298 results about "Particle filter" patented technology

The invention relates to the technical field of automatic workpiece grabbing and visual servo control, and discloses a workpiece grabbing method and system based on visual feedback, and the method comprises the steps: obtaining a real-time image through a front-view camera, a side-view camera and a top-view camera, and extracting surface features through a feature fusion network; inputting the features into a pose estimation model based on a particle filtering framework, and iteratively updating the pose in combination with a multi-dimensional observation likelihood function to generate prediction parameters; constructing a multi-target path planning model based on the parameters, and optimizing the path by adopting a dynamic planning algorithm with the shortest path and the minimum joint movement as targets; a hierarchical control model is established, a strategy layer performs global planning, an adjustment layer corrects a local path, and an execution layer realizes trajectory tracking through visual servo control and outputs a control instruction to complete grabbing. Through multi-view perception, robust pose estimation, global optimization path planning and hierarchical control, the precision, efficiency and stability of workpiece grabbing in a complex environment are improved.

The invention relates to the field of forest protection, and discloses an unmanned aerial vehicle intelligent monitoring system for forest protection. Comprising a multispectral data dynamic acquisition module, an environment field real-time modeling module, an image intelligent enhancement module, a multi-scale frequency domain feature extraction module, a Bayesian anomaly probability inference module, a space-time correlation verification module, an unmanned aerial vehicle cluster scheduling module and a heterogeneous hardware acceleration module. According to the system, real-time monitoring and accurate identification of the hidden ecological damage behavior are realized through a closed-loop process of multi-modal sensor collaborative acquisition, environmental parameter dynamic prediction, image illumination invariance transformation, cross-scale texture fingerprint extraction, particle filter dynamic threshold optimization, multi-constraint space-time clustering, resource allocation optimization and reconfigurable hardware acceleration. According to the invention, on the basis of an image preprocessing architecture based on illumination reflection separation and edge preserving enhancement, the feature distortion bottleneck of a traditional algorithm in a complex illumination environment is broken through, and a high-fidelity data base is provided for hidden ecological damage detection.

The invention discloses an error correction system and method for marine environment observation data. The error correction system comprises a data acquisition module, a compensation modeling module, an error analysis module, a data compensation module, a data fusion module and a feedback optimization module. The data acquisition module acquires ADCP flow measurement, GPS, IMU, temperature, pressure and water quality monitoring data. And the error analysis module detects a drift error by using Kalman filtering and a hidden Markov model, and analyzes an environmental error based on a long short-term memory network. The data compensation module optimizes a compensation model and improves data precision. And the data fusion module fuses multi-sensor data by adopting a particle filter and a Bayesian optimization algorithm. The feedback optimization module dynamically adjusts the weight of the compensation model according to the error correction data, and improves the adaptability of the system. According to the invention, the accuracy of ocean current monitoring data is improved, and more reliable data support is provided for underwater environment research.

The invention discloses a shield attitude control method and system based on machine vision, and the method comprises the steps: constructing a motion state model of a shield tunneling machine according to the motion track of a feature point, and carrying out the prediction and correction of a motion state through a Kalman filtering algorithm, and obtaining the real-time attitude data of the shield tunneling machine; vibration data of the shield tunneling machine are obtained, the vibration data are obtained based on a vibration compensation model of an inertial measurement unit, the vibration data and visual sensor data are combined, a data fusion algorithm based on particle filtering is adopted to suppress vibration interference, and stable attitude information is obtained; and constructing a multi-modal data fusion model based on a graph convolutional neural network, and fusing the visual data, the inertial data and the motion state data to obtain a high-precision attitude estimation result.

The invention discloses a digital twinning system and a construction method thereof, and relates to the technical field of digital twinning, and the construction method comprises the steps: carrying out the classified collection of multi-source heterogeneous data of a digital twinning entity, carrying out the filtering and denoising through a self-adaptive wavelet threshold, mapping the data into a multi-dimensional feature vector, screening key features, and outputting the key feature data; the method comprises the following steps: establishing a physical mechanism layer based on a general physical rule, defining core physical parameters and a constraint equation, initializing a particle swarm to establish a data driving layer, constructing an LSTM time sequence prediction module, executing particle filter state calibration, and selecting a model to configure a communication protocol to establish a virtual-real interaction layer, so as to realize state mapping and instruction feedback of a digital twin entity and a virtual model; and calculating virtual and real state deviation in real time and performing attribution diagnosis, adjusting model parameters or key features according to a deviation source, generating an optimization parameter combination based on a long time sequence prediction result and performing simulation verification in a virtual environment, and controlling entity parameter adjustment through an instruction feedback channel so as to realize predictive optimization.

The invention relates to the technical field of unmanned aerial vehicles, and particularly discloses an unmanned aerial vehicle millimeter wave radar detection system and method and an unmanned aerial vehicle, and the system comprises a millimeter wave radar module which is used for executing multiple detection tasks and detecting the distance, speed and angle information of a target object, and has the functions of beam scanning and signal transmitting and receiving; the signal processing module is used for processing the received radar echo signal, performing target object detection and positioning through signal analysis, and generating a target positioning result; the target tracking module is used for tracking the target according to the target positioning result and the sensor data of the unmanned aerial vehicle; the method improves the signal intensity and detection distance in different detection environments by optimizing the direction and focusing of radar beams in real time, effectively eliminates noise interference through the Kalman filtering and particle filtering technology, accurately estimates the distance, speed and angle information of a target, and improves the detection accuracy through combining a deep learning algorithm and an adaptive filtering strategy. And processing details of the radar echo signals are dynamically optimized.

The invention discloses an unmanned aerial vehicle track maintenance and autonomous positioning method in a communication decline scene, and relates to the technical field of navigation maintenance positioning, and the method comprises the steps: carrying out the signal intensity monitoring and smoothing of signal quality and decline information collected in real time, carrying out the decline trend prediction through a long-short-term memory network, and outputting a signal decline report; in combination with multi-unmanned aerial vehicle sensor data, local sensor data is processed in real time through edge calculation, and shared information is fused through particle filtering; a cooperative positioning algorithm and a distributed positioning algorithm are used to correct the shared information of the multiple unmanned aerial vehicles and output the precision for autonomous positioning; and adjusting the unmanned aerial vehicle to an energy-saving flight mode by using an energy consumption model in combination with the signal recession report and the optimal path. According to the method, the flight path of the unmanned aerial vehicle is optimized by adopting deep reinforcement learning and the game theory, and adaptive flight path adjustment is realized in combination with a model prediction control algorithm, so that the unmanned aerial vehicle can dynamically avoid a low-signal area, and flight stability is ensured.

The invention discloses an intelligent target detection system based on multi-sensor data fusion, particularly relates to the field of intelligent target detection, and comprises a heterogeneous sensor module, a data fusion module and a self-calibration module. According to the invention, at night or under low illumination, infrared thermal imaging dominates target detection, and the data weight of the visible light camera is reduced; under strong electromagnetic interference, the laser radar starts polarization filtering, and the millimeter wave radar performs frequency hopping interference resistance; data fusion adopts a three-level architecture: the first level is based on an improved D-S evidence theory to solve cross-modal data conflicts; in the second stage, aiming at sampling frequency and position difference, a time-space stamp compensation algorithm is used for alignment; the third level uses a bidirectional LSTM neural network of an attention mechanism to distribute weights; when the confidence coefficient of the sensor is low, a particle filter compensation mechanism is started, the deviation of the sensor is corrected, health assessment and predictive maintenance are carried out, historical data are recorded and calibrated, a health degree index is calculated, the remaining service life is predicted, and an alarm is triggered when the confidence coefficient is lower than a threshold value.

The invention discloses a method and a system for monitoring the temperature of an energy storage battery in a high-altitude area. Through environment adaptive feature reconstruction, thermodynamic model correction and multi-frequency EIS fusion feature extraction are utilized to generate a feature vector adaptive to the plateau environment. The thermodynamic model correction adjusts the convective heat transfer coefficient by introducing an air pressure correction coefficient; the multi-frequency EIS fusion feature selects a specific frequency point impedance value to construct a vector, and the weight is dynamically adjusted through a random forest algorithm. The lightweight hybrid machine learning model integrates the advantages of LightGBM, 1D-CNN and a physical constraint particle filter model, and realizes accurate prediction for different working conditions. The plateau exclusive training mechanism covers data enhancement, transfer learning and online calibration, and the generalization ability and adaptability of the model are improved. According to the method, the problems of low temperature monitoring precision and poor model adaptability of the energy storage battery in the high-altitude area are solved, the monitoring precision and the system reliability are remarkably improved, and a guarantee is provided for safe and stable operation of a high-altitude energy storage system.

The invention discloses a high-speed train multi-information fusion positioning method based on a Beidou satellite, and relates to the technical field of satellite navigation and positioning. The method comprises the following steps: designing a high-speed train multi-information fusion positioning scheme; based on a train multi-information fusion positioning scheme, establishing a nonlinear train state model based on a train kinematics equation; the position, speed, acceleration, course angle and pitch angle of the train are used as state vectors, and a train measurement model is constructed; based on a tracking target set by a train state model and a train measurement model, the train state is estimated by fusing IMM-UKF / PF models of unscented Kalman filter (UKF) and particle filter (PF) and introducing ACW and a short-term error feedback mechanism in state fusion. According to the method, the positioning precision and robustness can be improved, and the dynamic response capability in a complex scene can be enhanced.

The invention relates to the technical field of laser ranging, in particular to an unmanned aerial vehicle dynamic laser ranging method based on multi-source fusion and adaptive compensation, and the method comprises the following steps: obtaining a ranging pulse of a laser ranging module, and carrying out three-axis rotation and coordinate translation in combination with an IMU angle to generate a geographic registration coordinate set; radar and ultrasonic ranging are fused to construct redundant distance compensation data, particle filtering is executed based on IMU acceleration angular velocity to generate an attitude constraint distance, SLAM weight is adjusted according to illumination and meteorological parameters, and an unmanned aerial vehicle dynamic ranging coordinate result set is generated. Spatial registration is executed by combining longitude and latitude height data, by establishing dynamic association between the ranging data and IMU attitude data, smoothness and continuity of the ranging data can be kept when attitude disturbance is severe, the weight proportion of the vision ranging data in multi-source fusion is dynamically adjusted, and the high-precision estimation capacity of the target distance in the dynamic flight state is improved.

The invention discloses a generator state estimation method and system considering noise and parameter uncertainty constraints. The method comprises the following steps: acquiring model parameters and dynamic state vectors of a generator, and establishing augmented state vectors; performing unscented transformation on the augmented state vector to obtain a particle set; improving to obtain robust mixed Kalman particle filtering, and in the process of performing unscented Kalman filtering on an augmented state vector, taking correlation entropy maximization of a measurement information sequence as a target function, and solving by adopting a fixed point iteration method to obtain a filtering gain; determining a filtering gain according to the updated state of the measurement information; robust mixed Kalman particle filtering is executed, physical constraints of model parameters serve as a feasible region, after resampling, projections of the model parameters in new-generation particles exceed the feasible region, the model parameters are set to be closest boundary points, and then resampling is conducted again; a weighted average value of the particle set is an optimal joint estimation value, a dynamic state estimation value and a model parameter identification result are separated, and reliable uncertainty quantization is provided for state and parameter estimation.

The invention discloses an environmental event dynamic risk assessment method and system based on a Bayesian network, belongs to the technical field of artificial intelligence, and aims to solve the technical problems that in existing environmental risk assessment, dynamic data adaptability is poor, multi-source heterogeneous data fusion is difficult, and real-time performance is insufficient. Comprising the following steps: acquiring environment data through a sensor cluster deployed in an environment, and uploading the environment data to an edge computing node; performing data preprocessing on the environmental data through the edge computing node; extracting time series data fragments from the standardized data stream based on a predefined sliding time window; constructing a risk prediction model based on the dynamic Bayesian network; and taking the extracted time series data fragments as input, performing risk level analysis through a risk prediction model in combination with particle filter reasoning, predicting and outputting a risk level, a risk level probability value and a risk conduction path as prediction results, and constructing a visual risk conduction map based on the prediction results.

The invention relates to the field of gas leakage identification, and discloses a gas leakage identification method and system based on sound localization, a medium and equipment, and the method comprises the steps: collecting a leakage sound wave signal through a microphone array, and extracting acoustic features in a complex background; simulating a diffusion process of gas in a turbulence environment after gas leakage through an established leakage gas convection-diffusion model, and combining gas concentration distribution in the simulated diffusion process with acoustic characteristics to predict sound source localization at a leakage position; modeling a sound source localization search process as POMDP, and iteratively updating a confidence state of a sound source position through a particle filter; spatial distribution features are extracted from the confidence state through DBSCAN clustering to serve as input of the LSTM-DQN network, spatial and temporal features are fused through the constructed LSTM-DQN network, cross-scene generalization is achieved through transfer learning, and sound source coordinates are dynamically optimized and output through the Q network. According to the invention, the positioning accuracy and real-time performance in a complex environment are improved.

The invention belongs to the technical field of aircraft strength experiments, and particularly relates to a hybrid-driven metal multi-fatigue crack propagation fusion prediction method. The method comprises the following steps: establishing a stress intensity factor expression under multiple fatigue cracks; initial distribution of material parameters in the Paris physical model is obtained, and posterior distribution of the material parameters is adjusted; based on the stress intensity factor expression under the multi-fatigue crack and the posterior distribution of the material parameters, obtaining an improved Paris physical model for describing the multi-fatigue crack propagation process; a depth encoder network model is established, and a network bottleneck layer is utilized to extract guided wave deep features with strong correlation with the fatigue crack length; establishing a guided wave data driven multi-fatigue crack length online monitoring model; and building a particle filter fusion framework, and correcting and improving a distribution interval of uncertain material parameters in the Paris physical model according to guided wave monitoring data to realize dynamic tracking and prediction of metal multi-fatigue crack propagation.

The invention discloses a particle filtering fusion positioning method based on indoor and outdoor fuzzy judgment, which comprises the following steps of: firstly, dividing a region to be positioned into dynamic grid units, constructing a space-time signal distribution matrix, and measuring the matching degree of the space-time signal distribution matrix and a standard scene through weighted Jaccard similarity; secondly, designing a dual-channel adaptive membership function based on a fuzzy theory, and converting hard decision into probabilistic weight output; and then a four-parameter joint optimization mechanism based on environmental difference, model uncertainty, fuzzy entropy and confidence is constructed, a classification threshold and a confidence interval are dynamically adjusted, finally an improved particle filtering fusion model is constructed, a hybrid dynamics model is constructed to predict the positions of particles, and a dynamic resampling strategy is combined to improve the particle utilization rate. And the positioning accuracy and stability are improved. According to the invention, the problem that the positioning precision is reduced or the positioning result jumps frequently when indoor and outdoor scenes are switched is solved.

The invention discloses a working area personnel positioning method and system based on a data communication technology, and relates to the technical field of personnel positioning, and the method comprises the steps: estimating the position of a worker in real time through the fusion of an RFID tag and a multi-band signal, and the weighted fusion of a triangulation positioning algorithm and a dynamic signal; according to the method, extended Kalman filtering, multi-target tracking and particle filtering technologies are combined, signal interference is compensated, positioning precision is optimized, a fuzzy control algorithm is adopted for safety early warning, edge calculation and cloud calculation are combined, historical data are analyzed, and the moving trend of workers is predicted. According to the invention, by combining RFID tags, multi-band signals, dynamic signal fusion, multi-target tracking, particle filtering and fuzzy control technologies, high-precision and real-time personnel positioning and safety management are realized, the position of a worker can be accurately estimated, signal interference, attenuation and reflection effects in an environment can be effectively compensated, an early warning strategy is dynamically adjusted, and the working efficiency is improved. And the safety of the working area is improved.

The invention discloses a rail transit intelligent inspection robot positioning system based on laser SLAM and inertial navigation, and relates to the field of real-time positioning, the rail transit intelligent inspection robot positioning system is characterized in that data are synchronously acquired through a laser radar, an IMU, a speedometer and an RGB-D camera, and high precision in rail positioning is realized; the point cloud filtering module extracts orbit feature points through an improved voxel grid algorithm, and the inertial navigation initialization module calculates an initial pose based on IMU data and establishes a motion constraint model; the map feature layering module divides the point cloud into a track surface, equipment and a background layer; and the data tight coupling module optimizes the pose, and the dynamic object elimination module eliminates obstacles, so that the SLAM optimization precision is ensured. And the global positioning compensation module performs repositioning through particle filtering to generate a 6DoF pose estimation semantic map. The method has the advantages that by fusing the laser SLAM and inertial navigation technologies, high-precision and stable rail transit inspection is realized, the positioning precision and the environmental adaptability are optimized, and the intelligent operation and maintenance efficiency is improved.

The invention discloses a fuel cell vehicle control method integrating fault diagnosis and energy management. According to the method, vehicle key operation parameters are collected in real time, a whole vehicle operation state vector is constructed, and fuel cell health diagnosis and preliminary power action generation are carried out at the same time based on the state vector. The diagnosis module obtains a health factor and a severity index through residual calculation, particle filtering and generalized likelihood ratio test, and the action generation module outputs an intelligent agent power distribution vector. And according to the severity index, the dynamic reconstruction power, the SOC and the power change rate constraint, optimal power distribution is obtained through optimization solution, and meanwhile, a high-severity state is intervened in combination with a hierarchical fault-tolerant strategy. The method also comprises a demand power prediction and online learning module which is used for improving the robustness and the adaptive capability of the optimization strategy. According to the invention, by tightly combining the health state of the fuel cell with energy management optimization, high-energy-efficiency, long-service-life and reliable dynamic control of the fuel cell system is realized.

The invention relates to the technical field of fixed-wing unmanned aerial vehicle control, in particular to a state estimation method for a fixed-wing unmanned aerial vehicle in a gliding state during weak driving and long endurance. Comprising the steps of sensor data fusion and establishment of an observation model, a judgment process of entering a gliding state, switching of an unmanned aerial vehicle aerodynamic model, establishment of a lightweight particle filter-extended state observer PF-ESO inner and outer ring cascade filtering structure under a weak driving attitude, and working condition feedback control optimization of correlation coefficients. Through a lightweight particle filter-extended state observer cascade structure, the adaptive capacity of the system to airflow disturbance and modeling errors is enhanced, even if airflow change or control delay occurs in long-time gliding, the filter can quickly sense and correct estimation, and long-term state drift is avoided. The robustness of the system to slow disturbance and model mismatch is remarkably improved.

The invention discloses an intelligent substation virtual loop online transmission verification method based on GOOSE / SV closed loop test, and relates to the technical field of intelligent substation operation and maintenance. Through precise clock synchronization and an improved cross-correlation algorithm, in combination with wavelet noise reduction and spectral clustering analysis, nanosecond synchronization quality evaluation of GOOSE / SV signals is realized, and the hidden transmission risk discovery time is shortened from regular maintenance to real-time monitoring; a hierarchical hidden Markov model is adopted to analyze equipment-level to system-level behavior modes, real-time probabilistic reasoning is realized in combination with a dynamic Bayesian network and particle filtering, a multi-dimensional evaluation system is constructed, the reliability evaluation capability under complex working conditions is remarkably improved, an optimization scheme is generated based on network path characteristic analysis and bottleneck identification, and the reliability of the system is improved. Multi-scene closed-loop verification is carried out by means of a digital twin technology, safety and reliability of parameter optimization are ensured, and full-process intelligent operation and maintenance of the virtual circuit of the intelligent substation from state perception to optimization verification are realized.

The invention relates to the technical field of mine environment positioning, in particular to a mine scene self-adaptive repositioning method based on multi-modal perception, which comprises the following steps of: S1, acquiring information, and acquiring visual images, spatial features, wireless signal features and inertial motion data of an internal environment of a mine by using a multi-modal sensor; s2, extracting information, namely extracting the feature data obtained in the step S1, forming a dynamic topological map, and marking temporary obstacle information at the same time; s3, information processing: adaptively adjusting data fusion weights of different sensors according to the information extracted in the step S2 based on a dynamic environment sensing result; and S4, information updating: combining particle filtering and a graph optimization algorithm to realize robust pose estimation and real-time map updating. According to the invention, the failure risk of a single sensor is reduced through multi-mode complementation, and the positioning error is reduced by more than 40%; and through semantic auxiliary matching, the relocation time consumption is reduced.

The invention discloses an end-to-end automatic driving long tail scene data acquisition and automatic labeling method. The method comprises the following steps: firstly, collecting multi-modal time series data including images, point cloud and odometer information; thirdly, calculating a comprehensive long-tail scene judgment coefficient by constructing an occupation map and fusing perception uncertainty and prediction uncertainty, thereby automatically identifying a high-risk and high-uncertainty long-tail scene; then, only for the data judged as the long-tail scene, starting a particle filtering-based track truth value generation process, fusing the motion speculation of the odometer and the map matching of the laser radar point cloud in the process, and carrying out state prediction, observation updating, resampling and weighted averaging to obtain a track truth value of the long-tail scene; and generating a high-quality self-vehicle track point sequence conforming to the vehicle kinematics constraint as a truth value label. According to the method, accurate screening and true value automatic generation of the long-tail scene are realized, the data acquisition efficiency and the labeling quality are improved, and the safety and generalization ability of an end-to-end automatic driving model in a complex scene are enhanced.

The invention provides a pipeline defect prediction and evaluation method and system based on AKF and SR-UPF. The method comprises the following steps: obtaining observation data of a pipeline; based on the observation data, a growth rule of pipeline defects is obtained through a preset mechanism model; introducing random disturbance in the defect growth process through a preset random process model based on the growth rule of the pipeline defect, and generating a pipeline defect prediction result; wherein the mechanism model carries out dynamic estimation and correction on parameter uncertainty and noise distribution through adaptive Kalman filtering, and the random process model carries out particle weight and distribution optimization through square root unscented particle filtering. The problems that in the prior art, a pipeline corrosion and crack growth reliability evaluation method is insufficient in prediction precision and low in calculation efficiency are solved.

The invention discloses an improved particle filter map matching method based on rail transit 5G positioning, and the method mainly comprises the steps: 1) collecting original trajectory data obtained through 5G positioning, carrying out the sliding average processing according to a preset sliding window, generating a smooth new trajectory point sequence, and reducing the data redundancy; 2) constructing an improved particle filter map matching algorithm on the basis of the new track points, combining with subway tunnel fine map structure generation, considering space prior information such as a track center line and a track boundary, endowing particles with differentiated weights through a track center line judgment function and a track area judgment function, and determining the track area according to the differentiated weights; high-robustness matching of the track points and the tunnel environment is realized; and 3) in a particle filtering iteration process, setting an effective particle number threshold value, triggering a resampling operation when particles are seriously degraded, selecting a new particle set in a probability mode, avoiding falling into a degradation endless loop, and outputting a state posteriori estimation result in combination with a particle weight to generate an accurate trajectory.

The invention provides a Bayesian wireless channel estimation method and a Bayesian wireless channel estimation system suitable for same-frequency interference, and the method uses variational inference based on LSI to capture frequency points with large interference power in a considered frequency band, and can more effectively extract frequency points with real interference from an actual frequency selective fading channel. The method comprises the following steps: carrying out GI and LSI-based alternate variational inference for optimizing an algorithm model and reducing overfitting of the algorithm, introducing a hyper-parameter adaptive updating method of an interference level so as to introduce more prior information entropy during iteration, and completing position estimation through channel parameter estimation in a pilot frequency band of OFDM in combination with ToA / TDoA / AoA and other algorithms, so that the estimation accuracy is improved. Kalman / Particle Filter can also be combined to complete navigation prediction, and the communication quality of signal subcarriers can also be improved by using a related equalization algorithm.

The invention discloses a dynamic scene laser mapping and positioning method based on semantic information visual enhancement, and belongs to the field of navigation positioning. Comprising the steps that observation data of multiple sensors are acquired, and the multiple sensors comprise a laser radar and a visual sensor; establishing a coordinate conversion relation of the visual sensor relative to the laser radar according to the observation data; according to the coordinate conversion relation, mapping the visual semantic information to a laser radar coordinate system to obtain semantic enhancement data; according to the semantic enhancement data, pose estimation of the robot is updated under a particle filtering framework; constructing a composite map containing semantic information and geometric information according to the updated pose estimation; and executing a navigation task according to the composite map. Through deep fusion of visual semantic information and laser radar data, the positioning precision and navigation efficiency of the robot in a dynamic scene are effectively improved, and reliable technical support is provided for intelligent upgrading of the manufacturing industry.

The invention relates to the technical field of batteries, in particular to a battery capacity prediction method and system and a computer program product. The invention provides a battery capacity prediction method, which comprises the following steps of: obtaining a constant voltage stage time characteristic sequence of a battery, and inputting the constant voltage stage time characteristic sequence into a bidirectional long short-term memory network to obtain an initial prediction sequence; generating a capacity degradation sequence including battery capacity corresponding to the battery based on the capacity degradation model; constructing a mapping relation between the initial prediction sequence and the capacity degradation sequence through a dynamic time warping strategy; determining a target prediction residual error through a particle filtering model and based on the initial prediction sequence and the capacity degradation sequence which have the mapping relation; and correcting the initial prediction sequence through the target prediction residual error to obtain a capacity prediction value. The problem of reference correction error caused by time migration in a traditional battery capacity prediction method is solved, and the prediction precision and long-term stability in a capacity jump scene are improved.

A system is provided for localizing an autonomous work vehicle within mapped agricultural environments when GPS may be unavailable. Range sensors capture LiDAR images that are segmented by a trained model into features such as trunks and canopy. Trunk points are clustered to compute centroids, which are used to estimate left and right tree lines and a lane centerline relative to the vehicle. This sensor-derived centerline is compared with georeferenced lane and row geometry stored in a digital map. A particle filter maintains multiple pose hypotheses, propagates them using vehicle odometry, and weights them according to alignment between the lane centerline relative to the vehicle and the mapped lane geometry for the particle. Resampling and averaging of high-weight hypotheses produces an adjusted pose that corrects for lateral and heading errors. The adjusted pose serves as input to a navigation controller that issues steering and speed commands, enabling accurate autonomous operation.

The invention provides an indoor robot mapping precision optimization method based on Fast-SLAM, and relates to the technical field of mobile robot autonomous positioning and mapping. According to the method, in a front-end odometer module, a motion constraint relationship between laser point cloud frames is constructed based on a range flow model, and an adaptive iterative weighted least square method is introduced, so that the pose inference performance and the response rate are remarkably improved. In the state estimation module, a self-adaptive estimation Kalman filter based on a measurement residual error is adopted, and the measurement noise covariance is dynamically adjusted according to the measurement residual error, so that the adaptability and the stability of the system in a complex environment are enhanced. In a back-end particle filter module, a pose optimization method combining maximum likelihood estimation and gradient search is adopted, so that the resampling error is effectively reduced, and the mapping efficiency is improved. The algorithm framework has good universality and expandability, is suitable for autonomous navigation tasks of various mobile robot platforms, and is particularly suitable for being applied to environments with complex structures or without external positioning signal support.