326 results about "Odometry" patented technology

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 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.

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.

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.

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.

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.

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.

The present invention discloses a visual inertial odometry method and system based on transformation error state, and relates to the technical field of visual inertial odometry optimization. The technical points of the present invention include: estimating posture information using a transformation extended Kalman filter, including: establishing a system continuous-time motion model and measurement equation based on real-time acquired visual and IMU data; establishing a linearized error state system based on the system continuous-time motion model and measurement equation; designing a linear time-varying transformation, and using the linear time-varying transformation to transform the linearized error state system into an error state system in which the system state is independent of the system's unobservable subspace; performing state estimation based on the transformed linearized error state system to obtain estimated posture information. The present invention proposes a transformation-based method to solve the inconsistency problem in VINS, alleviates the observability mismatch problem, and ensures that the visual inertial odometry using the transformation extended Kalman filter has consistent estimation results.

An object of initial unknown position on a map may be determined by traversing through moving and turning to establish motion trajectory to reduce its spatial uncertainty to a single location that would fit only to a certain map trajectory. An artificial neural network model learns from object motion on different map topologies may establish the object's end-to-end positioning from embedding map topologies and object motion. The proposed method includes learning potential motion patterns from the map and perform trajectory classification in the map's edge-space. Two different trajectory representations, namely angle representation and augmented angle representation (incorporates distance traversed) are considered and both a Graph Neural Network and an RNN are trained from the map for each representation to compare their performances. The results from the actual visual-inertial odometry have shown that the proposed approach is able to learn the map and localize the object based on its motion trajectories.

The invention discloses a rapid robust monocular vision inertial positioning method and system. The method comprises the following steps: configuring IMU and monocular camera sensor parameters; preprocessing the IMU data; iMU attitude initialization is carried out; image features of the monocular camera are extracted, abnormal matching points are removed, and IMU pre-integration is carried out; monocular vision initialization is carried out; odometer attitude optimization: judging whether the feature points are mismatched or not based on an IMU pre-integration result, constructing a feature point re-projection error jacobian matrix, and performing blocking and diagonalization processing on the re-projection error jacobian matrix to respectively optimize inverse depths and image frame attitudes of the feature points; selecting a key frame based on a key frame identification rule after the current frame attitude update is completed; and judging according to the current frame, and removing a certain frame in the sliding window to reserve a sliding window space for adding the latest frame. According to the method, the inverse depth of the feature points and the image frame attitude are optimized respectively based on the counterweight projection error Jacobian matrix partitioning and diagonalization processing, and the calculation complexity is reduced.

A system for generating a map of a paved surface for a vehicle and localizing the vehicle on the map of the paved surface includes an imaging sensor, a vehicle odometry sensor, a memory, a processor, and a transceiver. The imaging sensor captures a series of image frames. The vehicle odometry sensor measures an orientation, a velocity, and an acceleration of the vehicle. The memory stores a mapping engine as computer readable code. The processor executes the mapping engine to generate a map. The transceiver uploads the map to a server such that the map is accessed by a second vehicle that uses the map to traverse the external environment.

The invention relates to a TDCP enhanced visual inertial odometer and a navigation positioning method thereof, the method aims at multi-sensor fusion positioning, uses an extended Kalman filter (EKF) to integrate visual features, IMU data and TDCP observation values, improves local pose estimation precision, and suppresses VIO error accumulation through absolute scale and course constraint provided by TDCP; meanwhile, the local estimation result and the global position of the GNSS are optimized and fused through a global pose map, and high-precision pose estimation which is globally consistent and within a global range is achieved. According to the method, optimization can be completed only through intermittent global positions, the problem of positioning failure of a traditional scheme in a GNSS challenge environment is solved, the problems of cycle slip accumulation and gross error interference when TDCP is independently used are effectively solved, and the method is suitable for automatic driving, unmanned aerial vehicles and other scenes with high real-time performance requirements.

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.

A system is provided for localizing an autonomous work vehicle within mapped agricultural environments when GPS may be unavailable. Range sensors capture LiDAR images that are segmented by a trained model into features such as trunks and canopy. Trunk points are clustered to compute centroids, which are used to estimate left and right tree lines and a lane centerline relative to the vehicle. This sensor-derived centerline is compared with georeferenced lane and row geometry stored in a digital map. A particle filter maintains multiple pose hypotheses, propagates them using vehicle odometry, and weights them according to alignment between the lane centerline relative to the vehicle and the mapped lane geometry for the particle. Resampling and averaging of high-weight hypotheses produces an adjusted pose that corrects for lateral and heading errors. The adjusted pose serves as input to a navigation controller that issues steering and speed commands, enabling accurate autonomous operation.

The invention relates to the field of robot autonomous navigation and environmental perception, and discloses a robot SLAM method and system fusing dual dynamic point cloud segmentation and multiple sensors, and the method comprises the steps: obtaining a three-dimensional point cloud frame, an inertial measurement unit and foot end odometer data, and obtaining a three-dimensional point cloud frame through a low-dynamic point cloud segmentation module and a high-dynamic point cloud segmentation module; the high-dynamic point cloud segmentation module identifies and filters dynamic point sets generated by low-speed and high-speed moving targets, static point cloud data are generated, the high-dynamic point cloud segmentation module detects free space state mutation, and motion state information of the dynamic targets is output. Data of the static point cloud, an inertial measurement unit and a foot end odometer are sent into a graph optimization SLAM fusion module for global pose optimization, in order to compensate segmentation delay, the graph optimization module also predicts a dynamic position and constructs an envelope frame, and the point cloud is temporarily shielded during point cloud registration. According to the method, various dynamic targets can be comprehensively filtered out, calculation delay can be compensated, and the positioning precision and mapping quality of the robot in a dynamic environment are improved.

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.

The application relates to a laser SLAM method and system based on height information. The method comprises the following steps: acquiring three-dimensional laser radar data, performing motion distortion and dynamic point filtering processing to obtain first point cloud data; acquiring robot IMU data, performing pre-integration processing to obtain first pose data; extracting features from the first point cloud data to obtain feature point cloud, recording the height of the feature point cloud, and obtaining laser radar odometry data according to the feature point cloud and the height of the feature point cloud; fusing the first pose data and the laser radar odometry data based on a factor graph to obtain second pose data as the robot pose; and determining corresponding point cloud data according to the second pose data and splicing to generate a three-dimensional point cloud map. The application can enable the robot to realize high-precision self-positioning and high-precision mapping in an unfamiliar cluster point scene.

Apparatus and methods for mitigating slip conditions and estimating ground friction for a robot having a plurality of feet are provided. In one aspect, a method includes estimating a coefficient of friction for a ground surface supporting the legged robot based on sensor data, odometry data, and a terrain map of an environment. The sensor data includes a set of joint angles and a set of joint torques for a set of joints of the legged robot, and the odometry data indicates a location of the legged robot in the environment. One of the plurality of feet of the robot applies a force on the ground surface based on the estimated coefficient of friction.

Techniques for determining whether a global navigation satellite system (GNSS) measurement generated by a sensor associated with a vehicle is reliable. In some cases, an example system determines whether a first GNSS measurement generated by a first sensor and associated with a first time is reliable based on at least one of: (i) one or more GNSS measurements generated by the first sensor and associated with one or more times before and / or after the first time, or (ii) one or more GNSS measurements generated by a second sensor and associated with the first time. For example, the system may determine whether a first GNSS measurement generated by a first sensor and associated with a first time is reliable based on whether a ratio of a sum of incremental distances associated with a GNSS measurement sequence including the first GNSS measurement over an odometry-based distance falls within a threshold range.

The present invention relates to a method and apparatus for expressing scan data detected by a LiDAR mounted on a moving object as a submap based on a Normal Distribution Transform (NDT), estimating a relative positional change (LiDAR odometry) of the moving object through scan matching using Particle Swarm Optimization (PSO) between the submap and real-time scan data, and determining a covariance of the estimated LiDAR odometry by recognizing the shape of the surrounding environment based on the orientation of normal distributions represented in the NDT-based submap.

A method and a apparatus for expressing scan data detected by a LiDAR mounted on a moving object as a submap based on a Normal Distribution Transform (NDT) estimate a relative positional change (LiDAR odometry) of the moving object through scan matching using Particle Swarm Optimization (PSO) between the submap and real-time scan data, and determine a covariance of the estimated LiDAR odometry by recognizing the shape of the surrounding environment based on the orientation of normal distributions represented in the NDT-based submap.

A robot or other device capable of movement includes a local position module and an RF communication module, the RF communication module being configured to communicate with RF anchor points to conduct one or more of navigation, positioning, exploration, tracking, and mapping. The robot or other device can include a transceiver configured to communicate with fixed location RF anchor points and a relative odometry unit. The robot or other device also can include a localization and navigation system that can conduct bearing measurements, which can be two-way bearing measurements, between the robot and one or more of the RF anchor points and integrates the bearing measurements with odometry measurements to navigate an environment of the RF anchor points.