553results about "Distance measurement" patented technology

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

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 application discloses a hanging rail type contact wire abrasion detection robot clamping and walking device, which realizes quick, automatic and reliable installation and locking of the detection robot on a narrow tunnel space and a Π-shaped busbar, and discards traditional heavy manual operation. The detection robot can automatically identify and smoothly pass through a pre-tightening bolt on the Π-shaped busbar, realizes uninterrupted continuous detection, overcomes the blockage of fixed obstacles, is not prone to be stuck, and improves the applicability of the overall device.

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.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

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.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention discloses a laser radar inertial odometer method based on block-by-block updating, which comprises the following steps: constructing a kinematics equation and an observation equation of a laser radar inertial odometer system based on a block-by-block updating scheme, and performing state propagation on point cloud block laser radar data and inertial measurement data to obtain a prior prediction state; compensating the pose of each point cloud point in the point cloud block, and aligning the point cloud point to the pose of the point cloud block at the last moment; registering the aligned point cloud blocks with a local map, executing nearest neighbor plane search, and then calculating a point-surface residual error; combining the prior state prediction with the posterior state residual error, and performing state iteration updating and iteration convergence to obtain a final result; and then local map updating is carried out. According to the method, the continuous laser radar point cloud data is divided into a plurality of small blocks for batch processing according to the extremely short time interval, so that the time overhead of single update can be remarkably shortened.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The embodiment of the invention provides a robot precise alignment method and system, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring a laser radar point cloud, a visual image and inertial measurement unit data of a robot; performing dynamic point cloud segmentation on the laser radar point cloud, filtering out dynamic interference points to obtain static point cloud, and extracting target point cloud from the static point cloud; constructing a tight coupling fusion model based on the visual image, the target point cloud and the inertial measurement unit data, and solving the tight coupling fusion model to obtain the current pose of the robot; and the deviation between the current pose and a preset standard pose is calculated, and the robot is controlled to move according to the deviation to complete alignment. According to the method, dynamic environment interference is filtered out, and the pose is solved in a multi-sensor tight coupling mode, so that the alignment precision and robustness of the robot in a complex dynamic scene can be effectively improved.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention relates to the technical field of pipeline detection navigation, in particular to an in-pipeline multi-feature fusion inertial navigation and mileage online correction method, which comprises the following steps: collecting detection data; determining observation information based on the detection data; inputting detection data to the observation information prediction model, and outputting an observation information prediction value; inputting the observation information into the observation information prediction model, and correcting an observation information prediction value to obtain an observation information estimation value; and based on the updated odometer wheel scale factor in the observation information estimation value, performing conversion processing on displacement pulse data in the detection data to obtain the target running mileage of the detector. According to the method, the feature events and the observation information of the detector in the operation process in the pipeline are generated, and the state-amplified extended Kalman filtering algorithm is utilized to perform online real-time joint estimation and updating on the zero offset of the inertial device and the scale factor of the odometer wheel, so that the positioning precision and the mileage reliability of the detector in the pipeline are improved.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

A method is provided. The method includes: obtaining global position and orientation information of a first trajectory point; determining at least one pair of second trajectory points based on the global position and orientation information; obtaining a relative position and orientation estimation information and a relative position and orientation measurement information of each pair of second trajectory points; and calculating a local rotation offset at the first trajectory point based on the relative position and orientation estimation information and the relative position and orientation measurement information of each pair of second trajectory points in the at least one pair of second trajectory points to correct the global position and orientation information.

A monorail hoist positioning method combining rail joint beacons and inertial navigation. An odometry kinematic model for a monorail hoist is established by means of an encoder on the monorail hoist, and an extended Kalman filter is used to fuse a heading angle obtained from a kinematic model solution and a heading angle obtained from a strapdown inertial navigation solution. Beacons are arranged at rail joints for the monorail hoist, and vibration signals are acquired by a vibration sensor. By combining a strapdown inertial navigation system, the encoder, and a rail installation diagram, a rail joint determination model is constructed to perform filtering, so as to obtain impact signals at rail joint beacons that meet a requirement. Beacon identifiers are used to match the identified rail joint beacons against specific beacon coordinates in the rail installation diagram, so as to obtain a time-position coordinate sequence for the monorail hoist arriving at the rail joints. A combined positioning model integrating rail joint beacons and strapdown inertial navigation is constructed to realize integration of local relative positioning and global absolute positioning of the monorail hoist. The method can improve the positioning accuracy of the monorail hoist while reducing positioning costs.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention discloses a convolutional neural network and multi-source data fusion-based nuclear radiation on-line monitoring method, which comprises the following steps of: acquiring laser radar point cloud data, a radiation dose rate and a radiation image of a monitoring area by adopting an unmanned aerial vehicle, and de-noising the radiation image by adopting a double-branch convolutional neural network; determining the track position of the unmanned aerial vehicle according to the laser radar point cloud and the de-noised image; performing space-time registration on the radiation dose rate and the track position of the unmanned aerial vehicle to form a data set, and obtaining a continuous radiation field of the monitoring area by adopting Kriging interpolation; according to the continuous radiation field, an L-BFGS-B algorithm is adopted to carry out minimization solution on a loss function with the radiation source position as an optimization variable, and position coordinates of the radiation source are obtained; performing data cleaning on the radiation dose in the continuous radiation field, and then performing multi-dimensional feature extraction to obtain a feature vector; and inputting the feature vectors into a plurality of prediction models to obtain a plurality of predicted radiation doses, and carrying out weighted fusion to obtain a predicted dose rate of the radiation source.

The invention discloses a clamping and walking device of a rail-mounted contact line abrasion detection robot, which realizes rapid, automatic and reliable installation and locking of the detection robot in a narrow tunnel space and on an n-shaped busbar, and abandons traditional heavy manual operation. The detection robot can automatically recognize and stably cross the pre-tightening bolts on the n-shaped busbar, uninterrupted continuous detection is achieved, blocking of fixed obstacles is overcome, clamping stagnation is not prone to occurring, and the applicability of the whole device is improved.

The invention provides a robot dynamic obstacle trajectory prediction navigation method based on a visual model, and relates to the field of power equipment maintenance, the method comprises the following steps: collecting original data of dynamic scene multi-modal perception, carrying out space-time alignment, extracting thermal infrared dynamic feature vectors and visual dynamic feature vectors, and carrying out dynamic scene multi-modal perception on the thermal infrared dynamic feature vectors and the visual dynamic feature vectors; simultaneously calculating the fuzzy degree of the visual image and the dynamic complexity of the scene; calculating a fusion weight of the thermal infrared feature and the visual feature through a dynamic modal weight algorithm, and outputting a fusion feature vector; inputting the fusion feature vector into a visual model, and calculating the trajectory probability distribution of the dynamic obstacle; calculating comprehensive uncertainty and risk coefficients based on the trajectory probability distribution; and based on the fusion feature vector and the trajectory probability distribution, integrating the uncertainty and the risk coefficient, and planning an obstacle avoidance path and a decision report in combination with an A algorithm, so as to realize the dynamic navigation of the robot. According to the technical scheme, the reliability and intelligence of robot navigation can be improved, and the navigation requirement of a complex environment is met.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.