9415results about "Navigation by speed/acceleration measurements" patented technology

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

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

The invention belongs to the technical field of positioning and navigation, and discloses a high-reliability positioning and navigation method for an air-ground cooperative unmanned system. According to the method, a negative obstacle is accurately extracted by using an RANSAC algorithm and a region growing method, a two-dimensional grid map is generated through three-dimensional point cloud re-projection, a negative obstacle region is marked on the map, and a prior map is provided for an unmanned vehicle. The unmanned vehicle constructs a local map in combination with a sensor of the unmanned vehicle, and the local map is matched with a prior map through an ICP algorithm to accurately position a global pose. According to a priori map and real-time sensor data, a grid cost map containing static obstacles, dynamic obstacles and negative obstacles is generated, a TSCNN predicts dynamic obstacle tracks according to time sequence data, map cost values are updated, an A * algorithm carries out optimal path planning based on the updated dynamic cost map, and the optimal path planning is carried out based on the updated dynamic cost map. And dynamic obstacles and negative obstacles are avoided in real time. According to the invention, efficient, accurate and safe navigation and obstacle avoidance can be realized in a dynamic environment and a complex terrain.

The invention discloses a logistics robot path planning method based on multi-modal perception, and relates to the technical field of robot path planning. Laser radar, visual camera and IMU data are fused, and environment state feature vectors are generated through multi-modal data synchronization and space-time alignment; the method comprises the following steps: analyzing environmental semantics by using models such as PointPill and YOLOv8, extracting dynamic characteristics, and identifying obstacles; constructing a space-time risk field, searching a path by a space-time algorithm, converting path points into a continuous trajectory, and optimizing the continuous trajectory; deviation is evaluated in real time, dynamic re-planning is triggered, and multi-robot cooperation and environment semantic understanding are included. Through multi-mode perception fusion, hierarchical planning, multi-target optimization and a cooperation mechanism, the obstacle detection accuracy and the obstacle avoidance success rate are improved, the path planning time is shortened, the energy consumption is reduced, the multi-robot conflict is reduced, the task efficiency is improved, the environment semantic understanding and task adaptive ability is enhanced, and the method is suitable for scenes such as intelligent storage and the like and has wide application prospects. The automation level is improved.

The invention discloses an integrated Beidou monitoring machine system integrating panoramic photographing and machine vision deformation monitoring, and relates to the technical field of safety monitoring. The system comprises a hardware structure unit and a software function unit, the hardware structure unit comprises a Beidou positioning module, an inertial navigation compensation module, a panoramic vision acquisition module, a machine vision processing module, a multi-mode communication transmission module and a power management module; the software function unit comprises a multi-source data fusion engine, a panoramic three-dimensional modeling sub-module, an intelligent early warning decision module and a cloud collaborative resolving interface. According to the invention, through real-time fusion of Beidou positioning data and panoramic vision imaging, an improved dense optical flow method is adopted to track local crack propagation, and global displacement monitoring of Beidou is combined, so that cross-scale synchronous sensing from macroscopic displacement to microscopic deformation is realized, and the comprehensive monitoring precision is improved.

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

The invention provides an IMU (inertial measurement unit)-assisted depth SLAM (simultaneous localization and mapping) method and system fusing language-vision multi-mode perception, and the system comprises functional modules such as initial calibration and semantic map initialization, pre-integration prediction and key frame judgment, dense point cloud reconstruction and relative pose estimation, semantic embedding extraction, semantic guidance loopback detection and semantic three-dimensional map incremental updating. IMU motion priori, depth geometric constraint and language model semantic factors are subjected to combined modeling through a graph optimization framework, and high-precision positioning and labeled map construction in a complex dynamic environment are achieved. Compared with the prior art which only depends on geometric or inertial information, the method has the advantages that the loop-back mismatching rate is reduced, the closed-loop convergence efficiency and the long-time relocation robustness are improved, and a semantic interface is provided for upper-layer tasks such as natural language navigation and target retrieval. The method can be widely applied to the fields of service robots, security inspection, intelligent driving, post-disaster search and rescue and the like.

The invention discloses a video analysis-based electric power operation personnel behavior identification and early warning system and method, which realize accurate identification and real-time early warning of electric power operation personnel behaviors by combining a video analysis technology with multi-modal data fusion, and effectively improve the safety management level of an operation site. Compared with a traditional safety supervision mode, the method employs a mode of combining deep learning target detection and time sequence behavior analysis, improves the recognition accuracy of operators and safety equipment, fuses the data of equipment worn by the operators with video data, improves the detection precision, and improves the safety supervision accuracy. And misjudgment caused by illumination change, shielding or complex environment is reduced. Besides, high-risk violation behaviors such as no safety helmet wearing, no safety belt fastening, violation climbing, high-altitude object throwing and the like are accurately recognized through the violation detection module, and different levels of alarm measures are adopted according to the severity of the violation behaviors in combination with an early warning feedback mechanism, so that the pertinence and response efficiency of early warning are improved.

The invention relates to an automatic driving vehicle path planning method and device based on a large language model, equipment and a medium. The method comprises the steps that a traffic scene RGB image, a traffic scene depth image, laser radar point cloud data and inertial measurement unit data of an automatic driving vehicle are acquired; performing feature extraction on the traffic scene RGB image, the traffic scene depth image, the laser radar point cloud data and the inertial measurement unit data to construct multi-modal traffic scene information; calling a preset large language model to determine the motion state of the autonomous vehicle and natural language description of traffic targets around the autonomous vehicle according to the multi-mode traffic scene information; and determining the attention weight of the target node by adopting an attention mechanism in a large language model according to the natural language description of the traffic target, and determining the optimal driving path by adopting a preset path planning algorithm according to the attention weight of the target node, the motion state of the autonomous vehicle and the target position. According to the invention, the adaptive capability of path planning is obviously improved.

The invention discloses an operation method of a humanoid agricultural robot based on path planning, which relates to the related technical field of agricultural robots and comprises the steps of multi-sensor data acquisition, data processing and map generation, map updating, target crop image recognition and positioning, global path planning, local path obstacle avoidance and picking action execution. The invention further discloses an operation system of the humanoid agricultural robot based on path planning. The operation system comprises a humanoid robot body, a visual sensor, a laser radar, a depth camera, an inertial measurement unit, an auxiliary sensor, an actuator module, a control system, a communication module and a power supply system. The inventor-shaped robot can recognize the positions and shapes of the tools in advance by using a sensitive visual perception system of the inventor-shaped robot, can skillfully cross over or bypass the tools by virtue of the flexibility of the inventor-shaped robot, can accurately determine the positions and distribution conditions of crops in a three-dimensional space, and generates a comprehensive picking environment map.

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

The invention belongs to the technical field of unmanned aerial vehicle intelligent navigation, and discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and the method comprises the steps: calculating the scene adaptation weight of multi-modal data through a dynamic attention mechanism based on a space-time alignment feature package, and carrying out the fusion to generate a multi-modal joint feature matrix; based on the multi-modal joint feature matrix, constructing a three-dimensional topological model of an urban airspace, predicting a dynamic obstacle trajectory in combination with a space-time diagram neural network, and generating a hierarchical navigation instruction set; the unmanned aerial vehicle executes a flight instruction according to the hierarchical navigation instruction set, and generates a flight state monitoring log by collecting data in flight of the unmanned aerial vehicle in real time; according to the method, the multi-modal joint feature matrix is generated through environment parameter driving weight distribution, and the complex scene sensing precision is remarkably improved.

The invention provides a robot obstacle avoidance and navigation method based on multi-modal fusion and a visual language model, and the method comprises the steps: collecting different modal data in real time through a plurality of sensors, and carrying out time synchronization processing and normalization processing; extracting features of different modal data and fusing the features through an intermediate layer; and inputting the fused multi-modal data into a visual language model, generating a semantic map of the environment by utilizing semantic segmentation of the model and a target detection result, generating an action strategy in combination with a natural language instruction and a visual analysis result, and converting the generated action strategy into a control signal which can be executed by the robot to realize closed-loop control. According to the method, the visual language model and the multi-modal sensor fusion technology are combined, and the perception, decision making and real-time response capabilities of the mobile robot in a complex dynamic environment are improved.

The invention discloses a multi-source information collaborative power equipment three-dimensional temperature field construction method. Firstly, an infrared camera, an IMU and a laser radar are utilized to obtain an accurate external parameter relation through joint calibration, point cloud distortion is eliminated, angular points and plane points are extracted, a re-projection residual error, a distance sampling residual error and an IMU pre-integration residual error are constructed, an error state iteration Kalman filter is adopted to optimize a global pose, and positioning is achieved. Providing a self-supervised depth completion network, combining an infrared temperature image and a sparse depth map generated by a laser radar as input, adopting a depth completion strategy guided by an infrared image, estimating relative motion of adjacent frames by using pose information, introducing a feature alignment module to reduce alignment errors, and combining the depth map, the infrared image and IMU data to obtain a self-supervised depth completion algorithm; and efficient construction of the three-dimensional temperature field of the power equipment is realized. According to the invention, the three-dimensional temperature field of the power equipment is constructed more accurately, and the capability of the substation inspection robot for state monitoring and fault diagnosis of the power equipment is improved.

The invention provides a power transmission line three-dimensional reconstruction method based on an unmanned aerial vehicle and a laser radar, and the method comprises the steps: dynamically adjusting the scanning field angle and scanning frequency of the laser radar according to the flight attitude of the unmanned aerial vehicle and the direction of a cable, obtaining the original point cloud data of the cable at different angles and different densities, carrying out the preprocessing of the obtained original point cloud data, and obtaining a pre-processed point cloud data; obvious noisy points and outliers are removed, and cable point cloud data after preliminary filtering are obtained; and in a local tangent coordinate system, point cloud simplification and curved surface reconstruction are carried out on each local cable point cloud segment to obtain a local cable three-dimensional model, and through coordinate system inverse transformation, the local cable three-dimensional models are spliced into a complete cable three-dimensional model to realize three-dimensional accurate reconstruction of the cable. According to the invention, the problem of accurate three-dimensional reconstruction of a power transmission line in a complex environment is effectively solved, the efficiency and precision of cable detection and maintenance are improved, and during later upgrading and reconstruction or new project construction, electric power personnel can conveniently and rapidly master cable information.

The invention relates to the technical field of building reconnaissance, in particular to a high-precision reconnaissance lofting intelligent positioning system and method based on multi-source data fusion, and the system comprises a data collection module, a fusion processing module, an intelligent positioning calculation module, a lofting path generation module, a wireless communication module and a visual terminal module. The data acquisition module synchronously acquires multi-source data; the data fusion processing module carries out space-time alignment and feature level fusion and detects the state of the sensor; the intelligent positioning resolving module outputs three-dimensional coordinates by adopting various algorithms; the lofting path generation module generates an optimized path according to the BIM model; the wireless communication module realizes high-speed data transmission and equipment communication; the visual terminal module provides a visual interaction interface. Multi-source data fusion is utilized, the positioning precision is improved, the reliability of the system is guaranteed through sensor failure detection, the system adapts to the complex environment, the lofting path is optimized, the overall performance of the system is enhanced, and an efficient and accurate solution is provided for building investigation lofting.

The present invention relates to the technical field of posture data analysis, and specifically to a posture data intelligent analysis method and system, comprising the following steps: obtaining acceleration, angular velocity, magnetic field strength, temperature and humidity, and vibration data of a posture sensor, calculating environmental disturbance parameters, and obtaining environmental disturbance values. In the present invention, the posture change rate is calculated in real time to match the trend, the instantaneous fluctuation is smoothed by a sliding window method, and the weight is adjusted adaptively to optimize data consistency and prediction capabilities. The instantaneous angular velocity and acceleration offset in a high-speed motion state are dynamically compensated to reduce errors, and the rotation matrix parameters and inertia error correction amount are adjusted at the same time to improve the stability of the posture solution, and the data processing flow is optimized as a whole, so that the posture data can maintain high stability and accuracy in different motion modes, and the long-term trend matching and inertia error compensation capabilities are improved, which is particularly suitable for complex environments and high-speed dynamic scenes.

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 discloses an ultra-wideband laser radar inertial navigation cooperative SLAM (Simultaneous Localization and Mapping) method and system, which are suitable for robot positioning and mapping tasks in a GPS (Global Positioning System)-free environment. According to the method, high-frequency motion priori of an IMU, relative pose constraint of LiDAR and absolute ranging information of UWB are fused, a unified factor graph optimization model is constructed, and multi-sensor cooperative positioning is realized; in the system initialization stage, IMU bias calibration, UWB base station coordinate configuration and LiDAR initial attitude estimation are completed; in the operation process, IMU pre-integration is utilized to predict the pose, LiDAR point cloud registration is utilized to obtain the relative motion between key frames, UWB ranging is combined to construct a residual item, a self-adaptive weight mechanism is introduced to suppress ranging abnormity, and the fusion robustness is improved; the system supports geometric feature loopback detection, cross-frame constraints are constructed in combination with UWB to carry out closed-loop optimization, and an optimization result is used for real-time incremental updating of a global map; the method has the advantages of high positioning precision, strong anti-interference capability, wide application scene and the like.

The invention discloses a tunneling equipment position calibration method and system adopting a cursor frame, and relates to the technical field of coal mine tunnel construction tagging. Geologic feature data in a coal mine tunnel are collected in real time through a dynamic environment data set, an environment adaptability index Szp is calculated, and the influence of the environment on equipment calibration is evaluated; a calibration error evaluation coefficient Gz and an error growth trend value Wczs are combined, a calibration strategy is dynamically optimized, adaptive filtering and a multi-modal fusion algorithm are adopted to improve positioning precision, and stable work in an environment with large geological change is ensured; meanwhile, calibration parameters are adjusted in real time by using a deep learning model and a self-adaptive path calibration algorithm, and the adaptability to complex geological conditions is enhanced; the interference compensation module processes electromagnetic interference through Kalman filtering, particle filtering and other algorithms, optimizes the error coefficient Ins of the inertial navigation system, corrects the deviation of the sensor, reduces the influence of external interference on the calibration precision, and guarantees the high precision and stability of the system.

The invention provides an underwater vehicle multi-source information fusion accurate navigation method and system. The method comprises the following steps: firstly, acquiring inertial navigation data, DVL data and ocean current information of an underwater vehicle and observation information of a distance between the underwater vehicle and a beacon, and preprocessing the inertial navigation data, the DVL data and the ocean current information; then, establishing a state model according to the kinematics principle of the underwater vehicle, and establishing an observation model by combining various types of information; carrying out fusion processing on the state model and the observation model by adopting a self-adaptive Kalman filtering algorithm; and finally, estimating the current position of the underwater vehicle according to the fused state vector, comparing the current position with a preset target position to calculate a deviation, and correcting the deviation by adjusting a rudder angle and the rotating speed of a propeller. According to the method, multi-source information is integrated, innovation is carried out on construction of a state model and an observation model and position estimation and deviation correction, adaptive Kalman filtering is adopted, and the method has the advantages that the navigation precision is improved, the ocean current influence is considered, adaptive adjustment is realized, and real-time navigation is realized.

The invention relates to the technical field of navigation, and provides a dynamic environment self-adaptive intelligent navigation method and system, target detection and tracking are performed after an image is corrected through pose priori, and the dynamic probability of feature points is estimated by fusing Bayesian filtering under multi-key-frame observation; dynamically optimizing the weights of the feature points in combination with a time decay factor and a re-projection error, extracting static map points, and jointly optimizing a pose based on an IMU pre-integration residual error and a visual error to construct a high-precision SLAM map; multi-view dynamic probability estimation and a feature point weight optimization mechanism based on a reprojection error and a time decay factor are innovatively introduced, and the feature screening precision and the pose estimation stability of the SLAM system in a dynamic environment are cooperatively improved; the problems of reduced positioning precision, unstable mapping and insufficient real-time performance of a traditional SLAM system in a dynamic environment are effectively solved.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle flight path positioning method and system based on multi-source information fusion. The method comprises the following steps: acquiring an original observation data stream of an unmanned aerial vehicle in real time through a sensor array, and preprocessing to output a multi-source data sample set; calculating relative motion increment between adjacent key frames through an IMU pre-integration model, and extracting local pose estimation through a visual geometric calculation method; evaluating the confidence of each sensor in real time to dynamically generate an adaptive weight coefficient; and constructing a tight coupling fusion filter taking position, speed and attitude errors as core state quantities, and outputting a global optimal trajectory of the unmanned aerial vehicle. According to the invention, the weight is dynamically distributed through the real-time confidence of the sensor, the multi-source data association is fully mined, and the flight path positioning precision and environmental adaptability of the unmanned aerial vehicle are improved.

The invention relates to the field of virtual reality positioning, and discloses a VR large-space positioning interaction system based on multi-modal perception, and the system comprises the steps: deploying a multi-modal sensor to obtain sensing data, carrying out the visual feature extraction and preprocessing, building a sparse point cloud map in a matching manner, carrying out the scale calibration, and constructing an environment model; pre-judging a UWB signal path based on an environment model, performing error optimization compensation on an NLOS state, and performing observation updating and fusion through degradation detection to obtain a predicted state change; constructing an interactive perception network, and tracking the hands and the whole body; tactile feedback is realized by using a layered tactile system, and a tactile effect is generated by using vibration frequency mapping; the transmission efficiency is improved by using a beam forming technology, an edge cloud server cluster renders a virtual scene, and the scene is pre-rendered in advance to offset network and rendering delay; an online calibration mechanism is designed, and system errors are corrected through visual loopback detection, UWB beacon dynamic correction and IMU drift compensation.

The invention discloses an unmanned aerial vehicle positioning system and method based on multi-source position signal fusion, and relates to the technical field of unmanned aerial vehicle positioning, and the system comprises a plurality of positioning data collection units which are carried on an unmanned aerial vehicle, and each type is specially used for collecting positioning data of a single source; the positioning data processing unit is suitable for calculating a confidence coefficient value based on the attribute parameter of each positioning data, and determining a weight of each positioning data according to the confidence coefficient value; and the positioning data fusion unit is suitable for fusing all the positioning data by applying a fusion algorithm and combining the weights of the positioning data to generate fused positioning data. According to the system, the confidence of each source positioning data is evaluated in real time, and the fusion weight is dynamically optimized according to the confidence, so that the dynamic evaluation and optimal fusion of the multi-source positioning data are realized, and the positioning precision and reliability of the unmanned aerial vehicle in a complex environment are improved.

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

The invention relates to an underwater robot navigation positioning method and system. The method comprises the following steps: S1, acquiring angular velocity and acceleration signals through an inertial measurement unit; s2, resolving a three-dimensional velocity observation value according to the beam radial velocity vector signal in combination with the angular velocity signal, and extracting environment feature point cloud data according to the acoustic image signal; s3, multi-source data time synchronization is carried out, and a fusion input signal with time-space alignment is generated; s4, constructing an adaptive factor graph optimization model, and dynamically adjusting an inertial navigation solution node based on a real-time weight coefficient; inputting the environment feature point cloud data into a closed-loop detection module to generate a loopback factor node, and adaptively correcting the weight of the node according to the feature matching degree; and S5, solving the adaptive factor graph optimization model through a nonlinear optimization algorithm. According to the underwater robot navigation positioning method and system, the problem that the fusion positioning precision of a multi-source heterogeneous sensor is insufficient in an underwater GPS-free environment can be solved.

The invention discloses an underwater vision-inertia-acoustic odometer positioning method fused with a DVL, and belongs to the technical field of robot navigation positioning. According to the method, firstly, an underwater image acquired by a camera and carrier motion data acquired by an IMU and a DVL are used as input information to carry out time alignment and space coordinate system conversion; performing feature point extraction on the image to construct a visual re-projection residual error, and dynamically adjusting a weight ratio of visual information based on the number of feature points and the re-projection residual error; calculating IMU data increment between adjacent frames based on pre-integration to construct an inertial residual error; calculating the displacement change between image frames according to the speed information measured by the DVL to construct a DVL residual error; establishing a nonlinear least square optimization function based on Bayesian maximum posteriori estimation; vision, inertia and acoustic speed measurement data are fused at the rear end through a sliding window mechanism, a multi-constraint joint optimization model is constructed, and the robustness and accuracy of a vision-inertia-acoustic odometer system in the underwater environment are improved.

The invention provides an all-terrain autonomous navigation and task planning method and system for a photovoltaic inspection robot, and relates to the technical field of photovoltaic inspection, and the method comprises the steps: constructing a three-dimensional grid map and generating an initial inspection path, dividing hexagonal sub-regions and calculating a passing cost matrix, and optimizing a global inspection path based on the cost matrix. The motion state is predicted in real time, the motion track is corrected, thermal imaging and visible light data are collected to generate an inspection report, autonomous navigation and efficient inspection under complex terrains can be achieved, the inspection efficiency and accuracy are improved, and the labor cost is reduced.

The invention provides a real-time code spraying and position retesting method and system for a positioning and testing robot in signal construction, and relates to the technical field of robot positioning and testing, and the method comprises the steps: obtaining the three-dimensional point cloud data of a construction area, and generating a grid map; identifying a positioning mark point and planning an optimal motion track; controlling the fixed measurement robot to move and keeping the code spraying device horizontal; when a preset position is reached, the height of the nozzle is adjusted and code spraying is performed; collecting a code spraying image and extracting contour features; comparing the actual sprayed code with a preset template, and calculating a deviation; optimizing the motion parameter when the deviation exceeds a threshold; and uploading the deviation data and generating a quality report. The method improves the accuracy and automation degree of signal construction.