WO2018133851A1 - Procédé et appareil de traitement des données de nuage de points, et support de stockage informatique - Google Patents

Procédé et appareil de traitement des données de nuage de points, et support de stockage informatique Download PDF

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Publication number
WO2018133851A1
WO2018133851A1 PCT/CN2018/073504 CN2018073504W WO2018133851A1 WO 2018133851 A1 WO2018133851 A1 WO 2018133851A1 CN 2018073504 W CN2018073504 W CN 2018073504W WO 2018133851 A1 WO2018133851 A1 WO 2018133851A1
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point cloud
cloud data
candidate
guardrail
point
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PCT/CN2018/073504
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English (en)
Chinese (zh)
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曾超
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腾讯科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05—Geographic models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/48—Extraction of image or video features by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation

Definitions

  • the present invention relates to electronic map technology, and in particular to a point cloud data processing method and apparatus, and a computer storage medium.
  • the common method for extracting the guardrail from the central divider of the road is the manual extraction method and Baidu's recently-published single-frame point cloud-based extraction method (Chinese Patent Application No. 201511025864.x, the patent name is the protection point point cloud extraction method and device).
  • the manual extraction method requires the internal operation personnel to open the road point cloud data in the special software, and manually extract the fence point cloud data.
  • the manual extraction method has the following disadvantages: the manual extraction method has large amount of point cloud data and complicated operation, resulting in large extraction workload and low efficiency; high cost and large-scale operation cannot be performed: due to low manual extraction efficiency, massive point cloud data A huge amount of manual work is required, which makes it impossible to promote the work on a large scale.
  • the single frame point cloud based extraction method uses the preset guard point cloud feature to extract and identify the single frame point cloud data to obtain the final guard point cloud data.
  • the extraction method based on single-frame point cloud has the following disadvantages: the use of single-frame point cloud for the extraction and recognition of guardrail features, due to the sparsity inherent in single-frame point cloud data, the preset guardrail features are not obvious, and it is easy to cause errors and omissions.
  • embodiments of the present invention are directed to providing a point cloud data processing method and apparatus, and a computer storage medium, which can efficiently and accurately extract point cloud data of a road center divider guardrail, thereby improving the robustness of identification and extraction.
  • a point cloud data processing method includes: classifying collected point cloud data of each frame to obtain low ground point cloud data; The direction extracts the candidate guardrail point cloud data from the low ground point cloud data; spatial clusters the candidate guardrail point cloud data to obtain the candidate guardrail point cloud set; and the candidate guardrail point cloud set obtained by the clustering Identifying, obtaining point cloud data of the road center divider with guardrail; performing three-dimensional curve fitting on the road center divider with guardrail point cloud data, and obtaining road central divider guardrail data represented in the high-precision map.
  • a point cloud data processing method including the steps of:
  • At least spatial feature recognition is performed on each candidate barrier point cloud set obtained by clustering, and point cloud data of the road center partition guardrail is obtained;
  • Three-dimensional curve fitting is performed on the point cloud data of the central divider of the road, and the road center divider guardrail data represented in the high-precision map is obtained.
  • a point cloud data processing apparatus includes: a classification unit configured to classify the collected point cloud data to obtain low ground point cloud data.
  • a first extracting unit configured to extract candidate guardrail point cloud data from the low ground point cloud data along a direction of the vehicle track;
  • the clustering unit is configured to perform spatial clustering on the candidate guardrail point cloud data, Obtaining a set of candidate guardrail point clouds;
  • the identifying unit is configured to identify each candidate guardrail point cloud set obtained by the cluster, and obtain point cloud data of the road center divider guardrail;
  • the fitting unit is configured to be separated from the road center
  • the 3D curve fitting is performed with the guardrail point cloud data, and the road center divider guardrail data represented in the high-precision map is obtained.
  • a computer storage medium storing computer executable instructions for executing point cloud data according to an embodiment of the present invention is provided. Approach.
  • the collected point cloud data of each frame is classified to obtain low point object cloud data; and the candidate is extracted from the low ground point cloud data along the direction of the vehicle track Guard point cloud data; spatial clustering of candidate guardrail point cloud data to obtain a set of candidate guardrail point cloud; identify the candidate guardrail point cloud collection obtained by clustering, and obtain point cloud data of the road center divider guardrail;
  • the road center partitions the guardrail point cloud data for three-dimensional curve fitting, and obtains the road center separation zone guardrail data represented in the high-precision map; thus, the road central separation zone guardrail can be quickly and automatically extracted from the vehicle laser point cloud.
  • the data provides basic data for high-end applications such as vehicle-assisted positioning and driverless driving, which can greatly improve the automatic extraction efficiency of the road center divider with guardrails, reduce the amount of manual work, and reduce the production cost of high-precision maps.
  • FIG. 1 is a schematic view of a road center divider belt guardrail according to an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of a method for processing a point cloud data according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of a typical single frame laser scan line data according to an embodiment of the present invention.
  • FIG. 4 is a side view of a road center divider with a guardrail according to an embodiment of the present invention.
  • FIG. 5 is a schematic diagram showing a vertical projection of a road center divider guard rail to a two-dimensional plane according to an embodiment of the present invention
  • FIG. 6 is a three-dimensional curve diagram of data of a road center divider with guard rails according to an embodiment of the present invention
  • FIG. 7 is a schematic structural diagram of a point cloud data processing apparatus according to an embodiment of the present disclosure.
  • FIG. 8 is a schematic structural diagram of an optional software and hardware of a point cloud data processing apparatus according to an embodiment of the present invention.
  • 9-1 is a schematic diagram of an optional scenario of point cloud data processing when the point cloud data processing device is distributed in the cloud according to an embodiment of the present invention.
  • 9-2 is a schematic diagram of an optional scenario of point cloud data processing when the point cloud data processing device is distributed on the vehicle side according to an embodiment of the present invention.
  • FIG. 10 is an optional schematic flowchart of a point cloud data processing method according to an embodiment of the present invention.
  • Point cloud data refers to data that is scanned and recorded in the form of dots by a scanning device (such as a laser scanner) installed in a vehicle or other mobile device (such as an aircraft), each point containing a three-dimensional point Coordinates, as well as attribute information of corresponding 3D points, such as red, green, blue (RGB, Red, Green, Blue) color information, or reflection intensity information (Intensity).
  • a scanning device such as a laser scanner
  • vehicle or other mobile device such as an aircraft
  • attribute information of corresponding 3D points such as red, green, blue (RGB, Red, Green, Blue) color information, or reflection intensity information (Intensity).
  • Vehicle laser point cloud Point cloud data collected by a scanning device installed on a mobile measuring vehicle.
  • High-precision maps maps that represent lane-level maps, including lane lines, markings, and road parameters. It has at least centimeter-level positioning accuracy and can also have road facility information (such as traffic lights, electronic eyes, and traffic signs). Among them, the road parameters can be static traffic information (such as whether the road is restricted or not), or dynamic traffic information such as traffic flow (whether it is unblocked, whether there is a traffic accident), road conditions (whether there is water, ice) Wait).
  • Road facilities auxiliary facilities near the road that are continuously distributed along the road, such as road guardrails, traffic signs, traffic lights, and electronic eyes.
  • Guardrail A longitudinal energy absorbing structure that absorbs collision energy by self-deformation or vehicle climb, thereby changing the direction of the vehicle, preventing the vehicle from going out of the road or entering the opposite lane, and minimizing the damage to the occupants.
  • it According to its longitudinal position in the highway, it can be divided into roadbed guardrails and bridge guardrails; according to its horizontal position in the road, it can be divided into roadside guardrails and central divider guardrails; according to the degree of deformation after collision, can be divided It is a rigid guardrail, a semi-rigid guardrail and a flexible guardrail.
  • Central divider with guardrail A guardrail placed in the central divider of the road to prevent uncontrolled vehicles from entering the opposite lane through the central divider and protecting the structure within the central divider.
  • Land objects refer to roads and various tangible objects on the ground around the road (such as road facilities, plants, buildings, etc.).
  • the point cloud data the point cloud data used to represent the feature in the point cloud data.
  • Ground point cloud data part of the cloud data used to represent the ground (such as the road surface, the surface connected to the road, the water surface).
  • the low point object point cloud data the point cloud data point in the point cloud data for indicating that the value from the ground is greater than the first threshold and less than the second threshold; wherein the first threshold is less than the second threshold.
  • point cloud data used in the point cloud data to indicate that the value from the ground is greater than or equal to the second threshold.
  • 3D curve fitting using a continuous curve to approximate or compare the 3D points in the point cloud data, so that as many 3D points as possible fit the distribution of a continuous 3D curve, such as on the continuous 3D curve or from the 3D
  • the curve is closer, and the three-dimensional curve is the result of three-dimensional curve fitting based on point cloud data.
  • An embodiment of the present invention provides a point cloud data processing method. As shown in FIG. 2, the method mainly includes:
  • Step 201 classify the collected point cloud data of each frame to obtain low ground point cloud data.
  • the step of obtaining low ground point cloud data includes:
  • the low point point cloud data is obtained.
  • the method before the collecting the collected point cloud data of each frame, the method further includes:
  • Each frame point cloud data is extracted from the point cloud data file based on the location information, the angle information, and the time information.
  • the vehicle environment is collected by setting a collection unit (such as a laser scanner, a three-dimensional camera), real-time positioning is performed by the positioning unit at each position in the traveling, and the environment is multi-angled by the collecting unit (for example)
  • a collection unit such as a laser scanner, a three-dimensional camera
  • real-time positioning is performed by the positioning unit at each position in the traveling
  • the environment is multi-angled by the collecting unit (for example)
  • an optional data structure for collecting point cloud data collected from any angle at each location is: geographic location, acquisition angle, three-dimensional point coordinates, and three-dimensional point attribute information.
  • the scanning device such as the laser scanner adopts a 360-degree rotational scanning mode
  • the point cloud obtained by rotating the laser scanner from 0 degrees to 360 degrees can be referred to as single-frame laser scanning line data.
  • each scan line data is continuously stored to form a point cloud data file. Therefore, after obtaining the point cloud data, it is necessary to extract the point cloud data of each frame according to the angle information of each point (ie, the angle value is Point between 0-360 degrees).
  • a typical single frame laser scan line data is shown in Figure 3.
  • the collected point cloud data of each frame is classified to obtain low ground point cloud data, including:
  • RANSAC Random Sample Consensus
  • the point cloud coarse classification is performed according to the distance from each point in the single-frame point cloud data to the ground plane.
  • the specific classification rules are as follows:
  • dThred1 can take a value of 0.3 meters and dThred2 takes a value of 1.5 meters. It should be noted that the dThred1 and dThred2 can be adaptively adjusted according to the extraction accuracy requirement.
  • Step 202 Extract candidate fence point cloud data from the low object point cloud data along a track direction.
  • the step of extracting candidate guardrail point cloud data from the low ground point cloud data includes:
  • the candidate guardrail point cloud data is extracted from the low ground point cloud data according to the distance between the low ground object and the specific side of the vehicle along the direction of the vehicle track.
  • the extracting, by the vehicle track, the candidate guardrail point cloud data from the low ground point cloud data including:
  • the low point object cloud data in a certain range of the preset direction is extracted from the low point point cloud data along the vehicle track to obtain the candidate guard point cloud data.
  • the preset direction may be the left side or the right side of the vehicle traveling direction.
  • the predetermined direction is specifically the left side or the right side, depending on the position of the steering wheel of the vehicle in the vehicle.
  • the preset direction is generally the left side; when the steering wheel is located on the right side of the vehicle, the preset direction is generally the right side.
  • the central separation zone of the road is generally between 0.5m and 1.5m from the ground, it falls right into the "low dwarf point” classification point; in addition, because China is driving right, the central separation zone of the road
  • the guardrail is generally located to the left of the driving track. Therefore, the low datum points in the multi-frame point cloud data are accumulated to obtain a complete set of low-lying object point clouds; then, the vehicle trajectory is taken within a certain distance from the left side of the vehicle (vertical track trajectory) (for example) A point with a value of 15 meters) as a candidate guardian point cloud dataset.
  • Step 203 Perform spatial clustering on the candidate guardrail point cloud data to obtain a set of candidate guardrail point cloud.
  • the spatial clustering of the candidate guardrail point cloud data includes but is not limited to the following manners:
  • Feature-based clustering method spatial clustering of candidate guardrail point cloud data.
  • the spatial clustering of the candidate guardrail point cloud data is performed to obtain a set of candidate guardrail point cloud clouds, including:
  • each candidate barrier point cloud collection includes a plurality of three-dimensional points and attribute information of the corresponding three-dimensional points.
  • the attribute information of the corresponding three-dimensional point such as red, green and blue (RGB) color information, or reflection intensity information (Intensity).
  • Step 204 Identify each candidate guardrail point cloud set obtained by clustering, and obtain point cloud data of the road center divider guardrail.
  • the step of performing at least spatial feature recognition on each candidate barrier point cloud set obtained by clustering includes:
  • At least the spatial distribution feature and the linear feature recognition are performed on each candidate barrier point cloud set obtained by clustering.
  • the candidate guardrail point cloud set obtained by the cluster is identified, and the interference point cloud set is eliminated, and the point cloud data of the road center partition guardrail is obtained.
  • the candidate point cloud set obtained by clustering is identified, and the point cloud data of the road center partition guardrail is obtained, including:
  • the candidate guardrail point cloud sets are identified by combining the spatial distribution features in the three-dimensional space, the linear features on the two-dimensional plane, and the spatial topological features.
  • the spatial distribution feature combined in a three-dimensional space, the linear feature on the two-dimensional plane, and the spatial topological feature identify each candidate barrier point cloud set, including:
  • a second type of candidate point cloud set that satisfies the spatial topological feature as an inclusion relationship is selected from the first type of candidate point cloud set, and the road center separation zone guard point cloud data is obtained based on the second type of candidate point cloud set.
  • the shape feature-based recognition method mainly considers that the road center divider guard rail presents a planar feature in three-dimensional space, as shown in FIG. 4; and on the two-dimensional plane after vertical projection, a continuous line is presented. Shape distribution characteristics, as shown in Figure 5. Therefore, this feature can be used to quickly remove the point cloud data (such as stationary vehicles on the road surface, anti-collision piers, and vegetation on both sides of the road).
  • point cloud data such as stationary vehicles on the road surface, anti-collision piers, and vegetation on both sides of the road.
  • the three-dimensional planar feature extraction may use a principal component analysis method (PCA) to calculate a spatial distribution feature of the candidate point cloud set;
  • PCA principal component analysis method
  • P1 and P2 represent spatial distribution characteristics.
  • the point cloud is distributed in a body shape, ⁇ 1> ⁇ 2> ⁇ 3, the three eigenvalues are close to each other, and both P1 and P2 are small; when the point cloud is distributed in a plane, ⁇ 1> ⁇ 2>> ⁇ 3, P1 is smaller, P2 is larger; when the point cloud is linearly distributed, ⁇ 1>> ⁇ 2> ⁇ 3, P1 is larger, and P2 is smaller.
  • the calculation method of the linear features on the two-dimensional plane may adopt the Hough transform method, or first extract the two-dimensional image gradient, and then perform the line segment tracking method.
  • the identification method based on spatial topological features mainly considers the spatial topological relationship between the guardrail of the central divider of the road and the ground. Since most of the central dividers of the roads are in the form of fences, they can penetrate the point cloud. Therefore, in the two-dimensional plane after vertical projection, the spatial topology between them and the ground is inclusive, as shown in Figure 5. As shown in the figure; and the moving vehicle and the wall and other objects, the spatial topology between them and the ground is in a connected relationship.
  • the extraction accuracy of the point cloud of the central divider of the road can be greatly improved, and the risk of false rejection can be reduced.
  • Step 205 Perform three-dimensional curve fitting on the point cloud data of the road center divider with the guardrail to obtain the road center divider guardrail data represented in the high-precision map.
  • the point cloud data extracted in step 204 is subjected to curve fitting by using a three-dimensional curve fitting method to obtain a final road guardrail three-dimensional curve data; wherein, a three-dimensional curve diagram of the road center divider with guardrail data is shown in FIG. 6.
  • the three-dimensional curve fitting of the point cloud data of the central divider of the road is performed, including but not limited to the following manners:
  • a three-dimensional curve fitting method based on a polynomial equation for a three-dimensional curve fitting of a point cloud data of a road center divider with a guardrail;
  • the curve fitting method based on the random sampling consensus algorithm performs three-dimensional curve fitting on the point cloud data of the road center divider with guardrail.
  • the three-dimensional curve fitting of the point cloud data of the central divider of the road is performed, and the road central divider guardrail data represented in the high-precision map is obtained, including:
  • the three-dimensional modeling is performed based on the point cloud data conforming to the features of the central divider of the road, forming a three-dimensional solid figure of the road center divider with a guardrail for rendering in a high-precision map.
  • the automatic extraction of the guard data of the road center separation zone based on the vehicle laser point cloud can be realized, and the high-precision road center separation zone guardrail data can be obtained.
  • the central divider of the road there is no distinction between the central divider of the road and the extraction method of the guardrails on both sides of the road.
  • the application requirements of the two types of guardrails are not the same and need to be distinguished.
  • the application can quickly extract the road central divider guardrail data from the vehicle laser point cloud, and can provide basic data for high-end applications such as vehicle assisted positioning and driverless driving.
  • the proposal of the present application can greatly improve the automatic extraction efficiency of the guardrail of the central divider of the road, reduce the workload of manual work, and reduce the production cost of the high-precision map.
  • the embodiment of the invention further provides a point cloud data processing method, comprising the steps of:
  • At least spatial feature recognition is performed on each candidate barrier point cloud set obtained by clustering, and point cloud data of the road center partition guardrail is obtained;
  • Three-dimensional curve fitting is performed on the point cloud data of the central divider of the road, and the road center divider guardrail data represented in the high-precision map is obtained.
  • FIG. 7 shows an optional logical function structure diagram of the point cloud data processing apparatus 10.
  • the point cloud data processing apparatus 10 includes: a classification unit 21, a first extraction unit 22, and a clustering unit. 23. Identification unit 24 and fitting unit 25, each unit will be described below.
  • the classification unit 21 is configured to classify the collected point cloud data to obtain low point point cloud data
  • the first extracting unit 22 is configured to extract candidate guardrail point cloud data from the low object point cloud data in a direction of the vehicle track;
  • the clustering unit 23 is configured to perform spatial clustering on the candidate guardrail point cloud data to obtain a set of candidate guardrail point cloud sets;
  • the identifying unit 24 is configured to identify each candidate guard point cloud set obtained by clustering, and obtain point cloud data of the road center partition guardrail;
  • the fitting unit 25 is configured to perform three-dimensional curve fitting on the road center separation barrier point cloud data, and obtain road central divider guardrail data represented in the high-precision map.
  • the device further includes:
  • the second extraction unit 26 is configured to:
  • Each frame point cloud data is extracted from the point cloud data file based on the location information, the angle information, and the time information.
  • the classification unit 21 is specifically configured to:
  • the classification unit 21 integrates the received point cloud data into discrete point cloud data collected by the vehicle side at different locations and different acquisition angles, and integrates the received point cloud data into a form of “frames” for subsequent Processing, for example, for the received point cloud data, distinguishing the collection locations according to the labels of the geographic locations of the point cloud data, and for each collection location point cloud data, forming point cloud data of different acquisition angles of the corresponding locations into a corresponding position Frame point cloud data, each frame point cloud data includes coordinates and attribute information of a three-dimensional point obtained by collecting the road environment at different angles at corresponding positions.
  • the first extracting unit 22 is specifically configured to:
  • the low point object cloud data in a certain range from the vehicle in the preset direction is extracted from the low point point cloud data, and the candidate fence point cloud data is obtained.
  • the clustering unit 23 is specifically configured to:
  • each candidate barrier point cloud collection includes a plurality of three-dimensional points and attribute information of the corresponding three-dimensional points.
  • the identifying unit 24 is specifically configured to:
  • the candidate guardrail point cloud sets are identified by combining the spatial distribution features in the three-dimensional space, the linear features on the two-dimensional plane, and the spatial topological features.
  • the identification unit 24 is further configured as follows:
  • a second type of candidate point cloud set that satisfies the spatial topological feature as an inclusion relationship is selected from the first type of candidate point cloud set, and the road center separation zone guard point cloud data is obtained based on the second type of candidate point cloud set.
  • the fitting unit 25 is specifically configured to:
  • the three-dimensional modeling is performed based on the point cloud data conforming to the features of the central divider of the road, forming a three-dimensional solid figure of the road center divider with a guardrail for rendering in a high-precision map.
  • the purpose of screening the point cloud data corresponding to the three-dimensional point that does not conform to the fitted three-dimensional curve is to further reduce the noise in the extracted road facility point cloud data.
  • the specific structures of the foregoing classification unit 21, the first extraction unit 22, the clustering unit 23, the identification unit 24, the fitting unit 25, and the first extraction unit 26 may all correspond to a processor.
  • the specific structure of the processor may be a Central Processing Unit (CPU), a Micro Controller Unit (MCU), a Digital Signal Processing (DSP), or a programmable logic device (PLC). Programmable Logic Controller) A collection of electronic components or electronic components with processing functions.
  • the processor includes executable code, the executable code is stored in a storage medium, and the processor may be connected to the storage medium through a communication interface such as a bus, when performing a corresponding function of each unit Reading and running the executable code from the storage medium.
  • the portion of the storage medium used to store the executable code is preferably a non-transitory storage medium.
  • the point cloud data processing device in this embodiment may be disposed on the vehicle side and the cloud server side.
  • the point cloud data processing apparatus provided by the embodiment of the present invention can be implemented in various manners, which will be exemplified below.
  • the point cloud data processing device is distributed on the cloud server side.
  • the point cloud data processing apparatus 10 includes a component layer, an intermediate layer, an operating system layer, and a software layer.
  • the structure of the point cloud data processing apparatus 10 shown in FIG. 8 is merely an example and does not constitute a limitation on the structure of the point cloud data processing apparatus 10.
  • the point cloud data processing apparatus 10 sets more components than FIG. 8 according to implementation needs, or omits setting part components according to implementation needs.
  • the hardware layer of the point cloud data processing device 10 includes a processor 11, an input/output interface 13, a storage medium 14, a positioning module 12, a communication module 15, and an acquisition module 16; each component can communicate with the processor 11 via a system bus connection.
  • the processor 11 can be implemented by using a central processing unit (CPU), a microprocessor (MCU, Microcontroller Unit), an application specific integrated circuit (ASIC), or a Field-Programmable Gate Array (FPGA).
  • CPU central processing unit
  • MCU microprocessor
  • ASIC application specific integrated circuit
  • FPGA Field-Programmable Gate Array
  • the input/output interface 13 can be implemented using input/output devices such as a display screen, a touch screen, and a speaker.
  • the storage medium 14 may be implemented by using a non-volatile storage medium such as a flash memory, a hard disk, or an optical disk, or may be implemented by using a volatile storage medium such as a double rate (DDR) double data rate cache, where the storage is useful to execute the point cloud data.
  • DDR double rate
  • the storage medium 14 may be centrally located or distributed across different locations.
  • the communication module 15 provides the processor 11 with the access capability of the external data such as the storage medium 14 disposed off-site.
  • the communication module 15 can implement Near Field Communication (NFC) technology, Bluetooth technology, and purple.
  • NFC Near Field Communication
  • the short-range communication by the ZigBee technology can also implement communication systems such as Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), and its evolution system. Communication.
  • the collection module 16 is configured to perform multi-angle acquisition and output point cloud data, which may be implemented by a laser scanner or a three-dimensional camera.
  • the point cloud data includes at least three-dimensional point coordinates, and the point cloud data according to the specific type of the collection module 16 further includes related
  • the attribute information such as the attribute information when the camera is a depth camera, is RGB information, and the attribute information is, for example, the reflection intensity information of the three-dimensional point (related to the gray scale) when the laser scanner is used.
  • the driver layer includes a middleware 17 for the operating system 18 to identify and communicate with the hardware layer components, such as a collection of drivers for the various components of the hardware layer.
  • the software layer includes providing a user with a high-precision map-based application such as a navigation application 19, and can also package various services based on high-precision maps into a callable application programming interface (API).
  • a high-precision map-based application such as a navigation application 19
  • API application programming interface
  • the software layer can provide a high-precision map-based service to the application in the in-vehicle terminal, including locating the current location of the vehicle, the navigation route query, and the like.
  • the point cloud data processing device is distributed on the cloud server side.
  • a typical implementation scenario is shown in Figure 9-1.
  • the point cloud data processing device sets the aforementioned acquisition module (such as a laser scanner) on the vehicle side.
  • the point cloud data of different locations is collected at multiple angles (such as 0-360 degrees) along the road where the vehicle is traveling, and the label of the collection angle can be added for the collected point cloud data.
  • the point cloud data processing device may also be deployed with the foregoing positioning module in the vehicle side, and the positioning device is located in a real-time position of the vehicle based on a Global Positioning System (GPS), a Beidou satellite positioning navigation system, etc.
  • GPS Global Positioning System
  • Beidou satellite positioning navigation system etc.
  • the collected point cloud data can be added to the collected geographical location label, and sent to the cloud server through the communication module deployed by the point cloud data processing device on the vehicle side, by the point cloud data processing device
  • the processor of the server disposed in the cloud extracts point cloud data of the road facility from the point cloud data, and three-dimensionally models the road facility through the point cloud data of the road facility to form A three-dimensional solid figure of a road facility that can be used for rendering in a high-precision map.
  • the point cloud data processing device is distributed on the vehicle side.
  • FIG. 8 An optional hardware and software structure diagram of the point cloud data processing device can still be seen in FIG. 8.
  • the point cloud data processing device is distributed on the vehicle side.
  • a typical implementation scenario is shown in FIG. 9-2, while the vehicle is running.
  • the point cloud data processing device is provided with an acquisition module (such as a laser scanner) on the vehicle side to collect point cloud data at different positions (such as 0-360 degrees) to form different points of the point cloud data, which may be collected point clouds.
  • the data adds a label for the acquisition angle.
  • the point cloud data processing device may also be provided with a positioning module in the vehicle side, and the positioning device locates the real-time position of the vehicle based on a global satellite positioning system (GPS), a Beidou satellite positioning navigation system, etc. (for example, using various forms of coordinate recording)
  • GPS global satellite positioning system
  • Beidou satellite positioning navigation system etc.
  • the road facilities are three-dimensionally modeled to form road facilities that can be used for rendering in high-precision maps.
  • the point cloud data of the extracted road facilities can be sent to the cloud server, and the cloud server provides services based on the high-precision map of the road facilities. .
  • FIG. 10 shows an alternative flow diagram of a point cloud data processing method. As shown in FIG. 10, the flow mainly includes:
  • step 301 the road environment is collected when each vehicle travels along the road.
  • the vehicle environment is collected by setting an acquisition module (such as a laser scanner, a three-dimensional camera), real-time positioning is performed by the positioning module at each position in the traveling, and the environment is multi-angled through the acquisition module ( For example, the acquisition of all angles from 0 to 360, an optional data structure for collecting point cloud data collected from any angle at each location is: geographic location, acquisition angle, three-dimensional point coordinates, and three-dimensional point attribute information.
  • an acquisition module such as a laser scanner, a three-dimensional camera
  • real-time positioning is performed by the positioning module at each position in the traveling
  • the environment is multi-angled through the acquisition module
  • an optional data structure for collecting point cloud data collected from any angle at each location is: geographic location, acquisition angle, three-dimensional point coordinates, and three-dimensional point attribute information.
  • Step 302 Each vehicle sends the point cloud data collected along the road to the cloud server side with the extraction function of the road center divider guardrail data.
  • each vehicle can send the point cloud data collected by the collection module to the cloud server in real time through the set communication module, and the cloud server with high computing capability extracts the point cloud corresponding to the road facility from the point cloud data as soon as possible.
  • each vehicle may send the point cloud data collected by the collection module to the cloud server when the predetermined transmission condition is reached, for the cloud server to extract the point cloud data corresponding to the road facility from the received point cloud data.
  • the cloud server may send the point cloud data collected by the collection module to the cloud server when the predetermined transmission condition is reached, for the cloud server to extract the point cloud data corresponding to the road facility from the received point cloud data.
  • each vehicle may send point cloud data collected in a corresponding time period to the cloud server when the predetermined time (which may be periodic or non-periodic) arrives, for example, sending the collection every 5 minutes.
  • Point cloud data may be sent.
  • each vehicle may transmit point cloud data collected at a corresponding mileage when the mileage traveled meets a predetermined mileage, for example, point cloud data to be collected within 1 km per 1 km travel is transmitted to the cloud server.
  • Step 303 The cloud server extracts each frame point cloud data according to the angle information of each point as needed.
  • the point cloud data received by the cloud server is discrete point cloud data collected at different locations and different collection angles.
  • the server integrates the received point cloud data into a "frame" format for subsequent processing, wherein Each acquisition location corresponds to at least one frame of point cloud data, and the number of frames of point cloud data formed for each location depends on the dwell time at that location and the speed at which the road environment is scanned during acquisition.
  • the cloud server receives point cloud data collected at various angles (0 to 360 degrees) at different locations, and for the received point cloud data, the collection location is distinguished according to the geographical location of the point cloud data, for each collection location point
  • the cloud data forms point cloud data of different acquisition angles of corresponding positions to form a frame point cloud data of the corresponding position, and each frame point cloud data includes coordinates and attribute information of the three-dimensional points obtained by collecting the road environment at different angles at corresponding positions. .
  • point cloud data having a tag of position 1 is first extracted from the received point cloud data, and point cloud data of a tag having position 1 is collected according to the point cloud data of each point cloud data. Arrange sequentially to form a corresponding one-frame point cloud data.
  • an optional data structure of a frame of point cloud data at position 1 is (position 1, acquisition angle 0 - 3D point 1 coordinate - 3D point 1 attribute information; acquisition angle 1 - 3D point n coordinate - 3D point n attribute Information; ... acquisition angle 360 - 3D point 1 coordinates - 3D point 1 attribute information; acquisition angle 360 - 3D point n coordinates - 3D point n attribute information).
  • Step 304 The cloud server classifies the collected point cloud data of each frame to obtain low ground point cloud data.
  • a plane equation corresponding to the ground plane is established according to the coordinates of the three-dimensional point of the point cloud data of each frame, and the height of each three-dimensional point in the frame point cloud data relative to the ground plane is obtained according to the plane equation, and the ground and the ground are
  • the value range of the height corresponding to the object is divided into at least point cloud data (ground point cloud data) corresponding to the ground and point cloud data (ground point cloud data) corresponding to the ground object.
  • point cloud data point cloud data
  • point cloud data point cloud data
  • it can also be divided into other types of point cloud data (referred to as other point cloud data) above the height of the feature.
  • road facilities on the ground level differ in height from other plants, such as plants.
  • the height of traffic lights is more than 1 meter, and the height of road barriers is generally between 0.3 and 1 meter. Plants near the road. Generally, flowers or other low-lying plants are generally below 0.3 meters.
  • the three-dimensional point of the cloud data of each frame can be preliminarily determined that the three-dimensional point is a corresponding ground plane, a corresponding feature or a corresponding higher object, and the three-dimensional point is divided into corresponding category point cloud data.
  • the point cloud data types that are highly adapted to the road facilities are also different, for example, the following may be included:
  • the point cloud data categories that are highly adapted to the road facilities can be processed later, and the other types of point cloud data are filtered out (the subsequent processing is not necessary). This achieves the effect of preliminary screening of point cloud data including road facilities, reducing the amount of subsequent data processing.
  • Step 305 The cloud server extracts candidate guardrail point cloud data from the low object point cloud data along the vehicle track.
  • the guardrail of the central divider of the road is generally 0.5m to 1.5m above the ground, which falls right into the "low-lying object point" classification point.
  • a point within a certain distance (for example, 15 meters) from the left side of the vehicle (vertical track trajectory) is taken along the vehicle trajectory as a candidate guard point cloud data set.
  • a method for judging a predetermined spatial distribution characteristic of a point cloud data of a guardrail based on a road center partition is a method for judging a predetermined spatial distribution characteristic of a point cloud data of a guardrail based on a road center partition.
  • Step 306 The cloud server spatially clusters the candidate guardrail point cloud data to obtain a set of candidate guardrail point cloud.
  • the spatial clustering method includes, but is not limited to, a clustering method based on Euclidean distance, a clustering method based on graph theory, and a clustering method based on features.
  • Step 307 The cloud server identifies each candidate fence point cloud set obtained by the clustering, and removes the interference point cloud set to obtain point cloud data of the road center partition guardrail.
  • the cloud server combines the spatial distribution features in the three-dimensional space, the linear features on the two-dimensional plane, and the spatial topological features to identify the candidate guardrail point cloud sets. Combining the identification methods of shape and spatial topological features can greatly improve the extraction accuracy of the point cloud of the central divider of the road and reduce the risk of false rejection.
  • Step 308 The cloud server performs three-dimensional curve fitting on the road center separation barrier point cloud data, and obtains the road center separation zone guardrail data represented in the high-precision map.
  • the cloud server performs a three-dimensional curve fitting on the road center separation barrier point cloud data, and filters the point cloud data corresponding to the three-dimensional point that does not conform to the fitted three-dimensional curve;
  • the feature point cloud data is three-dimensionally modeled to form a three-dimensional solid figure of the road center divider with a guardrail for rendering in a high precision map.
  • the embodiment of the present invention further provides a computer storage medium, which may be a computer readable storage medium included in the memory in the above embodiment, or may be separately readable by a computer that is not assembled into the terminal.
  • Storage medium stores one or more computer executable instructions that are used by one or more processors to perform the community discovery method of embodiments of the present invention.
  • the computer executable instructions are configured to perform: classifying the collected point cloud data to obtain low ground point cloud data; and in the direction of the vehicle track, from the low ground point cloud
  • the candidate guardrail point cloud data is extracted from the data; the candidate guardrail point cloud data is spatially clustered to obtain the candidate guardrail point cloud set; the candidate guardrail point cloud set obtained by the cluster is identified, and the point of the road center divider guardrail is obtained.
  • the computer executable instructions are configured to: before collecting the collected point cloud data of each frame, acquiring a point cloud data file in a preset time period; according to the location information, the angle information, and The time information extracts each frame point cloud data from the point cloud data file.
  • the computer executable instructions are configured to: determine, according to a distance from each point in the cloud data of each frame point to a ground plane, a point whose distance value is greater than a first threshold and less than a second threshold as a low ground An object point; wherein the first threshold is less than the second threshold.
  • the computer executable instructions are configured to: extract, in the direction of the track of the vehicle, from the low point object cloud data, a low ground object in a certain range from a side of the preset direction Point cloud data to get candidate fence point cloud data.
  • the computer executable instructions are configured to: extract spatial features from candidate guard point cloud data of each frame; and compare the same spatial features of each frame candidate point cloud data by using Each frame candidate cluster point cloud data has the same spatial feature, and each frame candidate guard point cloud data is clustered to form a plurality of candidate guardrail point cloud sets, each candidate guardrail point cloud set includes a plurality of three-dimensional points and corresponding The attribute information of the 3D point.
  • the computer executable instructions are configured to: determine spatial distribution features of each candidate point cloud set in three-dimensional space; determine linear features of each candidate point cloud set on a two-dimensional plane; determine each candidate The spatial topological features of the point cloud set on the two-dimensional plane after vertical projection and the ground; the spatial distribution features in the three-dimensional space, the linear features on the two-dimensional plane, and the spatial topological features of the candidate guardrail point clouds The collection is identified.
  • the computer executable instructions are configured to: extract a first type of candidate point that satisfies a planar feature in a three-dimensional space and a continuous linear distribution feature on a two-dimensional plane after vertical projection a cloud set; determining a spatial topological feature between the first type of candidate point cloud set on a two-dimensional plane after vertical projection and the ground; and selecting, from the first set of candidate point cloud sets, a second satisfying spatial topological feature as an inclusion relationship
  • the computer executable instructions are configured to: perform a three-dimensional curve fitting on the road center divider fence point cloud data, and filter out point cloud data corresponding to the three-dimensional point that does not conform to the fitted three-dimensional curve. 3D modeling based on point cloud data conforming to features of the central divider of the road, forming a three-dimensional solid figure of the road center divider with a guardrail for rendering in a high precision map.
  • the disclosed method and apparatus may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the modules is only a logical function division.
  • there may be another division manner for example, multiple modules or components may be combined, or Can be integrated into another system, or some features can be ignored or not executed.
  • the communication connections between the various components shown or discussed may be indirect coupling or communication connections through some interfaces, devices or modules, and may be electrical, mechanical or otherwise.
  • the modules described above as separate components may or may not be physically separated.
  • the components displayed as modules may or may not be physical modules, that is, may be located in one place or distributed to multiple network modules; Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may be separately used as one module, or two or more modules may be integrated into one module;
  • the module can be implemented in the form of hardware or in the form of hardware plus software function modules.
  • the foregoing storage medium includes: a mobile storage device, a random access memory (RAM), a read-only memory (ROM), a magnetic disk, or an optical disk.
  • RAM random access memory
  • ROM read-only memory
  • magnetic disk or an optical disk.
  • optical disk A medium that can store program code.
  • the above-described integrated unit of the present invention may be stored in a computer readable storage medium if it is implemented in the form of a software function module and sold or used as a standalone product.
  • the technical solution of the embodiments of the present invention may be embodied in the form of a software product in essence or in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for making A computer device (which may be a personal computer, server, or network device, etc.) performs all or part of the methods described in various embodiments of the present invention.
  • the technical solution of the embodiment of the present invention obtains the low point object point cloud data first; extracts the candidate guardrail point cloud data from the low ground object point cloud data along the vehicle track; and performs spatial clustering on the candidate guardrail point cloud data.
  • the basic data can greatly improve the automatic extraction efficiency of the guardrail on the central divider of the road, reduce the workload of manual work, and reduce the production cost of high-precision maps.

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Abstract

La présente invention porte sur un procédé et sur un appareil de traitement de données de nuage de points, ainsi que sur un support de stockage informatique. Le procédé consiste à : classifier des trames de données de nuage de points collectées pour obtenir des données de nuage de points d'objet de faible garde (201) ; extraire des données de nuage de points de glissières de sécurité candidates à partir des données de nuage de points d'objet de faible garde dans la direction d'une voie de circulation (202) ; regrouper dans l'espace les données de nuage de points de glissières de sécurité candidates pour obtenir des ensembles de nuage de points de glissières de sécurité candidates (203) ; identifier les ensembles de nuage de points de glissières de sécurité candidates obtenus dans le regroupement spatial, de façon à obtenir des données de nuage de points d'une glissière de sécurité de séparation centrale d'une route (204) ; et effectuer un ajustement de courbe tridimensionnel sur les données de nuage de points de la glissière de sécurité de séparation centrale de route, de façon à obtenir des données de la glissière de sécurité de séparation centrale de route, qui est indiquée sur une carte de haute précision (205).
PCT/CN2018/073504 2017-01-22 2018-01-19 Procédé et appareil de traitement des données de nuage de points, et support de stockage informatique WO2018133851A1 (fr)

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Cited By (36)

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Publication number Priority date Publication date Assignee Title
CN109215112A (zh) * 2018-08-13 2019-01-15 西安理工大学 一种单侧点云模型的标注方法
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CN110379007A (zh) * 2019-07-25 2019-10-25 厦门大学 基于车载移动激光扫描点云的三维公路曲线重建方法
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CN111009040A (zh) * 2018-10-08 2020-04-14 阿里巴巴集团控股有限公司 点云实体标注系统、方法、装置及电子设备
CN111142116A (zh) * 2019-09-27 2020-05-12 广东亿嘉和科技有限公司 一种基于三维激光的道路检测与建模方法
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CN111353512A (zh) * 2018-12-20 2020-06-30 长沙智能驾驶研究院有限公司 障碍物分类方法、装置、存储介质和计算机设备
CN111488411A (zh) * 2019-01-28 2020-08-04 阿里巴巴集团控股有限公司 道路设施的构建方法以及装置、渲染方法、介质、终端
KR20200096726A (ko) * 2019-01-30 2020-08-13 바이두닷컴 타임즈 테크놀로지(베이징) 컴퍼니 리미티드 자율 주행 차량을 위한 맵 파티셔닝 시스템
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US10983526B2 (en) * 2018-09-17 2021-04-20 Huawei Technologies Co., Ltd. Method and system for generating a semantic point cloud map
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WO2022016311A1 (fr) * 2020-07-20 2022-01-27 深圳元戎启行科技有限公司 Procédé et appareil de reconstruction tridimensionnelle basée sur un nuage de points, et dispositif informatique
CN112034449B (zh) * 2020-08-03 2024-01-12 河北德冠隆电子科技有限公司 基于物理空间属性实现对车辆行驶轨迹修正的系统及方法
CN112149572A (zh) * 2020-09-24 2020-12-29 知行汽车科技（苏州）有限公司 路沿检测方法、装置和存储介质
CN112446314B (zh) * 2020-11-19 2022-07-29 武汉中海庭数据技术有限公司 一种基于投影图提取护栏高程的方法及系统
CN112560747B (zh) * 2020-12-23 2024-08-20 园测信息科技股份有限公司 基于车载点云数据的车道边界交互式提取方法
CN112802126A (zh) * 2021-02-26 2021-05-14 上海商汤临港智能科技有限公司 一种标定方法、装置、计算机设备和存储介质
CN114494366B (zh) * 2022-01-19 2024-04-30 中铁第一勘察设计院集团有限公司 一种影像与点云配准的道路坑槽提取方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103605135A (zh) * 2013-11-26 2014-02-26 中交第二公路勘察设计研究院有限公司 一种基于断面剖分的道路特征提取方法
CN105551082A (zh) * 2015-12-02 2016-05-04 百度在线网络技术（北京）有限公司 一种基于激光点云的路面识别方法及装置
CN105631459A (zh) * 2015-12-31 2016-06-01 百度在线网络技术（北京）有限公司 防护栏点云提取方法及装置
CN105701478A (zh) * 2016-02-24 2016-06-22 腾讯科技（深圳）有限公司 杆状地物提取的方法和装置
CN105701798A (zh) * 2015-12-31 2016-06-22 百度在线网络技术（北京）有限公司 柱状物体的点云提取方法及装置

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7995054B2 (en) * 2005-11-21 2011-08-09 Leica Geosystems Ag Identification of edge regions from 3D point data
CN101975952A (zh) * 2010-09-13 2011-02-16 天津市星际空间地理信息工程有限公司 一种机载lidar单片模式下的数字线划图半自动测图方法
CN103954953B (zh) * 2014-05-16 2016-04-06 武汉大学 一种基于数据驱动的机载激光雷达盲源误差补偿方法
US9747505B2 (en) * 2014-07-07 2017-08-29 Here Global B.V. Lane level traffic
CN105373814B (zh) * 2014-08-26 2019-04-02 南京林业大学 一种基于激光点云数据的真实阔叶树器官分类识别方法
US9589355B2 (en) * 2015-03-16 2017-03-07 Here Global B.V. Guided geometry extraction for localization of a device
CN105184852B (zh) * 2015-08-04 2018-01-30 百度在线网络技术（北京）有限公司 一种基于激光点云的城市道路识别方法及装置
CN105550688B (zh) * 2015-12-04 2019-03-29 百度在线网络技术（北京）有限公司 点云数据的分类方法及装置
CN105701449B (zh) * 2015-12-31 2019-04-23 百度在线网络技术（北京）有限公司 路面上的车道线的检测方法和装置
CN106127153B (zh) * 2016-06-24 2019-03-05 南京林业大学 车载激光扫描点云数据的交通标牌识别方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103605135A (zh) * 2013-11-26 2014-02-26 中交第二公路勘察设计研究院有限公司 一种基于断面剖分的道路特征提取方法
CN105551082A (zh) * 2015-12-02 2016-05-04 百度在线网络技术（北京）有限公司 一种基于激光点云的路面识别方法及装置
CN105631459A (zh) * 2015-12-31 2016-06-01 百度在线网络技术（北京）有限公司 防护栏点云提取方法及装置
CN105701798A (zh) * 2015-12-31 2016-06-22 百度在线网络技术（北京）有限公司 柱状物体的点云提取方法及装置
CN105701478A (zh) * 2016-02-24 2016-06-22 腾讯科技（深圳）有限公司 杆状地物提取的方法和装置

Cited By (55)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109215112B (zh) * 2018-08-13 2022-12-13 西安理工大学 一种单侧点云模型的标注方法
CN109215112A (zh) * 2018-08-13 2019-01-15 西安理工大学 一种单侧点云模型的标注方法
CN109285220A (zh) * 2018-08-30 2019-01-29 百度在线网络技术（北京）有限公司 一种三维场景地图的生成方法、装置、设备及存储介质
CN111009040A (zh) * 2018-10-08 2020-04-14 阿里巴巴集团控股有限公司 点云实体标注系统、方法、装置及电子设备
CN111009040B (zh) * 2018-10-08 2023-04-18 阿里巴巴集团控股有限公司 点云实体标注系统、方法、装置及电子设备
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CN111174788A (zh) * 2018-11-13 2020-05-19 北京京东尚科信息技术有限公司 一种室内二维建图方法和装置
CN111353512A (zh) * 2018-12-20 2020-06-30 长沙智能驾驶研究院有限公司 障碍物分类方法、装置、存储介质和计算机设备
CN111353512B (zh) * 2018-12-20 2023-07-28 长沙智能驾驶研究院有限公司 障碍物分类方法、装置、存储介质和计算机设备
CN111488411B (zh) * 2019-01-28 2023-03-21 阿里巴巴集团控股有限公司 道路设施的构建方法以及装置、渲染方法、介质、终端
CN111488411A (zh) * 2019-01-28 2020-08-04 阿里巴巴集团控股有限公司 道路设施的构建方法以及装置、渲染方法、介质、终端
KR102334641B1 (ko) 2019-01-30 2021-12-03 바이두닷컴 타임즈 테크놀로지(베이징) 컴퍼니 리미티드 자율 주행 차량을 위한 맵 파티셔닝 시스템
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US11668831B2 (en) 2020-06-28 2023-06-06 Beijing Baidu Netcom Science Technology Co., Ltd. High-precision mapping method and device
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