469 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 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-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 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 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 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 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 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.

The invention discloses a fusion positioning method for a well mining long corridor environment and a medium. The fusion positioning method comprises the following steps: acquiring sensor data of a laser radar and an IMU (Inertial Measurement Unit); performing preprocessing and motion distortion removal on the sensor data; according to the distortion-removed point cloud data, a target area is extracted through vertical slicing and RANSAC fitting, and a dense point cloud area containing a pipeline and an indication board is obtained; according to the distortion-removed point cloud data and the dense point cloud area, performing dynamic obstacle detection by adopting a method based on a multi-dimensional grid map descriptor, and removing detected dynamic point cloud to obtain static point cloud; and constructing a tight coupling factor graph model according to the static point cloud and IMU measurement data, solving the tight coupling factor graph model through a nonlinear optimization algorithm, and outputting a global pose estimation result. Aiming at the special geometric degradation problem of the mine long corridor environment, the method effectively solves the problem of positioning error accumulation in the direction along the roadway and the problem of underground dynamic obstacle interference.

The invention belongs to the technical field of information and communication, and relates to an active user detection and data decoding method of an asynchronous massive machine type communication system. Aiming at the problem that the detection and estimation performance is reduced due to unknown user time delay in asynchronous transmission, the method comprises the following steps: initial estimation and preprocessing: detecting initial active users and transmission time delay thereof through correlation peaks, and whitening received signals; the iterative joint detection and channel estimation module is used for iteratively updating channel information and data symbol information for multiple rounds based on a factor graph and a message passing algorithm; the data decoding module is used for decoding data based on a soft decision device; the detection and decoding performance is improved by transmitting external information among the modules; and the time delay parameter updating adopts a greedy search algorithm to obtain the optimal time delay. According to the method, the active user detection rate, the channel estimation precision and the data decoding accuracy under the condition that the receiving and transmitting ends are completely asynchronous are improved.

The invention provides an unmanned aerial vehicle formation collaborative navigation method based on an open information fusion architecture, belongs to the field of unmanned aerial vehicle navigation, and aims to solve the navigation information fusion problem of asynchronous updating of multi-sensor data and available dynamic change of measurement information in a traditional formation collaborative navigation method. In the prior art, when asynchronous data fusion and measurement information availability change are coped, the defects of precision loss, poor flexibility and expansibility and the like exist. According to the method, a state variable and a measurement information set are constructed under an open architecture, a probability model of collaborative navigation state estimation is established based on Bayesian reasoning, and on this basis, a factor graph model is constructed by combining an incidence relation between a measurement model in posterior probability and prior information. By designing an independent measurement factor for each sensor, when sensor data is updated, flexible response to asynchronous data fusion and dynamic change of measurement information is realized by optimizing changed measurement factor nodes in real time and dynamically adding and deleting the nodes. And gradually minimizing an error cost function of each factor node through a nonlinear least square optimization method to realize efficient fusion of multi-sensor data and real-time updating of a navigation state. According to the method, the efficiency, flexibility and expansibility of asynchronous information fusion of the collaborative navigation system are remarkably improved, and reliable technical support is provided for unmanned aerial vehicle formation collaborative navigation.

The invention belongs to the technical field of polarization codes, and discloses a large nuclear polarization code BP decoding algorithm based on a pruning permutation factor graph, and the algorithm comprises the steps: firstly, randomly generating a permutation factor graph; secondly, deleting nodes with low contribution degree and non-contribution degree to decoding through pruning operation; and when the stop condition is not met, replacing the factor graph to execute BP decoding of the pruned and permutated factor graph until the stop condition is met. For a large nuclear polarization code, a permutation factor graph can improve the decoding performance, and a pruning factor graph can reduce the decoding complexity. A simulation result shows that compared with a BP decoding algorithm, the algorithm provided by the invention can ensure that the complexity is not too high and the decoding performance is improved at the same time.

A method of digital document review using factor graph document databases comprising an extract method and a summarise method, wherein the extract method uses a Large Language Model (LLM) to decompose into variables and elements a document or series of documents and to identify new terms semantically associated with the description of the interest of the desired audience for the document review process, and representing the variables and elements in a factor graph document database, and wherein the summarise method includes indexing the factor graph document database based on terms of interest to the audience of the review, and inferring which of the new variables and elements are relevant to the audience, and reconstructing a human interpretable document out of the variables and elements inferred in the factor graph document database.

The invention provides an underwater integrated navigation method and system based on speed prediction, and relates to the technical field of underwater navigation.The underwater integrated navigation method comprises the steps that observation data of an inertial navigation system (INS), a Doppler log (DVL) and a pressure sensor (PS) are obtained, and when the observation speed of the Doppler log (DVL) fails or meets the gross error condition, the pressure sensor (PS) is determined; predicting the observation speed based on the corrected rotating speed of the propeller; based on the observation data, calculating an IMU pre-integration factor, a DVL factor and a PS factor, constructing a factor graph, and fusing the observation data through a factor graph optimization method to obtain a final underwater vehicle state, including position coordinates, speed, attitude, accelerometer and gyroscope zero offset errors; according to the method, the pseudo DVL factor constructed by the speed predicted value replaces the DVL factor to participate in the navigation framework, so that the navigation precision divergence is effectively inhibited in the DVL failure state.

The invention belongs to the technical field of underwater navigation, and particularly relates to a multi-AUV (Autonomous Underwater Vehicle) collaborative SLAM (Simultaneous Localization and Mapping) method based on multi-beam water depth measurement data, which comprises the following steps: a plurality of AUVs synchronously run in a task area, and a multi-beam depth sounding sonar is respectively used for acquiring local water depth data and generating an internal water depth sub-graph set; each AUV performs sparse processing on the internal water depth sub-graph; the AUV receiving the sub-graph performs interpolation reconstruction on the sparse sub-graph by using a GPR algorithm; each AUV constructs a collaborative SLAM factor graph containing a pushing factor, an internal water depth closed-loop factor, an external water depth closed-loop factor and an inter-AUV distance observation factor based on self navigation data, the internal water depth sub-graph and the external water depth sub-graph; and solving the factor graph through a distributed optimization algorithm, and outputting an optimized pose and a global map of each AUV. According to the method, the positioning precision and reliability of the underwater SLAM are remarkably improved.

The invention discloses an unmanned aerial vehicle high-precision construction lofting method and system based on an RTK / PPK technology, and belongs to the technical field of construction lofting, and the method comprises the steps: based on a LiDAR point cloud, an IMU attitude and a camera image of timestamp alignment, realizing space-time registration through extended Kalman filtering or factor graph optimization, and generating a high-precision construction area three-dimensional model; based on the deviation thermodynamic diagram and the text report and after adjustment of construction personnel, the unmanned aerial vehicle performs automatic reinspection until the verification deviation of comparison of two times of actual measurement data is within a convergence threshold value; and static baseline calculation is performed on PPK original data stored by the unmanned aerial vehicle, and accurate positioning data of the unmanned aerial vehicle in a signal interruption period is calculated in combination with synchronous observation data of a base station, so that full-process automatic high-precision lofting in a complex environment is realized.

The present application relates to the technical field of data processing, and particularly relates to a power monitoring method and system for remote electric energy meter, the method comprising: constructing a time series factor graph model with voltage and current phasor as hidden variables and original electric parameter data as observation nodes; performing synchronous compression wavelet transform on current phasors in the electric energy state sequence to generate a time-frequency energy distribution graph and obtain a monitoring feature vector sequence; inputting the monitoring feature vector sequence into a deep auto-encoder pre-trained on normal power consumption working condition data to calculate a reconstruction error, simultaneously calculating a Lyapunov index of the sequence within a preset time window, and obtaining a negative log-likelihood probability according to a pre-established Gaussian mixture model describing the distribution of the index under normal working condition; and weighting and fusing the reconstruction error and the negative log-likelihood probability to generate a comprehensive abnormality index for judging power consumption events. The present application can realize high-precision and low-false-alarm-rate detection of power consumption events.

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 multi-vehicle cooperative positioning method based on progressive non-convex factor graph optimization, and the method comprises the steps: firstly calculating a GNSS pseudo-range error factor, an inter-vehicle distance measurement error factor and an inter-epoch constraint factor according to the collection data of a vehicle-mounted terminal GNSS sensor and a UWB distance measurement sensor; then a progressive non-convex welsch cost function is constructed, and an equivalent objective function is constructed in combination with a GNSS pseudo-range error factor, a workshop distance measurement error factor and an inter-epoch constraint factor; and finally, carrying out factor graph optimization on the equivalent objective function, solving an optimal weight and state, and outputting a global state and final weights of a GNSS pseudo-range error factor, a workshop distance measurement error factor and an inter-epoch constraint factor. According to the method, abnormal measurement values in cooperative positioning are suppressed by constructing a progressive non-convex welsch cost function, and meanwhile, multi-epoch and multi-node measurement information is fully fused by adopting a factor graph optimization architecture, so that the reliability of cooperative positioning is improved.

The invention relates to a computer vision technology, and discloses a degraded environment map construction method based on adaptive pose estimation, which comprises the following steps: acquiring an IMU (Inertial Measurement Unit) measurement value, and calculating an IMU pre-integration factor; acquiring laser radar data, and performing point cloud distortion removal processing to obtain standard laser radar data; extracting feature points in the standard laser radar data, and performing degradation environment identification to obtain a degradation environment identification result; calculating a degradation factor, and adjusting a feature weight corresponding to the feature point; carrying out adaptive pose updating according to a degradation environment identification result, and carrying out point cloud matching to obtain a laser radar factor; and integrating the IMU pre-integration factor and the laser radar factor into a factor graph to carry out factor graph optimization, outputting pose information, and obtaining a degraded environment map according to the pose information and the adaptive pose. The invention further provides a degradation environment map construction device and equipment based on self-adaptive pose estimation and a medium. The method can improve the success rate of degraded environment mapping.