321 results about "Local map" patented technology

The invention discloses an AR home experience method in a large scene. On the basis of combination of a natural feature identification-based three-dimensional registration method and a binocular tracking positioning and local mapping method, the camera attitude is estimated by using feature points of a real-time scene and corresponding three-dimensional points thereof under the binocular trackingpositioning and local map construction technology. According to the mode, on-site environment features shot in real time are used as recognition tracking objects, a virtual home model can still be normally positioned and tracked under the condition that no identification graph exists, the problems that an existing AR home experience application is small in use range and poor in stability are solved, and therefore the AR home experience of virtual and real fusion can be met in a wider range and more truly.

The invention relates to a distributed trajectory planning method and system for an unmanned aerial vehicle cluster in an obstacle environment. The method comprises the steps of initializing a system, constructing a local Euclidean symbol distance site map, and realizing real-time interaction of state information of the unmanned aerial vehicle. An initial collision-free path is generated using jump point search. And solving an optimal control point through an L-BFGS algorithm through multi-constraint trajectory optimization in combination with a load swing dynamics model, four-rotor dynamics limitation, cluster collision avoidance and environment obstacle avoidance requirements. And a dynamic time redistribution strategy is adopted, and the track time interval is adjusted according to speed and acceleration overrun conditions. The method further comprises an adaptive re-planning mechanism, and local target points are updated in real time and adjacent aircraft collaborative optimization is triggered based on local map boundary detection and quadrotor track safety detection. According to the method, efficient, safe and stable trajectory planning of the hanging load quad-rotor unmanned aerial vehicle cluster in a complex environment is realized, the requirement of autonomously and efficiently completing tasks is met, and the task execution efficiency and safety are improved.

The invention relates to a dynamic visual map matching autonomous optimization method based on IMU sensing, and belongs to the technical field of positioning and navigation of autonomous mobile equipment. The method comprises the steps of obtaining IMU high-frequency motion data and visual image data, performing noise reduction to obtain carrier short-time attitude change information, performing semantic segmentation on an image to recognize a dynamic target, extracting feature points and performing dynamic target weight evaluation; calculating confidence degree scores of the feature points, and mapping fusion weights of the IMU and the vision through a self-adaptive credibility distribution mechanism; s3, predicting a trajectory and a potential matching area based on carrier short-time attitude change information, incrementally updating a local map, and matching by taking an IMU trajectory as a constraint to obtain an initial result; and detecting the matching validity, reducing the re-matching range by using an IMU short-time track during mismatching, and correcting an initial value to obtain a final result. The map matching precision and stability in a complex dynamic scene are improved, the real-time performance and continuity are balanced, and the problems that a traditional scheme is redundant in calculation power and slow in mismatch recovery are solved.

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 invention relates to the technical field of map construction, and provides a mapping method and device based on fusion of a laser radar and an inertial measurement unit. According to the scheme, motion distortion compensation is carried out on original point cloud data through motion estimation based on inertial data, the compensated point cloud is matched with a local map, and the local map is obtained through a filter; carrying out tight coupling fusion on the inertial data and the point cloud matching result, and outputting the pose information of the sensor at the current moment; according to the pose information of the sensor, the compensated point cloud is added into the dynamically managed local map, and the updated local map is periodically published, so that a complete mapping function from data acquisition, processing and fusion to map construction is realized. And through motion distortion compensation, the inertial data and the point cloud matching result are tightly coupled and fused, and the updated local map is periodically published, so that high mapping precision can still be kept when the mapping area is large.

The invention provides a mobile robot autonomous navigation method based on pedestrian trajectory prediction obstacle avoidance, which comprises the following steps: firstly, initializing a robot H, and acquiring 3D laser radar information, camera information, local map data and pose information of the robot H; secondly, according to the position information of the robot H, the multi-modal fusion information and the local map data, the dynamic obstacle state and the static obstacle position are obtained, the types of obstacles are distinguished, and passable areas are divided; next, a prediction trajectory is generated by using a progressive learning trajectory prediction network of LSTM + GAN, sub-target points are generated in a passable area in combination with an RRT algorithm, an optimal sub-target point is selected through an evaluation function, and global path planning is performed by using BIRRT; and finally, inputting the predicted trajectory into a DWA algorithm to realize local path planning and real-time obstacle avoidance of the robot. The obstacle avoidance capability and navigation efficiency of the robot are remarkably improved, the detouring distance and time are reduced, and the adaptability and flexibility of the robot in logistics storage and other scenes are enhanced.

An embodiment method of generating a local map for travel control of a mobility includes loading the local map from an entire map stored in a memory, wherein the local map includes at least a portion of the entire map, acquiring a two-dimensional (2D) light detection and ranging (LiDAR) point data from a LiDAR mounted on the mobility, acquiring a three-dimensional (3D) feature point data by receiving a front image of the mobility from a camera mounted on the mobility, reducing a dimension of the 3D feature point data to a 2D feature point data, binding the 2D feature point data to the 2D LiDAR point data, and publishing the 2D feature point data bound to the 2D LiDAR point data on the local map.

The invention discloses an obstacle avoidance trolley control method based on multi-sensor fusion and Kalman filtering, and particularly relates to the field of obstacle avoidance trolley environment adaptation and control. A data acquisition module acquires multi-sensor original data of an obstacle avoidance trolley in different scenes; the Kalman filtering fusion module carries out data fusion; the environment feature recognition module performs feature marking on the fused data; the local map construction module constructs a local map under a two-dimensional coordinate system according to different scenes; the path feasibility evaluation module calculates a traffic safety index of each node in a differentiated manner; calculating the deviation between the actual safety index of each node and the reference value; the obstacle avoidance control module generates a corresponding motor control instruction; according to the method, after the node traffic safety index is calculated, the differential motor instruction is generated, operation delay caused by excessive braking of a traditional fixed control strategy or potential safety hazards caused by insufficient control are avoided, and unnecessary energy consumption waste and fault shutdown cost are reduced.

The invention discloses a synchronous positioning and mapping method and system based on multi-sensor fusion, a storage medium and electronic equipment. The method comprises the following steps: acquiring IMU data, an image and point cloud data measured by a laser radar; performing pre-integration on the IMU data to obtain real-time pose estimation, and performing laser probability updating and visual probability updating based on the real-time pose estimation to obtain an updated local map; a laser point cloud matching residual error, a visual reprojection residual error, an IMU pre-integration residual error and a marginalization residual error are constructed, a residual error equation is constructed based on the laser point cloud matching residual error, the visual reprojection residual error, the IMU pre-integration residual error and the marginalization residual error, a sliding window is adopted for nonlinear optimization, and robot pose estimation is obtained; and visual loopback detection, laser loopback detection and pose global optimization are carried out. According to the method, the calculation rate, the positioning precision and the reliability can be improved in a complex or feature degradation scene.

The invention provides a robot obstacle avoidance method, a robot obstacle avoidance device and a computer storage medium. The robot obstacle avoidance method comprises the following steps: dividing a current obstacle point cloud from current point cloud data; mapping the current obstacle point cloud to a map coordinate system, and updating an obstacle grid of a global map; acquiring a current obstacle grid of a local map based on the current position of the robot; determining an actual obstacle distance according to the distance between the current obstacle point cloud and the robot; determining a virtual obstacle distance according to the distance between the current obstacle grid and the robot; and making an obstacle avoidance decision for the robot based on the actual obstacle distance and the virtual obstacle distance. When an obstacle avoidance decision is made through the robot obstacle avoidance method, comprehensive evaluation can be carried out based on the actual obstacle distance and the virtual obstacle distance, and the reasonability and accuracy of obstacle avoidance opportunity selection are improved.

The invention provides an unmanned vehicle layered road network path planning method and system based on air-ground cooperation, and belongs to the technical field of unmanned equipment path planning. The method comprises the following steps: S1, carrying out preliminary global planning on a road network according to a satellite map so as to extract an ordered global road point set with an interval of n1; s2, the high-altitude unmanned aerial vehicle goes to all global road points in sequence to obtain a finer local map, two layers of road networks are aligned and fused, part of road network information is supplemented, and the road points are calculated and updated; s3, taking the working range of a sensor carried by the unmanned vehicle as a planning step length, considering dynamic constraint and local dynamic and static obstacle avoidance, performing local planning on the unmanned vehicle to obtain an unmanned vehicle road point, and smoothing to obtain a final expected track of the unmanned vehicle; and S4, moving according to the position of the unmanned vehicle, and continuously iterating the steps S2 and S3 until the unmanned vehicle reaches the target point. The method improves the planning success rate.

The invention relates to a map construction method and device, computer equipment and a readable storage medium. The method comprises the steps that in the moving process of a robot in a pipeline, laser radar point cloud, pipeline environment data, pipeline visual data and motion data collected by different sensors are obtained; under the condition that the current map building moment is reached, the initial pose of the robot is predicted according to the motion data, and a refractive index compensation factor of the pipeline is determined according to the pipeline environment data; determining an initial map point cloud of the target time period according to the initial pose, the refractive index compensation factor, and the laser radar point cloud and pipeline visual data in the target time period; according to a preset curvature threshold and a refractive index compensation factor, correcting the initial map point cloud to obtain a target local map; and updating the initial global map at the previous map construction moment according to the target local map to obtain a target global map. By adopting the method, the accuracy of the constructed map can be improved.

The invention relates to the technical field of sweeping path planning, in particular to a map optimization method and device for a sweeping robot. The method comprises the following steps: collecting a historical cleaning log, performing three-dimensional point cloud reconstruction of a cleaning area, and constructing a normalized cleaning map; when it is recognized that a new round of cleaning task is started, a latest cleaning monitoring video is collected in real time, difference comparison detection is conducted on a normalized cleaning map, and latest position information of movable elements is calculated; local map updating is carried out according to the latest position information, and an incremental optimization map is constructed; and performing full-coverage cleaning planning based on the incremental optimization map, and constructing a full-coverage cleaning path. According to the invention, real-time and dynamic incremental updating of the cleaning map is realized, the cleaning efficiency is improved, and the cleaning path planning is optimized.

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.

The invention relates to the technical field of robot intelligent control, in particular to an intelligent path planning method and system based on a complex environment. Comprising the steps that through heterogeneous laser radar combination (air-ground global obstacle perception is achieved, a layered map (a static global map and a dynamic local map) is constructed, airspace safety corridor constraints are introduced based on an improved RRT * algorithm, and cooperative obstacle avoidance of double mechanical arms, a three-axis platform and an AGV is ensured. Real-time local path optimization is realized in combination with a dynamic window method (DWA), the path risk is monitored through a safety index (SI), and millisecond-level re-planning or emergency stop is triggered. The method has the advantages that the airspace obstacle detection height reaches 3 m, the path planning success rate is increased to 98.7%, the re-planning response time is smaller than or equal to 180 ms, the collision risk is remarkably reduced, and the method is suitable for autonomous navigation of complex scenes such as electric power inspection and warehouse logistics.

The invention discloses a multi-unmanned aerial vehicle integrated planning and control method based on distributed model predictive control. The method comprises the following steps: S1, judging an unmanned aerial vehicle communication topology mechanism in an offline stage, determining an expected target point of each unmanned aerial vehicle, initializing a predictive state value of each unmanned aerial vehicle, and setting controller parameters; s2, the front end performs unmanned aerial vehicle positioning and local map construction according to the sensor data, and searches a reference trajectory according to the local map; s3, the rear end uses an IDMPC controller to solve according to the error between the measured value and the predicted value, and an unmanned aerial vehicle input optimal value is obtained; s4, the solved optimal input is converted into unmanned aerial vehicle attitude control quantity input through differential flatness, and unmanned aerial vehicle formation trajectory tracking is achieved. According to the method, quick response and anti-disturbance capability under the high frequency of 100Hz are realized, and the superiority of the method in the aspects of success rate, calculation efficiency and anti-disturbance performance is verified through a simulation experiment.

The invention relates to the technical field of data analysis, and discloses an intelligent driving teaching method and device based on USS and RTK data fusion, and the method comprises the steps: collecting environment perception data and global positioning data corresponding to a vehicle driven by a student during the driving training of the student; the environment sensing data comprises USS data, and the global positioning data comprises RTK data; determining a dynamic local map of the environment where the vehicle is located and motion state data of the vehicle according to the environment perception data and the global positioning data; generating a dynamic scene view according to the dynamic local map and the motion state data; according to the dynamic local map and the motion state data, analyzing the driving behavior of the student to obtain a driving behavior real-time analysis result; and according to the driving behavior real-time analysis result, real-time driving guidance information for the driving behavior is fed back to the trainee. Therefore, the guidance flexibility and the guidance accuracy of driving teaching can be improved by implementing the method, so that the safety and the reliability of the driving teaching process are improved.

The invention provides a power grid wide area inspection method and system based on cooperation of multiple unmanned aerial vehicles, and the method comprises the steps: obtaining local map data in a power grid inspection path collected by multiple unmanned aerial vehicles, carrying out the preprocessing, obtaining a preliminarily aligned local map data set, and obtaining deviation correction parameters between local maps; correcting the local map data set based on the deviation correction parameter and fusing the local map data set to obtain a global consistent power grid map view; extracting line structure features and power grid equipment state features of a power grid inspection path in the globally consistent power grid map view to obtain an abnormal region identifier; acquiring abnormal position information corresponding to the abnormal region identifier in the global consistent power grid map view, and acquiring hidden danger point information based on the abnormal position information; and fusing real-time data shared by multiple unmanned aerial vehicles based on the hidden danger point information, updating a global consistent power grid map view, obtaining position distribution information of all hidden danger points in the updated power grid map view, and realizing power grid wide-area routing inspection.

The invention discloses a multi-radar calibration method and system based on mapping, and is suitable for an automatic driving vehicle and multi-sensor fusion scene. According to the method, firstly, time sorting and random sampling are carried out on top radar point cloud, distortion correction is carried out in combination with local geometric feature extraction and a continuous time GICP factor model, and high-precision mapping is completed under an iVox voxel structure and factor graph optimization framework. Then, on the basis of a top radar optimization pose, a local map is cut, and main radar calibration is achieved through ICP registration, ground normal vector alignment and height compensation; and with the main radar as a reference, blind compensation radar pose optimization is completed step by step. And finally, unifying the poses of the radars to a main radar and IMU coordinate system, and outputting color point clouds and intermediate data. Compared with an existing calibration mode depending on external hardware, the method has the advantages of being low in cost, high in precision, high in robustness, high in automation degree and the like.

Provided are systems and techniques for automated navigation of vehicles, such as drones. The systems generally include processing unit(s) that, collectively, perform several steps. Such steps include generating metric depth estimates, using a pre-trained model, for each pixel in received image(s) from a monocular camera, or transformed image(s) based on the received image(s). Such steps may also include generating a pose estimate from visual odometry, then generating a truncated signed distance function representation of an environment based on the absolute depth estimates and the pose estimate. The steps may include creating and / or updating a local map based on the truncated signed distance function representation. The steps may include plan a collision-free route towards a goal based on the local map. This may include using motion primitives, which may be generated in a single offline step and stored in a trajectory library.

The invention discloses a complex environment-oriented body agent navigation and three-dimensional reconstruction method, and relates to the technical field of body agent navigation, and the method comprises the following steps: collecting environment data around a navigation body, using a fine-grained octree for close-range key areas (narrow slits and dead corners), adopting coarse voxel acceleration for a far area, and adopting coarse voxel acceleration for a near-range key area (narrow slits and dead corners); extracting large planes such as wall surfaces, beam columns and the like by using RANSAC (Random Sample Consensus), and constructing an environment'abstract skeleton 'for navigation reference; establishing a temporary three-dimensional space based on the point cloud of the whole body environment, dividing a plurality of detection planes in the space, performing position correction on an obstacle edge dot matrix in the detection planes, determining a movable range, and generating a safe motion path in a feasible space by applying a local planning algorithm; octree + differential coding is carried out on a local map, and only a current activity area and crisis point cloud are reserved; local full-stack deployment enables navigation to be free of dependence on remote communication, and sensing, mapping and navigation closed loop can still be completed when a link is broken.

Path planning and navigation for a mobile machine is disclosed. A path planning method plans a path for the mobile machine having a plurality of sensors by: receiving, from each of the sensors of the mobile machine, sensor data; creating, based on the received sensor data from each of the sensors, a plurality of local sensor layers each corresponding to the received sensor data from each of the sensors; creating a local map by integrating all the created local sensor layers; inflating the local map; creating a global costmap by fusing an inflated global map and the inflated local map; planning, according to the costmap, the path for navigating the mobile machine; and providing the planned path to the mobile machine for navigating the mobile machine using the planned path.

The invention discloses a self-adaptive walking system of a photovoltaic panel cleaning robot and a path planning method of the self-adaptive walking system, and relates to the technical field of robots, and the method comprises the steps: dynamically tracking the position and posture of the robot based on a multi-source sensor, calculating the inclination angle and surface flatness of a photovoltaic panel in real time, and extracting the edge contour of the photovoltaic panel; based on a robot sensor, the working environment state is sensed in real time, and a real-time local map with a robot body coordinate system as a benchmark is constructed; based on the real-time local map, the inclination angle, the surface flatness and the edge contour of the photovoltaic panel are fused, and a robot self-adaptive walking strategy is generated; dynamically planning an optimal global connection path from the current position of the robot to the photovoltaic panel by combining a known photovoltaic array layout and a real-time local map on the basis of a generated robot self-adaptive walking strategy; and calculating the deviation degree of the actual trajectory of the robot and the optimal global connection path, and generating a robot speed and steering control instruction in real time by using a path tracking control algorithm. According to the invention, the autonomous operation efficiency is improved.

The invention discloses a mapping and polling positioning method and system based on a laser radar inertial odometer, and the method comprises the steps: carrying out the distortion correction of current point cloud data based on inertial data, and obtaining a point set after distortion removal and a prediction global point set; adaptively selecting a target voxel level and a target neighborhood scale of the predicted global points to obtain a neighborhood point set of each predicted global point, and constructing a point-to-plane residual vector based on the neighborhood point set; based on the predicted pose, the covariance and the residual vector, error state iteration updating is carried out through iterative extended Kalman filtering to obtain a posteriori state; local map updating is carried out based on the posterior state; and obtaining a corrected transformation matrix based on the key frame pose, and obtaining a corrected release pose based on the corrected transformation matrix. According to the method, self-adaptive adjustment of voxels and neighborhoods can be realized, data pollution is effectively prevented, and smooth and global consistency of a closed-loop track is realized under the constraint of a non-re-optimization state and a non-re-mapping map.

The invention discloses a passable lane identification method and a vehicle, and belongs to the technical field of automatic driving. According to the technical scheme provided by the embodiment of the invention, under the condition that the target vehicle is in the automatic driving state, the target lane mask is determined by utilizing the environmental perception information, the local map information and the navigation instruction set in the driving direction of the target vehicle, namely, the position identification of the passable lane is realized by utilizing the multi-source and multi-mode data. The passable lane is determined by using the environmental perception information and the target lane mask subsequently, and the accuracy of the obtained passable lane is high.

A method and system for obtaining, from at least one camera associated with a vehicle traveling through a road network, a sequence of images of a road or a road environment, each image being associated with a location where that image is captured, generating a local map representation of an area of the road network using at least some images from the sequence of images and the locations associated therewith, the generating including: processing the at least some of the images to detect an object in the road or the road environment, determining at least one transformation for tracking the object between the at least some of the images and, based on the at least one transformation and the locations associated with the at least some of the images, generating a two- or three-dimensional representation of the object relative to the area of the road network, comparing the local map representation with a reference map covering the area of the road network, and determining, based on the comparison, a geographical location and an orientation of the vehicle within the road network.

The invention discloses a multi-modal knowledge graph construction method for oilfield equipment supervision and manufacture, and the method comprises the steps: constructing a domain knowledge graph covering multi-dimensional entities such as stratum parameters, equipment components, fault modes and the like, and integrating the real-time data of the Internet of Things, equipment maintenance records, geological reports and the like; cross-database semantic mapping is realized by adopting a neural network, and data traceability credibility is guaranteed in combination with a block chain technology; real-time acquisition of operation data and generation of a local map are realized by utilizing edge nodes, global knowledge fusion and deep learning model training are completed by a cloud end, inference rules are dynamically optimized through digital simulation, an equipment-fault causal chain model is constructed to realize accurate fault positioning, and an energy efficiency optimization scheme is generated by integrating carbon footprint calculation; and on the basis of an automatic knowledge node expansion mechanism of transfer learning, VR three-dimensional visual display of the equipment disassembly process and risk early warning is supported, and the industrial pain points of difficulty in multi-source data integration, intelligent decision lag and the like are solved.

The invention relates to the technical field of multi-sensor information fusion positioning, and discloses a multi-sensor fusion-based 3D visual guidance positioning method, which comprises the following steps of S1, acquiring data of an inertial measurement unit, a visual sensor and a laser radar, and performing time-space synchronization and preprocessing; s2, 3D / 2D feature points are extracted and matched, cross-modal feature matching is carried out, a local map is constructed, and the quality of the feature points and the motion state of a carrier are evaluated in real time. According to the method, the 3D / 2D feature points are extracted and matched, cross-modal feature matching is performed, the local map is constructed, and the local map is maintained by adopting a sliding window method, so that the map is updated in real time, and the calculation efficiency is kept. According to the efficient data fusion strategy, the real-time requirement can be met while the positioning precision is guaranteed, and the method is suitable for application scenes with high response speed requirements, such as real-time obstacle avoidance and path planning in automatic driving.

The invention provides a 3D laser SLAM method and device based on optimized loopback detection and a medium, and relates to the technical field of simultaneous positioning and mapping of robots, and the method comprises the following steps: (1) a data preprocessing step: receiving original data of an IMU and a laser radar Lidar, and carrying out the denoising and time synchronization processing; (2) a feature extraction and map construction step: extracting angular point and surface point features from the preprocessed data, performing pre-integration on IMU data to obtain an initial pose, and performing registration on a current Lidar point cloud and a local map to construct a global map; (3) loopback detection: generating an enhanced descriptor based on the key frame point cloud, and constructing a KD-Tree structure with dynamic resolution for storage and indexing; and carrying out rough search on loopback candidate frames by inquiring the KD-Tree, carrying out two-stage pose matching on the candidate frames to confirm loopback, and dynamically distributing the weight of loopback constraint according to the environmental complexity. The loopback detection success rate can be improved, and dynamic scene mapping drift is reduced.

The invention relates to the technical field of intelligent inspection, and discloses an inspection method based on multi-device cooperation and global semantic map sharing, which comprises the following steps: acquiring first device state information and a local map of a plurality of inspection devices; generating a global semantic map based on the first device state information and a local map; according to the device semantic state feature priority and the real-time position of the inspection device in the global semantic map, distributing an inspection task for each inspection device; and when equipment exception information or task execution information fed back by the inspection equipment is received, updating the global semantic map and distributing the inspection task. The global semantic map is generated by acquiring the device state information of the multiple inspection devices and the local map, and task allocation is performed based on the device semantic state feature priority and the real-time position in the global semantic map, so that information sharing and cooperative work among the multiple devices are realized, and the inspection efficiency and the comprehensiveness of device state monitoring are remarkably improved.