924 results about "Odometer" patented technology

The invention discloses a multi-modal information fusion odometer construction method for star catalogue positioning, and relates to the technical field of star catalogue patroller positioning. The method comprises the following steps: carrying out space joint calibration on a monocular camera, a laser radar and an inertial measurement unit, reconstructing a laser radar point cloud by using a timestamp of a camera image, and realizing time synchronization of the camera image and the laser radar point cloud; establishing an IMU pre-integration error model; and performing motion compensation distortion removal on the laser point cloud by using an IMU pre-integration result, and extracting geometric features of the distorted laser point cloud based on a neighbor region smoothness calculation method of a fixed measurement distance. By researching a multimodal information fusion odometer method, the accumulative error of motion measurement is reduced, the positioning precision and stability are improved, technical support is provided for design and development of a star catalogue navigation system, and the problems that a single-modal star catalogue positioning method is weak in environment adaptive capacity and poor in algorithm generalization are solved.

The invention relates to the technical field of digital surveying and mapping of constructional engineering and construction quality control, and discloses a house surveying and mapping system for constructional engineering, which comprises a data acquisition module for synchronously generating a panoramic image signal and an inertial measurement unit signal; the preprocessing module receives a panoramic image signal and an inertial measurement unit signal, and fuses the panoramic image signal and the inertial measurement unit signal through a visual inertial odometer to generate a pose calibration signal; the fusion modeling module receives the purification point cloud signal and the pose calibration signal, and generates building component parameterized data based on a deep learning component recognition algorithm; the result output module receives the building component parameterized data and generates a construction deviation analysis report based on a preset tolerance threshold value; the intelligent control module generates an acquisition quality feedback signal through a real-time quality evaluation algorithm to the data acquisition module so as to adjust a scanning path, and sends a dynamic sampling rate control signal to the preprocessing module so as to optimize a calculation load. The problems of low house surveying and mapping efficiency, poor modeling precision and weak field real-time feedback are solved.

The invention provides an image acquisition method for three-dimensional point cloud reconstruction and component identification driving of a contact network, and particularly relates to the field of image acquisition of the contact network, and the method comprises the steps: constructing a three-dimensional point cloud model of a contact network structure through a two-dimensional laser radar and a high-precision odometer which are installed on a pushing device; identifying a work support, a carrier cable, a dropper, an electric connection and a positioning point through clustering and principal component analysis, and obtaining key parameters such as a guide height, a pull-out value and a support column limit; and in combination with camera view field parameters, a geometric model is established, a pitch angle and a yaw angle are solved simultaneously, and the camera is controlled to complete image acquisition at the optimal angle and position. Through the method provided by the invention, self-adaptive shooting control under structure identification driving can be realized, complete and clear imaging of key components is ensured, blind shooting and repeated shooting are avoided, and the method has the advantages of high precision, high efficiency and strong real-time performance, and is suitable for a complex overhead line system inspection scene.

The invention discloses an unmanned aerial vehicle autonomous navigation system based on multi-source vision assistance, and relates to the technical field of unmanned aerial vehicle autonomous navigation, and the system comprises the following modules: a terrain recognition module. A camera and a laser radar are used for synchronously collecting environment data, a visual odometer and a three-dimensional point cloud analysis technology are combined, a dynamically updated grating map is constructed, obstacle and topographic features are recognized through point cloud clustering, an improved Dijkstra algorithm is combined with an information entropy evaluation mechanism to plan a global optimal path, a B spline curve is used for eliminating path mutation, and the optimal path is obtained. The method comprises the following steps that: a dynamic speed decision-making module adjusts a flight parameter in real time on the basis of an obstacle distance-response model, and finally realizes trajectory tracking and dynamic deviation correction through closed-loop PID control and visual pose correction, so that full-link closed-loop control of'perception-planning-execution 'is formed, and the method has high precision, strong robustness and real-time response capability.

The invention relates to the technical field of high-precision positioning, in particular to an outdoor immersive text and travel hybrid positioning and dynamic rendering optimization method, and the method comprises the steps: obtaining a positioning signal of a mobile terminal in a target service area through a hybrid positioning model; the positioning signals comprise an ultra-wideband ranging signal, a visual inertial odometer signal, a real-time carrier phase difference signal and an inertial navigation signal; processing the positioning signal through a multi-layer signal processing filtering model to obtain coordinate data, and generating a space coordinate; constructing a user movement situation prediction model based on the space coordinates, generating a navigation prediction track and navigation confidence, and generating a preloading strategy; acquiring geographical environment parameters of the target service area, and integrating the space coordinates, the geographical environment parameters, the preloading strategy and the virtual object through a multi-dimensional virtual-real fusion model to generate an augmented reality space scene; and analyzing the user movement track data and the augmented reality space scene through a multi-modal driving model to generate interactive content.

The invention discloses a rapid measurement method and system for geometric irregularity of a track in a turnout zone. The method comprises the steps that complete three-dimensional point cloud data of the surface of a railway turnout steel rail are obtained in real time through a multi-angle line structure laser scanner on a special detection vehicle body; accurate attitude and position data of the detected vehicle body in the measurement process are obtained in real time through an inertial navigation measurement unit and an odometer; obtaining a high-precision pose of a detected vehicle body through a robust dead reckoning method of an IMU (Inertial Measurement Unit) assisted by a speedometer dynamic lever arm value based on a variable wheel-rail contact point; constructing a turnout area three-dimensional point cloud model; extracting geometric irregularity key points of turnout steel rail point cloud from the turnout area three-dimensional point cloud model; calculating parameters of track gauge, horizontal and rail direction irregularity and unevenness; the special detection vehicle body can quickly and stably pass through a turnout area to complete continuous and accurate measurement of geometric irregularity of a track.

The invention relates to a three-dimensional reconstruction method based on a visual odometer and deep learning. The method comprises the following steps: obtaining related parameters of a monocular camera through camera calibration; shooting continuous image frames of the target area under the plurality of camera poses through the monocular camera to obtain image frames of the target area after distortion correction under the plurality of camera poses; estimating relative poses of the camera among different image frames by using a visual odometer according to related parameters of the monocular camera; constructing a cost body of the image frame by using a cost body generation model according to the relative pose of the camera; optimizing the cost body generation model by using the geometric consistency loss and the depth consistency loss, and generating a depth map of the image frame through the optimized cost body generation model; performing noise elimination and hole filling on the depth map of the image frame to obtain an optimized three-dimensional map; according to the method, a stable reconstruction effect can be kept in a complex environment, a low-texture region and a scene with a large visual angle span, and compared with an existing single deep learning reconstruction method, the method has higher robustness and reconstruction precision.

The invention discloses a pedestrian inertia / vision / satellite integrated navigation method based on a handheld terminal PDR. The pedestrian inertia / vision / satellite integrated navigation method comprises the following steps: calculating to obtain inertial navigation pose information based on strapdown inertial navigation; when the pedestrian is static, constructing an observation constraint of zero-speed motion for correction, and updating inertial navigation position information based on step length information when a new step is carried out; obtaining visual pose information of the pedestrian based on an inertia / visual fusion odometer, obtaining satellite position information of the pedestrian based on satellite positioning, and carrying out coordinate system alignment and step length coefficient correction factor estimation; a pedestrian multi-source factor graph optimization model with a handheld terminal PDR as a core is constructed, inertial navigation pose information and step length information are used as local state constraints, visual pose information and satellite position information are used as global state constraints, and a target optimization function is constructed to obtain a final pose of a pedestrian. The method is applied to the field of pedestrian navigation, has full-scene robust and continuous positioning capability, and can effectively realize high-precision navigation positioning under long endurance.

The invention discloses a multi-source navigation data fusion method for an unmanned loader, which comprises the following steps: data acquisition: acquiring environmental perception data of satellite navigation, an inertial measurement unit IMU, a wheel type odometer and a laser radar and camera in real time; performing space-time synchronization preprocessing, realizing multi-source data time synchronization, unifying environment sensing data to a body coordinate system, eliminating abnormal data, and complementing missing data; carrying out dynamic weight calculation, establishing an error model of each sensor, and adjusting a fusion weight by using an error reciprocal exponential weighting method; layering fusion is carried out, a satellite and an IMU are fused through bottom-layer extended Kalman filtering (EKF), a laser radar and a high-precision map are fused through middle-layer ICP, a middle-layer result and a wheel type odometer are integrated through high-layer federated filtering, and high-precision fusion is achieved; and outputting and optimizing a result, outputting navigation data, performing closed-loop optimization, monitoring the health degree of the sensor, and executing redundancy switching when a fault occurs. The system comprises a corresponding processing unit, and a storage medium stores a program for executing the method.

The invention relates to the technical field of unmanned aerial vehicle intelligent inspection path planning, and discloses an unmanned aerial vehicle intelligent inspection path planning method and system based on visual inspection, and the method comprises the steps: collecting an image, and carrying out the differential superposition processing, and obtaining environment visual data; feature correction is extracted to determine a candidate area, and a template is matched to determine the type and severity of an abnormal event; performing local path planning according to an event generation mode switching instruction; according to the trajectory parameters meeting the minimum turning radius constraint, a safe fly-around path is obtained; the method comprises the following steps of: acquiring a real-time image stream, adjusting a speed parameter to obtain an optimized trajectory, processing the real-time image stream and inertial measurement data generated in the optimized trajectory through a visual inertial odometer technology to obtain environment three-dimensional point cloud data, and performing cyclic verification according to the environment three-dimensional point cloud data to obtain an event processing verification result. According to the invention, hierarchical response and dynamic local path re-planning of abnormal events can be realized.

The invention relates to an automatic guided vehicle (AGV) adaptive path tracking method based on MPC. The method comprises the following steps: S1, establishing a kinematic discretization error model based on the kinematic characteristics of the two-wheel differential AGV, processing a continuous kinematic equation by adopting an Euler discretization method, expressing a dynamic change relationship between a transverse deviation distance and an angle deviation in a state-space equation form, and generating a kinematic discrete state-space model; the method has the advantages that the continuous equation is processed by establishing the kinematics discretization error model and adopting the Euler discretization method, the deviation dynamic relation is expressed through the state space, the calculation process is simplified, the precision is kept, sensor data are fused, the wheel type odometer, IMU and laser data are integrated through the extended Kalman filtering algorithm, and the precision is improved. The real-time position and angle deviation are calculated, the positioning accuracy is improved, a model prediction controller objective function is designed, a weight matrix and boundary constraint are introduced according to constraint conditions, and the effect of obstacle avoidance constraint is combined.

The invention provides a land unmanned equipment semantic preserving large model deployment method and system, and relates to the technical field of navigation control. According to the deployment method, a preliminary control suggestion is directly output through a lightweight fusion model, optimization fusion is carried out through an MPC framework, underlying dynamics and environmental constraints, and seamless connection from semantics to control is achieved; in combination with a VILO odometer and instance segmentation, a global map containing dynamic obstacle semantic information is constructed and updated in real time, and an accurate context is provided for planning and decision making; model pruning, knowledge distillation and edge calculation scheduling are adopted, so that a complex multi-modal semantic model can run in real time on an embedded platform; the decision basis from instruction analysis to control execution is recorded and visualized in the whole process, and the transparency and credibility of the system are improved; on-line re-planning, multi-stage fault detection and switching strategies are integrated, and the robustness of the system in a dynamic environment and an abnormal condition is remarkably improved.

The invention provides an SLAM positioning method and system based on multi-sensor fusion, and the method comprises the steps: firstly receiving data of a plurality of sensors, and obtaining initial data including visual feature data, I MU pre-integration data and UWB distance data through anomaly detection and processing; initializing by using visual feature data and I MU pre-integration data, estimating an initial pose, and predicting a UWB anchor point position as a fixed constraint in combination with UWB distance data; inputting the initial data into a sliding window, outputting drifting-free odometer data, judging the motion state of the SLAM system according to the data, and if the motion state is static, executing zero-speed updating to obtain target pose data; and finally, determining a new key frame based on the parallax or visual feature data quantity of the current frame and the previous key frame. According to the invention, the problems of relatively weak robustness and relatively low positioning accuracy of the system facing extreme scenes are solved; by means of motion state judgment and zero-speed updating, the robustness of the system in long-term operation is improved.

Disclosed in the present invention are an image generation method, system and device for subway tunnel clearance, and a storage medium. The method comprises: a LiDAR collecting three-dimensional laser point cloud data along a railway, and an odometer collecting encoder data along the railway; determining current subway tunnel section point cloud data on the basis of a subway clearance detection project, the three-dimensional laser point cloud data and the encoder data; determining a clearance violation area on the basis of the current subway tunnel section point cloud data and clearance; optimizing intrinsic and extrinsic parameters of a camera on the basis of point cloud information corresponding to the clearance violation area, so as to obtain calibrated intrinsic and extrinsic parameters of the camera; and on the basis of the calibrated intrinsic and extrinsic parameters, generating a camera projection map from the current subway tunnel section point cloud data, and constructing an image of subway tunnel clearance on the basis of the camera projection map. The present invention realizes, on the basis of point cloud data and clearance, track clearance violation detection during a high-speed inspection process, thus facilitating inspection and viewing by means of constructing an image of subway tunnel clearance.

The invention relates to the technical field of artificial intelligence, in particular to a video GIS intelligent analysis method based on deep learning, and the method comprises the steps: detecting a target in a video in real time through a predefined target detection model, and generating a space-time mark which comprises a bounding box and category information; thirdly, associating target tracks of different cameras by using a dynamic graph model and a dynamic graph neural network to form a space-time ID and a track chain; then, combining a visual inertial odometer and a geographic information system to calibrate a homography matrix, and mapping the trajectory chain to a geographic coordinate system to obtain space-time trajectory data; then, constructing a neural network model and a trajectory generation model, and respectively predicting crowd density distribution and pedestrian motion trajectories; and finally, carrying out real-time alarm according to the track and the multi-level geo-fencing rule. According to the invention, intelligent analysis of video data is realized, target tracking and alarm capabilities are improved, and the method is widely applicable to the fields of crowd management and safety monitoring.

The invention relates to the technical field of agricultural intelligent equipment, in particular to a field sowing grain distance detection system and method based on computer vision, and the method comprises the steps that an image collection module collects 4KRGB and near-infrared data of a sowing area; the image processing module is used for processing and correcting the acquired image in real time; the grain distance feature deep learning analyzer strengthens seed feature extraction through an attention mechanism based on an improved YOLOv5 algorithm, accurately distinguishes weeds from seeds, and calculates a center distance deviation value of adjacent seeds based on a preset target grain distance; the positioning fusion module fuses Beidou positioning data and a visual odometer track, generates seed longitude and latitude and burial depth coordinates by using a high-precision farmland surface elevation model, and maps the seed longitude and latitude and burial depth coordinates to a farmland electronic map; and the dynamic regulation and control execution mechanism adjusts the rotating speed of the seeder or the seeding gap according to the grain distance deviation value and a priority strategy, and dynamically corrects the grain distance. Therefore, the problems of high detection error rate, single detection algorithm, poor data relevance and the like in the prior art are solved.

The invention relates to the field of mobile robot positioning, in particular to a LiDAR-inertia-vision fusion SLAM switching positioning method applied to a degraded environment. According to the method, through a multi-sensor system composed of a monocular camera, an inertial measurement unit and a laser radar which are installed on a mobile robot, original measurement data such as images, angular velocity, linear acceleration and dense space point cloud are acquired, a system state initialization model is constructed, and the multi-sensor system is used to acquire the measurement data. According to the method, the vision-inertia poses and the laser radar poses or linear interpolation of the vision-inertia poses and the laser radar poses are fused, the degradation state can be effectively recognized in real time, and multi-sensor system information switching can be carried out, so that the problem of positioning failure of the laser radar and the vision-inertia odometer module in the degradation environment can be effectively solved; the overall visual SLAM positioning precision and robustness are improved, and the long-term stable operation requirements under the severe conditions of single structure, complex illumination, sparse texture and the like are met.

The invention relates to the technical field of real-time positioning and mapping (SLAM), in particular to an SLAM method for tightly coupling a laser odometer and an inertial odometer, which comprises the following steps of: S1, receiving IMU (Inertial Measurement Unit) data and laser radar point cloud data, carrying out pre-integration processing on the IMU data and compensating laser frame motion distortion; s2, feature point selection is carried out on the laser point cloud based on curvature calculation, and angular points and plane points are distinguished; s3, uniform filtering is carried out on the feature points to optimize the point cloud distribution density; s4, calculating a lower pose change matrix of the laser speedometer according to the filtered feature point matching, and correcting IMU data of the inertial speedometer through the pose change matrix; and S5, adding multiple factors into the factor graph to optimize the global pose, and constructing a global map. Through a tight coupling bidirectional correction mechanism, the advantages of a laser odometer and an inertia odometer are combined, deep bidirectional compensation of sensor errors is realized, the pose estimation precision is effectively improved, and particularly, accumulative errors are remarkably inhibited in a dynamic interference scene.

A method and an apparatus for constructing hierarchical semantic map based on deep-learning based perception, a server, a storage medium are provided. The method includes: obtaining 3D point cloud data, scene images and motion state data; projecting 3D point cloud data onto an image plane to obtain a depth image; performing a point cloud registration and a pose prediction on the 3D point cloud data and the motion state data to obtain an odometer pose; generating a point cloud map according to the 3D point cloud data and the odometer pose of multi-sensor system; based on the depth image and the odometer pose of the multi-sensor system, performing an external ellipse fitting process on each semantic region in the depth image to obtain an object-enveloped map for respective object images; fusing the coordinately aligned point cloud map and object-enveloped map into a semantic map.

The invention relates to the technical field of rail transit automatic driving, and particularly discloses a rail transit train automatic driving control method, which is characterized in that a vehicle-mounted control center cooperates with a signal system, a traction unit and a braking system, and centimeter-level real-time positioning is realized by adopting extended Kalman filtering and fusing satellite positioning, an odometer and trackside beacon data; the parking precision of the platform is controlled based on a graded braking strategy (coasting-common-precise braking) and pulse type terminal point fine adjustment; the method dynamically adapts to line condition changes in combination with feedforward-feedback composite speed control, and establishes a multi-level safety monitoring network (obstacle detection / overspeed protection / vehicle door linkage) to realize full-process closed-loop control, and finally, high-precision, high-safety and high-coordination operation under unmanned driving is achieved.

The invention relates to a multi-mode interactive indoor unmanned aerial vehicle target detection and tracking method and system. The system comprises a target detection and tracking module, a laser radar inertial odometer module, a video display and interaction module and a flight control module. The method comprises the steps that an unmanned aerial vehicle end receives and processes RGB images, laser radar point cloud and IMU data; and optimizing a YOLOv8 target detection model and an OSTrack target tracking model through a TensorRT inference engine. By fusing LiDAR and IMU data, accurate state estimation and map construction are provided. And the ground station end displays a video stream and generates a control command through the video display and interaction module. And calculating a flight speed and a yaw angle rate by estimating an error between the target image position and the current position of the unmanned aerial vehicle, and generating a flight control instruction. The problems that an indoor unmanned aerial vehicle system often faces low target detection precision, poor tracking stability and difficult user interaction experience are solved, and the method and the system are more efficient, more accurate and higher in interactivity.

The invention discloses a multi-steering-wheel calibration method and device and a storage medium, and the method comprises the steps: constructing a conversion relation between the motion state of each steering wheel and the motion state of a vehicle body, and obtaining a motion model parameter; indicating the vehicle body to move according to the motion instruction, and calculating and calculating pose parameters based on the motion model parameters and the motion speed and the motion angle of each steering wheel in the motion instruction; obtaining an actual pose parameter after the vehicle body moves, and carrying out difference calculation on the calculated pose parameter and the actual pose parameter to obtain a pose residual error; and updating the steering wheel zero offset of each steering wheel by using the pose residual error to obtain a finally calibrated target steering wheel zero offset of each steering wheel. The scheme is suitable for multi-steering-wheel vehicle type speedometer calibration, the vehicle body pose can be calculated according to the speedometer data by deducing the conversion process of the multi-steering-wheel encoder and the vehicle body motion state, then the difference value between the vehicle body pose and the actual pose parameter is calculated to adjust the zero offset, and the accuracy of zero offset adjustment is improved.

The invention provides a multi-source data fusion system and method for laser radar assisted train positioning, and the system comprises a front-end odometer and a rear-end diagram optimization device. The front-end odometer comprises a multi-satellite multi-frequency external measuring antenna module, a double-antenna GNSS navigation receiver module, an SINS inertial navigation module and a laser radar information acquisition module; the rear-end map optimization device comprises an inertial navigation calculation module, a loopback detection module, an information fusion navigation module, a map optimization module, a map construction module and a navigation result display module. According to the multi-source data fusion system for laser radar assisted train positioning, a satellite, a gyroscope and a laser signal are fused, and the purpose of improving the positioning reliability and accuracy in a complex environment is achieved.

The invention relates to a quadruped robot laser odometer method based on ground point optimization, hardware and application, and the method comprises the steps: obtaining an original point cloud collected by a laser radar, carrying out the preprocessing, dividing a polar coordinate region, jointly extracting ground plane feature points and ground geometric edge feature points based on normal vector and local curvature features, and carrying out the calculation of the ground plane feature points and the ground geometric edge feature points; ground geometric edge feature points are utilized to optimize ground model fitting; optimizing a point set of the ground based on a time sequence reference plane of the ground; the optimized ground points are removed, and non-ground point features are extracted from the remaining point clouds; establishing feature association between the feature points in the current frame and the target point cloud, and screening effective matching pairs; a cascade weight optimization objective function associated with the ground point plane constraint, the non-ground point line feature constraint and the surface feature constraint is constructed, and continuous pose transformation of the quadruped robot is solved; hardware is realized based on the method, and the method is applied to real-time pose estimation and environment mapping of the quadruped robot in outdoor unstructured terrains and complex dynamic environments.

The invention discloses a multi-modal information fusion and active sensing pipeline defect absolute positioning method and device, which are suitable for a complex pipeline environment. The method comprises a cable deformation error compensation model, a cable speedometer and wheel type speedometer fusion algorithm based on visual auxiliary dynamic slip rate adjustment, and a pipeline defect positioning and measuring algorithm based on an improved monocular distance measurement algorithm and an active sensing strategy. And a pipeline defect geographic coordinate solving algorithm based on mileage information, GNSS and IMU information fusion. The cable deformation error compensation model corrects a measurement error caused by self-weight deformation of the cable; a cable odometer and wheel type odometer fusion algorithm is used for inhibiting positioning drift caused by tire slipping; an improved monocular distance measurement algorithm is used for calculating depth information and measuring the defect size by using the actual radius of the pipeline and the defect pixel radius; the active sensing strategy dynamically adjusts the observation distance to improve the defect positioning precision; and fusing the mileage information, the GNSS information and the IMU information to solve the position of the defect in an earth coordinate system.

A method for calibrating a mobile vehicle includes the following. The mobile vehicle performs a calibrating process, during which an odometer module of the mobile vehicle obtains a movement dataset related to movements of the mobile vehicle, and a lidar module of the mobile vehicle obtains a distance dataset related to distances to surroundings detected by the lidar module. A control module generates a variation dataset based on the movement dataset, where the variation dataset is related to changes in pose of the lidar module during the calibrating process. The control module generates a calibrated coordinate parameter and a calibrated angle parameter based on the variation dataset and the distance dataset, where the calibrated coordinate parameter is related to a position of the lidar module on the mobile vehicle, and where the calibrated angle parameter is related to an angle of the lidar module relative to the mobile vehicle.

The invention provides an AGV global positioning controller based on double-filter arbitration and space-time verification. The AGV global positioning controller comprises a visual motion estimator, a global absolute positioner and an intelligent arbitration center. The visual motion estimator generates relative pose and visual positioning confidence through data of a depth camera, an inertial measurement unit and a wheel type odometer; the global absolute locator outputs an absolute pose and a global positioning confidence coefficient through ultra-wideband base station ranging data; the intelligent arbitration center dynamically selects a fusion strategy according to the confidence degree, performs weighted fusion in a normal state, and adopts speed interpolation or drift compensation to realize positioning source switching in an abnormal state, so that the problem of positioning jump caused by failure of a single sensor in a complex environment is effectively solved; the positioning stability can be improved, smooth switching and abnormity correction are realized, and the AGV global positioning precision is further improved.

The invention relates to the field of positioning, in particular to a laser radar wheeled robot positioning mapping method and system based on ROS. The method comprises the following steps: generating Gaussian proposal distribution by fusing laser radar and odometer data through an improved RBPF Gmapping algorithm, dynamically balancing particle degradation and computing resources in combination with an adaptive resampling threshold Neff, and reducing mapping errors and memory occupation; the optimization A * algorithm adopts heuristic function weight segmentation adjustment, redundant search directions are eliminated, and path nodes are smoothed in combination with a Bezier curve; an improved DWA algorithm introduces an obstacle trajectory prediction factor, and a speed window is dynamically adjusted; iMU and laser radar weights are dynamically distributed based on EKF multi-sensor fusion positioning, pose errors are corrected and fed back to the mapping and obstacle avoidance module, and closed-loop control is formed. According to the method, the mapping precision is improved, the path planning efficiency is improved, the positioning error is reduced, and the problems of mapping accumulative error, path redundancy and insufficient dynamic obstacle avoidance capability in a traditional scheme are effectively solved.

The invention discloses a magnetic flux leakage internal detection system for gas pipe network detection and a control method, and mainly relates to the technical field of gas pipeline internal detection. Comprising a main power section, a magnetic leakage section, an auxiliary power section and a universal joint, the main power section comprises a power section sealing cabin, the power section sealing cabin is of a hollow cylindrical structure, an odometer wheel module is arranged on the outer side wall of the main power section sealing cabin, and a control circuit support and a battery control support are arranged in the main power section; the two ends of the main power section are provided with an end cover for debugging and a rear end cover respectively, the rear end cover and the magnetic leakage section are connected through a universal joint, and the magnetic leakage section and the auxiliary power section are connected through a universal joint. The device has the beneficial effects that the walking friction resistance of the magnetic flux leakage inner detector is reduced, and the bidirectional movement in a pipe network is also realized.

The invention relates to the technical field related to automatic driving, in particular to a vehicle global positioning deviation correction method based on a roadside sensing result, and the method comprises the steps: obtaining the global pose of a vehicle through a 3D target detection network in which roadside equipment is fused with time sequence features, and carrying out the uncertainty quantification; meanwhile, a vehicle end adopts a tight coupling laser radar and IMU data to realize high-frequency local positioning, a time domain self-adaptive interpolation mechanism is designed for compensating time deviation aiming at the problem of roadside sensing time delay, roadside observation is constructed into a roadside sensing factor of self-adaptive covariance, and finally, through factor graph fusion optimization, a road-side sensing model is obtained. According to the present invention, by combining the IMU pre-integration factor, the laser radar odometer factor and the roadside perception factor, the accurate vehicle pose estimation of the accumulative error correction and the global coordinate system alignment is achieved, and the experiment shows that the method significantly improves the positioning precision and the robustness in the complex environment, and effectively solves the positioning drift problem when the GNSS signal is missing.