481 results about "Odometry" patented technology

A method of footstep contact detection includes receiving joint dynamics data for a swing phase of a swing leg of the robot, receiving odometry data indicative of a pose of the robot, determining whether an impact on the swing leg is indicative of a touchdown of the swing leg based on the joint dynamics data and an amount of completion of the swing phase, and determining when the impact on the swing leg is not indicative of the touchdown of the swing leg, a cause of the impact based on the joint dynamics data and the odometry data.

The invention relates to a multi-sensor cross-scene dynamic preferential fusion positioning and mapping method, and the method comprises the steps: obtaining the data of a plurality of sensors, and completing the unification of the time-space relation of the data of the plurality of sensors; processing the data, carrying out loopback detection on image key frame data acquired by a camera, constructing to obtain an I MU pre-integration factor, a visual inertial odometer factor, a laser radar odometer factor, a GPS inertial odometer factor, a UWB factor, a GNSS factor and a loopback detection factor, and adding the factors into a factor graph for optimization; a global positioning pose and a map are obtained; and optimizing the multi-sensor data fusion strategy based on a deep fuzzy neural network. According to the multi-sensor cross-scene dynamic preferential fusion positioning and mapping method provided by the invention, high-precision positioning and navigation of an agricultural robot in different scenes are realized through real-time fusion of various sensor data, and the problems of scene dependence and insufficient precision of an existing single sensor scheme are solved.

The invention discloses a distribution network tree barrier real-time analysis method and system based on dynamic vision and SLAM. The method comprises the following steps: generating a dynamic visual baseline by cooperatively controlling the translation and flight displacement of an unmanned aerial vehicle holder, and constructing a bionic binocular model to simulate a time sequence image into a binocular image pair; generating a depth point cloud through epipolar correction and stereo matching; key targets are recognized and extracted through a semantic segmentation network, and semantic point clouds are generated; establishing a dimensionality reduction motion model by utilizing pan-tilt stability augmentation, and fusing a visual inertial odometer and RTK data by adopting a filtering or optimization algorithm to realize centimeter-level pose estimation; and finally, performing optimization processing on the semantic point cloud, completing three-dimensional reconstruction based on multi-modal fusion, and outputting a risk assessment result through tree line spacing calculation and safety margin analysis. According to the invention, accurate, efficient and automatic routing inspection and risk early warning of the distribution network tree obstacles are realized.

The invention discloses an unmanned ship positioning method and a berth selection method based on a high-precision semantic map, and the method comprises the steps: carrying out the semantic segmentation of a three-dimensional point cloud of a berth area scene, and extracting angular points and plane points based on the curvature; constructing a local sub-graph according to the historical key frame point cloud, and constructing a semantic weighting-based line-plane matching residual error between the current frame point cloud and the local sub-graph based on the angular points and plane points of the current frame point cloud; constructing a shore surface matching residual error based on fitting prediction between the fitting shore surface of the current frame and the fitting shore surface of the subgraph; and forming a semantic laser odometer factor by the semantic weighting-based line-surface matching residual error and the fitting prediction-based shore surface matching residual error, further constructing an overall factor graph, and optimizing to obtain the optimal global pose of the unmanned ship. According to the method, the structured features are subjected to weighted matching through semantic information and constraint constructed by utilizing a fitting prediction shore surface are jointly added into speedometer optimization, so that real-time high-precision positioning of the unmanned ship can be realized.

An external rotation 3D lidar device and a simultaneous localization and mapping (SLAM) method comprises a 3D hybrid solid-state lidar device that is driven to rotate by an external motor. The device significantly improves the horizontal field of view of the lidar and can be mounted on a ground robot to comprehensively improve its 360-degree environment sensing capabilities. Error-state Kalman filtering and pose graph optimization are combined and the overall framework is divided into two parts: front-end odometry and back-end loop-closure optimization. Therefore, high-frequency odometry that meets the requirements of the robot can be output in real time and cumulative errors can be eliminated through the back-end loop-closure optimization.

The invention discloses a ring main unit inspection robot autonomous navigation method and system based on SLAM, particularly relates to the technical field of robot autonomous navigation and intelligent inspection, and is used for solving the problem of positioning drift caused by repeated features of an existing ring main unit scene. Semantic feature analysis and topological constraints are introduced into an SLAM processing flow, acquired image data and point cloud data are processed through a deep learning model, objects such as an electrical cabinet, a corridor channel and a cable trench are identified, and a semantic feature set with category labels and spatial position information is generated; and constructing a topological graph containing node spacing, connectivity and directivity constraints based on the semantic features, adding the topological graph as a constraint factor into SLAM back-end optimization, and performing joint optimization in combination with vision, a laser odometer and inertial prior information, thereby avoiding only depending on repeated geometric feature positioning, and improving the positioning accuracy. The problems of loopback misjudgment and drifting caused by feature confusion are reduced, and the pose resolving stability in the ring main unit environment is improved.

The invention provides an unmanned fire extinguishing robot navigation method and device based on a 4D millimeter wave radar, and solves the technical problem that in the prior art, effective data cannot be accurately captured for positioning navigation, so that fire cannot be accurately extinguished. The method comprises the following steps: acquiring an initial attitude angle, a local map and an optimal path of a robot, and navigating the robot according to the optimal path; the method comprises the following steps: acquiring feature point clouds, preprocessing the feature point clouds and a local map to obtain a preprocessed feature point cloud set and a preprocessed local map, performing point cloud matching based on a variant ICP algorithm to obtain pose change parameters, and superposing the pose change parameters with an initial attitude angle of a robot to form an incremental odometer. The preliminarily estimated real-time motion state of the robot is obtained and corrected to obtain the high-precision robot positioning and motion state, and the process is cycled until the robot reaches the fire source position, and then water spraying and fire extinguishing are conducted. The method can be widely applied to the technical field of robot navigation.

Techniques for map refinement (e.g., tracking data refinement) by an inside-out location tracking system may include computing a transform from a reference point to an epipolar line using an Essential Matrix derived from a reference frame camera motion in a live frame, computing several appearance errors between a feature associated with the reference point and other projected and optimized features, using a perpendicular projection hypothesis and an optimized point generated on the epipolar line, and evaluating the appearance errors using a greedy, ordered optimization. If the appearance error is less than a predetermined quality threshold, the optimized point is retained in tracking data. Otherwise the tracking data may be reset at the reference point location for reinitialization. An updated map and associated map data reflecting updated optimized points may be provided, for example, to a client device, as well as returned to a visual inertial odometry system.

The invention provides an unmanned aerial vehicle fusion positioning method based on four-anchor-point UWB pose dynamic quality evaluation, and the method comprises the following steps: defining an unmanned aerial vehicle coordinate system and a world coordinate system, and selecting a first frame point cloud coordinate system of a laser radar as the world coordinate system; defining a state transition model required by front-end state estimation; obtaining a front-end laser radar-inertial odometer factor by using an iterative Kalman filter; based on the four-anchor-point ultra wide band, the accurate unmanned aerial vehicle position and attitude are obtained to serve as ultra wide band factors; based on the obtained ultra-wideband factor, judging the position and attitude quality of the ultra-wideband factor, and determining whether the ultra-wideband factor participates in factor graph optimization or not by adopting an event triggering mechanism; and factor graph optimization is used to obtain the accurate position and attitude of the unmanned aerial vehicle after optimization. And the calculation burden of an airborne computer of the unmanned aerial vehicle is reduced, and the accurate unmanned aerial vehicle position and attitude obtained by combining the four-anchor-point ultra wide band are combined, so that the unmanned aerial vehicle real-time positioning with higher precision and lower calculation burden is realized.

In variants, the method can include: receiving a first and second image, determining system motion data, determining a set of correspondences based on the first and second image, determining a pixel capture time for each matched pixel, determining an adjusted sensor pose based on the pixel capture time and the motion data, determining a set of depth measurements, optionally performing odometry, optionally creating a depth map, optionally operating a vehicle, and / or other processes. The method can function to determine depth measurements using a rolling shutter camera.

Systems, methods, and other embodiments described herein relate to estimating a gap between positioning and odometry speed-signals through time warping for aligning the speed-signals. In one embodiment, a method includes computing a positioning speed-signal and an odometry speed-signal temporally by a vehicle from positioning data and odometry data, the positioning data and the odometry data generated at different frequencies. The method also includes calculating a cost matrix for the positioning speed-signal and the odometry speed-signal using dynamic time warping (DTW). The method also includes extracting a time gap using the cost matrix and align the positioning speed-signal and the odometry speed-signal by correcting a lag with the time gap.

The invention provides an unmanned aerial vehicle navigation positioning method and system based on a visual inertial odometer, and relates to the technical field of unmanned aerial vehicle navigation, and the method comprises the steps: synchronously obtaining image data collected by an airborne visual sensor, inertial data output by an inertial measurement unit, and a motor control instruction signal generated by a flight control system; synchronously executing noise compensation on the inertial data by combining a preset thrust-rotating speed mapping relation according to the motor control instruction signal to obtain compensated inertial data; in a preset sliding window optimization framework, solving an optimal state estimation value based on the image data, the motor control instruction signal and the compensated inertial data; according to the method, through synchronous acquisition, noise compensation, state estimation optimization and navigation control strategies, the navigation positioning precision and the anti-interference capability of the unmanned aerial vehicle in a complex environment are remarkably improved.

The invention discloses a LiDAR-IMU-camera tight coupling positioning and mapping method and device for a mobile platform, and belongs to the technical field of robot SLAM. Synchronously triggering the color camera, the laser radar and the IMU through hardware, and unifying timestamps; performing compensation and distortion removal on the laser point cloud motion by the IMU data; based on curvature, intensity and density self-adaptive downsampling, local plane fitting errors are used for distributing observation weights; the IMU pre-integration pose is used as an initial value, point-to-surface registration of the weighted point cloud and the local map is carried out, and a laser-IMU tight coupling odometer factor is obtained; the color image and the laser intensity graph are fused into a multi-mode loopback descriptor, and loopback factors are generated through global retrieval and geometric verification; and inputting an odometer, an IMU, a laser loopback factor and a visual loopback factor into an increment factor graph optimizer, jointly solving a global optimal key frame pose, and outputting a dense laser point cloud map and a color visual point cloud map. The method can be operated in real time on an embedded platform, effectively inhibits drifting, and realizes centimeter-level global consistent positioning and mapping.

The provided is a LiDAR odometry using a piecewise linear continuous-time trajectory. The LiDAR odometry includes the following steps: dividing an initial LiDAR point cloud time sequence through time windows to obtain LiDAR point cloud subsequences in the multiple time windows, and constructing an initial local point cloud map; and optimizing, based on the initial local point cloud map, a LiDAR pose for the LiDAR point cloud subsequence in each time window to obtain an overall continuous-time trajectory and a final local point cloud map. The provided achieves high-precision motion estimation of the LiDAR, with high robustness and real-time performance, and is suitable for fields such as robot navigation, simultaneous localization and mapping, and autonomous driving. The provided gives a simple and efficient solution that effectively utilizes the characteristics of the LiDAR as a streaming sensor.

An apparatus is configured to classify indicator lights of surrounding vehicles as either active or inactive. The apparatus may use a Siamese network to determine respective feature vectors from respective images captured by a vehicle at different times. The apparatus may also embed speed and time information in the respective feature vectors based on odometry information associated with the vehicle at a time each respective image was captured, and fuse, using a temporal attention mechanism, features and the speed and time information from the respective feature vectors to produce a fused feature vector. The apparatus may further process the fused feature vector using capsule modules to produce an indicator feature vector, calculate a similarity metric from the indicator feature vector, and process the similarity metric with a classifier to output an indicator classification.

The invention provides a self-adaptive multi-modal odometer dynamic fusion method which specifically comprises the following steps: extracting edge and plane features of a laser point cloud, and generating a laser pose factor by utilizing iterative motion compensation and residual optimization; multi-scale angular point features are extracted from an original image, and visual pose factors are generated in combination with optical flow tracking and local light beam adjustment. And a dynamic factor graph is constructed, laser and visual pose factors are used as constraints, an iSAM2 engine is used for optimization solution, and a global pose is obtained. Calculating the residual mean value and variance of Lagrange factors in real time, dynamically checking the state of the sensor based on chi-square test, triggering failure response during continuous overrun, gradually removing abnormal factors, keeping historical data, aligning timestamps through SE (3) manifold interpolation, and optimizing external parameter calibration. And after the sensor is recovered, factors are re-introduced through progressive weight fusion, and a sliding window mechanism is adopted to smooth the track. The method provides a robust and high-precision fusion algorithm for the field of robots and automatic driving.

Exemplary embodiments of the present disclosure are directed towards an indoor and outdoor navigation system for autonomous mobile robots and the methods employed therein. The system comprises a mobile robot equipped with sensors, including IMU, encoders, GPS, and a 3D camera, all managed by a processing device. During indoor navigation, the device utilizes an extended Kalman filter for precise odometry estimation, adapting to outdoor mode with an additional clearance base. User-defined waypoints guide the robot, supported by safety lanes generated through a lane generation algorithm to prevent off-roading in mapless environments. Visual markers facilitate odometry correction for enhanced accuracy, while map-based navigation employs 2D LIDAR sensors for environment mapping. Safety is ensured through obstacle detection and ground removal algorithms, with alerts transmitted via IoT protocols upon detecting obstacles beyond designated safety lanes, thereby enhancing navigation reliability and safety.

The invention discloses an enhanced RTK (Real Time Kinematic) positioning method for blind compensation of a laser inertial navigation odometer, which belongs to the field of computer vision and comprises the following specific steps: I, calibrating an initial sensor and performing real-time data fusion; iI, predicting a motion state and carrying out real-time monitoring and evaluation on a GNSS signal; iII, switching a positioning mechanism according to the change condition of the GNSS signal; iV, optimizing a switching process and analyzing radar data in real time to establish an environment model; according to the invention, positioning schemes can be rapidly and accurately switched, positioning errors caused by reduction of signal quality are reduced, the problem of inaccurate positioning caused by unstable GNSS signals is reduced, the robustness and stability of the system in a complex environment are improved, the strain capacity of the whole positioning system is improved, and the positioning accuracy is improved. The positioning error caused by improper path selection is reduced, the strategy can be dynamically adjusted according to the environment change, the dependence on the GNSS signal is reduced, and the response speed and the calculation efficiency of the system are improved.

The invention discloses a pipeline robot positioning method, system and device based on an odometer wheel and an IMU and a medium, and relates to the technical field of robot positioning and pipeline detection. The method comprises the following steps: determining an initial attitude angle of the pipeline robot in a static state according to acceleration data and magnetic force data collected by an IMU (Inertial Measurement Unit); based on a compensated angular velocity obtained by denoising angular velocity data collected by an IMU by using a complementary filtering algorithm, updating an initial attitude angle by using an Euler angle method to obtain an attitude angle matrix, and performing optimal estimation by using a nonlinear state estimation algorithm; performing consistency verification on the pulse data of the plurality of odometer wheels, determining corresponding effective pulse data through weighted correction, and then determining a mileage increment; and based on the optimized attitude angle matrix, the mileage increment is projected to a navigation coordinate system so as to solve the real-time position coordinate of the pipeline robot. The problems that the pipeline robot is poor in trafficability and low in positioning precision when facing a complex pipe network can be solved.

The embodiment of the invention provides an electric control clutch semi-combination point position self-learning method and device, and relates to the technical field of clutch control, and the method comprises the steps: controlling a motor to carry out zero position self-learning, and recording a mechanical zero position; determining that the vehicle meets a first vehicle state condition, and performing clutch semi-joint point self-learning; a semi-combination point position obtained through self-learning is stored in a memory, and current odometer data is recorded; determining whether the clutch is completely run-in; through self-learning, change of a clutch semi-joint point caused by long-time work of the clutch and semi-joint point deviation caused by manufacturing and assembling differences of the clutch are corrected, and torque transmission capacity reduction caused by fast change of the semi-joint point when running-in of the clutch in the initial life cycle is not complete is prevented. Therefore, the torque transmission accuracy of the clutch is ensured.

A method and system are provided for characterizing a vehicle motion of an autonomous mobile robot in response to a triggering event. The method and system involve an autonomous mobile robot and a vehicle processor operable to navigate the autonomous mobile robot. The system further includes a motion characterization system coupled to the autonomous mobile robot, the motion characterization system comprising an odometry system operable to collect vehicle motion data associated with the vehicle motion; a triggering component; a storage component for storing an event start time, an event end time and the vehicle motion data between the event start time and the event end time; and a motion characterization processor operable to: receive an initialization input to initiate the triggering event; generate a trigger signal to cause the triggering component to cause the triggering event; and identify the event start time and an event end time.

The invention discloses an underground mine positioning and mapping trajectory optimization method based on underground control point constraint, and belongs to the field of positioning and mapping, and the method comprises the steps: respectively obtaining GNSS data, laser data, IMU data and image data; according to the GNSS data, the laser data, the IMU data and the image data, completing initialization of a laser radar-vision-inertial odometer system and GNSS initialization; extracting point cloud features of the distributed underground control points in real time based on an RANSAC algorithm, and generating a global coordinate constraint factor of nonlinear least square optimization; and performing relative attitude calculation according to the global coordinate constraint factor to complete underground mine positioning and mapping trajectory optimization. According to the method, the problems of accumulated error overrun and point cloud dislocation layering easily caused by pose drift in a special environment of an underground mine are solved.

Autonomous ground vehicles that are outfitted with radar sensors and inertial measurement units accurately determine states of the autonomous ground vehicles, e.g., estimates of the vehicles' positions, orientations, or velocities or accelerations along or about one or more axes, based on data captured by the radar sensors and the inertial measurement units. Where objects are detected in radar scans, the objects are determined to be static (or fixed), or dynamic (or moving), and landmarks representing static objects are identified. Constraints on estimates of states may be calculated based on doppler effects, inertial measurement unit effects, or locations of landmarks, and the states may be determined as solutions to optimization problems based on the data and the calculated constraints. Odometry messages representing the determined states may be generated and stored or utilized for any purpose.

The invention discloses an unmanned simultaneous positioning and mapping method and device, a medium and equipment, and relates to the technical field of computers, and the method comprises the following steps: obtaining image data of the surrounding environment of unmanned equipment, carrying out semantic segmentation through employing an improved semantic segmentation model, and determining the semantic information of each pixel; acquiring multi-frame point cloud data, and performing distortion correction on the multi-frame point cloud data by performing pre-integration on the inertial measurement unit data; projecting each frame of point cloud data to the time-space synchronized image data, and determining a semantic tag of a three-dimensional point in each frame of point cloud data; removing three-dimensional points of the dynamic object according to the speed and the space overlapping degree; and optimizing a surrounding environment map and a pose map of the unmanned equipment by selecting a key frame and a loopback candidate frame. According to the method, the semantic segmentation technology and the laser radar inertial odometer are fused for positioning and mapping, so that the positioning and mapping precision in a dynamic similar environment is greatly improved.

An external device for use with one or more robotic tools, the external device including a display and an electronic processor, where when an initiate setup button is selected by a first user input, the processor is configured to send a signal to the first robotic garden tool to travel from a dock and along a perimeter of an operating area. When the add start point button is selected by a second user input, the processor is configured to retrieve a first position of the first robotic garden tool, the first position being indicative of a first start point remote of the dock, and where the first robotic garden tool is configured to return to the dock after traveling along the perimeter and to communicate a calculated boundary length based on the data gathered by the odometry unit to the processor.

A system may include an electronic device such as a head-mounted device and a handheld input device for controlling the electronic device. A lanyard may be removably attached to the handheld input device or a non-electronic object. The lanyard may include visual markers, such as infrared light-emitting diodes and / or fiducials, that can be detected by an external camera and used to track a location of the lanyard. For example, the lanyard may be fabric, and the visual markers may be incorporated into the fabric or attached to the fabric. The lanyard may also include motion sensors, visual-inertial odometry cameras, or other sensors to determine the location of the lanyard. The lanyard may be electrically coupled to the handheld input device, such as to transfer power and / or data. Alternatively, the lanyard may be coupled to a non-electronic object and may include a battery and / or a haptic output component.

The invention discloses a fusion positioning method, system and device based on an aerial view, and a medium, and relates to the field of fusion methods, and the method comprises the steps: S1, obtaining the satellite position information of a vehicle; s2, resolving position information and attitude based on a strapdown inertial navigation system, and correcting an inertial navigation error; s3, acquiring point cloud data to generate an aerial view image, and performing association matching with a high-precision map; s4, when the GNSS signal is not good, providing short-term position information based on a speedometer so as to support inertial navigation positioning; and S5, performing error estimation and correction through multi-sensor data, and outputting corrected real-time position information and attitude of the vehicle. After error correction and filtering are carried out on the fused data, accurate position information can be provided for scenes such as automatic driving and robot navigation. Through cooperative work of the GNSS / RTK, inertial navigation, the laser radar and the odometer, the positioning precision and robustness of the system are improved.

The invention discloses a VIO positioning method for dynamically adjusting covariance by using normalized cross-correlation (NCC), and the method comprises the steps: constructing a new observation model, introducing a tracking error of a feature point on a pixel value, taking the NCC as an index for quantifying the matching quality between the feature points, and reflecting the tracking error of the feature point by calculating the NCC matched with the feature points. Therefore, the covariance matrix of the observation noise item is dynamically adjusted to adapt to the change of the matching quality of the feature points, and the method can obtain a more accurate and robust matching result in a complex environment in which the matching quality difference of the feature points is significant.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for improving visual inertial odometry (VIO). One of the methods includes identifying two or more parameters of a robot; generating, using the two or more parameters, a multi-dimensional space; generating two or more configurations for the robot by sampling the multi-dimensional space; determining, for each of the two or more configurations, a visual inertial odometry (VIO) trajectory; generating, for each of the trajectories using the corresponding trajectory and a ground truth trajectory, (i) error data representing a difference of the corresponding trajectory from the ground truth trajectory and (ii) processing data representing processing metrics from the determination of the corresponding trajectory; selecting, using (i) the error data and (ii) the processing data, a configuration of the two or more configurations; and providing, to the robot, the selected configuration for navigating an area.

A method for aligning coordinate systems from separate augmented reality (AR) devices is described. In one aspect, a first device accesses first pose data from a first Visual Inertial Odometry (VIO) system of the first device. A camera of the first device captures an image of a machine-readable code that is displayed on a display of a second device. The second device encodes the machine-readable code with second pose data from a second VIO system of the second device. The first device decodes the second pose data from the machine-readable code, and determines a relative pose between the first device and the second device based on the first pose data and the second pose data.