221 results about "Odometry" patented technology

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 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 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.

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.

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.

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.

The invention relates to the technical field of robots and automation, and discloses a dynamic environment-oriented laser SLAM (simultaneous localization and mapping) system and an efficient loopback detection method thereof, the system comprises a data preprocessing module, a probability persistence map management module, a persistence weighted front-end odometer module, a loopback detection and state reverse correction module and a rear-end pose map optimization module, static and dynamic elements in an environment are distinguished by utilizing a probability persistent map, structural conflicts in the environment are intelligently identified by comparing the similarity of a structural anchoring descriptor synthesized by high-persistent voxels with an error of geometric verification, and when the structural conflicts are identified, a system performs reverse correction on old structural information in the map; and when the geometric verification is passed, generating a loopback constraint. According to the method, transient dynamic object interference and long-term structural change of the environment can be effectively dealt with, and the robustness, loopback detection accuracy and map long-term consistency of the SLAM system in the complex dynamic environment are improved.

The invention discloses a multi-source fusion continuous positioning method in combination with a prior feature map, and aims to solve the problem that an existing vision-inertial odometer easily generates accumulative errors in a GNSS denial environment, the method comprises the following steps: constructing a prior feature map comprising key frame poses, feature point descriptors and 3D coordinates based on an open source algorithm; a track displacement difference alignment strategy is adopted to complete visual-inertial system and GNSS joint initialization, and ESIKF is used to replace EKF to improve fusion precision; robust alignment of a local coordinate system and a map coordinate system is achieved through a single-frame geometric constraint accumulation-batch manifold alignment strategy; based on the reverse PnP, the current 3D point cloud and the historical 2D observation constraint are utilized to resolve the relative pose, the map matching result is used as observation to be fused with the GNSS or replace GNSS observation, the ESIKF loose coupling is utilized to eliminate accumulative errors, and continuous high-precision positioning in the GNSS failure scene is achieved.

The invention belongs to the field of multi-sensor fusion systems, and discloses a multi-sensor fusion system simultaneous positioning and mapping method and a related device, and the method comprises the steps: obtaining an environment image, a laser point cloud and IMU data; scene semantic information is extracted through a multi-modal large model; dynamically generating a weight matrix of vision and laser radar relative to an IMU (Inertial Measurement Unit) by utilizing a vision and point cloud condition deep neural network in combination with scene information and IMU observation; and performing local and global optimization by combining each sensor speedometer factor, an IMU pre-integration factor and the weight matrix, and finally outputting robot state estimation and map point coordinates. Through semantic understanding and a self-adaptive weight mechanism driven by a conditional deep neural network model, a fusion strategy can be intelligently adjusted before environment change or sensor degradation, the robustness, precision and consistency of a system in an unstructured scene are improved, and the problems of positioning drift and map failure caused by fixed weight in a traditional method are solved.

To improve the accuracy of the stopping position when an autonomous mobile robot stops at a target location. [Solution] The autonomous mobile robot autonomously moves along a first target path to a first stop position just before the target stop position, while measuring and comparing the surrounding conditions using laser scanning (S1, S2). While stopped at the first stop position, it recognizes the position of a marker placed near the target stop position (S3), generates a second target path approaching the marker (S4), performs odometry movement to the second stop position following the generated second target path (S5, S6), recognizes the marker while stopped at the second stop position (S7), estimates the error with the target trajectory to the target stop position, performs odometry movement to correct the error (S8, S9), and stops at the target stop position (S10).

The application belongs to the technical field of unmanned aerial vehicle navigation. A pipeline inspection unmanned aerial vehicle navigation method and system based on reinforcement learning are provided. By collecting inertial measurement, visual inertial odometry, cross array ranging and electronic governor feedback data, a state vector is constructed and high-frequency motion prediction and measurement update are realized through extended Kalman filtering to output high-precision unmanned aerial vehicle body state. The best anti-interference height is solved by combining the pipeline wind disturbance and rotor airflow disturbance model, and the external disturbance torque is decoupled. A dual-frequency reinforcement learning state is constructed and control instructions are output, and safe speed is obtained through safety shield filtering. With safe speed as the target, the motor speed is directly output through the reinforcement learning network to control flight, and the three-dimensional topological mapping of pipeline defects is completed synchronously. The application can realize high-precision navigation, active disturbance rejection and safe inspection in the pipeline, and improve the flight stability and operation reliability in complex aerodynamic environment.

A computer implemented system useful for determining turns in a user's trajectory, including one or more processors; one or more memories; and one or more programs stored in the one or more memories, wherein the one or more programs executed by the one or more processors (1) receive input data, comprising orientation data and acceleration from one or more sensors carried by a user taking steps along a trajectory; (2) analyze the input data using a first trained machine learning algorithm so as to detect a plurality of n straight sections when the user is walking along an approximately straight path; (3) comprise an orientation tracker tracking an orientation of the user in each of the n straight sections, wherein the orientation comprises an estimated orientation taking into account drift of the input data outputted from the one or more sensors; and (4) comprise a turn detector detecting each of the turns, wherein each of the turns is a change in the estimated orientation of the user in the nth straight section as compared to the estimated orientation in the (n−1)th straight section.

The invention relates to a measuring device (E2) with a connecting means (VEM) for use in positioning a primary coil (PSP) of a charging unit (E1) of a charging device (LAV) and a secondary coil (SSP) of an electric vehicle (EV) for wireless charging of the electric vehicle (EV), wherein the measuring device (E2) is configured such that it can be connected to, or is connected to, the charging unit (E1) by means of the connecting means (VEM), wherein at least one primary positioning sensor (S11, S21, S22) for detecting primary position data of the primary coil (PSP) and at least one secondary positioning sensor (ES1, ES2) of the electric vehicle (EV) for detecting secondary position data of the secondary coil (SSP) during the positioning of the electric vehicle (EV) to be charged relative to the charging unit (E1) are equipped, wherein the primary positioning sensor (S11, S21, S22) and the secondary positioning sensor (ES1,ES2) communicate wirelessly, characterized in that odometry data of the electric vehicle (EV) to be charged can be supplied to or are supplied to the measuring device (E2), wherein the measuring device (E2) is configured to determine and / or update an instantaneous vehicle position of the electric vehicle (EV) during positioning based on the transmitted odometry data and the acquired primary position data and the acquired secondary position data. Furthermore, the invention relates to a charging device, an electric vehicle and a charging method.

A method for managing the longitudinal speed of a motor vehicle, the motor vehicle traveling on a planned trajectory, the motor vehicle being equipped with at least one detection means for detecting the environment of the vehicle and with an odometry means, wherein the method comprises detecting, based on data from the at least one detection means, a speed change point located on the given trajectory and ahead of the motor vehicle, and determining a limit speed applicable at the speed change point.

The application discloses a GNSS denial and multi-source information fusion based unmanned aerial vehicle autonomous navigation method and belongs to the technical field of navigation measurement. The application adopts or logic to determine the GNSS environment state through multi-dimensional indexes, and realizes navigation mode jump-free switching through a weighted smoothing mechanism. When GNSS is effective, GNSS / IMU loose combination filtering is adopted, and when GNSS is denied, a factor graph optimization framework is constructed, visual inertial pre-integration, laser odometry, height constraint and heading constraint in a non-electromagnetic interference scene are uniformly accessed, and digital orthographic image and elevation model joint landscape matching correction, fault adaptive adjustment and online parameter calibration are combined. The application significantly improves the precision, stability and robustness of unmanned aerial vehicle navigation in a complex environment, effectively suppresses the cumulative drift in a long-time denial environment and is suitable for unmanned aerial vehicle autonomous flight in satellite blocking, electromagnetic interference and other scenes.

The present application belongs to the technical field of mobile robot navigation, and particularly relates to a SLAM navigation method and system for a mobile intelligent cabinet, which comprises the following steps: acquiring a laser point cloud sequence and a grayscale image of the mobile intelligent cabinet and performing data cleaning; obtaining a geometric feature index based on the spatial jump distribution of the laser point cloud sequence; obtaining a visual texture index based on the local dispersion of the image gradient; calculating laser dynamic weight and visual dynamic weight by using the geometric feature index and the visual texture index, weighting and fusing the pose change quantity calculated by the single-line laser radar odometry and the visual odometry to obtain a fused pose quantity; and updating the global state based on the fused pose quantity and driving autonomous navigation. The present application can adjust the sensor weight in real time according to the environmental characteristics, solves the problem of positioning divergence in the long corridor of a shopping mall or a high-reflectivity environment, and improves the robustness of navigation.

This invention provides a 4D radar odometry method for high-dynamic scenarios, relating to the field of autonomous driving perception and sensor fusion technology. The method includes: acquiring raw point cloud data using a 4D millimeter-wave radar-based perception system; extracting corresponding clustering parameters from the raw point cloud data, wherein the clustering parameters include search radii in different directions and minimum neighborhood density; clustering the raw point cloud data using the DBSCAN clustering method based on the clustering parameters to obtain different point cloud clusters; extracting static points from the different point cloud clusters; and calculating the vehicle speed based on the static points using an angle-distance weighted least squares method. This technical solution effectively improves the accuracy of vehicle speed measurement.

The invention relates to the technical field of robot autonomous navigation and environmental perception, in particular to a multi-sensor fusion SLAM and autonomous exploration method in a complex agricultural environment. The method comprises the following steps: correcting motion distortion through IMU forward propagation and laser radar back propagation; fusing the laser point-surface residual error and the visual luminosity residual error by adopting error state iteration Kalman filtering with sequence updating; carrying out object-level segmentation on the local point cloud map, and removing dynamic objects based on a weighted residual mechanism; carrying out loopback detection based on feature matching of deep learning; fusing three factors of a laser-visual odometer, IMU pre-integration and loopback through a factor graph model to obtain a global consistent track and map; autonomous exploration is carried out based on an optimization result, and automatic map construction is realized through terrain analysis and hierarchical path planning. The method effectively improves the positioning precision, robustness and automation level in an agricultural environment with dynamic interference and sparse features, and supports long-time reliable operation.

The application provides a mapping method, equipment and readable storage medium of an engineering machine, and the technical scheme is as follows: IMU data and IMU odometry are used to correct motion distortion of a current frame laser point cloud, point lines and point surface features are extracted according to the curvature of each point of the current frame laser point cloud, a local map is matched with the current frame laser point cloud, and factor graph optimization is performed on a key frame. The output laser odometry is used to correct IMU zero offset, the IMU pre-integrator is reset, and the IMU odometry is output. When the current frame laser point cloud meets a loop detection condition, a normal distribution transformation algorithm is used to perform loop detection on the current frame laser point cloud, and a global pose is updated, so that the mapping problem in the degradation scene of the engineering machine is solved, and fast mapping and positioning in a large scene are realized.

Embodiments of the present application disclose a kind of external parameter calibration method, device, equipment and storage medium.The method comprises: determining the first motion trajectory of camera according to image data, determining the second motion trajectory of wheeled odometry according to encoding value and preset wheeled odometry internal parameter;Get all adjacent key frames in the first motion trajectory, calculate the first rotation increment and the first translation increment between adjacent key frames;According to the acquisition time stamp of adjacent key frame, determine the corresponding chassis trajectory point in the second motion trajectory, and calculate the second rotation increment and the second translation increment between the corresponding chassis trajectory point;Based on hand-eye calibration equation set, according to the first rotation increment, the first translation increment, the second rotation increment and the second translation increment newly obtained, recursive calculation new internal parameter and external parameter, until internal parameter and external parameter converge.The above technical means, solve the technical problem that the external parameter calibration precision is low due to internal parameter change in prior art.

The invention provides a PPPRTK / LiDAR / INS fusion positioning method based on hierarchical optimization. The PPPRTK / LiDAR / INS fusion positioning method comprises the following steps: firstly, constructing a front-end tight coupling module of LiDAR and INS, extracting point cloud geometric constraints, and combining the point cloud geometric constraints with IMU pre-integration to form LIO odometer factors; then obtaining multi-system multi-frequency GNSS original observation, unifying time, coordinates and antenna reference, and carrying out multi-stage correction on the observation; a PPP-RTK / INS tight coupling observation model is established under an SRIF framework, linearization and residual construction are carried out on a non-difference and non-combination equation, a joint state is constructed, and a floating point-fixed two-stage ambiguity resolving and quality control method is adopted; and finally, performing joint optimization on the front-end LIO factor and the PPP-RTK / INS factor by utilizing a factor graph, eliminating abnormal observation in combination with a robust kernel and a residual error threshold, and outputting a continuous and accurate vehicle pose. The method can effectively improve the continuity, convergence speed and precision of positioning in complex scenes such as urban canyons and elevated interchange, and is suitable for the fields of navigation, automatic driving, free flow charging and the like.

In an approach to two-way ranging, a system includes a first entity. The first entity includes: a first inertial measurement unit (IMU) circuitry configured to measure a first change in position of the first entity; radio circuitry configured to communicatively couple with one or more additional entities; and processor circuitry. The processor circuitry is configured to: measure a range to a second entity using two way ranging (TWR); receive an odometry measurement from the second entity; and determine a range estimation to the second entity based on a previous range to the second entity and the odometry measurement.