1218 results about "Vehicle positioning" patented technology

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

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

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle positioning method and system combining environment characteristics. According to the technical scheme, the method comprises the steps that multi-modal data of the environment where the unmanned aerial vehicle is located are collected through a multi-source heterogeneous sensor, and the multi-modal data comprise GNSS signal data, visual image data, laser radar point cloud data, inertia measurement data and electromagnetic interference monitoring data; and carrying out cooperative processing on the multi-modal data by utilizing the fusion deep learning model. A comprehensive basis is provided for subsequent processing through multi-source data collection, data quality and feature matching reliability are improved through fusion processing, timely and accurate positioning is guaranteed through real-time calculation, safe and efficient flight is achieved through a decision-making model based on accurate positioning, and the flight speed is high. The method not only solves the traditional technical problems of signal shielding, interference, dynamic interferent influence and contradiction between real-time performance and precision in a complex environment, but also shows excellent positioning stability and adaptability in various complex scenes.

A hitch point where a receiving vehicle is coupled to a following vehicle is located relative to a leading vehicle. The trajectory or route of the following vehicle is detected and a dynamic model estimates a location of a receiving area in the receiving vehicle relative to the leading vehicle based on the trajectory or route of the following vehicle and the location of the hitch point relative to the leading vehicle.

The invention discloses a method and system for cooperatively positioning a vehicle through a door lock, and the method comprises the steps: obtaining and dynamically updating a neighbor door lock list through the detected door lock of the vehicle according to the broadcast and scanning data of a Bluetooth tag on a moving vehicle; according to the periodic detection report of the door locks on the vehicle and the global analysis of the management system, the cooperative timers of the related door locks are synchronized, and timing management for preventing repeated triggering is implemented; according to the response of the obtained neighbor door lock which initiates multicast query after the collaborative timer expires, the door lock which initiates the query verifies whether all responders are in the neighbor list, so as to judge whether the vehicle is stably parked; and according to the vehicle parking information confirmed by the door lock, the management system updates the global state and sends a parking position notification to the owner. According to the embodiment of the invention, accurate and low-misjudgment judgment of the parking state of the vehicle can be realized, and the reliability and real-time performance of vehicle positioning management are improved by means of global state synchronization.

A vehicle-mounted GNSS positioning method based on multi-motion model interaction includes: establishing a position-constant velocity (PCV) model and a position-constant steering angular velocity (PCSAV) model for two attitudes of a carrier (i.e., linear motion and turning motion) respectively to obtain a state estimation vector and a state transition matrix of the carrier of the PCV model and the PCSAV model at a previous moment, introducing an interacting multiple model (INM), establishing a heuristic position-velocity filtering (HPV)-IMM model based on the IMM model to achieve an information filtering interaction between the PCV model and the PCSAV model, and obtaining a state estimation vector and an error covariance matrix of the carrier at a current moment, so as to obtain a position and velocity of the carrier at the current moment. The present disclosure solves the problem of low accuracy of a traditional single kinematic model in multi-motion attitude vehicle positioning.

The invention discloses an unmanned vehicle environment perception and obstacle avoidance control method in a complex dynamic scene, relates to the technical field of unmanned vehicle positioning and navigation, and is used for solving the problems of low positioning accuracy, low stability, poor real-time response capability and the like in a dynamic complex environment in an existing method. The method is specifically achieved through an unmanned vehicle environment sensing and obstacle avoidance control system, the unmanned vehicle environment sensing and obstacle avoidance control system is provided with a sensor module, a vehicle-mounted computer module and a chassis control module, and the vehicle-mounted computer module is connected with the sensor module and the chassis control module. The sensor module is provided with a solid-state laser radar, a multi-line mechanical laser radar and an inertial measurement unit which are installed on the unmanned vehicle, and the chassis control module is provided with an accelerator and brake calibration meter and a driving execution unit. The method has the advantages that the positioning accuracy, stability and real-time response capability of the unmanned vehicle in a dynamic complex environment are effectively improved.

The invention provides a mountainous road driving assistance method and device and a vehicle, and relates to the technical field of vehicles. The method is based on a vehicle carrying an unmanned aerial vehicle. The method comprises the following steps: in response to a mountain road navigation instruction, controlling the unmanned aerial vehicle to acquire traffic information about a mountain road where the vehicle is located; constructing a three-dimensional terrain model about the mountain road according to the traffic information; and generating driving path suggestion information and / or early warning information according to vehicle positioning information and the three-dimensional terrain model. On the basis of the vehicle carrying the unmanned aerial vehicle, the traffic information of the mountain road where the vehicle is located is obtained timely and accurately, the three-dimensional terrain model about the mountain road is constructed, and the driving path suggestion information and / or early warning information are / is generated, so that active guidance and risk prompt for the driving behavior of a driver are achieved, and the driving safety of the driver is improved. And the driving safety of the vehicle on the mountainous road and the driving experience of a driver are improved.

The invention relates to the technical field of vehicle control, and discloses a vehicle positioning and control method and system based on machine vision, and the method comprises the steps: building a vehicle kinematics prediction model, and generating a reference pose sequence according to a driving instruction through the vehicle kinematics prediction model; recording a first reference pose during image acquisition; calculating a deviation vector between a positioning result and the first reference pose when positioning calculation is completed; determining a dynamic damping coefficient according to a direction included angle between the velocity vector and the deviation vector, generating a compensation gain sequence, and converting the compensation gain sequence into a compensation torque of a current loop given value; according to the method, the effective decoupling of the sensing time delay of the sensor and the dynamic response of the carrier is realized, the phase lag is reduced on the premise of ensuring the momentum continuity of the controlled object, the instantaneous control oscillation of positioning correction is eliminated, and the mechanical stress impact of an execution mechanism is inhibited.

The invention provides an unmanned aerial vehicle positioning method and system, an unmanned aerial vehicle and a medium, and the method comprises the steps: firstly, obtaining inertial motion data and visual perception data in real time, and continuously providing the inertial motion data through employing the characteristic that an inertial sensor is not limited by a signal environment, so as to avoid a positioning interruption problem when a positioning signal is weak; meanwhile, the spatial pose data is generated by performing pose analysis on the visual perception data, and the first position information is generated by combining the preset satellite map database in a matching manner, so that the defect of inertial navigation accumulative errors is made up. The inertial motion data, the spatial pose data and the first position information are fused and calculated to generate the second position information, and the second position information is converted into the GPS signal and accessed to the GPS system of the unmanned aerial vehicle, so that the error defect of a single sensor can be effectively inhibited, continuous and accurate positioning in a complex environment is realized, and the positioning accuracy is improved. The problem of application limitation of a traditional GPS positioning system in special environments with weak positioning signals and the like is fundamentally solved from the technical level.

Various systems and methods are presented regarding utilizing technology onboard a vehicle to mitigate the effects of communication signals being deleteriously affected / lost when operational surroundings cause occlusion / loss of receipt of positioning / location signals at the vehicle. The vehicle can be operating non-autonomously, partially autonomously, or autonomously. To supplement the lost / occluded signals position information of other systems proximate to the vehicle can be obtained / utilized. The other systems can include another vehicle, a mobile device, an internet, and suchlike. Also, onboard devices can be utilized to determine the location, such as an onboard camera system providing information regarding street signs, building facades, and suchlike.

The invention discloses an unmanned aerial vehicle autonomous positioning navigation method and device based on a cross-view azimuth regression network. The method comprises the steps of 1, constructing a cross-view-angle data set which is oriented to a cross-view-angle scene and contains azimuth information, 2, constructing an unmanned aerial vehicle position heading regression model oriented to the cross-view-angle scene, and 3, training and verifying the unmanned aerial vehicle position heading regression model oriented to the cross-view-angle scene by using a training set and a verification set. And 4, carrying out regression performance test on the unmanned aerial vehicle position heading regression model facing the cross-view scene by using the test set. The method only depends on a visual image, can reduce the dependence degree of unmanned aerial vehicle positioning navigation on a sensor, improves the survivability in an extreme environment, uses a cross-visual-angle remote sensing map to construct simulation training data and generate the absolute orientation regression capability, can eliminate the drift problem, and also has natural advantages in the aspect of migration from simulation to a real scene. The model is light in weight and is more suitable for unmanned aerial vehicle continuous positioning and directional navigation scenes with limited computing power resources.

The invention discloses a cascading filtering positioning method and device in a complex and severe environment of a well mine and a medium, and achieves a high-precision positioning function of a well mine unmanned vehicle based on a cascading filtering method. According to the method, the IMU, the wheel speed and the wheel rotation angle are subjected to data fusion through adaptive Kalman filtering, the influence of non-Gaussian noise on wheel speed measurement is reduced through non-Gaussian noise feature extraction, and robust prior positioning information is obtained. According to the method, a regular term representing feature degradation is constructed, a laser radar observation model based on a prior map and laser radar data is constructed, and finally, a result of the laser radar observation model and prior positioning information are fused according to the regular term under a regularization Kalman filtering framework to obtain posterior positioning information. The precision and robustness of the method meet the positioning requirements of the mine unmanned vehicle.

The invention provides a position calculation method and system for combined processing of vehicle and gun positioning information, and relates to the field of fusion positioning, and the method comprises the steps: determining a plurality of time windows corresponding to vehicle positioning data and gun positioning data based on the size of an optimal time window and an optimal sliding interval; for each time window, determining an alignment reference time point of the time window, generating a vehicle position and a gun position corresponding to the alignment reference time point of the time window, and further calculating a vehicle-gun separation distance corresponding to the alignment reference time point of the time window; determining the vehicle-gun state based on the vehicle-gun separation distance and the dynamic separation threshold value corresponding to the alignment reference time points of the plurality of time windows; when the vehicle-gun state is the fusion state, the vehicle fusion position and the gun fusion position are determined based on the gun positions and the vehicle positions corresponding to the alignment reference time points of the multiple time windows, and the method has the advantage that the accuracy of combined positioning of the vehicle and the gun is improved.

The application relates to the technical field of automatic driving of vehicles, and provides a vehicle obstacle avoidance trajectory planning method, a vehicle obstacle avoidance trajectory planning system, a vehicle and a storage medium.The method comprises the following steps: obtaining coordinate point information of a lane line and coordinate point information of a lane center line according to vehicle positioning information; taking the lane center line of the lane where the vehicle is located as a reference line, and converting the position, heading, speed and acceleration of the vehicle into a longitudinal position, a lateral offset, a lateral speed, a longitudinal speed, a lateral acceleration and a longitudinal acceleration in a frenet coordinate system; taking obstacle information, if there is an obstacle, converting the obstacle coordinates into S obs ,L obs , if there are multiple obstacles, converting the coordinates of each obstacle into S obsi ,L obsi , and calculating a target point through dynamic programming by means of point sampling; converting the target point into coordinates in a natural coordinate system, smoothing the coordinates through a smoothing algorithm to obtain a trajectory point set, and fitting the trajectory point set to generate a trajectory equation. The application has a simple calculation process and lower cost.

The invention relates to an unmanned aerial vehicle positioning method and device based on multi-camera joint calibration. The method comprises the following steps: according to a plurality of cameras arranged according to different orientations in an operation environment in which the unmanned aerial vehicle equipment is located, obtaining calibration object images obtained by shooting a preset calibration object by each camera; determining an internal parameter set corresponding to each camera according to a coordinate mapping relationship between the feature points in each calibration object image and the calibration object; determining an external parameter set of each camera relative to the calibration object according to a matching relationship between the feature points in each calibration object image; and in combination with the internal parameter set and the external parameter set corresponding to each camera, generating a visual perception network based on multi-camera joint calibration so as to obtain pose information and three-dimensional coordinates of the unmanned aerial vehicle equipment in real time and plan a flight path. The method can be used for improving the capability of autonomous positioning and navigation operation of unmanned aerial vehicle equipment in a GPS-free environment.

The invention discloses a three-dimensional positioning method for an inspection unmanned aerial vehicle in an underground pipe gallery based on multi-sensor fusion, and relates to the technical field of unmanned aerial vehicle positioning, and the method comprises the following steps: obtaining UWB distance measurement information, IMU attitude information, optical flow motion information and TOF distance measurement information, correcting the UWB and TOF information based on the IMU and optical flow information, and analyzing the global coordinates of a UWB label on the unmanned aerial vehicle; acquiring original point cloud data of the laser radar, performing motion distortion compensation on the original point cloud data through an interpolation algorithm based on the pose data of the unmanned aerial vehicle, and converting the original point cloud data from local coordinates to global coordinates; further calculating the Euclidean distance between each point cloud coordinate and the UWB label coordinate, setting a dynamic threshold interval based on the UWB label positioning result, precision and pipe gallery mapping data, and filtering the point cloud data with the distance in the threshold interval; and finally, analyzing the residual point cloud data through a laser positioning algorithm, and outputting a high-precision positioning result of the unmanned aerial vehicle.

The invention discloses a low-altitude unmanned aerial vehicle dynamic visual positioning method and device under an ultra wide band, and relates to the technical field of unmanned aerial vehicle positioning and navigation.The method comprises the steps that UWB anchor points are arranged in an operation area, a low-altitude unmanned aerial vehicle calibration operation track is obtained, and an anchor point set is configured through signal fitting; when the unmanned aerial vehicle works along the track, the anchor point group is activated according to the front authentication track point; receiving a UWB signal and carrying out abnormity identification; if the threshold value is triggered, activating a visual acquisition device, and obtaining IMU data; visual key feature points are extracted, the dynamic fusion network is initialized, the pose is corrected based on multi-source data linkage, and a linkage pose correction result is generated. The technical problems that in non-electrical variable control and adjustment of the low-altitude unmanned aerial vehicle pose in the prior art, due to the complex environment, positioning deviation and failure are prone to occurring, and the requirement for precise operation is difficult to meet are solved, precise positioning of the low-altitude unmanned aerial vehicle pose is achieved, precise positioning in the complex environment is achieved, the failure is avoided, and the working efficiency is improved. And the technical effect of precise operation requirements is met.

Provided are methods, systems, and computer program products for lane keep tracking and / or localization of a vehicle using a radar to detect metallic particles in paint applied to lane markings or curb markings. An example method may include: causing a radar system of a vehicle to output radio waves; receiving a radar image from the radar system corresponding to returned radio waves; determining a portion of the radar image includes lane or curb markings, wherein the lane or curb markings are embedded with metallic particles; and determining a location of the vehicle based at least in part on the determined portion of the radar image that includes the lane or curb markings.

The invention discloses a vehicle positioning method based on a reflective column. The problems that in the prior art, system integration is complex, cost is high, mapping dependence is high, robustness is poor, and scene recognition capacity is insufficient are solved. The method comprises the following steps: S1, acquiring point cloud data of a surrounding environment scanned by a laser radar on a vehicle; s2, identifying at least three reflective columns based on reflection intensity information in the point cloud data, and determining a first coordinate of each reflective column in a laser radar coordinate system; step S3, based on the first coordinates of the at least three reflective columns and in combination with pre-stored global layout information of the reflective columns, determining a sub-region where the current vehicle is located; and S4, according to the second coordinate and the first coordinate corresponding to the current sub-region, calculating the pose information of the vehicle in the global map coordinate system through coordinate transformation.

A vehicle GNSS positioning method based on multi-motion model interaction. The method comprises: respectively establishing a PCV model and a PCSAV model of a carrier on the basis of a straight driving pose and a turning driving pose of the carrier, so as to obtain a state estimation vector and a state transition matrix, which are based on the PCV model and the PCSAV model, of the carrier at a previous moment; introducing an interactive multiple model to establish an interactive-multiple-model-based heuristic position-velocity filtering (HPV-IMM) model; realizing information filtering interaction between the PCV model and the PCSAV model; and obtaining a state estimation vector and an error covariance matrix of the carrier at the current moment, so as to obtain the position and velocity of the carrier at the current moment. The method solves the problem of the low precision of a conventional single kinematic model in terms of multi-motion-pose vehicle positioning, and is thus suitable for dynamic navigation of multi-pose vehicle motion in an urban environment.

The invention relates to the technical field of parking lot positioning, in particular to a vehicle positioning management system in a parking lot based on dynamic weight multi-sensor fusion. Comprising a multi-source sensor data acquisition unit; an environment entropy perception and weight pre-adjustment unit; a reinforcement learning weight optimization unit; and an edge end vehicle positioning management unit. According to the method, an image histogram entropy value and a UWB signal RSSI fluctuation variance are calculated in real time through a heterogeneous sensor array in combination with an environment entropy perception and weight pre-adjustment unit, meanwhile, a space correlation matrix is constructed through sensor plane coordinate mapping, and interference space correlation characteristics are extracted. Based on reinforcement learning, a causal correlation model of an environmental entropy change rate, interference characteristics and sensor failure time is established, a sensor failure window can be predicted in advance, the weight of each sensor accessing a fusion filter is dynamically adjusted, and the problems of environmental influence perception deficiency, sensor correlation analysis and failure pre-judgment are solved.

A system including a plurality of first wires and a plurality of second wires is disclosed. Each second wire overlaps over one or more first wires at a respective overlapping area of one or more first wires. A dielectric material is disposed in a vertical gap at each respective overlapping area forming a capacitance sensor including a first electrode corresponding to a first wire and a second electrode corresponding to a second wire. The first and second electrodes are connected to a positive bias and a negative bias of at least one voltage source, respectively. The system includes at least one processor configured to periodically measure change in respective electric field of each capacitance sensor of a subset of capacitance sensors of a plurality of capacitance sensors, and store data corresponding to periodically measured change in the respective electric field of each capacitance sensor of the subset of capacitance sensors.

The invention discloses a vehicle spatial position uncertainty modeling method based on a factor graph, and relates to the technical field of automatic driving. Establishing a factor graph model, and constructing state variable nodes and factor nodes; an objective function of the factor graph optimization problem is converted into a nonlinear least square problem, and optimal state estimation is obtained through iterative solution by adopting a Gauss-Newton method; on the basis of an objective function under a Gaussian Newton method framework, a posterior covariance matrix is obtained through calculation of a Hesse matrix and inversion; performing incremental optimization on the factor graph, and updating a global Jacobian matrix through matrix decomposition and Givens transformation; and carrying out confidence ellipse geometric quantization, and representing the positioning uncertainty in a geometric form. Based on a factor graph and a confidence ellipse theory, through multi-sensor space-time constraint fusion and geometric interpretable representation, a vehicle positioning uncertainty modeling framework is constructed, and the safety and robustness of an automatic driving system downstream decision planning algorithm are improved.

The invention discloses a multi-source data fusion positioning method and device, electronic equipment, a medium and a product. The method comprises the following steps: acquiring target data; determining a target time window according to the quality score of each target data and the scene factor corresponding to the target vehicle; wherein the scene factor is determined according to the scene feature of the target vehicle; determining the weight of each piece of target data; and in the target time window, based on the weight of each piece of target data, performing fusion processing on each piece of target data, resolving a fusion result, and outputting a target vehicle positioning result. According to the technical scheme, the multi-source data fusion process is optimized through dynamic time window setting and dynamic weight distribution, and the precision and the real-time performance of the vehicle high-precision positioning system can be improved.

The invention discloses a telescopic control box intelligent adjustment method fusing unmanned aerial vehicle positioning data, and relates to the technical field of automatic control, and the method comprises the following steps: a multi-source data collection step: collecting unmanned aerial vehicle positioning, environment and control box periphery related data; in the positioning data preprocessing step, a standardized data set is generated through filtering, synchronization and abnormal value elimination; in the control box state monitoring step, travel, load, temperature and locking state data are collected in real time; in the data fusion analysis step, positioning and state data are associated, and key adjusting features are extracted; the adjustment demand evaluation step is used for judging the suitability and the operation risk; in the intelligent adjustment decision-making step, specific adjustment parameters such as telescopic speed and stroke are formulated; and an execution and closed-loop feedback step: driving the mechanism to execute an action, and dynamically correcting parameters to realize accurate adaptation. According to the invention, unmanned aerial vehicle positioning and multi-source data are fused; different operation scenes and complex environments are adapted, stable and efficient operation is guaranteed, and the application boundary of the unmanned aerial vehicle is expanded.

Systems and methods of performing localization of a vehicle are provided. The system receives data comprising one or more of: raw images from one or more vision sensors, motion sensor data, or lane-level map information. The system determines, based on the data, a lane in which the vehicle is located and lateral displacement of a center of the vehicle relative to a center of the lane.

A system and method are provided for facilitating safe transport of a work machine. During a transport stage for a work machine, wherein the work machine is positioned for transport with respect to a transport vehicle, a current effective height of the work machine is determined, for example relative to the ground surface. The current effective height may for example be based on a work implement assembly pose determined based on sensed relative positions of the various components. An intervention state for the transport stage may be determined based at least in part on the effective height of the work machine, for example by comparison to a threshold value, which may correspond to a minimum possible effective height for the type of machine. Output signals may be automatically generated to execute a specified intervention in a current transport plan, corresponding to the determined intervention state.

The invention provides a target vehicle pose detection method and system based on a single-line laser radar point cloud, and relates to the technical field of vehicle positioning, and the method comprises the following steps: S1, carrying out the preprocessing of point cloud data collected by a laser radar based on a DBSCAN algorithm, screening out noise points, and generating an effective cluster; s2, performing L-shaped feature fitting on each cluster, and calculating a tire center point coordinate; s3, calculating vehicle pose data according to the at least three tire center points, wherein the vehicle pose data comprises a wheelbase, a vehicle center coordinate and a parking angle; and S4, transmitting the vehicle pose data to a parking AGV control system through a communication protocol. The method has the beneficial effects that by adopting the positioning method integrating the laser radar and AGV four-wheel positioning, the vehicle navigation and positioning precision is further improved; a laser radar point cloud is combined with an L-shaped feature fitting algorithm, the pose of a target vehicle is obtained in the walking process of the parking AGV, the pose detection efficiency of the parking AGV is improved, and the walking fluency of the parking AGV is improved.

The invention discloses a multi-sensor fusion unmanned aerial vehicle positioning and attitude determination system and method. The system deeply fuses data of a millimeter wave radar, a real-time dynamic differential positioning technology and an inertial measurement unit; the method comprises an initialization stage S1 and a loop execution stage S2, and specifically comprises the steps of S21, data acquisition and synchronization; s22, performing state prediction based on an inertial measurement unit (IMU); s23, preprocessing the measurement data; s24, state updating is carried out based on the real-time dynamic differential positioning unit RTK and the millimeter wave radar unit MMW; s25, calculating a relative position; and S26, outputting. According to the method, high-precision and high-reliability absolute positioning and attitude determination are realized through a fusion algorithm, accurate and robust relative position estimation is completed through complementation of multiple sensors, maintenance of relatively good pose estimation and real-time calculation of an accurate relative geometrical relationship between the unmanned aerial vehicle and a target, so that high-update-rate state output is completed, and real-time control requirements are met.