811results about "Navigation by speed/acceleration measurements" patented technology

According to the multi-source information fusion robot positioning method and system for the unstructured environment provided by the invention, fusion processing is carried out on multi-source information data such as laser point cloud, environment image information and acceleration which are acquired in real time, so that the multi-source information fusion robot positioning method and system for the unstructured environment can be realized in a severe weather state and a dynamic unstructured environment. Carrying out high-robustness real-time positioning under the condition of violent illumination change; in a geometrically degraded roadway area, registration positioning of laser point cloud emission fails due to lack of enough external characteristics, and at the moment, the visual positioning module can still work normally and complete robot repositioning by detecting matched image information; in an environment lacking enough illumination, the laser point cloud and acceleration information can still provide enough positioning fusion data input for the computing unit.

The application provides a wind field measurement method and system based on a wave glider laser wind radar, comprising: measuring a wind field radial velocity based on a laser wind radar carried on a wave glider platform, measuring attitude data by an IMU, and measuring speed data by a GPS; obtaining an installation external parameter of an optical axis of the laser wind radar relative to a ship body of the wave glider platform through installation calibration; fusing and solving the attitude data measured by the IMU and the speed data measured by the GPS to obtain attitude angles and motion parameters of the wave glider platform; performing attitude compensation on the wind field radial velocity according to the attitude angles and the installation external parameter to obtain a real measurement direction, and performing motion compensation on the wind field radial velocity in the real measurement direction according to the motion parameters; performing vector inversion and height mapping on the wind field radial velocity after the motion compensation to obtain a wind profile. The application realizes high-precision measurement of an air wind field above a sea surface to a height of 2 km.

The application provides a three-dimensional structure multi-layer mapping method and system based on ground plane correlation constraints, comprising: a real-time maintenance height state machine, which is used for controlling the robot to switch between a cruising state representing cruising on the same floor and a crossing state representing height change; in the cruising state, the robot acquires environment point cloud data in real time and is correlated to a floor sub-map corresponding to the current height; in the crossing state, the robot suspends updating the floor sub-map and maintains a channel map containing a topological connection relationship; layered map data of each floor is generated and output, and the layered map data comprises a floor index file, a layered point cloud file and a topological connection file. Through the introduction of the ground plane geometric prior constraint and the height probability distribution statistical mechanism, accurate pose determination in the vertical direction and structured management of the multi-floor map are realized.

The specification discloses a multi-sensor-based combined navigation method and device, which can acquire sensor data collected by multiple sensors. Then, the sensor data corresponding to the sensors is input into a second-level filter according to a first combination mode to perform information fusion, so as to obtain a state estimation value of a local state quantity. Then, the output result of at least part of the second-level filter or the sensor data of several sensors is input into a third-level filter according to a second combination mode to perform information fusion, so as to obtain a state estimation value of a global state quantity. Finally, the state estimation values of each navigation state quantity from different data sources are input into a fourth-level filter, so as to obtain a final estimation value of each navigation state quantity. By combining different sensors, multiple combined navigation systems with independent navigation capabilities are constructed to redundantly estimate the navigation state quantity, so that the navigation state quantity remains independent of each other in the combined navigation process, failure coupling is avoided, and the reliability and fault tolerance of the navigation system are improved.

In accordance with various embodiments of the present disclosure, a method for confirming kickback is provided. In some embodiments, the method for confirming kickback comprises, in response to an indication of a kickback occurrence, comparing an input current to a predetermined current threshold; if the input current is greater than the predetermined current threshold for a first predetermined amount of time, generating a kickback interrupt signal; if the input current is not greater than the predetermined current threshold, comparing an input voltage to a predetermined voltage threshold; if the input voltage is less than the predetermined voltage threshold for a second predetermined amount of time; generating a kickback interrupt signal; and if the input voltage is not less than the predetermined voltage threshold and the input current is not greater than the predetermined current threshold, generating a false positive (FP) signal.

The application provides a laser industrial quality detection method and device based on semantic perception, and the method comprises the following steps: acquiring multi-modal data information of an industrial area to be detected; performing space-time calibration on the multi-modal data information to obtain a multi-dimensional feature vector of each sampling point after fusion; inputting the multi-dimensional feature vector of each sampling point into a pre-trained semantic segmentation network for semantic segmentation to generate a three-dimensional semantic map; acquiring a detection instruction, analyzing and target reasoning the detection instruction to obtain a detection target coordinate; performing path planning according to the detection target coordinate and the three-dimensional semantic map to obtain an optimal execution path for moving to a target position; moving to the target position according to the optimal execution path, and selecting a corresponding scanning mode according to the information of the three-dimensional semantic map to perform quality detection on the target to obtain a corresponding detection result; thereby improving the intelligent level and detection efficiency of the quality detection robot.

The present disclosure provides a kind of adaptive obstacle detection method and system, applied to unmanned driving technical field, comprising: by autonomous navigation module, the passable area information is calculated, and global environment navigation map is obtained, according to global environment navigation map, the point cloud data and IMU information of predetermined frequency are collected, and the unmanned vehicle attitude information is obtained based on IMU information;Through the road surface slope information of unmanned vehicle attitude information, the ground segmentation is carried out to point cloud data based on road surface slope information, and the non-ground point cloud data after ground segmentation is obtained, the obstacle detection is carried out to the non-ground point cloud data after ground segmentation, and the obstacle data is obtained.The present disclosure can solve the problems that the current mainstream ground segmentation algorithm generally exists poor real-time, over-segmentation, cannot completely segment ground with noise, and serious under-segmentation occurs when encountering slope road surface, improve the real-time and accuracy of obstacle detection.

The application provides an assembled trolley control system, and relates to the technical field of trolley intelligent control systems.The technical scheme points of the application are as follows: an environment sensing module, a posture detection module, an intelligent control module and an execution driving module are communicated;the environment sensing module comprises a laser radar submodule, a visual camera submodule, an ultrasonic sensor submodule and a data fusion submodule;the laser radar submodule is used for scanning the three-dimensional profile of a work area;the visual camera submodule is used for identifying component marker points;the ultrasonic sensor submodule is used for detecting close-range obstacles;and the data fusion submodule adopts a Kalman filtering algorithm to perform noise reduction processing on the multi-source sensor data.The assembled trolley control system provided by the application has the advantages of high positioning accuracy, strong environmental adaptability and good control coordination, and can meet the high-precision assembly requirements.

The application discloses a three-dimensional blasting vibration response non-contact measuring device and a surrounding rock state evaluation method, and relates to the field of solid deformation measurement. The three-dimensional blasting vibration response non-contact measuring device and the surrounding rock state evaluation method use a single high-speed camera and a two-stage reflection structure to acquire images of each target point in a monitoring area when blasting operation is performed in a tunnel, simultaneously use an inertial sensing unit to collect vibration data of the high-speed camera, calculate real vibration displacement of each target point in a world coordinate system according to the images of each target point in the monitoring area acquired by the high-speed camera and the vibration data collected by the inertial sensing unit, further extract a wave velocity field of the monitoring area, calculate a wave velocity reduction rate, and evaluate the surrounding rock state of the monitoring area according to the wave velocity reduction rate. The three-dimensional blasting vibration response non-contact measuring device can realize non-contact measurement of the three-dimensional response of the tunnel blasting vibration by using a single high-speed camera, remove errors caused by the vibration of the high-speed camera itself on the evaluation result, and effectively improve the evaluation precision.

Improved method for simultaneous localization and mapping; associated computer system and program. This method, performed by a computer (40) mounted on board a vehicle (1), consists of: acquiring a set of points at the current time, provided by a Doppler radar system (30) of the vehicle; acquiring the attitude at the current time of the vehicle, provided by an inertial measurement unit (20) of the vehicle; orienting the set of points at the current time with respect to a ground reference frame (X0Y0Z0), taking into account the attitude at the current time; processing the radial velocities of the points to calculate an estimated velocity at the current time of the vehicle (1); calculating a position at the current time of the vehicle (1) from the estimated velocity at the current time; and executing a simultaneous localization and mapping algorithm from the position at the current time of the vehicle over a plurality of successive times. Figure for the abstract: Figure 1

The application discloses an indoor unmanned aerial vehicle multi-sensor fusion navigation positioning method and system, belongs to the technical field of unmanned aerial vehicle navigation positioning, and solves the technical problems of low positioning precision, poor robustness of a single sensor, UWB non-line-of-sight (NLOS) ranging deviation, IMU cumulative error and the like in an indoor environment without GNSS. The method comprises the following steps: modeling the UWB ranging deviation under the NLOS condition, describing the multi-modal characteristics of the ranging error through a mixed Gaussian observation model, combining an adaptive measurement noise adjustment (IAE) mechanism and a robust kernel function to suppress abnormal measurement; constructing a bias-aware extended Kalman filter (Bias-Aware EKF) fusion framework, and integrating position, velocity, attitude, IMU zero offset and UWB bias into a unified state vector model; fusing multi-source information of IMU, UWB, height sensor and optical flow to realize high-precision estimation of the state of the unmanned aerial vehicle; and building a simulation and physical verification platform to complete algorithm verification. The application improves the precision, robustness and long-term stability of unmanned aerial vehicle positioning in a complex indoor environment, and can be widely applied to unmanned aerial vehicle operation scenes such as indoor inspection, logistics and security.

The present application relates to a kind of mine personnel vital signs monitoring and early warning bracelet, relate to mine personnel vital signs monitoring and early warning technical field.The main body rear cover is externally fixedly arranged rubber ring, the inside of the rubber ring is opened gas storage cavity, located in body, is equipped with the gas supply mechanism for the gas storage cavity in rubber ring inflation;Second gas leakage passage leading to the space surrounded by rubber ring is opened on the gas storage cavity, the notch independent of gas storage cavity is opened in the bottom of rubber ring;Switching component for opening or closing second gas leakage passage is provided in the gas storage cavity.The present application is provided with the rubber ring that can inflate and deflate on bracelet, when wearing bracelet, utilize the rubber ring that inflates and expands and skin close adhesion, form closed loop sealing surface, to avoid external coal dust, rock dust into sensor sensing area, ensure the monitoring effect of sensor;Again, the inflation of rubber ring improves the comfort of wearing.

This invention belongs to the field of underground autonomous exploration technology and discloses a synchronous localization and mapping method for complex underground environments. This method aims to explore complex underground environments without satellite signals, making breakthroughs in key areas such as hardware system design, data synchronization between sensors, kinematic modeling of quadruped robots, and multi-sensor fusion technology. This results in a precise and robust SLAM system, thereby promoting the development of autonomous exploration in the field of underground mobile robots. The underground SLAM method based on a quadruped robot dog features reliable performance, strong obstacle-crossing ability, good real-time performance, and strong robustness. Besides applications in underground resource exploration, natural disaster relief, and digital management of mine environments, it can also meet the application needs of underground spaces in large cities, large caverns, and subway spaces. This invention will fill a gap in the development of my country's underground autonomous exploration field and has significant economic and social benefits in both civilian and military applications.

This invention provides a self-following ground-penetrating radar (GPR) detection system and its control method. A handheld terminal provides real-time positioning of the operator, issues detection tasks marked with target areas, paths, or follow instructions, and receives detection results. The self-following GPR device adopts a modular architecture. The mobile platform flexibly connects the chassis and drive mechanism via suspension arms and shock-absorbing damping components, enabling all-terrain capability. The self-following control module integrates sensors such as positioning, lidar, vision, and IMU, fusing multimodal data to complete path planning, follow control, and obstacle avoidance decisions, and actively adjusts the suspension based on attitude recognition to maintain vehicle stability. The GPR is installed at the bottom of the platform to collect echo data. The GPR control unit jointly processes the radar data and positioning information to generate radar profile maps, 3D imaging maps, and detection reports, which are then transmitted back to the handheld terminal. This invention balances the efficiency of automated surveys with the flexibility of manual intervention, enabling reliable positioning and following even in environments with satellite signal rejection, and suppressing vibration interference through active attitude adjustment to ensure data quality.

The invention provides a vehicle scene reconstruction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring image data acquired by a vehicle, multi-frame millimeter wave radar data and inertial measurement unit data; when it is recognized that the weather state of the environment where the vehicle is located is severe weather, the severity level of the severe weather is determined; according to the severity level, performing fusion processing on the multiple frames of millimeter wave radar data to obtain accumulated radar data; performing enhancement processing on the accumulated radar data to generate enhanced radar data; performing dedegradation processing on the image data according to the enhanced radar data to generate an enhanced image; and generating three-dimensional scene reconstruction data of the surrounding environment of the vehicle according to the enhanced image, the enhanced radar data and the inertial measurement unit data. Therefore, a stable and accurate three-dimensional scene reconstruction result can be output in severe weather.

The application relates to the technical field of high-precision positioning, in particular to a power distribution network tower coordinate dynamic verification method based on cooperation of satellites and unmanned aerial vehicles, which comprises the following steps: an unmanned aerial vehicle formation receives a satellite navigation signal, and synchronously collects relative ranging data in the formation and observation data of towers, a unified time stamp is added to all the data, time and space correlation deviation between different data sources is solved, an absolute delay compensation vector for time synchronization is calculated, original time and space data are corrected in time synchronization, accurate positions and postures of the unmanned aerial vehicle formation are calculated based on fused data after correction, and the corrected tower coordinates are output. The purpose of the application is to accurately obtain the spatial positions of power distribution network towers through cooperation of satellites and unmanned aerial vehicles.

The application discloses an inertial satellite integrated navigation method and system based on an iterative adaptive filtering principle, and mainly comprises the following steps: step 1, acquiring sensor and satellite navigation measurement data, wherein the sensor measurement data comprises gyroscope data and accelerometer data, and the satellite navigation measurement data output information comprises satellite speed and position; step 2, based on the sensor and satellite navigation measurement data, an error model is used to construct an integrated navigation prediction estimation model, which is used for integrated navigation filtering prediction estimation; step 3, based on the satellite navigation measurement data, an iterative adaptive filtering estimation system noise is used to construct an adaptive correction covariance update model; and step 4, the adaptive correction covariance update model and the integrated navigation prediction estimation model are combined to update integrated navigation measurement data, so that updated integrated navigation results are obtained. The application solves the problem that an inertial satellite integrated navigation system is difficult to suppress time-varying noise.

A system and method for high-confidence error overbounding of multiple optical pose solutions receives a set of candidate correspondences between 2D image features captured by an aircraft camera (102) and 3D constellation features including at least one ambiguous correspondence. A candidate estimate of the optical pose of the camera (102) is determined for each of a set of candidate correspondence maps (CMAP), each CMAP resolving the ambiguities differently. Each candidate pose estimate is evaluated for viability and any non-viable estimates eliminated. An individual error bound is determined for each viable candidate pose estimate and CMAP, and based on the set of individual error bounds a multiple-pose containment error bound is determined, bounding with high confidence the set of candidate CMAPs and multiple pose estimates where at least one is correct. The containment error bound may be evaluated for accuracy as required for flight operations performed by aircraft-based instruments and systems.

A laser radar inertial odometry module is described herein that employs a hybrid continuous-time optimization technique to mitigate errors introduced by human motion when implementing a laser radar inertial odometry method using a low-cost and compact wearable mapping system to collect laser radar-based three-dimensional maps of structures and complex environments.

The application discloses a road disease image recognition positioning system for artificial intelligence, and relates to the technical field of artificial intelligence image recognition and road maintenance management. A multi-source data acquisition module is used for synchronously acquiring road pavement images and spatial position data. The multi-source data acquisition module comprises a high-resolution image acquisition unit, a satellite positioning unit, an inertial measurement unit and an odometer unit. Each unit realizes hardware-level time synchronization through a time synchronization controller. The application has the advantages that an integrated system architecture comprising an image acquisition module, a positioning sensor module and a coordinate mapping module is constructed, end-to-end mapping of disease detection results from pixel coordinates to geographic coordinates is realized, each detected disease has accurate longitude and latitude position information, and the road maintenance management geographic information system can be directly accessed.

This invention provides a real-time 3D environmental reconstruction system based on semantic interaction. It integrates semantic prior information into the mesh optimization process, enabling clear differentiation between adjacent but semantically different objects, thus generating a mesh model that is more topologically accurate and geometrically precise. By employing an energy function that includes a geometric term, a semantic consistency term, and a semantically modulated smoothness term to optimize the vertex positions of the initial semantic mesh, it can actively reduce smoothness constraints at the boundaries of different semantic objects while maintaining surface smoothness, effectively preventing the blurring of object edges and generating object representations with clear outlines and sharp boundaries. Therefore, this invention transforms the traditional passive mapping process of "perception-processing-mapping" into an intention-driven active mapping process of "query-perception-processing-highlighting on the map," generating high-fidelity, textured 3D mesh models with semantic interaction capabilities in real time.

The application provides a vehicle fusion positioning method and device and a vehicle, and relates to the technical field of automobile electronics and intelligent control.The method comprises the following steps: identifying a current driving scene of the vehicle; adjusting a weight parameter of each sensor according to the driving scene; fusing positioning data collected by each sensor based on the weight parameter; and outputting current positioning information of the vehicle.The vehicle fusion positioning method and device and the vehicle provided by the application can fuse the positioning data collected by each sensor based on the weight parameter and output the current positioning information of the vehicle.By adjusting the weight parameter of each sensor, the sensor can be more suitable for the current application scene, and by fusing the positioning data of multiple sensors, the precision and accuracy of the positioning data can be effectively improved.

The application discloses a robot unknown environment autonomous exploration navigation method based on CTSAC, which combines the powerful sequence perception ability of the Transformer with the Soft Actor-Critic deep reinforcement learning framework, and introduces a periodic review mechanism, course learning, laser radar partition optimization processing and refined reward setting. The state of the robot is represented by the laser radar data and the gyroscope data after laser radar partition optimization processing. Meanwhile, the state information is used to obtain the corresponding reward value after refined reward setting, and the corresponding experience sequence is stored in the experience pool. The data sampled from the experience pool is used to train the neural network. The application is suitable for robots to perceive the surrounding environment through sensors, plan paths to reach the specified target point or establish an environment map in an unknown environment, and is used for complex tasks such as space exploration, search and rescue and reconnaissance.

This invention relates to the field of orchard navigation technology, specifically a 3D method for orchard operations based on a grid map. The method includes: constructing an orchard point cloud map using a mapping algorithm; filtering the acquired 3D point cloud map and constructing a triangular grid map of the orchard, embedding the orchard's GPS information into the triangular grid map; based on the triangular grid map with GPS information, performing ICP registration between the point cloud data scanned by the LiDAR and the points on the triangular grid map using the RMCL 3D positioning algorithm, outputting the 3D pose of the operation robot in the orchard; updating the 3D pose of the operation robot in the orchard using GNSS information; planning the navigation path of the operation robot using grid navigation based on the GPS information of the target point; and controlling the operation robot to dynamically avoid obstacles and navigate to the target point. This invention enables precise positioning and autonomous navigation of the operation robot in hilly orchards, allowing it to complete tasks such as orchard inspection, plant protection, harvesting, and transportation.

The application discloses a kind of ship compliance monitoring methods based on unmanned aerial vehicle and AIS data fusion.The method controls satellite positioning module, IMU and vision sensor of unmanned aerial vehicle to collect data;Satellite positioning antenna is compensated using IMU real-time attitude lever arm effect, and the corrected unmanned aerial vehicle coordinates are obtained;Based on vision SFM algorithm and unmanned aerial vehicle positioning displacement baseline, the relative position vector of ship is solved, and the absolute coordinate of ship is obtained by space projection;Through linear interpolation, the time and space alignment of measured data and AIS data is realized;Finally, coordinate deviation is calculated, and dynamic threshold based on flight height, sea state and other environmental parameters is combined to determine the compliance of ship.The application realizes independent high-precision solution and full-dimensional verification of ship position, heading and speed, effectively solves the problems of AIS data fraud identification difficulty, large environmental interference and other problems, and greatly improves the accuracy and intelligent level of maritime non-site law enforcement.

This invention discloses a method, system, medium, and device for zero-speed detection of a wheeled robot. The method includes the following steps: acquiring inertial sensor data of the wheeled robot; selecting inertial sensor data based on a preset length sliding window, and calculating the modulus of the selected inertial sensor data to obtain one-dimensional inertial data; standardizing the one-dimensional inertial data; performing a discrete Fourier transform on the standardized data to obtain a Fourier spectrum; extracting time-domain feature data and frequency-domain feature data from the standardized data and the Fourier spectrum based on a preset one-dimensional convolutional neural network; and performing zero-speed detection on the time-domain feature data and frequency-domain feature data based on a preset fully connected neural network. Therefore, a high-performance zero-speed detection method that does not rely on a preset threshold is provided, improving the zero-speed detection capability of the wheeled robot in different motion modes and reducing the impact of engine and other component vibrations on the zero-speed detection effect when the robot is parked.

The invention relates to a device (1) for implementation with a distance sensor (3) and an acceleration sensor (9), wherein distance signals d(K·T1) are generated with the distance sensor (3) in a clock cycle T1 and acceleration signals b(K·T2) are generated with the acceleration sensor (9) in a clock cycle T2 and with a fusion unit (11), which is designed to generate noise-reduced distance measurements D (K·T3) in a clock cycle T3, in which distance signals d(K·T1) and acceleration signals b(K·T2) determined simultaneously in this unit are combined.