549 results about "Factor graph" patented technology

The invention relates to a multi-sensor cross-scene dynamic preferential fusion positioning and mapping method, and the method comprises the steps: obtaining the data of a plurality of sensors, and completing the unification of the time-space relation of the data of the plurality of sensors; processing the data, carrying out loopback detection on image key frame data acquired by a camera, constructing to obtain an I MU pre-integration factor, a visual inertial odometer factor, a laser radar odometer factor, a GPS inertial odometer factor, a UWB factor, a GNSS factor and a loopback detection factor, and adding the factors into a factor graph for optimization; a global positioning pose and a map are obtained; and optimizing the multi-sensor data fusion strategy based on a deep fuzzy neural network. According to the multi-sensor cross-scene dynamic preferential fusion positioning and mapping method provided by the invention, high-precision positioning and navigation of an agricultural robot in different scenes are realized through real-time fusion of various sensor data, and the problems of scene dependence and insufficient precision of an existing single sensor scheme are solved.

The invention provides an unmanned aerial vehicle high-precision autonomous navigation method and system based on GNSS and IMU deep fusion, and aims to solve the problems of insufficient navigation precision and poor robustness in a complex electromagnetic environment. Through a tight coupling architecture, GNSS original observed quantity and IMU pre-integration results are jointly modeled in an observation layer, and multi-source constraints are introduced in combination with factor graph optimization, so that the positioning precision and consistency in weak signal and shielding scenes are remarkably improved. For abnormal observation, a robust kernel function is adopted to dynamically adjust the weight, and the influence of electromagnetic interference and a multipath effect is effectively inhibited. Meanwhile, navigation calculation and model prediction control MPC are combined, and sub-meter hovering and high-precision trajectory tracking are achieved. According to the method, in high-voltage transmission line inspection, dependence on a high-cost sensor is reduced, the engineering application value is high, the method can be widely applied to the fields of electric power inspection, disaster emergency, infrastructure monitoring and the like, and reliable technical support is provided for high-precision autonomous navigation of the unmanned aerial vehicle in a complex environment.

The invention provides a bionic polarization semantic SLAM (Simultaneous Localization and Mapping) method based on a neural radiation field. A system comprises three parts, namely a bionic polarization pose estimation module based on factor graph optimization, a dense mapping module based on the neural radiation field and a semantic information positioning module based on bionic polarization infrared rays. The system firstly collects multi-sensor data, establishes a state equation and a measurement equation, and optimizes and estimates the pose by using a factor graph. Secondly, dense mapping is carried out by using the estimated pose and laser radar data and by adopting a neural implicit representation (NeRF-LOAM) model constructed by a laser radar odometer and a map through voxel embedding map generation and grid reconstruction. And finally, processing the polarization infrared fusion image through Yolov8 and SegNeXt, and realizing semantic positioning in combination with laser radar features. According to the system, the anti-interference performance and the environmental adaptability are remarkably enhanced, the mapping training time is shortened, and the system can be applied to multiple fields such as automatic driving, intelligent transportation and intelligent logistics.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention discloses a multi-pose source fusion positioning method based on a factor graph, and the method comprises the steps: S1, building a factor graph: taking RTK state information at a fusion starting moment as a prior factor in a current state, and building the factor graph; s2, point cloud map relocation odometer information optimization: performing forward propagation by using IMU data, and establishing a state quantity error model; carrying out backward propagation on the original point cloud of the LiDAR by utilizing a predicted value of forward propagation so as to carry out distortion removal operation, and establishing an observation equation by utilizing the point cloud after distortion removal; then IEKF is used for updating; s3, optimizing wheel speed-IMU odometer information: optimizing rotation information provided by an IMU through error Kalman, and calculating a state quantity at an IMU data moment in combination with wheel speed data; and S4, multi-pose source fusion: carrying out cumulative multiplication on the RTK priori factor, the point cloud map relocation odometer factor and the wheel speed-IMU odometer factor to obtain a system state with the maximum probability. Through fusion optimization of data of a plurality of sensors, accurate autonomous positioning information of the mine card is obtained.

The invention relates to a low-orbit communication satellite Doppler positioning method and device based on factor graph optimization. The method comprises the following steps: after required observation data is received and an initial state value of a ground receiver is solved, a factor graph framework is introduced to set a priori factor, a Doppler frequency shift factor, a state constraint factor and a state variable consisting of a three-dimensional position and frequency drift of the receiver according to a Doppler positioning observation model; and constructing a Doppler positioning factor graph model by taking the factors and variables as nodes, introducing a sliding window mechanism to update a factor graph in a factor graph optimization solution process, and solving state variables by adopting a Gaussian-Newton algorithm to finally obtain a Doppler positioning result. According to the method, factor graph modeling is carried out on the Doppler positioning observation model, the advantages of nonlinear modeling, global optimization and capability of fully utilizing historical observation of a factor graph optimization method are exerted, and a relatively small positioning error and a higher convergence speed can be obtained.

The invention discloses a multi-unmanned aerial vehicle cluster cooperative positioning method based on multi-sensor fusion, and the method comprises the following steps: S1, collecting and processing the multi-sensor data of unmanned aerial vehicles, compensating VIO accumulated drift through fusing ultra-wideband ranging information, guaranteeing the time-space consistency of the multi-sensor data through employing a layered time synchronization strategy, and carrying out the collection and processing of the multi-sensor data of the unmanned aerial vehicles; suppressing multipath interference of the UWB signal and eliminating an NLOS error by using adaptive Kalman filtering, and obtaining initial positioning data of the unmanned aerial vehicle; s2, acquiring key frames of multiple unmanned aerial vehicles based on a window sampling method, and performing key frame optimization and outlier processing to balance noise suppression and real-time performance; and S3, constructing a factor graph model of the key frame data based on a graph optimization theory, and obtaining optimized cluster position information by solving a nonlinear least square optimization problem. The method effectively improves the accuracy and stability of multi-unmanned aerial vehicle cluster cooperative positioning, and can be applied to the fields of unmanned aerial vehicle logistics distribution, environment monitoring and the like.

The invention relates to the field of robots, and discloses a mapping method based on multi-sensor fusion, electronic equipment and a storage medium. According to the method, firstly, a laser radar, a millimeter-wave radar and a visual and motion inertial odometer are generated by collecting multi-sensor data, and it is ensured that other sensors can still provide effective information when part of the sensors are degraded due to insufficient illumination, noise interference or extreme dynamic scenes. And secondly, through real-time health degree evaluation, unstable data generated due to sensor degradation or abnormity is automatically eliminated, and it is ensured that only health data is used for generating a pose map, so that the robustness of data fusion is improved. And finally, a factor graph is constructed by utilizing head IMU data, global pose optimization is realized by virtue of short-time high-frequency stability and continuity of the factor graph, and even if other sensors are partially degraded, the accuracy and consistency of overall positioning can be maintained.

The invention discloses a Gaussian representation SLAM method based on dense matching priori and factor graph constraint, which comprises the steps of inputting a current image and a key frame image, outputting a point graph corresponding to the image through a pre-trained model, returning a matching condition of two frame image points and respective point cloud information, and obtaining a point-level matching result based on a point-level matching result. The method comprises the following steps: constructing a joint optimization problem of a current frame and a key frame by taking a luminosity consistency error and a geometric projection error as targets, performing joint estimation on a camera pose and a point cloud of the current frame, realizing high-precision pose solution, generating point diagram data after Gaussian scene representation and rasterized rendering processing, and transmitting the point diagram data to a rear end for global optimization. And the rear end receives the pose and point cloud data, executes loopback detection to identify repeated key frames, and performs Gaussian rendering through an optimized key frame image to complete global dense three-dimensional reconstruction. The method effectively solves the problem of track drift and scene inconsistency caused by lack of pose priori and global geometric constraints in an existing system.

The invention discloses a factor graph multi-source information fusion integrated navigation method based on a correlation entropy theory. The method comprises the following steps: constructing a heterogeneous sensor information factor graph model; sensor measurement and prediction state variables are equivalent to two kinds of random variables according to the correlation entropy theory, the random variables are expressed in a correlation entropy kernel function mode, cost function optimization is carried out by introducing an adjustment factor and maximum correlation entropy information, and real-time dynamic adjustment is carried out on the information weight of each sensor; constructing an initial pre-integration object, continuously reading IMU (Inertial Measurement Unit) data, carrying out pre-integration calculation, and storing a current pre-integration result into a sliding window state list; a residual factor is added, the state in the sliding window is set as an optimization variable, iterative optimization is carried out, an optimization objective function is solved, and an iterative optimization combination navigation result is obtained; and evaluating the navigation precision. According to the method, on the premise of ensuring the robustness of the system, the inhibition capability on the abnormal value of the underwater complex environment is remarkably improved, and the pose determination accuracy is improved.

The embodiment of the invention discloses a multi-sensor fusion anti-degradation SLAM mapping method and system. The method can effectively solve the problem of pose drift of a robot in a mapping process in structure degradation environments such as an indoor long corridor, constructs a globally consistent three-dimensional point cloud map and a robot trajectory, and comprises the following steps: realizing depth coupling of an IMU and a wheel speedometer based on extended Kalman filtering, and generating high-frequency pose prediction; denoising, down-sampling and motion distortion correction are carried out on the 4D laser radar point cloud, and the normal vector and intensity characteristics of the point cloud are extracted; a normal vector and intensity feature enhanced scanning matching algorithm is adopted, and a target function is optimized through a multi-feature weight, so that the matching precision in a degradation scene is improved; loopback detection is realized through candidate key frame screening and geometric registration verification, and a closed-loop constraint is incorporated into a factor graph for global correction; and finally, incrementally updating the global point cloud map and carrying out consistency optimization, and outputting a robust three-dimensional point cloud map and a high-precision robot track.

The invention provides an unmanned aerial vehicle fusion positioning method based on four-anchor-point UWB pose dynamic quality evaluation, and the method comprises the following steps: defining an unmanned aerial vehicle coordinate system and a world coordinate system, and selecting a first frame point cloud coordinate system of a laser radar as the world coordinate system; defining a state transition model required by front-end state estimation; obtaining a front-end laser radar-inertial odometer factor by using an iterative Kalman filter; based on the four-anchor-point ultra wide band, the accurate unmanned aerial vehicle position and attitude are obtained to serve as ultra wide band factors; based on the obtained ultra-wideband factor, judging the position and attitude quality of the ultra-wideband factor, and determining whether the ultra-wideband factor participates in factor graph optimization or not by adopting an event triggering mechanism; and factor graph optimization is used to obtain the accurate position and attitude of the unmanned aerial vehicle after optimization. And the calculation burden of an airborne computer of the unmanned aerial vehicle is reduced, and the accurate unmanned aerial vehicle position and attitude obtained by combining the four-anchor-point ultra wide band are combined, so that the unmanned aerial vehicle real-time positioning with higher precision and lower calculation burden is realized.

The invention discloses a self-adaptive multi-sensor fusion SLAM (simultaneous localization and mapping) system implementation method and system, a hierarchical fusion asynchronous architecture with a front-end sensing layer, a middle fusion layer and a rear-end optimization layer is designed, and the front-end sensing layer comprises a feature extraction and matching and timestamp asynchronous processing mechanism. The middle fusion layer comprises sliding of a time window and alignment of data to a time axis, and the rear-end optimization layer comprises a multi-source factor graph model, state estimation, map updating and calculation optimization; a self-adaptive weight adjustment module is arranged between the front-end sensing layer and the middle fusion layer; multi-dimensional evaluation, self-adaptive adjustment and engineering practicability of SLAM spatial positioning mapping are realized; the front-end sensing layer performs data acquisition and preprocessing on each sensor for spatial positioning mapping, and then independently performs feature extraction and matching and timestamp asynchronous processing mechanism implementation. The problem of fusion time sequence synchronization is effectively solved, the environmental adaptability is improved, and the calculation complexity is reduced.

The invention discloses an ambiguity determination method for satellite positioning, a terminal and a medium in the field of satellite navigation and positioning, and the method comprises the steps: obtaining observation data of a base station and a mobile station, eliminating errors through employing a double-difference technology, and carrying out the cycle slip correction, and obtaining the corrected observation data; preliminarily estimating the speed and position of the mobile station; based on the corrected observation data, extracting an optimal ambiguity subset through satellite-level and ambiguity-level screening; constructing a factor graph model by using the optimal ambiguity subset, taking the preliminarily estimated speed and position of the mobile station as initial values, minimizing an error function in a factor graph, and optimizing to obtain a floating solution of ambiguity; obtaining an integer ambiguity solution based on the floating point solution; and verifying the stability of an ambiguity resolving result through consistency, and finally obtaining an ambiguity fixed solution. According to the method provided by the invention, high precision, high efficiency and high stability of ambiguity calculation are realized, and the method is suitable for a high-precision satellite positioning system in a complex environment.

The invention discloses a GNSS (Global Navigation Satellite System) / inertial navigation tightly integrated navigation method and system based on robust auto-velocity constraint factor graph optimization, and the method specifically comprises the following steps: extracting pseudo-range and Doppler observation data from original observation information of a GNSS satellite, and obtaining accelerometer and gyroscope data from an INS (Inertial Navigation System); constructing a pseudo-range residual block, a Doppler residual block, an inertial navigation pre-integration residual block and a speed constraint factor, and integrating into a sliding window estimator based on factor graph optimization; according to the size of a set sliding window, adding all residual blocks in the window to form an optimized objective function; performing a first round of factor graph optimization to obtain a preliminary state estimation result, and endowing the observed quantity containing gross error with a relatively low weight; and updating the prior weight matrix, and carrying out second round of optimization to obtain a joint optimal estimation result of a plurality of moment states in the current time window. According to the method, the influence of gross error-containing observation information on weight estimation and state estimation is suppressed, the positioning precision is high, the robustness is strong, and the reliability is high.

In estimating a position of a vehicle utilizing a global navigation satellite system (GNSS), it is desirable to exclude outliner GNSS measurements due to navigation signals via non-light-of-sight paths from the GNSS to the vehicle, but it leads to a distorted satellite geometry distribution. Complementariness between low-lying visual landmarks and healthy but high-elevation satellite measurements is explored to improve the geometry constraint. Measurements of an inertial measurement unit, low-lying visual landmarks captured by a forward-looking camera onboard the vehicle, and healthy but high-elevation satellite measurements are tightly-coupled integrated via sliding window optimization of system states used in a factor graph. To improve estimation performance, good initial guesses of system states are important. As such, initial guesses of velocity set and position set inside a sliding window are estimated simultaneously based on data of Doppler measurement, double-differenced (DD) pseudorange measurement and DD carrier-phase measurement as obtained in GNSS measurements.

According to the VLA-based body robot SLAM method and device and the storage medium, a VLA large model is introduced on the basis of multi-modal fusion SLAM of traditional point cloud geometry, vision and the like, perception is improved from a geometric layer to semantic concept alignment, and the SLAM is more accurate. A VLA large model is used for carrying out dynamic prediction updating on dynamic interference filtering, key frame screening, factor graph relation construction, noise estimation, loopback detection and the like, the real-time requirement is met in the modes of incremental optimization and the like, and the dislocation problem of geometric constraints is corrected through global semantic constraints. The stability of the robot SLAM in extreme scenes such as excessive environmental dynamic interference, loud sensor noise and environmental degradation is improved, and the constructed hierarchical situation map can meet the requirement of a high-order navigation task while the geometric accuracy is met, so that the robot SLAM can be deployed to carriers such as a body-equipped intelligent carrier for subsequent application.

The invention belongs to the technical field of intelligent robot navigation and positioning, and discloses an intelligent robot autonomous mapping method in a GNSS rejection environment, which comprises the following steps of: monitoring a GNSS signal state in real time, and switching to pure laser SLAM mapping when detecting that the number of available satellites, PDOP or pseudo-range residual exceeds a threshold; the method comprises the following steps: acquiring three-dimensional point cloud data, filtering and denoising, obtaining a filtered and denoised current frame point cloud, performing laser radar mapping and positioning registration, selecting a key frame point cloud, constructing lightweight neural implicit scene representation, and training an MLP network; loopback detection is carried out, a sparse factor graph is constructed, key frame poses are corrected, then the key frame poses are applied to original three-dimensional point cloud data, and a two-dimensional grid map is generated. According to the invention, under the condition that the GNSS signal is blocked or fails, the environment map with high precision and low drift is continuously generated, and the real-time performance and robustness of autonomous mapping are remarkably improved.

The invention belongs to the field of mechanism control, and provides a mechanical arm control method and system based on adaptive adjustment, and the method comprises the steps: coding a plurality of mechanical arm control behaviors into memetic fragments, endowing each memetic fragment with survival fitness, and carrying out the selection, intersection and mutation operation on the memetic fragments based on the survival fitness, generating a control behavior evolution graph; constructing a task semantic parameter map, and dynamically activating or disabling the semantic nodes by a monitoring mechanism according to the state of the current task to generate real-time working condition description of the current task; according to the similarity between the working condition embedding vector and the semantic embedding of each memetic fragment, extracting the memetic fragment most relevant to the current working condition from the control behavior evolution graph to form a candidate memetic factor graph; selecting the control path with the highest score as an optimal control path; and performing fine tuning on the control parameters through a local disturbance mechanism, and taking the fine-tuned control parameters as final optimal motion control parameters.

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

The invention relates to a time-frequency system integrity monitoring method, device and equipment and a storage medium. The method comprises the following steps: acquiring a time difference measurement result of each time-frequency signal link and a temperature measurement result of a time-frequency system; and inputting the time difference measurement result and the temperature measurement result into a time difference measurement combination estimation model to generate a temperature change coefficient and a noise normalization coefficient. And according to the temperature change coefficient and the temperature measurement result, calculating a temperature removal time difference measurement result to obtain a first time difference measurement result, and according to the first time difference measurement result and a preset sliding window length, constructing a factor graph model. According to the factor graph model, time difference forecast deviation and time difference frequency deviation of each time frequency signal link are estimated, and according to the time difference forecast deviation, the time difference frequency deviation, the noise normalization coefficient and preset integrity monitoring parameters, an RAIM algorithm is adopted to carry out integrity monitoring on the time frequency system. By adopting the method, the deviation alarm efficiency and accuracy of the time-frequency system can be improved.

The invention discloses a factor graph RTK positioning method based on ambiguity detection, recognition and repair, which comprises the following steps: detecting an unreliable solution by using an ambiguity domain and a state domain, recognizing possible cycle slip through forward and backward repair of window information, and finally adding a cycle slip repair factor to adjust a factor graph so as to ensure reliable and correct fixation of ambiguity. And therefore, the RTK positioning robustness in a complex environment is improved. In a complex urban environment, the method can basically realize continuous position estimation with centimeter-level precision. Besides, when the available satellites are few, the ambiguity fixing and positioning effects of the method are far better than those of the existing method, which shows that the method has better robust robust capability, and the RTK positioning capability in a complex environment is effectively improved.

The invention provides a robust factor graph integrated navigation method based on adaptive MCMC (Markov Chain Monte Carlo), and in an INS / GNSS (Inertial Navigation Satellite System / Global Navigation Satellite System) integrated navigation system, factors such as multipath effect and non-line-of-sight can greatly reduce the positioning accuracy of satellites, so that the effect of the whole integrated navigation system is influenced. In urban traffic, many canyon environments greatly influence the positioning precision of vehicles, so that a combined navigation method with robust performance and estimation precision needs to be researched. The invention provides a robust factor graph integrated navigation method based on the adaptive MCMC. The method comprises the following steps: firstly, introducing adaptive MCMC into a factor graph optimization framework based on a prior stage and a posterior stage so as to accurately estimate GNSS time variable measurement noise covariance; secondly, introducing an innovation x2 detection algorithm to detect and eliminate gross errors; the method can effectively estimate GNSS time-varying variable measurement noise while reducing gross error interference. Compared with a robust adaptive factor graph algorithm based on M estimation and a robust factor graph combined navigation algorithm based on variational Bayes, the method has the advantage that the positioning precision is obviously improved.

The invention discloses a construction robot positioning and mapping method in a long tunnel environment, and relates to the technical field of computer vision, and the method comprises the steps that a robot obtains data information in the long tunnel environment through a carried sensor, and the data information comprises IMU information, an RGB image and a depth image; the method comprises the following steps: calculating IMU pre-integration according to IMU information; carrying out adaptive ORB point feature extraction and EDlines line feature extraction and optimization on an RGB image; constructing a deep learning network to carry out feature matching on extracted point-line features to obtain a matching result; constructing a point-line feature re-projection error model based on the matching result; preliminarily estimating an inter-frame pose based on the depth image and a point-line feature re-projection error model; the method comprises the following steps: selecting a key frame and detecting an identification code based on a depth image and a preliminarily estimated inter-frame pose, constructing a factor graph model based on the key frame, an identification code detection result and IMU pre-integration, updating global positioning based on a factor graph optimization result, and constructing a global map.

The invention discloses a power distribution network topology state estimation method, electronic equipment, a medium and a product. The method comprises the steps of obtaining a topological structure and measurement data of a power system; constructing variable nodes and factor nodes according to the topological structure and the measurement data, and constructing a state-topological joint factor graph model according to the variable nodes, the factor nodes and the topological structure; based on the state-topology joint factor graph model, performing state estimation through a belief propagation algorithm to obtain an estimated value of state variable correction; if the estimated value of the state variable correction meets the convergence condition, correcting the topological state of the switch branch according to the active power and reactive power of the head end of the switch branch in the estimated value of the state variable correction; and outputting an estimation result of the topological state of the switch branch until the on-off state of the switch branch obtained according to the state variable is consistent with the original topological state of the switch branch. According to the method, asynchronous real-time updating of the topological state of the power system can be realized.

The invention discloses an indoor UWB-IMU fusion trajectory tracking system and method based on factor graph optimization, and belongs to the technical field of UWB indoor positioning. The system adopts a multi-stage processing framework, and specifically comprises the following steps: firstly, collecting channel pulse response and distance information by virtue of a wireless communication system, constructing a dynamic environment sensing mechanism, and evaluating the current channel state and the availability of each base station in real time; secondly, for the ranging values of the available base stations in the NLOS environment, compensating by using historical information, and for the ranging values of the unavailable base stations, estimating and correcting by using a time step length; and finally, collaborative optimization is carried out on UWB ranging data, IMU inertial information and base station state information through an SW-FGO algorithm, so that positioning stability is ensured, and adverse effects of obstacle shielding and a multipath effect on trajectory tracking precision are reduced. The invention provides a fusion positioning system which combines the advantages of UWB and IMU, can realize the tracking of a target motion track, and is suitable for most LOS and NLOS indoor mixed scenes.

The invention provides a fusion positioning mapping method using a single anchor point external confidence source. The method comprises the following steps: step 1, arranging UWB anchor points; 2, initializing the mobile robot; 3, processing a front-end point cloud map; step 4, processing UWB ranging information; step 5, key frame judgment; and step 6, optimizing a back-end factor graph. The invention provides a multi-sensor synchronous positioning and mapping system integrating a 3D laser radar, an inertial measurement unit and an ultra-wideband sensor, and aims to improve the positioning precision and trajectory estimation performance of a mobile robot in a complex environment. According to the method, a robust and efficient positioning scheme is provided by combining data of three sensors through a factor graph optimization method.

The invention provides an IMU (inertial measurement unit)-centered multi-sensor fusion automatic driving positioning method, which comprises the following steps of: acquiring a GNSS (global navigation satellite system) signal, a laser radar point cloud, image data and an IMU state of a vehicle through a receiver, a camera, a laser radar and an IMU of a global navigation satellite system (GNSS), and calculating the position, the speed and the attitude of the vehicle in real time; an IMU state, a camera pose and a laser radar frame are used as nodes, IMU pre-integration, a visual re-projection error, a laser radar matching error and a GNSS measurement value are used as factors, and a factor graph is constructed; taking the weighted residual sum of all minimized factors as a target function, taking the state of the IMU as a core variable, solving the target function by adopting a sliding window mechanism and a variable elimination algorithm, and updating nodes in the factor graph; the updated nodes are fed back to the IMU, the IMU state is updated, the steps are repeated, and automatic driving positioning of the vehicle is achieved.

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 dynamic semantic SLAM (Simultaneous Localization and Mapping)-driven robot full life cycle navigation method, which comprises the following steps: synchronously acquiring multi-modal data through a multi-modal sensor array, and carrying out joint coding based on a cross-modal contrast learning framework to obtain embedded features. A dynamic three-dimensional Gaussian radiation field is constructed, a scene is represented as a parameterized Gaussian kernel set, and mixed Gaussian field representation is obtained through micro rendering and joint training optimization. A Gaussian mixture field is divided into a global static layer and a local dynamic layer, retention constraints are applied to the static layer, and an incremental online updating strategy is adopted for the dynamic layer. Based on the optimized Gaussian field parameters and the multi-modal data, a factor graph containing Gaussian rendering residual factors and inertia pre-integration factors is constructed and solved, the corrected robot pose is obtained, and the space occupancy probability is generated. Operation control parameters are adjusted on line according to the pose and the occupancy probability through a strategy network, and autonomous navigation is achieved in combination with path planning and trajectory tracking.