615 results about "Odometer" patented technology

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 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 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 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.

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 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.

The invention is suitable for the technical field of artificial intelligence, and provides a robot visual navigation optimization system based on edge computing, the system comprises a visual sensor module, an edge computing unit, a navigation algorithm module, a communication module and a robot executor, the visual sensor module is used for collecting high-definition images or video streams, and the edge computing unit is used for processing the high-definition images or video streams; the edge calculation unit is responsible for locally executing a visual navigation algorithm, the navigation algorithm module is deployed on the edge calculation unit, and the navigation algorithm module is used for executing a visual odometer, synchronous positioning and map construction, environment perception, obstacle detection, local path planning and motion control; and the communication module is used for carrying out model updating, task scheduling and non-real-time data uploading with a cloud. Complex visual data processing and navigation decision are sunk to the robot body or near-end edge equipment, so that the navigation precision and the response speed of the robot in complex and diversified environments are remarkably improved, and the energy consumption efficiency is optimized.

The invention belongs to the technical field of traction robot control, and relates to an intelligent sensing control method and system for a traction robot. Checking the consistency of satellite positioning, ultra-wideband positioning and odometer information, calculating the credibility, fusing to obtain a fused pose and pose confidence, and when the confidence is lower than a threshold, rejecting low-credibility information to degrade and re-fuse; environment representation is constructed based on laser point cloud and image semantics, and dynamic obstacles are updated; generating and updating a target track according to environment change or confidence change under environment representation and motion constraint; and generating a control instruction according to the target track, the fused pose and the traction connection angle, and parking when a safety condition is triggered. According to the technical scheme, through trailer motion constraint, multi-source positioning consistency check, dynamic obstacle environment representation and track self-adaptive updating cooperation, pose reliability maintenance and track-control closed loop stability are achieved, and the trailer / motor home shifting and parking requirements of a camping site and a parking lot are met.

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 discloses an urban canyon positioning method and system fusing GNSS, vision and low orbit satellites, GNSS signals and images near a visual sign are collected in an urban canyon to serve as mapping points, and sign angular point coordinates are recorded; and analyzing to obtain a GNSS latitude and longitude list corresponding to each mark, and extracting LoFTR feature descriptors and storing the LoFTR feature descriptors in a map database. During positioning, firstly performing rough matching with the current position through the KNN, and then realizing fine positioning in the candidate area through LoFTR feature matching and homography matrix calculation; and if the matching fails, calculating a joint confidence coefficient based on the satellite PDOP / number, and estimating the current position through dynamic weighting by fusing Kalman filtering with a visual odometer. According to the method, a two-stage positioning strategy is adopted, GNSS and visual information are preferentially used for high-precision matching positioning, a fusion positioning mechanism based on low-orbit satellite joint confidence is started after failure, and the matching success rate and the positioning precision under the complex visual condition are improved.

A method for previewing the terrain in front of an emergency rescue vehicle in a geometric feature degradation scene relates to the technical field of road surface recognition, realizes accurate alignment of LiDAR point cloud and IMU data through timestamp synchronization and linear interpolation, and combines a spherical projection model and a degradation perception complementary feature selection algorithm to realize the previewing of the terrain in front of the emergency rescue vehicle. Converting the three-dimensional point cloud into a robust intensity image and extracting gradient significant features; based on a double-observation secondary filter frame, a motion state is predicted by utilizing IMU forward propagation, a point cloud geometric residual error and an intensity image luminosity residual error are synchronously fused, timestamp deviation is compensated through back propagation, and a multi-source observation model under a global coordinate system is constructed. Finally, the error state is iteratively optimized to realize collaborative output of the high-precision odometer and the three-dimensional terrain map, and the problems of data asynchronism, feature degradation and dynamic interference in a complex scene are effectively solved.

The invention discloses an inertial navigation error compensation method and system based on gravity assistance, and belongs to the field of navigation. The inertial navigation error compensation method is suitable for long-time indoor inspection of the robot. Firstly, channel state information, odometer data and inertial measurement data of a robot are obtained; calculating an initial attitude through angular velocity integration, and calibrating a pitch angle and a roll angle by using the separated gravity vector; calculating a motion increment based on odometer data, and performing coordinate conversion in combination with the calibration attitude to obtain navigation position information; inputting the channel state information into a neural network to obtain a channel fingerprint, and matching target position information; and finally, calculating the cross covariance between the navigation position and the target position to obtain a state correction value, and compensating the navigation position and course angle error. According to the invention, when inertial navigation error compensation is carried out on the robot for long-time indoor inspection, the compensation accuracy and effect can be improved.

The invention discloses a multi-mode SLAM implementation method for intelligent garden supervision, a software system and a hardware device, and belongs to the technical field of intelligent garden supervision, synchronous positioning and map construction. According to the technical scheme, the multi-mode SLAM implementation method for intelligent garden supervision comprises the following steps: S1, sensor calibration: acquiring a camera internal reference matrix and distortion parameters by adopting a checkerboard calibration method; compensating motion distortion through a constant-speed model, cutting sub-frames, and calculating a transformation matrix from the radar to the IMU in combination with the output of a pure laser odometer and IMU data; generating a transformation matrix from the radar to the camera through feature extraction, matching and external parameter optimization; through multi-modal sensor fusion and space-time synchronization technologies and in combination with collaborative optimization of the laser-vision-inertial odometer, high-precision map construction with geometric structure and color information is realized in a complex garden scene, and the method has the advantages of improving positioning precision and map information integrity in a complex garden environment.

The invention discloses an infant airway management system and method based on navigation and high-precision dynamic registration, and the method comprises the steps: constructing a personalized airway deformation model through a physical constraint-based few-sample transfer learning network, generating an optimal instrument matching scheme, and providing a semantic weighted tight coupling visual inertial odometer; the confidence coefficient of feature points is dynamically adjusted by segmenting airway anatomy semantics in real time, data are fused under a factor graph optimization framework, and sub-millimeter robust positioning is achieved. Based on the high-precision trajectory, the system introduces a space-time attention fusion network, captures a long-range time sequence dependency relationship between operation micro-actions and physiological parameters, calculates a real-time dynamic risk score, and compensates airway soft tissue deformation in real time through a non-rigid deformation field model. The technical problems that an existing navigation system is prone to being lost in a microcosmic dynamic environment and risk early warning lags behind are effectively solved, and the safety and the success rate of the infant airway management operation are remarkably improved.

The invention discloses a tunnel face geological information acquisition and image processing method based on machine vision, which belongs to the technical field of image processing, and comprises the following steps: resolving the pose of a terminal in real time through a visual inertial odometer, generating augmented reality guide information, and guiding the terminal to move to a standard acquisition point; at each acquisition point, performing multi-stage verification of scene semantic compliance, motion blur and defocus and illumination uniformity on the image flow to ensure the acquisition quality; for tunnel high-dynamic illumination, adopting an adaptive brightness segmentation and multi-resolution fusion technology based on an improved Otsu algorithm to perform high-dynamic range reconstruction on a single-frame image so as to recover details; and finally, performing clustering optimization based on the feature matching similarity, and screening out an optimal image subset for three-dimensional reconstruction. According to the method, the problems of subjective and random acquisition process, many image quality defects, detail loss under extreme illumination and large image data redundancy are solved.

The invention discloses an industrial AGV navigation method based on visual BEV representation and hierarchical asynchronous control, and the method comprises the steps: collecting a multi-view perspective view of AGV navigation, and achieving an intermediate-frequency visual odometer and a high-frequency wheel type odometer through a prediction cost map and 3-DoF relative pose transformation in a BEV space; hierarchical asynchronous self-adaptive navigation based on multiple frequencies: a parameter tuning network operates at a low frequency and adaptively tunes planner parameters based on a local cost map and a relative target point provided by a planner; the local planner runs at an intermediate frequency, and generates a local track and a feed-forward speed based on the optimized planner parameters; the controller operates at a high frequency and predicts a feedback speed based on a local track, a feed-forward speed, a robot state and a local cost map, the feedback speed and the feed-forward speed are superposed and then used for controlling AGV navigation, and a complete navigation solution with low cost, high precision and high adaptability is provided.

The invention relates to the technical field of medical instruments, in particular to a capsule endoscopy focus automatic identification and positioning system. The system comprises an image acquisition module, an intelligent analysis module and a space positioning module. The image acquisition module receives an alimentary canal image sequence captured by the capsule endoscopy; the intelligent analysis module is integrated with a pre-trained deep convolutional neural network, analyzes the image frame by frame, and outputs a pixel-level segmentation mask identifying the contour and type of the focus; and the spatial positioning module estimates the pose of the capsule endoscopy and constructs a topological map of the inner wall of the digestive tract by analyzing the motion of feature points in the image sequence and combining a visual odometer and a graph optimization SLAM algorithm, and finally maps the recognized focus to specific coordinates of the map. According to the invention, automatic identification and accurate positioning of the nidus of the digestive tract are realized.

The invention discloses a degradation scene-oriented multi-sensor fusion mapping method and device, and the method comprises the steps: receiving multi-source data through a front-end fusion odometer, and carrying out the alignment of a coordinate system, so as to obtain an initial pose; through IMU integration and extrinsic parameter transformation, point cloud distortion removal is completed, and a laser radar priori pose is generated; based on an ICP Hessian matrix, decomposing and analyzing point pair contribution vectors, and judging pose degeneration; constructing a laser point-surface residual error and an RTK pose residual error for a non-degraded frame, updating a state through ESIKF, and outputting a key frame pose, a point cloud and a degradation label; performing back-end factor graph optimization: screening non-degraded key frames through a sliding window, and adjusting and optimizing the pose through a point cloud beam; carrying out secondary verification on the RTK pose to remove invalid values; and constructing an odometer, a loop and an RTK factor, and fusing and optimizing to obtain a globally consistent high-precision point cloud map. According to the method, robustness is enhanced through degradation detection, global consistency is guaranteed through factor graph fusion, and the method is suitable for complex scene mapping.

The invention provides a pose map optimization method and device and XR equipment, and relates to the technical field of image processing, and the method comprises the following steps: calculating speedometer constraints between adjacent key frames; calculating a speedometer covariance matrix corresponding to speedometer constraints through an uncertainty propagation model; wherein the uncertainty propagation model is obtained by deducing an odometer constraint formula through an analytical method; calculating a loopback constraint between the current frame and the closed-loop candidate frame and a loopback covariance matrix corresponding to the loopback constraint; according to the speedometer constraint, the speedometer covariance matrix, the loopback constraint and the loopback covariance matrix, constructing a pose map optimization problem; and establishing a global information matrix, carrying out optimization solution on the global information matrix, and updating the pose of the key frame. The method can be operated in real time at an XR equipment end with limited computing power, low-delay and high-precision pose estimation is realized, and stronger robustness can be shown in a complex environment.

The invention relates to a mobile welding robot weld path planning method fused with binocular vision, and belongs to the technical field of mobile welding robots. Comprising the following steps of S1, weld joint feature recognition, specifically, weld joint images are collected and processed through a global vision unit fixed to a movable chassis and a local vision unit fixed to the tail end of a mechanical arm, and weld joint feature information is recognized and extracted; s2, generating and fusing double-level tracks; s3, track execution and correction; and through a visual odometer of the movable chassis, the translation drift distance of the movable chassis in the linear direction is collected in real time, the controller controls the mechanical arm to move according to the track according to the final welding track, and weld joint welding is completed. The recognition robustness is improved through double vision, welding seam global positioning and local detail recognition are both considered, interference of welding fume and arc light is effectively resisted, and the feature point leak detection / false detection rate is reduced; the adaptive region adapts to multiple processes, the trajectory precision is optimized by dynamic weight, and the overall precision of trajectory planning is greatly improved.

The invention provides an outdoor cross-modal robust positioning and navigation method for extreme severe weather, and relates to the field of online positioning and navigation of mobile robots. The method comprises the following steps: acquiring and preprocessing data; generating a path point set based on a CMR network, perfecting the path point set through local trajectory fitting and error optimization, and constructing a path point relative pose measurement-topology hybrid map; motion prior estimation is carried out based on Doppler velocity, effective matching prior estimation is generated in combination with LiDAR odometer information, cross-modal pose estimation is carried out through a CMR network, and a pure tracking strategy and PD controller path point tracking are adopted; and geometric and intensity alignment is carried out on the forward 4D millimeter wave radar point cloud and the omnidirectional 3D laser radar point cloud through a CMR network, a rotation matrix and a translation vector are output, and accurate positioning is completed. According to the invention, the sensing and positioning defects in extreme weather in the prior art are overcome, and the positioning precision and robustness are improved.

The application discloses a robot area traversal method and belongs to the field of intelligent robots. The method first judges whether a feedback signal of a laser sensor is received when the robot receives a start signal for starting traversal, and when the feedback signal of the laser sensor is not received, the robot can rely on sensors such as a photosensitive sensor, a gyroscope and an odometer to perform area traversal in a non-laser navigation mode, so as to clean the bottom area of furniture with a relatively low bottom space, thereby improving the application range of the robot and remarkably improving practicability.

The invention discloses an autonomous mobile robot path planning obstacle avoidance method and system, relates to the technical field of autonomous mobile robot path planning and obstacle avoidance, and is used for solving the problems that in a deep mine multi-layer roadway, multiple environmental risks are difficult to quantify, and once a key bottleneck channel is blocked, task failure is easily caused, and the reliability of path planning is insufficient. After an autonomous mobile machine is powered on, sensor calibration parameters are loaded, pose fusion is completed through extended Kalman filtering based on a multi-source odometer, an inertia sensor and a laser vision sensor, and an occupation grid map with static obstacles, dynamic candidates and passable marks is constructed. The method comprises the following steps: constructing a simultaneous multi-cause environmental risk feature vector to obtain an environmental comprehensive risk cost, performing layered screening and comprehensive cost sorting on search nodes in combination with task blocking sensitivity of off-line calculation on a mine laneway traffic map, and then generating a time parameterized speed reference trajectory; the Mecanum wheel omni-directional drive mechanism is guided to pass through the high-risk area along the safe and smooth path.

The invention discloses a wheel-foot type robot dog navigation method and a wheel-foot type robot dog navigation system based on ROS Native. According to the method and the system, high-precision odometer information provided by FastLIO is combined, a perceptual layer is newly added in Costmap2D, a perceptual algorithm is utilized to identify a passable area, and a Theta * algorithm is adopted as a global path planner to generate a smooth path. Through the technical fusion, the problems of adaptability, positioning stability and planning efficiency of the robot dog in a complex environment are effectively solved, so that efficient and accurate navigation of the robot dog is realized, and the reliability and the overall performance of the system are remarkably improved.