a vehicle positioning result. According to the method and the device, the technical problem that the positioning precision is affected due to data distortion caused by the influence of the vehicle motion on the sensor data is solved, the distortion caused by the vehicle motion is eliminated through motion distortion correction and compensation, and the positioning precision and stability are improved.

The invention provides an unmanned ship charging cable control method based on visual identification, and relates to the technical field of data processing, and the method comprises the steps: collecting continuous image frames of an unmanned ship charging area, identifying the spatial displacement and inclination angle change of an unmanned ship, analyzing the attitude offset of the unmanned ship caused by sea waves, calculating a water surface disturbance factor, and calculating the water surface disturbance factor. The method comprises the following steps: identifying the position of a charging interface, obtaining an initial positioning coordinate, carrying out prediction analysis on a disturbance trend in a preset time window, predicting a position change range of the charging interface, obtaining prediction coordinate data, planning a butt joint path of a cable and the charging interface, obtaining a compensation path sequence, and generating a cable propulsion instruction. And collecting an area image of the charging interface to obtain path tracking image data, judging whether the butt joint of the charging interface is successful or not, and if not, dynamically correcting the compensation path sequence. According to the invention, the cable is controlled through visual identification to charge the unmanned ship.

A LiDAR and inertial navigation integrating positioning method and system, a storage medium, and an electronic device. The method comprises: according to a first position and orientation state of a vehicle, predicting a second position and orientation state of the vehicle, the first position and orientation state being used for indicating the corrected position and orientation state of the vehicle in a first cycle, the second position and orientation state being used for indicating a predicted position and orientation state of the vehicle in a second cycle, and the first cycle being one cycle ahead of the second cycle (310); observing the vehicle in a local point cloud map and in a vector map to construct observation residuals (320); and, according to the observation residuals, correcting the second position and orientation state to obtain a third position and orientation state (330). The method introduces the observation residual in the local point cloud map and the observation residual in the vector map, corrects the position and orientation state in the current cycle according to the observation residuals and, in the case of the minimum observation residuals, obtains the most accurate corrected position and orientation state in the current cycle, effectively improving the precision of LiDAR and inertial navigation integrating positioning.

The invention provides a road marking quality intelligent evaluation system based on image analysis, and relates to the technical field of road facility monitoring, and the system comprises a detection unit, a marking quality evaluation unit, a marking wear prediction unit, a GPS positioning unit, a vehicle driving information unit, a control unit and a remote central control unit. The marking quality evaluation unit is constructed based on a differential geometry theory and comprises a curvature flow edge detection module, a marking geometric characteristic manifold representation module and a multi-scale differential invariant evaluation module, the system regards a marking as a two-dimensional manifold, and the marking quality is evaluated by calculating differential geometric quantities such as a Gaussian curvature, an average curvature and a shape index. And constructing geodesic distance measurement on the feature manifold, and evaluating the completeness, visibility and reflective performance of the marked line. And the marking wear prediction unit predicts the service life of the marking based on the traffic flow information and the environment characteristic mapping relation model, and generates a maintenance suggestion.

The invention discloses a method for determining the distance between blasting unit groups for a surface mine, a rapid adjusting device and a blasting operation system. The determination method comprises the following steps: acquiring multi-dimensional data through a geological radar, a sensor and the like, and constructing a model through normalization processing; a finite element-discrete element coupling algorithm is adopted to carry out three-dimensional geology-blasting coupling modeling, an improved NSGA-III algorithm is combined to solve a multi-objective function to determine an optimal interval, and Kalman filtering is utilized to realize real-time feedback correction. The rapid adjusting device comprises a multi-angle adjusting module, an intelligent navigation module and a cooperative control module, and accurate positioning and rapid adjusting can be achieved. According to the method, a modeling-calculating-adjusting-feedback full-process automatic system is formed, and compared with a traditional technology, the lumpiness qualification rate can be increased by 25% or above, the interval adjusting time is shortened to be within 15 minutes, and the mine blasting efficiency and safety are effectively improved.

The invention belongs to the technical field of coal mine underground automatic driving, and provides a coal mine tunnel simultaneous positioning and mapping method and system based on multi-modal data fusion in order to solve the problem of reliable positioning and navigation of an automatic driving vehicle in a large coal mine underground tunnel. According to the invention, a high-precision mine environment three-dimensional point cloud map of a unified navigation coordinate system in a global range from a mine ground plaza to an underground haulage roadway can be established, and the six-degree-of-freedom pose of an underground autonomous vehicle is calculated. Accurate positioning of the underground automatic driving vehicle in a degraded environment such as the coal mine underground roadway is achieved, a coal mine underground roadway simultaneous positioning and mapping system is built, the posture state of the underground automatic driving vehicle can be accurately estimated in an underground space where global satellite navigation signals are lacked, the underground accurate positioning requirement is met, and the positioning accuracy of the underground automatic driving vehicle is improved. The method is of great significance in realizing application of intelligent unmanned transportation equipment in underground coal mines and promoting intelligent and unmanned construction of coal mine auxiliary transportation systems.

The invention provides a tunneling equipment autonomous navigation positioning method and system based on multi-source data, and relates to the technical field of inertial navigation, the method comprises the following steps: connecting multi-source monitoring equipment, obtaining a multi-source data set, carrying out space-time alignment, and establishing a monitoring data network; butting the transparent geologic model, and fusing the transparent geologic model with a monitoring data network to construct a virtual model; fitting the physical structure parameters and the operation state parameters of the tunneling equipment to the virtual model, positioning the tunneling equipment, and performing tunneling path optimization search; performing attitude and inertia analysis in the virtual model based on the planned path to obtain a path attitude strategy; and carrying out operation navigation control on the tunneling equipment. According to the method and the device, the technical problem of high difficulty in long-time navigation and positioning in a complex environment due to lack of autonomous navigation of the tunneling equipment is solved, and accurate positioning of the tunneling equipment in the complex environment is realized through fusion of multi-source monitoring and the virtual model, so that the tunneling efficiency and safety are improved.

The invention discloses an ocean multi-mode environment perception and intelligent ship navigation decision-making method based on a double-branch vision-semantic encoder. The method comprises the following steps: S1, acquiring a multi-source data image containing a ship and a surrounding environment thereof from an existing public maritime data set or platform; s2, training a double-branch vision-semantic encoder by using the multi-source data image, and inputting a to-be-processed image extracted in real time into a multi-modal feature matrix in the trained double-branch vision-semantic encoder; s3, based on the multi-modal feature matrix, obtaining positioning information of the ship and surrounding environment elements, and constructing a dynamic security domain model; and S4, in combination with the dynamic security domain model and the multi-ship relative position relationship, carrying out quantitative evaluation on the navigation risk, and generating a self-adaptive navigation strategy based on an evaluation result. According to the invention, high-precision ship positioning and environment element identification under complex weather and illumination conditions are realized by using all-weather characteristics and multi-scale visual feature coding of SAR imaging.

The invention is suitable for the technical field of satellite navigation, and provides an accelerated convergence PPP-B2b precision positioning method, which comprises the following steps: calculating satellite precision orbit parameters of each epoch; for the first epoch, obtaining a corresponding satellite precision clock difference parameter based on the basic navigation broadcast clock difference parameter; for all epochs except the first epoch, on the basis of the PPP-B2b clock error correction number of the current epoch and the clock error parameter data of the previous epoch, satellite precision clock error parameters of the current epoch are obtained through calculation; obtaining O-C residual information of the double-frequency observation data in each epoch and constructing partial derivative information of the double-frequency observation data to navigation resolving parameters; and based on the normal equation matrix and the residual vector, calculating to obtain a navigation solution data set of the corresponding epoch, and according to the method, satellite precision clock correction of the first epoch and satellite precision clock correction of the subsequent epoch are processed respectively, so that deviation errors in PPP-B2b clock correction numbers are avoided, and user convergence time is shortened.

The invention discloses a vehicle navigation positioning method and system when a navigation signal is lost, and relates to the technical field of vehicle autonomous localization, and the method comprises the steps: carrying out the tight coupling matching based on a geomagnetic feature map, obtaining the geomagnetic sequence similarity through a sliding window dynamic time warping algorithm, and generating a fusion positioning track; extracting a road network topology connection relation from the high-precision map, constructing a road connection graph Laplacian matrix with a weight, and obtaining a topology constraint correction coefficient through a graph convolutional network in combination with the fusion positioning track; and constructing a joint optimization function based on the topological constraint correction coefficient, and solving an optimal positioning coordinate by adopting an asymmetric Gaussian-Newton iterative algorithm. According to the method, the defects of topology modeling stiffness and non-linear optimization instability of a traditional method are overcome, so that high-precision and high-robustness vehicle continuous positioning is realized in a GNSS signal loss scene.

The invention discloses a high-precision train locomotive positioning method and system based on a GNSS (Global Navigation Satellite System) and INS (Inertial Navigation Satellite System) loose combination mode, and the method realizes the precise positioning of a train by combining the data of an IMU (Inertial Measurement Unit) and a GNSS (Global Navigation Satellite System). The method comprises the following steps: firstly, acquiring IMU data, performing error correction, and outputting corrected acceleration and angular velocity; meanwhile, a GNSS signal is obtained and a pseudo-range measurement value is extracted. Then, inertial navigation calculation is carried out, including attitude calculation, position and speed calculation and sensor deviation updating; through Kalman filtering fusion, state estimation and error correction are carried out by using complementary characteristics of the INS and the GNSS, so that the positioning precision is improved. In the initialization stage, the normal work of the system is ensured through the installation and parameter setting of the IMU and the GNSS. Finally, through Kalman filtering processing, accurate train position, speed, attitude and sensor deviation data are output, and reliable data support is provided for a train control system.

The embodiment of the invention provides an airborne multi-sensor integrated navigation and positioning method, device, equipment and medium, and relates to the technical field of aviation navigation, and the method comprises the following steps: a Beidou navigation system, an altimeter and a radio navigation system are respectively combined with an inertial navigation system, each combination outputs an observation value through a sub-filter, and the observation value is calculated through a sub-filter; the observation value output by each sub-filter and the predicted value of the inertial navigation system are fused and then a positioning result is output, and fault detection and repair are respectively carried out on data output by the inertial navigation system, the Beidou navigation system and the altimeter; carrying out anomaly detection on the observation value output by each sub-filter by utilizing the state parameter estimation and the residual vector, and repairing the detected abnormal value by adopting a robust filtering mode; and performing anomaly detection and restoration on the observation value output by each sub-filter, calculating new state parameter estimation, and performing anomaly detection on the dynamic model of the sub-filter based on the new state parameter estimation. The scheme is beneficial to improving the fault tolerance rate of integrated navigation positioning.

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

The invention provides a rail car positioning method and system based on information fusion of a laser radar and a sensor. The method comprises the steps that S1, off-line calibration of the laser radar and inertial navigation is carried out; s2, establishing vehicle pose calculation based on point cloud features according to the laser radar point cloud and inertial navigation data; according to an RFID coordinate observation solution graph optimization problem, constructing a globally optimized point cloud map; s3, based on the point cloud map, matching the map with the real-time laser radar data to obtain a vehicle positioning result; and performing pose optimization by using an error state Kalman filtering method and fusing inertial navigation data to obtain an accurate positioning result of the vehicle. Compared with a pure sensor data fusion positioning algorithm, the positioning method combining high-precision point cloud map data and fusing other sensor information has better adaptability and precision.