646 results about "Lidar data" patented technology

An apparatus and method for aligning a line scan camera with a Light Detection and Ranging (LiDAR) scanner for real-time data fusion in three dimensions is provided. Imaging data is captured at a computer processor simultaneously from the line scan camera and the laser scanner from target object providing scanning targets defined in an imaging plane perpendicular to focal axes of the line scan camera and the LiDAR scanner. X-axis and Y-axis pixel locations of a centroid of each of the targets from captured imaging data is extracted. LiDAR return intensity versus scan angle is determined and scan angle locations of intensity peaks which correspond to individual targets is determined. Two axis parallax correction parameters are determined by applying a least squares. The correction parameters are provided to post processing software to correct for alignment differences between the imaging camera and LiDAR scanner for real-time colorization for acquired LiDAR data.

The invention discloses an automatic driving system based on enhanced learning and multi-sensor fusion. The system comprises a perception system, a control system and an execution system. The perception system high-efficiently processes a laser radar, a camera and a GPS navigator through a deep learning network so as to realize real time identification and understanding of vehicles, pedestrians, lane lines, traffic signs and signal lamps surrounding a running vehicle. Through an enhanced learning technology, the laser radar and a panorama image are matched and fused so as to form a real-time three-dimensional streetscape map and determination of a driving area. The GPS navigator is combined to realize real-time navigation. The control system adopts an enhanced learning network to process information collected by the perception system, and the people, vehicles and objects of the surrounding vehicles are predicted. According to vehicle body state data, the records of driver actions are paired, a current optimal action selection is made, and the execution system is used to complete execution motion. In the invention, laser radar data and a video are fused, and driving area identification and destination path optimal programming are performed.

A system for capturing live-action three-dimensional video is disclosed. The system includes pairs of stereo cameras and a LIDAR for generating stereo images and three-dimensional LIDAR data from which three-dimensional data may be derived. A depth-from-stereo algorithm may be used to generate the three-dimensional camera data for the three-dimensional space from the stereo images and may be combined with the three-dimensional LIDAR data taking precedence over the three-dimensional camera data to thereby generate three-dimensional data corresponding to the three-dimensional space.

A method for extracting a 3D terrain model for identifying at least buildings and terrain from LIDAR data is disclosed, comprising the steps of generating a point cloud representing terrain and buildings mapped by LIDAR; classifying points in the point cloud, the point cloud having ground and non-ground points, the non-ground points representing buildings and clutter; segmenting the non-ground points into buildings and clutter; and calculating a fit between at least one building segment and at least one rectilinear structure, wherein the fit yields the rectilinear structure with the fewest number of vertices. The step of calculating further comprises the steps of (a) calculating a fit of a rectilinear structure to the at least one building segment, wherein each of the vertices has an angle that is a multiple of 90 degrees; (b) counting the number of vertices; (c) rotating the at least one building segment about an axis by a predetermined increment; and (d) repeating steps (a)-(c) until a rectilinear structure with the least number of vertices is found.

A three dimensional shape correlation computer software program which uses laser radar data for target identification. The correlation program obtains a scan of laser radar data of a target from a Ladar sensor. The data includes a plurality of X,Y,Z coordinate detection points for the target. The software simulates the sensor scan using a 3D wire-frame model of the target. An X,Y,Z coordinate location is computed for every point in the computer model of the target. The software compares the X,Y,Z coordinate detection points for the target with the X,Y,Z coordinate points for the computer model to determine if the points match. When there is a match a target identification is declared.

The invention discloses a laser radar road reconstruction and expansion exploratory survey design method. The method comprises the steps of A designing a result coordinate benchmark, measuring basic control and measuring pavement control points; B determining parameters including the data density, acquisition route and the like, and acquiring vehicle laser radar data along a main road and a ramp; C determining parameters including the data density, flight design and the like, and acquiring airborne laser radar data according to a designed flight strip; D realizing laser radar data fusion by refining laser point cloud plane coordinates and elevation coordinates and refining track line data; E acquiring characteristics of road traffic lane lines by using point cloud intensity information, and realizing extraction of characteristic lines of road pavements, protection and the like by adopting a method of projecting three-dimensional point clouds to a two-dimensional plane; F recovering planar elements and longitudinal surface elements of an existing road; G producing a DEM (digital elevation model), a DOM (digital orthophoto map) and a DLG (digital line graphic); H collaboratively designing laser radar measurement and road reconstruction and expansion CAD (computer-aided design), designing flat, longitudinal and transverse cross sections of a road, comparing and selecting schemes, and outputting final design drawings and charts.

The invention discloses a routing inspection method applied to a high-speed railway machine room. The routing inspection method comprises the following steps that (1), a routing inspection robot is started, and start-up status self-check and equipment initialization are performed; (2), a routing inspection task file is read, the target equipment cabinet number to be detected is obtained, and the position information of equipment cabinets to be detected is exported from the equipment cabinet position database; (3), the routing inspection robot performs real-time positioning according to laser radar data, performs global path planning and local path planning and reaches the target equipment cabinet positions in sequence, when the routing inspection robot reaches the target equipment cabinetpositions, environment detection is performed, and detection is performed on the equipment cabinet state according to a deep learning neural network model and an image recognition algorithm. The invention provides a corresponding routing inspection robot system at the same time, and the routing inspection robot system can be used for performing automatic regular routing inspection on high-speed railway machine room equipment and can finish emergency routing inspection tasks through remote control when an emergency occurs.

The invention discloses an optimal route selection method for overhead transmission line route. Onboard lidar equipment is adopted to acquire laser point cloud data and aerial digital photo data of the transmission line route corridor range; the onboard lidar data is processed after the wave filtering and the sorting of the laser point cloud data, and the points of the ground surface are made into a digital elevation model with high precision; then data processing is carried out by utilizing the data of the digital elevation model with high precision and ortho-rectification is carried out to the aerial photo by utilizing the internal and external orientation elements of the aerial digital photo to generate digital orthophoto maps; through the overlying of the digital elevation model and the digital orthophoto maps, the tridimensional visualization of the transmission line route corridor can be realized to optimize the transmission line route selection; finally prearrangement of power pole and power pole arrangement are carried out according to the data of plane cross sections. The route selection platform of the invention has simple operation and lifelike tridimensional scene, thus being convenient for full roaming and multi-view observation and greatly improving the efficiency of the inner plane cross section survey operation. Compared with the optimal route selection technology based on the aerial photographing measuring method, the efficiency of the inner plane cross section survey operation can be improved by about 75 percent.

The invention provides a three-dimensional measurement device for a driving range and an evaluation system for the driving range. A three-dimensional measurement device based on a rotary laser radar and a camera is included; three-dimensional laser scanning is realized by the motor rotary single-line or four-line laser radar and image characteristics of the monocular camera are matched with a position posture change of an evaluation platform; and a three-dimensional point cloud of a surrounding is generated by motion compensation. A three-dimensional evaluation method for the driving region comprises the following steps: fusing data of the camera and the laser radar; and establishing traversing analysis of the possibility and evaluating the driving region. Compared with the international similar three-dimensional measurement device, the device has the characteristics of low cost, lower domestic difficulties and the like, and is easy to maintain and hide; and the evaluation on the driving region of a complicated cross-country environment can be effectively realized.

The invention discloses a power channel corridor routing-inspection method based on a tilt photography three-dimensional reconstruction technology, wherein the method includes the steps: A, combining a laser radar data measurement technology and a tilt photography technology, and rapidly constructing power transmission line channel high-precision three-dimensional base map data by using the laser radar measurement technology; B, acquiring three-dimensional real scene data of a channel corridor by using the tilt photography measurement technology; and C, carrying out fused analysis of the three-dimensional real scene information and a laser radar channel base map, effectively identifying and extracting trees and obstacles in the channel corridor, and then generating a tree barrier assessment report. By using the method, qualitative analysis of the tree barrier can be effectively turned into quantitative analysis, the position of a to-be-felled tree in the channel can be calculated, the felling area is statistical, and thus the safe operation of the line corridor is ensured.

The invention discloses a target detection method and a target detection system of visual radar spatial and temporal information fusion. The target detection system comprises an acquisition unit, a sampling unit, a superposition unit, a model building unit and an execution unit. The acquisition unit is used for collecting RGB image data and 3D point cloud data to calculate discretized LIDAR depthmap in grayscale; the sampling unit is used for up-sampling and densifying the LIDAR depth map so that the RGB image and the data form of the LIDAR depth map are unified and corresponded to each otherone by one; the superposition unit is used for combining the RGB image and the LIDAR depth map into an RGB-LIDAR picture and superposing the RGB-LIDAR pictures which are continuously collected for multiple times to obtain a superposed RGB-LIDAR picture, wherein the number of the continuous collection of the RGB-LIDAR pictures is equal to or more than 1; the model building unit is used for establishing an RGB-LIDAR data set for the multiple superposed RGB-LIDAR pictures to enter the deep learning network for training and learning and establish a classification model; the execution unit is usedfor taking corresponding decisions according to target analysis results from the classification model. Consequently, the effects of long-distance recognition and high classification accuracy are achieved.

The invention relates to the technical field of airborne laser radar data processing, in particular to a semi-supervised classification method for airborne laser radar data fusing images. In the method, based on a semi-supervision concept, rough classification results of point cloud data are utilized to extract high-accuracy training sample data which is used for classifying high-resolution images; and in the post-processing process, based on target complexity, spurious building points are removed, the classification results are refined, and LiDAR (Laser Intensity Direction and Ranging) pointcloud multiple characteristics are fused for cross validation so as to finally realize fine classification of airborne laser radar data. The method is a fusion classification method with high reliability and high classification accuracy. In the precondition without using near infrared data, the method achieves good effect of classification for point cloud tall vegetation and low vegetation areas.

Methods and systems for performing three dimensional LIDAR measurements with an integrated LIDAR measurement device are described herein. In one aspect, a return signal receiver generates a pulse trigger signal that triggers the generation of a pulse of illumination light and data acquisition of a return signal, and also triggers the time of flight calculation by time to digital conversion. In addition, the return signal receiver also estimates the width and peak amplitude of each return pulse, and samples each return pulse waveform individually over a sampling window that includes the peak amplitude of each return pulse waveform. In a further aspect, the time of flight associated with each return pulse is estimated based on a coarse timing estimate and a fine timing estimate. In another aspect, the time of flight is measured from the measured pulse due to internal optical crosstalk and a valid return pulse.

The invention belongs to the field of high-voltage transmission line operation inspection and particularly relates to a transmission line unmanned aerial vehicle automatic driving inspection method. The method comprises the steps that S1, according to the model, range, opening angle and communication distance of a laser radar, the area of a scanning region of the laser radar is calculated; S2, RTKlaser radar unmanned aerial vehicle base station positioning is performed through Qianxun precise positioning service; S3, a horizontal distance value maintained between a transmission line center and an unmanned aerial vehicle, a vertical distance theoretical value maintained between the unmanned aerial vehicle and a horizontal ground and a designed height value between the unmanned aerial vehicle and the horizontal ground are calculated; S4, unmanned aerial vehicle manual flight laser modeling is performed, and an automatic driving air route of the unmanned aerial vehicle is planned; and S5, according to a transmission line laser point cloud entity model obtained through laser radar modeling, an intelligent inspection automatic driving air route of the unmanned aerial vehicle is generated. Through the method, flight air route planning, automatic driving, refined inspection and laser radar data collection of the unmanned aerial vehicle in a strong electromagnetic field are realized,and transmission line modeling and inspection quality is greatly improved.

The invention discloses a mobile robot navigation method based on a binocular camera and a two-dimensional laser radar. The mobile robot navigation method includes the steps that a two-dimensional grid map model is built; a mobile-robot pose model is built; a laser radar data model is built; a joint calibration model of the binocular camera and the laser radar is established; route planning and obstacle avoidance navigation are carried out with the Dijkstra algorithm and the A-Star algorithm. According to the mobile robot navigation method, the relative position and the relative orientation ofthe binocular camera and the two-dimensional laser radar can be determined with the triangle joint calibration method; in the known-environment two-dimensional grid map environment, the binocular camera can be used for detecting barriers higher than and lower than the two-dimensional laser radar plane, the barriers can be mapped into an environment map, through the shortest path algorithm and heuristic search optimization, a navigation path line capable of avoiding the barriers is obtained, and a mobile robot can reach the destination along a path capable of avoiding the barriers in the movement process.

The invention discloses a building area extraction method based on LiDAR data, which belongs to the field of extraction of building through the LiDAR data and includes early-age processing of the LiDAR data, resampling of original LiDAR data, reverse iteration mathematical morphological filtering and separation of building and dense trees. The building area extraction method directly processes three-dimensional (3D) point cloud data instead of converting the point cloud into depth images, avoids information loss and added calculation quantity in the conversion process, simultaneously utilizesdifferent windows to conduct mathematical morphological filtering operation through reverse gradual iteration, basically removes effects of terrain fluctuation on the building in the mathematical morphological filtering and obtains high extraction accuracy. The building area extraction method can quickly and accurately extract LiDAR points belonging to the building area from a large range of LiDAR points and is capable of providing reliable data support for urban three-dimensional modeling.

The invention belongs to the field of image processing methods, and discloses a three-dimensional roof reconstruction method based on LiDAR data and ortho images. The method comprises the following steps of: (1) LiDAR point roof facets segmentation based on a triangular cluster; (2) roof ridge extraction based on LiDAR data nad ortho images; (3) three-dimensional roof model reconstruction. In the method, for meeting the requirement on accurate reconstruction of the three-dimensional roof model, the LiDAR data and the high-resolution images are integrated, the complementary advantages of the elevation characteristic of the LiDAR data and the high-resolution characteristic of the images are comprehensively used, 'roof facets segmentation, ridge extracton and three-dimensional roof model reconstruction' is taken as a principal line, a LiDAR point roof facets segmentation algorithm based on the triangular cluster and a roof ridge extraction algorithm based on the LiDAR data and the ortho images are realized, and a new method for three-dimensional roof model reconstruction is formed. Tests show that the method of the invention has high automation degree, higher accuracy and integrity and high location precision in modeling, and meets the needs of actual application.

Point cloud data is received and a ground plane is segmented. A two-dimensional image of the segmented ground plane is generated based on intensity values of the segmented ground plane. Lane marking candidates are determined based on intensity within the generated two-dimensional image. Image data is received and the generated two-dimensional image is registered with the received image data. Lane marking candidates of the received image data are determined based on the lane marking candidates of the registered two-dimensional image. Image patches are selected from the two-dimensional image and from the received image data based on the determined lane markings. Feature maps including selected image patches from the registered two-dimensional image and received data are generated. The set of feature maps are sub-sampled, and a feature vector is generated based on the set of feature maps. Lane markings are determined from the generated feature vector.

Examples of the disclosure are directed to using vehicle users' subjective evaluation of autonomous vehicle performance to adjust operation of one or more autonomous vehicle systems, such as an adaptive cruise control system. In some examples, a vehicle can perform an autonomous maneuver, and can use automotive sensors to collect data describing one or more quantitative aspects of that vehicle during the maneuver, such as make, model, GPS coordinates, mileage, speed, engine activity, or LIDAR data, among other possibilities. In some examples, a control system can receive subjective feedback inputted by the vehicle's user that indicates one or more qualitative evaluations of the completed autonomous maneuver. In some examples, a control system may perform statistical analysis of collected vehicle data and of subjective feedback input by vehicle users to identify correlations between quantitative vehicle parameters, specified types of user feedback and / or autonomous vehicle maneuvers.

The invention provides a method for inverting effective leaf area index by utilizing geometric projection and laser radar and belongs to the field of research on methods for acquiring forest canopy structure parameter. The method includes the following steps: acquiring and preprocessing three-dimensional laser point cloud data of a plant canopy layer, converting a coordinate system of point cloud data, utilizing different geometric projections to project the three-dimensional point cloud data into a two-dimensional plane space and convert the data into a grid image and further utilizing the linear least square inversion algorithm to conduct estimation of porosity and effective leaf area index. Compared with a traditional observation method, the method is small in work amount and free of contact type observation and damage to the canopy structure and radiative characteristics, has the advantages of being objective, high in efficiency and accurate. A method for extracting three-dimensional structure and biological physical diversity information from laser radar data is developed, and characterization is conducted on the horizontal and vertical distribution change rule of leaves.

The invention discloses an overhead high-voltage line intelligent autonomous flight tour inspection system and method along wires. The system comprises a ground control station, laser radar, a laser radar data processing module and a multi-rotor unmanned aerial vehicle, the laser radar and the laser radar data processing module are both arranged on the multi-rotor unmanned aerial vehicle, the laser radar is used for acquiring information of the angle and the distance of a high-voltage line relative to the multi-rotor unmanned aerial vehicle, the laser radar data processing module transmits theabove information to the ground control station in a wireless mode, a motor driving module of the multi-rotor unmanned aerial vehicle performs rotating speed control on propeller motors in the multi-rotor unmanned aerial vehicle under the control of an unmanned aerial vehicle control center, the unmanned aerial vehicle and the overhead high-voltage line maintain a fixed distance, and the unmannedaerial vehicle performs cruise flight along the overhead high-voltage line. According to the system and method, in the tour inspection process, personnel operation is avoided, and high-efficiency, multifunctional and omnibearing intelligent overhead high-voltage line monitoring tour inspection can be realized.

The invention realizes a new robot autonomous navigation system based on double single-line laser radars. Compared with mainstream indoor robot navigation systems in the prior art, only by use of two low-cost single-line laser radars as a source of information, navigation and obstacle avoidance effects of the new robot autonomous navigation system are obviously superior to that of schemes with same cost in the prior art. The new robot autonomous navigation system based on the double single-line laser radars includes a double single-line laser radar system, an upper navigation system, a bottom control algorithm and a motion execution system; the upper navigation system includes a SLAM algorithm and a coordinate transformation algorithm; a current space two-dimensional map can be dynamically constructed by use of original laser radar data acquired by horizontally-installed radars by use of the SLAM algorithm, and displacement information of a robot platform can be calculated.

An object detecting apparatus and an operating method thereof are disclosed. An apparatus for detecting an object includes: a sensor unit including a light distance and ranging (LiDAR) sensor for collecting LiDAR data and a camera sensor for collecting image data; an area dividing unit configured to divide a common detection area of the LiDAR sensor and the camera sensor into a plurality of areas; a data analyzing unit configured to analyze the LiDAR data to extract a first object information and / or analyze the image data to extract a second object information; and a sensor signal converging unit configured to determine whether a dangerous object exists for each of the divided areas based on the first object information and / or the second object information.

The invention discloses an airborne laser radar data-based building three-dimensional reconstruction method and aims to propose a three-dimension reconstruction method combining a building roof boundary with a roof topologic graph so as to realize automatic detection and three-dimensional reconstruction of a building. The method comprises the steps of firstly, filtering airborne LiDAR (Airborne Light Detection And Ranging) data to obtain a ground point and a non ground point; secondly, extracting building point cloud from non ground point cloud in combination with point cloud characteristic information; thirdly, segmenting a roof surface and extracting a boundary contour line; and finally, obtaining key line segments of a roof in combination with the building boundary and the roof topologic graph, and constructing closed polygons of each root surface and combination of the closed polygons so as to obtain a building roof model. The wall surface can be obtained by elevation information of DTM or the ground point, so that the building 3D (three-dimensional) model reconstruction is realized. According to the method, the application bottleneck of the airborne LiDAR data at the present stage is broken through to a certain extent, the complexity of a reconstruction process is lowered, the flexibility of 3D reconstruction is improved, and a breakthrough is provided for city 3D reconstruction.

The invention discloses a data fusion calibration method of a catadioptric panorama camera and a laser radar. A data fusion calibration structure comprises the laser radar and the single-viewpoint catadioptric panorama camera mounted on an environment sensing system body. The calibration method comprises the steps that 1) a parameter K in the camera is calibrated; 2) parameters XmYmZm of refraction points of the catadioptric surface are solved; 3) parameters XwYwZw of world coordinate points of the panorama camera are solved; 4) parameters Xw*Yw*Zw* of f world coordinate points of the laser radar are measured; and 5) the panorama camera and the laser radar are calibrated in a fused manner. According to the invention, reasonable and fused data of the laser radar and the panorama camera are designed, internal parameters of the panorama camera can be calibrated effectively, and reasonable, rapid and effective schemes are provided for range finding and positioning in the environment sensing system.

The invention discloses a multi-source remote sensing image classification method based on a two-way attention fusion neural network, and mainly solves the problem of low classification precision of multi-source remote sensing images in the prior art. The implementation scheme comprises the following steps: 1) preprocessing and dividing hyperspectral data and laser radar data to obtain a trainingsample and a test sample; 2) designing an attention fusion layer based on an attention mechanism to carry out weighted screening and fusion on spectral data and laser radar data, andestablishing a two-way interconnection convolutional neural network, (3) training the interconnection convolutional neural network by taking multiple types of cross entropies as a loss function to obtain a trained network model, and (4) predicting a test sample by using the trained model to obtain a final classification result. The method can extract the features of the multi-source remote sensing data and effectively fuse and classify the features, improves the problem of overhigh dimension in fusion, improves the average classification precision, and can be used for fusing remote sensing images obtained by two different sensors.

The invention provides a method for non uniformity correction of range finding of an array push-scan type laser radar. An array push-scan type laser radar with N laser channels are used for detecting a target plate, the relative position of the array push-scan type laser radar and the target plate is changed, and the target plate is detected for M times, so laser radar data and total station data corresponding to each laser channel are obtained. By carrying out operation like coordinate transformation, a non uniformity correction model is established, and an actual range finding value of any one laser channel is used as a reference correction value to correct actual range finding values of other laser channels.

The invention discloses a rapid estimation method for solar energy in a construction size. The method includes the steps that by the comprehensive utilization of LiDAR data and high-resolution remote sensing image data, and three-dimension digital surface modeling is performed on objects such as urban constructions and trees; through simulation analysis of the attenuation effect, on direct solar radiation to the ground, of the atmosphere and the sheltering influence, on direct solar radiation energy obtained by construction surfaces including roofs and wall faces, by the urban objects, the solar energy obtained by the roofs and the wall faces of the urban constructions and dynamic estimated values are obtained. The rapid estimation method is suitable for specific application of using solar energy resources in the construction size in an urban region, wherein the specific application includes optimal configuration of energy resources, installation of solar photovoltaic equipment, design of green and environment-friendly constructions and the like.