1322 results about "Lidar data" patented technology

An agricultural vehicle includes cameras and a LIDAR sensor operably coupled to the agricultural vehicle, and an imaging controller operably coupled to the LiDAR sensor and the cameras. The imaging controller includes at least one processor, and instructions that cause the processor to receive image data from the cameras, receive LiDAR data from the LiDAR sensor, analyze the image data from each of the cameras to generate labeled image data, analyze the LIDAR data to generate labeled LiDAR data, and fuse the labeled image data with the labeled LIDAR data. Related agricultural vehicles and methods are also disclosed.

An agricultural vehicle may include a propulsion system, a steering system, and a vehicle controller. An agricultural vehicle may include a camera sensor having a camera field of view (CFOV), a light detection and ranging (LiDAR) sensor having a LiDAR field of view (LFOV), and an imaging controller in operable communication with the camera sensor and the LiDAR sensor. The imaging controller may include a processor and at least one non-transitory computer-readable storage medium having instructions store thereon that, when executed by the at least one processor cause the imaging controller to, at least, receive image data, segment the image data, fuse LiDAR data and the image data, create a fused instance including depth information; and provide at least the fused instance to the vehicle controller, wherein the vehicle controller is configured to provide at least one navigational command based at least partially on the fused instance.

The invention discloses a geological disaster deformation monitoring method based on remote sensing data, and relates to the technical field of environment monitoring, and the method comprises the following steps: S1, data source selection and preprocessing; s2, region feature extraction; s3, differentiating the multi-temporal remote sensing images; s4, analyzing a time sequence; s5, performing three-dimensional reconstruction on the deformation area; s6, settlement and landslide monitoring; s7, performing fusion analysis on environment and meteorological data; s8, performing data anomaly detection and automatic early warning; and S9, risk assessment and influence analysis. According to the geological disaster deformation monitoring method based on the remote sensing data, SAR, optical images and LiDAR data are combined, the defect of dependence of a single data source in the prior art is overcome, dynamic data fusion and time sequence analysis are used, multi-dimensional real-time monitoring of earth surface deformation is achieved, the monitoring precision and timeliness are improved, and the geological disaster deformation monitoring method based on the remote sensing data has good application prospects. More comprehensive geological disaster early warning can be provided, and the effectiveness of disaster prevention and control is greatly enhanced.

The invention relates to the technical field of hydraulic engineering intelligent operation and maintenance, and discloses an AI unmanned aerial vehicle-based auxiliary decision-making platform for hydraulic engineering operation and maintenance, and the platform comprises a data collection module which is used for obtaining multi-modal data of a water conservancy facility through an AI unmanned aerial vehicle, the multi-modal data comprises a visible light image, thermal imaging data, multispectral data and laser radar data; the data processing and fusion module is used for carrying out formatting processing, space-time alignment and feature fusion on the multi-modal data; the health assessment and prediction module is used for assessing the health state of the water conservancy facility based on the processed data and predicting the future change trend of the facility state; and the auxiliary decision-making module is used for generating inspection priority planning of the water conservancy facilities. According to the invention, through multi-source data fusion, dynamic risk assessment and intelligent optimization distribution, the effects of accurate monitoring, efficient operation and maintenance and risk visual management of the whole life cycle of the water conservancy facilities are realized.

The invention discloses a local path planning method, system and device fusing improved speed obstacle and a TEB algorithm and a medium, and the method comprises the steps: building a basic environment of local path planning, and obtaining a global static map, positioning information and a global path planning point; the method comprises the following steps: acquiring laser radar data, and realizing clustering and boundalization of obstacle information by using DBScan and a monotonic chain convex hull algorithm so as to construct a local cost map; adding linear velocity, adding angular velocity, curvature and velocity obstacle constraint are introduced to improve a TEB algorithm; and constructing an improved algorithm model, adjusting the weight coefficient of the speed obstacle constraint according to the environmental complexity, and solving the improved algorithm model. When the method is used for local path planning, a smooth and safe path can be output, and the calculation amount of processing a local cost map by a TEB algorithm is reduced; on the basis of considering the influence of the real environment, the speed obstacle and the TEB algorithm are fused and improved, and the obstacle avoidance capability of obstacles, especially dynamic obstacles, is improved.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a multi-mode remote sensing data driven refined city three-dimensional reconstruction method and system, and the method comprises the steps: obtaining an original satellite optical image for providing city surface information; synthetic aperture radar (SAR) data is obtained and used for assisting reconstruction of a point cloud data missing region; the method comprises the following steps: acquiring laser radar data carried by a flight aerial carrier for acquiring high-precision three-dimensional point cloud; performing data preprocessing; carrying out fusion on the preprocessed multi-modal data; on the basis of the fused features, complementing space detail information; on the basis of the refined features, progressive reconstruction is carried out, vertexes and edges are gradually added from the initial vertexes, and a grid model is constructed; in the reconstruction process, performing semantic segmentation on the grid model by using a multi-category segmentation network, and judging the category of each triangular patch; time sequence reconstruction is completed through fusion of multi-temporal data, and a dynamic city change model is generated; and complementation of advantages of different modal data is realized.

The invention discloses a robot path planning and obstacle avoidance method combining an artificial potential field and reinforcement learning. The method comprises the following steps: carrying out sensor data acquisition and fusion; environment complexity evaluation is carried out, a multi-scale planning mechanism is established, and global planning and local planning are determined based on switching conditions; carrying out artificial potential field path planning based on global planning; carrying out reinforcement learning path planning based on local planning; and judging whether the target point is reached, and returning to the determined plan until the target point is reached. According to the invention, the data of the infrared sensor and the laser radar are fused, the complementary characteristics of the two sensors are fully utilized, the comprehensive perception of static and dynamic obstacles is realized, and the adaptability of the robot in different environments is improved. According to the method, through reinforcement learning, strategy optimization and uncertainty estimation, the obstacle avoidance capability of the robot in a dynamic environment is enhanced, and the local optimum problem of a traditional artificial potential field method is solved.

A method for operating a robot, including: capturing images of a workspace; capturing data indicative of movement of the robot; capturing LIDAR data as the robot moves within the workspace; generating a map of the workspace based on the LIDAR data; actuating the robot to drive; discriminating between an object on a floor surface along a path of the robot and the floor surface based on the captured images; actuating the robot to drive until determining all areas of the workspace are discovered and included in the map; and executing a cleaning function.

The invention provides a laser radar data processing method for a complex forest environment, and the method comprises the steps: obtaining the original point cloud data of a target forest region through an airborne laser radar system or a backpack type laser radar system, carrying out the preprocessing of the original point cloud data, obtaining the preprocessing point cloud data, and filtering out the ground points in the preprocessing point cloud data, obtaining non-ground point cloud data, and classifying ground features in the non-ground point cloud data to obtain ground feature classification point cloud data; based on the ground feature classification point cloud data, constructing a three-dimensional canopy point cloud model, and based on an individual tree parameter extraction method, obtaining individual tree spatial positions and key forest parameters of individual trees in the target forest area, the individual tree parameter extraction method including but not limited to crown segmentation and different-speed growth equations. According to the method, the vertical structure information of the ground features is obtained through the radar system, the ground point data is removed, the ground features are classified, and high-precision obtaining of the horizontal parameters of the single tree and the forest stand in the complex forest environment is achieved.

The invention discloses an intelligent driving track prediction method, device and equipment and a storage medium, relates to the technical field of intelligent driving, and discloses an intelligent driving track prediction method which comprises the following steps: acquiring environment laser radar data, environment radar data and environment image data; converting the environment laser radar data, the environment radar data and the environment image data into aerial view features; performing feature alignment on the aerial view features based on a sensor field angle identifier to obtain multi-modal sensing features; determining a candidate trajectory according to the multi-modal sensing features; and determining a target trajectory from the candidate trajectories according to a target cost function to complete intelligent driving trajectory prediction. According to the scheme, the complex driving environment can be accurately understood in the intelligent driving process so as to predict the optimal driving track.

The invention provides a water area safety patrol method and system based on multi-modal feature analysis, and relates to the technical field of image recognition. The method comprises the steps of collecting a visible light image, an infrared image and laser radar data corresponding to a target water area, and performing time synchronization and space alignment to obtain visible light alignment data, infrared alignment data and laser radar alignment data; in response to the condition that the definition of the visible light image is higher than a quality reference value, carrying out edge detection and region segmentation processing on the visible light alignment data, and generating a candidate region set containing a target contour; for the infrared alignment data corresponding to the candidate region set, screening out a thermal anomaly region from the candidate region set; and carrying out three-dimensional space feature extraction and motion state feature extraction on the laser radar alignment data corresponding to the thermal anomaly region, and carrying out anomaly identification on the thermal anomaly region. According to the scheme, high-precision safety patrol of the target water area in various environments can be realized.

The invention discloses a navigation matching correction method based on an inspection robot, and the method comprises the steps: building a feature database through the deployment of a physical calibration object and the recognition of a natural feature object, providing a reliable positioning reference for a robot, achieving the coarse positioning through the fusion of visual and laser radar data in a positioning process, and introducing a dynamic credibility evaluation mechanism. The positioning reliability is quantified in real time through an exponential decay model, when the credibility is lower than a threshold value, the system automatically triggers a compensation behavior to re-search features, in the aspect of multi-robot cooperation, secondary positioning correction is achieved through track matching and data fusion, high-confidence-coefficient reference data are screened through a clustering algorithm, the group positioning precision is improved, and the positioning accuracy is improved. Aiming at a key inspection area, multi-angle image matching is adopted to realize fine positioning, a positioning error is dynamically corrected through a sliding window, the continuity and accuracy of robot navigation in a complex environment are remarkably improved through closed-loop correction and self-adaptive optimization, and the method is suitable for intelligent inspection requirements of railway trains.

The invention relates to the technical field of wind power generation, and discloses a wind power plant energy management method based on laser radar wind measurement, comprising the following steps: S1, deploying laser radar equipment, and collecting real-time data of wind speed, wind direction and turbulence intensity in a wind power plant; s2, constructing a three-dimensional dynamic model of a wind field, and modeling wind speed, wind direction and turbulence distribution in combination with laser radar data and historical meteorological data; s3, performing short-term prediction on the future wind speed by using the deep learning model, and outputting minute-level to hour-level wind energy prediction data; s4, optimizing a dynamic scheduling strategy of the wind turbine generator based on the prediction data; and S5, docking an energy storage system, optimizing charging and discharging behaviors of energy storage equipment, and realizing smooth power output. According to the invention, by fusing the laser radar wind measurement data, the deep learning prediction model and the intelligent optimization algorithm, high-precision wind condition monitoring, dynamic scheduling optimization and power output stability improvement of the wind power plant are realized.

The invention discloses a fault detection method and system for a mine unmanned vehicle, and relates to the field of positioning navigation, and the method comprises the steps: collecting the data of each sensor; according to the laser radar data, a slam point cloud map is constructed through a cartographic algorithm; according to the IMU data and the vehicle speed data, performing fusion through a Kalman filtering algorithm to obtain odometer data; the preprocessed laser radar data, IMU data, vehicle speed data, camera data and odometer data are converted to a slam point cloud map coordinate system through coordinate conversion, fusion is carried out through a Kalman filtering algorithm, and state estimation of the unmanned vehicle is obtained; taking the state estimation of the unmanned vehicle as a predicted value, comparing the predicted value with laser radar data, IMU data, vehicle speed data and camera data, and calculating an observation residual error; and performing unmanned vehicle fault detection according to the observation residual error. Aiming at the low positioning precision of the mine unmanned vehicle in a complex environment in the prior art, the positioning precision is improved through multi-sensor fusion, fault-tolerant switching and the like.

Techniques for detecting an obstruction associated with a lidar sensor are discussed herein. For example, a computing device can implement an obstruction detection component to detect rain, mud, dirt, dust, snow, ice, animal droppings, etc., on and / or near an outer surface of the lidar sensor. The obstruction detection component can apply one or more heuristics and / or models to the lidar data and / or compare pulse information associated with the lidar data to a threshold to determine a size, a type, or a location of an obstruction blocking a lidar beam.

The invention discloses a mobile robot positioning and navigation method based on laser SLAM (Simultaneous Localization and Mapping). The method relates to the fields of machine learning, automatic driving, computer vision and the like. Aiming at the balance of real-time performance and calculation efficiency, the method comprises the following steps: firstly, tracking and estimating a motion track of a sensor by using laser radar data in real time, and fusing LiDAR and IMU; secondly, selecting a key frame and expressing the key frame by using a neural network implicit scene; adjusting parameters represented by a key frame pose and a neural scene through joint optimization to minimize a loss function; and finally, dynamically adjusting the loss function margin of laser radar rays by adopting a JS dynamic margin loss function, and converting an implicit neural representation three-dimensional scene into an explicit three-dimensional network model, thereby facilitating visualization, evaluation and use. Compared with other methods, the method has the advantages that the method has continuous and fine scene modeling capability, has real-time performance of positioning and mapping, and can be expanded to the fields of automatic driving, urban planning and the like.

In various examples, feature location identification for autonomous and semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use LiDAR data to determine locations of road markings within an environment. For instance, the LiDAR data may be used to generate one or more images, such as a top-down image, birds-eye-view (BEV) image, and / or an intensity image, representing the environment. The image(s) may then be processed using one or more machine learning models that are configured to determine information associated with the road markings, such as bounding shapes (e.g., bounding boxes) indicating the locations of the road markings within the image(s), classifications associated with the road markings (e.g., the types of road markings), and / or any other information. The information may then be used to perform one or more processes, such as updating a map of the environment and / or navigating a vehicle within the environment.

A method for operating a robot, including: capturing images of a workspace; capturing data indicative of movement of the robot; capturing LIDAR data as the robot moves within the workspace; generating a map of the workspace based on the LIDAR data; actuating the robot to drive; discriminating between an object on a floor surface along a path of the robot and the floor surface based on the captured images; actuating the robot to drive until determining all areas of the workspace are discovered and included in the map; and executing a cleaning function.

A LiDAR apparatus, including: a laser to generate an optical signal; modulation components configured to receive the optical signal as an input and to output at least two corresponding modulated optical signals at respective output ports, wherein each modulated optical signal is modulated by a corresponding pseudo-random bit sequence, and: (i) the optical signals have respective different delays such that the modulations do not overlap in time; or (ii) the pseudo-random bit sequences have low cross-correlation; for each of the at least two modulated optical signals, a corresponding optical transmitter configured to transmit the corresponding modulated optical signal towards a corresponding surface spaced from the LiDAR apparatus by a corresponding distance, and a corresponding optical receiver configured to receive a portion of the transmitted optical signal scattered and / or reflected by the surface, the received portion of the optical signal having a phase shift and / or Doppler shifted angular frequency due to radial motion of the LiDAR apparatus relative to the surface; at least one photodetector to receive the optical signals received by the optical receivers, interfered with a reference beam, and to generate a corresponding output signal; at least one analogue to digital converter to generate a digital signal representing the output signal from the at least one photodetector; and a digital signal processing component configured to process the digital signal to generate LiDAR data representing the distances to the surfaces and / or relative velocities of the surface(s) with respect to the apparatus.

A request signal that indicates a quality for a determination of an orientation of a road user is received. The orientation of the road user is determined based on a) image data when the request signal indicates the quality is below a predetermined quality for the determination of the orientation of the road user, or b) on LIDAR data and image data when the request signal indicates the quality is the predetermined quality.

The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: receives light detection and ranging (LIDAR) data generated from a LIDAR camera via wireless communication or data transmission over one or more radio frequency links, the LIDAR data associated with a store or a store layout; generates a LIDAR-based virtual map of the store from processor analysis of the LIDAR data; determines locations of individual goods in the store; overlays the locations of the individual goods onto the LIDAR-based virtual map; and generates an updated LIDAR-based virtual map of the store displaying aisles of the store and the overlaid locations of the individual goods within the store.

The invention discloses a crop canopy height estimation method based on laser radar and multispectral fusion, and the method comprises the steps: scanning a crop field through the laser radar, and obtaining the field point cloud data; aerial photography is carried out on a crop field through an airborne multispectral camera to obtain field multispectral image data, and after data image splicing, crop region extraction and land parcel segmentation, two point cloud feature parameters with the strongest correlation with crops are extracted from point cloud data to obtain a point cloud feature parameter; the method comprises the following steps of: extracting a vegetation index with the strongest correlation with the crop height from multispectral image data, solving through multiple linear regression, and establishing a height estimation model; according to the invention, the multispectral vegetation index and the laser radar data are combined, so that the crop canopy height monitoring precision is improved, the crop growth monitoring precision and reliability can be improved, the crop growth condition can be comprehensively understood, and an agricultural decision maker can determine the crop canopy height based on the crop canopy structure data. And a more accurate, timely and effective farmland management decision can be made.

The invention provides a bionic polarization semantic SLAM (Simultaneous Localization and Mapping) method based on a neural radiation field. A system comprises three parts, namely a bionic polarization pose estimation module based on factor graph optimization, a dense mapping module based on the neural radiation field and a semantic information positioning module based on bionic polarization infrared rays. The system firstly collects multi-sensor data, establishes a state equation and a measurement equation, and optimizes and estimates the pose by using a factor graph. Secondly, dense mapping is carried out by using the estimated pose and laser radar data and by adopting a neural implicit representation (NeRF-LOAM) model constructed by a laser radar odometer and a map through voxel embedding map generation and grid reconstruction. And finally, processing the polarization infrared fusion image through Yolov8 and SegNeXt, and realizing semantic positioning in combination with laser radar features. According to the system, the anti-interference performance and the environmental adaptability are remarkably enhanced, the mapping training time is shortened, and the system can be applied to multiple fields such as automatic driving, intelligent transportation and intelligent logistics.

The invention provides a tunnel quality safety detection system and method based on laser radar data, and the method achieves the fusion and complementation of crack detection based on point cloud density change and crack detection based on curvature change, and achieves the quick recognition and precise detection of a crack region. The potential crack area is rapidly positioned through point cloud density change detection, and the boundary of the crack is further accurately positioned in combination with curvature change detection, so that the accuracy and integrity of crack detection are improved. For tiny deformation which is very tiny in change in point cloud density and curvature and is difficult to directly detect, a projection plane is further determined on the basis of the identified crack, and after point cloud data is projected to a two-dimensional plane, the characteristics of the tiny crack in the image are highlighted through image enhancement, so that the crack is identified. And then an edge detection algorithm is used to identify the micro crack so as to make up for the defects of a previous point cloud density and curvature detection method in the aspect of micro crack detection, and the accuracy and integrity of crack detection are further improved.

The invention discloses a robot unknown environment autonomous exploration navigation method based on CTSAC, and the method combines the powerful sequence perception capability of Transform and a Soft Actor-Critic deep reinforcement learning framework, and introduces curriculum learning of a regular review mechanism, laser radar partition optimization processing, and refined reward setting. The state of the robot is represented by laser radar data and gyroscope data which are subjected to laser radar partition optimization processing, meanwhile, after state information is subjected to refined reward setting, corresponding reward values are obtained, corresponding experience sequences are stored in an experience pool, and data obtained through sampling of the experience pool are supplied to a neural network for training. The method is suitable for the robot to perceive the surrounding environment through the sensor in an unknown environment to perform path planning so as to reach a specified target point or establish an environment map, and is used for complex tasks such as space exploration, search and rescue, reconnaissance and the like.

The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: receives LIDAR data generated from a LIDAR camera; measures plurality of dimensions of the home based upon processor analysis of the LIDAR data; builds a 3D model of the home based upon the measured plurality of dimensions; and displays a representation of the 3D model by visually navigating through the 3D model.

The invention discloses an intelligent lighting management system and method based on multi-source data, and relates to the technical field of intelligent lighting management, and the system comprises a multi-source data collection module, an environment feature anchoring module, a dynamic error modeling and calibration module, an edge-cloud collaborative decision module and a lighting strategy execution module. The multi-source data acquisition module fuses environment sensor, visual perception and laser radar data to construct a'sensor original data + environment characteristic data 'dual-input system; the environment feature anchoring module extracts stable features from the visual / laser radar data to serve as a calibration reference; the dynamic error modeling and calibration module quantifies the error in real time based on the reference and generates calibration parameters; the edge-cloud collaborative decision-making module and cloud long-term optimization form a hierarchical collaborative architecture through edge node real-time control; and the lighting strategy execution module dynamically adjusts lighting parameters according to the calibration data and feeds back an effect to form a closed loop.

The invention discloses a laser radar mapping method based on loopback detection and a visual-inertial odometer. The visual-inertial odometer (VIO) and laser radar data are combined with loopback detection. Through comprehensive utilization of rich perception of VIO to the environment, high-precision distance measurement provided by the laser radar and a correction function of loopback detection, the method can significantly improve the accuracy and robustness of the autonomous navigation system. In a dynamic and uncertain environment, the method can effectively solve the technical problems of sensor error accumulation, environment change, positioning errors caused by dynamic obstacles and the like, and provides a more reliable and effective solution for navigation of an automatic driving vehicle and an advanced robot in a complex environment.

The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: receives LIDAR data generated from a LIDAR camera; measures a plurality of dimensions of a room of the home based upon processor analysis of the LIDAR data; builds a 3D model of the room based upon the measured plurality of dimensions; receives an indication of a proposed change to the room; modifies the 3D model to include the proposed change to the room; and displays a representation of the modified 3D model.

The invention discloses an external parameter calibration method and system for a mining laser radar and a camera, and belongs to the technical field of computer vision and robotics.The method comprises the steps that an original image and an original point cloud in a to-be-calibrated target scene are obtained; obtaining a preliminary alignment result according to the original point cloud data; optimizing the preliminary alignment result to obtain a laser radar fusion frame point cloud; performing panoramic segmentation on the original image, and constructing a 2D-3D effective target centroid corresponding relation; and based on a laser radar fusion frame point cloud and a 2D-3D effective target mass center corresponding relation, through panoramic segmentation, an effective target is extracted, an effective target panoramic information matching degree target function is constructed, the target function is optimized, and an external parameter optimal solution of the laser radar and a camera is obtained. According to the method, the automatic calibration precision of the external parameters of the mining laser radar and the camera can be effectively improved, and a research basis is provided for subsequent mining laser radar data and image fusion.