System and method for correcting road polyline objects using results of automated point cloud analysis

Abstract

Disclosed are a system and a method for correcting road polyline objects using results of automated point cloud analysis. The system according to the present invention uses the results of automated point cloud analysis in which the distance between point cloud data is measured using point cloud data collected from roads, abnormal sections having distances exceeding a preset reference value are identified, determination is made of whether first and second lines connecting the point cloud data are parallel, and interpolated points are inserted in the abnormal sections to extract traffic lanes of greater accuracy.

Description

System and method for road alignment object correction using automatic point cloud analysis results

[0001] The present invention relates to a system and method for correcting a road linear object using a result of automatic point cloud analysis, wherein the system and method are used to correct a point cloud collected on a road.

[0002] The present invention relates to a system and method for correcting a road linear object using the results of automatic point cloud analysis, which measures the gap between point cloud data using PCL data, searches for an abnormal section where the gap exceeds a preset reference value, determines whether the first and second lines connecting the point cloud data are parallel, and inserts an interpolation point into the abnormal section to extract a more accurate lane.

[0003] Autonomous vehicles are vehicles capable of sensing their surroundings and movements with little or no human intervention or input. To realize these autonomous vehicles, a variety of sensors capable of sensing their surroundings, such as lidar, radar, computer vision, GPS, odometry, and inertial measurement units, must be integrated. More specifically, the sensing behavior of various physical sensors must be combined with logical road information technology pre-configured using HD-Map data authoring technology.

[0004] In particular, real-time data and transmission technologies are needed to provide autonomous vehicles with vehicle operation support information, facilitating their ability to perceive difficult-to-detect surrounding traffic conditions. For example, LDM (Local Dynamic Map) is a technology that links, stores, and manages standardized vehicle operation support information for autonomous cooperative driving. It integrates road traffic and surrounding vehicle conditions (dynamic information) based on a lane-level precision electronic map (or static information) to provide real-time information to autonomous vehicles.

[0005] The unmanned autonomous driving of a vehicle (autonomous vehicle) can be broadly divided into three stages: recognizing the surrounding environment, planning a driving route based on the recognized environment, and driving along the planned route.

[0006] The technology for recognizing the environment around a vehicle will vary depending on the target environment in which the autonomous vehicle is driving. In particular, in order to drive autonomously in a road environment designed and constructed for existing manned vehicles, technology for recognizing various rules existing on the road may be essential.

[0007] In particular, recognizing lanes and following a designated lane is the most basic technology for safe driving with manned vehicles.

[0008] While methods for recognizing road lanes rely on images captured by cameras, they suffer from reduced recognition accuracy when ambient light is insufficient. Consequently, lane recognition technology using Lidar sensors is currently being developed.

[0009] A lidar sensor is a device that precisely draws the surroundings by measuring the distance to objects and other things through the intensity (strength) of the signal that is reflected back from the medium where the pulse collided after firing a laser pulse onto the road surface. It has the same principle as radar using radio waves, but since it uses the frequency of the visible light range, which is called light among electromagnetic waves, the actual technology and scope of use can be seen to be different.

[0010] These lidars are one component of the Mobile Mapping System (MMS), and in addition to lidars, the MMS includes a camera, an inertial measurement unit (IMU), GPS, and a distance measurement indicator (DMI).

[0011] Accordingly, the information acquired through the MMS collection equipment (vehicle) that integrates lidar sensors, image sensors, IMU, and GPS equipment can obtain specific road configuration information (signal reflection intensity of the medium, absolute position of the medium, continuity, etc.), i.e., high-precision digital map data.

[0012] Typically, point cloud data collected by vehicles equipped with MMS equipment includes various information such as structures, buildings, and vehicles around the road. However, the information actually needed for autonomous driving in point cloud data is road floor data including information such as lanes, road signs, crosswalks, and stop lines. If point cloud data collected by lidar equipment is provided directly to autonomous vehicles, there is a problem that the distance of the cloud point data is omitted due to noise from vehicles in the adjacent lane (such as weak signal collection missed sections, driving traces of vehicles driving side by side in the adjacent lane, etc.), resulting in inconsistent distance intervals.

[0013] The purpose of the present invention is to provide a system and method for correcting a road linearity object using the results of automatic analysis of a point cloud, which evaluates the continuity and consistency of a road surface linearity (lane, center line, curb, etc.) cartographed with HD Map data, measures the interval between point cloud data using point cloud (PCL) data collected on the road, searches for an abnormal section in which the interval exceeds a preset reference value, determines whether the first and second lines connecting the point cloud data are parallel, and inserts an interpolation point into the abnormal section to extract a more accurate lane, as well as provides a method for examining the suitability of an entire road linearity created for the purpose of constructing an HD-MAP.

[0014] The purpose of the present invention is not limited to the purposes mentioned above, and other purposes not mentioned will be clearly understood by those skilled in the art from the description below.

[0015] According to one embodiment of the present invention, a road linear object correction system using a point cloud automatic analysis result is configured to include: a point cloud data storage unit for storing point cloud (PCL) data collected on a road; an interval measurement unit for measuring an interval between the point cloud data; an abnormal interval search unit for finding an abnormal interval in which the interval exceeds a preset reference value; a straight line composition unit for forming a first line and a second line; a parallel judgment unit for determining whether the first line and the second line are parallel; and an interpolation point insertion unit for calculating one or more intermediate data between the abnormal intervals and inserting an interpolation point.

[0016] The above straight line component may comprise a first line connecting a first point which is a starting point of the above-mentioned section, a second point which is an ending point, the first point, and a 0 point which is spaced apart from the first point by the preset reference value in the reverse direction of the driving direction, and a second line connecting the second point and a third point which is spaced apart from the second point by the preset reference value in the forward direction of the driving direction.

[0017] The above interpolation point insertion unit determines that the first line and the second line are parallel in the parallel determination unit, determines that the ideal section is a straight section, and can insert the interpolation point by calculating one or more intermediate data points of the first point and the second point in the ideal section.

[0018] The above interpolation point insertion unit determines that the first line and the second line are not parallel in the parallel determination unit, determines that the abnormal section is a curved section, calculates an extended intersection point (pK) of the first line and the second line, and generates a Bezier curve connecting the first point and the second point using the first point, the second point, and the extended intersection point.

[0019] The above interpolation point insertion unit can insert the interpolation point by calculating one or more intermediate data of the first point and the second point on the Bezier curve.

[0020] According to one embodiment of the present invention, a road linear object correction method using a point cloud automatic analysis result is configured to include a point cloud data storage step, which is performed in a point cloud data storage unit and stores point cloud (PCL) data collected on a road; an interval measurement step, which is performed in an interval measurement unit and measures an interval between the point cloud data; an abnormal interval search step, which is performed in an abnormal interval search unit and searches for an abnormal interval in which the interval exceeds a preset reference value; a straight line construction step, which is performed in a straight line construction unit and constructs a first line and a second line; a parallel confirmation step, which is performed in a parallel determination unit and determines whether the first line and the second line are parallel; and an interpolation point insertion step, which is performed in an interpolation point insertion unit and generates one or more intermediate data between the abnormal intervals and inserts an interpolation point.

[0021] The above straight line configuring step may include a step of configuring the first line by connecting a first point which is a starting point of the above-mentioned section, a second point which is an ending point, the first point, and a 0 point which is spaced apart from the first point by the preset reference value in the reverse direction of the driving direction; and a step of configuring the second line by connecting the second point and a third point which is spaced apart from the second point by the preset reference value in the forward direction of the driving direction.

[0022] The above interpolation point insertion step may include a step of, when the parallel determination unit determines that the first line and the second line are parallel, determining the abnormal section as a straight section, and calculating at least one intermediate data point of the first point and the second point in the abnormal section to insert the interpolation point.

[0023] The above interpolation point insertion step may include a step of determining that the abnormal section is a curved section when the parallel determination unit determines that the first line and the second line are not parallel, calculating an extended intersection point (pK) of the first line and the second line, and generating a Bezier curve connecting the first point and the second point using the first point, the second point, and the extended intersection point.

[0024] The above interpolation point insertion step may include a step of inserting the interpolation point by calculating at least one intermediate data of the first point and the second point on the Bezier curve.

[0025] According to one aspect of the present invention, a system and method for correcting a road linear object using a point cloud automatic analysis result are provided, which uses point cloud (PCL) data collected on a road to measure an interval between point cloud data, search for an abnormal section in which the interval exceeds a preset reference value, determine whether a first line and a second line connecting the point cloud data are parallel, and insert an interpolation point into the abnormal section to extract a more accurate lane.

[0026] And, the system and method for correcting a road linear object using the result of automatic point cloud analysis according to one embodiment of the present invention inserts interpolation points to complete a linear connection between lanes of a road by granting continuity to a missing lane based on continuity between lanes, thereby extracting a linear connection by maintaining a constant interval between cloud data even when there is a large interval between cloud data.

[0027] The effects that can be obtained from the present invention are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by a person having ordinary skill in the art to which the present invention belongs from the description below.

[0028] FIG. 1 is a drawing illustrating an example of an automatic analysis correction method using a road linear object correction system and method using the results of automatic point cloud analysis.

[0029] FIG. 2 is a drawing illustrating a road linear object correction system (200) using the results of automatic point cloud analysis.

[0030] FIG. 3 is a drawing illustrating an example of determining whether a first line and a second line are parallel when they are parallel from a parallel detection unit by a road linear object correction system and method using a point cloud automatic analysis result according to one embodiment of the present invention.

[0031] FIG. 4 is a diagram schematically illustrating a process of calculating a second point and inserting an interpolation point when the first line and the second line are parallel according to one embodiment of the present invention.

[0032] FIG. 5 is a drawing illustrating an example of determining whether a first line and a second line are parallel when they are not parallel from a parallel detection unit by a road linear object correction system and method using a point cloud automatic analysis result according to one embodiment of the present invention.

[0033] FIG. 6 is a diagram schematically illustrating a process of calculating a second point and inserting an interpolation point when the first line and the second line are not parallel according to one embodiment of the present invention.

[0034] FIG. 7 is a block diagram showing a computer system for implementing a road linear object correction method using point cloud automatic analysis results according to one embodiment of the present invention.

[0035] FIG. 8 is a block diagram showing a computer system for implementing a road linear object correction method using point cloud automatic analysis results according to one embodiment of the present invention.

[0036] The advantages and features of the present invention, and the methods for achieving them, will become clear with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and these embodiments are provided only to make the disclosure of the present invention complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Meanwhile, the terminology used in this specification is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated in the phrase. The terms "comprises" and / or "comprising" as used in the specification do not exclude the presence or addition of one or more other components, steps, operations, and / or elements mentioned.

[0037] In describing the present invention, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the present invention, the detailed description is omitted.

[0038] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. In order to facilitate an overall understanding in describing the present invention, the same reference numbers will be used for the same means regardless of the drawing numbers.

[0039] FIG. 1 is a drawing illustrating an example of an automatic analysis correction method using a road linear object correction system and method using the results of automatic point cloud analysis.

[0040] Referring to FIG. 1, according to the system and method for correcting road linear objects using the results of automatic point cloud analysis according to an embodiment of the present invention, only data including lane information, which is information practically necessary for autonomous driving, can be extracted. As illustrated, it can be confirmed that point cloud data of structures, buildings, vehicles, etc. around the road that are unnecessary for autonomous driving of an autonomous vehicle have been removed. In this way, the system and method for evaluating road linear objects using the results of automatic point cloud analysis according to an embodiment of the present invention solves the problem that the distance of cloud point data is missing due to noise (a collection omitted section due to weak signal collection, a driving trace of a vehicle driving side by side in the adjacent lane, etc.) due to a vehicle in the adjacent lane, and thus the distance interval is not constant by calculating the interval between cloud data, calculating the midpoint for an interval exceeding a certain distance, inserting an interpolation point to interpolate into a suitable linearity, and extracting a more accurate lane.

[0041] FIG. 2 is a drawing illustrating a road linear object correction system (200) using the results of automatic point cloud analysis.

[0042] Referring to FIG. 2, the road linear object correction system (200) using the cloud automatic analysis results is configured to include a point cloud data storage unit (210), an interval measurement unit (220), a straight line generation unit (230), a parallel determination unit (240), and an interpolation point insertion unit (250). The road linear object correction system (200) using the cloud automatic analysis results illustrated in FIG. 2 is according to one embodiment, and the components of the road linear object correction system (200) using the cloud automatic analysis results according to the present invention are not limited to the embodiment illustrated in FIG. 2, and may be added, changed, or deleted as needed.

[0043] The point cloud data storage unit (210) stores point cloud (PCL) data collected on the road. Point cloud data is data collected by Lidar sensors, RGB-D sensors, etc. These sensors send light / signals to objects and record the time it takes for them to return, calculate distance information for each light / signal, and create a single point. Point cloud data refers to a set cloud of multiple points spread across a three-dimensional space collected from these sensors. In one embodiment, the point cloud data stored in the point cloud data storage unit (210) may be data collected from a Lidar sensor equipped in the MMS.

[0044] In one embodiment, point cloud data may be stored in the point cloud data storage unit (210) in the LAS file format. Due to the characteristics of the LAS file format, when point cloud data is collected on a road using a lidar sensor, the data is collected by dividing it into left and right along the driving path of the MMS vehicle. Accordingly, when point cloud data is collected on a road, the left and right division points are collected as points and converted into lines to form lanes (driving lines) that can be expressed on the point cloud data.

[0045] The interval measuring unit (220) can measure the interval between point cloud data based on information collected from the point cloud data storage unit (210). In one embodiment, when collecting point cloud data, any one point data can be p0, and data can be repeatedly collected at intervals of a certain distance along the driving direction (forward direction), such as p1, p2, and p3, and the interval between previously collected cloud data can also be measured in the forward and reverse directions based on any reference point.

[0046] The abnormal interval detection unit (230) can find abnormal intervals where the interval between point cloud data exceeds a preset reference value. Here, the preset reference value can be separately specified by direct input, or set as a threshold value through an average measurement of the entire distance value.

[0047] The straight line generation unit (240) can generate a first line and a second line, wherein the first line refers to the first line connecting the first point (p1) which is the starting point of the ideal section, the second point (p2) which is the ending point, the first point, and the 0th point (p0) which has a distance of a preset reference value in the reverse direction of the driving direction from the first point, and the second line connecting the second point (p2) and the third point (p3) which has a distance of a preset reference value in the forward direction from the second point (p2).

[0048] The parallel judgment unit (250) can check whether the first line and the second line are parallel. Here, it can be determined whether the first line formed by the 0th point (p0) and the first point (p1) and the second line formed by the second point (p2) and the third point (p3) are parallel. The first line and the second line refer to lines formed by the same distance, and the first line and the second line can be determined to be parallel if they are formed horizontally in the driving direction (forward direction). In addition, the first line and the second line can be determined to be not parallel if they are not formed horizontally in the driving direction (forward direction).

[0049] The interpolation point insertion unit (260) can insert an interpolation point by producing one or more intermediate data on the abnormal section searched by the abnormal section search unit.

[0050] Here, the ideal section may mean any point passing through a straight line if the first and second lines are judged to be parallel, and may mean any point passing through a curve if the first and second lines are judged not to be parallel.

[0051] If the parallel verification unit determines that the first line and the second line are parallel, the interpolation point insertion unit (260) determines that the abnormal section is a straight section, and can insert an interpolation point by calculating one or more intermediate data points of the first point and the second point in the abnormal section.

[0052] In addition, if the parallel verification unit determines that the first line and the second line are not parallel, the interpolation point insertion unit (260) determines that the abnormal section is a curved section, calculates the extended intersection point (pK) of the first line and the second line, and can generate a Bezier curve connecting the first point and the second point using the first point, the second point, and the extended intersection point, and can insert an interpolation point by calculating one or more intermediate data of the first point and the second point on the Bezier curve.

[0053] In addition, according to one embodiment, a road linear object correction system (200) using the results of automatic point cloud analysis can evaluate the continuity and consistency of linearity (lanes, center lines, curbs, etc.) of a road surface cartographed with HD Map data.

[0054] FIG. 3 is a drawing illustrating an example of determining whether a first line and a second line are parallel when they are parallel from a parallel detection unit by a road linear object correction system and method using a point cloud automatic analysis result according to one embodiment of the present invention.

[0055] FIG. 4 is a diagram schematically illustrating a process of calculating a second point and inserting an interpolation point when the first line and the second line are parallel according to one embodiment of the present invention.

[0056] Referring to FIGS. 3 and 4, in one embodiment, the point cloud data storage unit (210) repeatedly collects point cloud data (point cloud data of p0, p1, p2, p3, ..., pn), and the interval measuring unit (220) can measure intervals, such as the interval between p0 and p1, the interval between p1 and p2, and the interval between p2 and p3. The interval measuring unit (220) measures each data interval, and the abnormal section detection unit (230) can determine that the interval exceeding the preset reference value is an abnormal section (sections p1 and p2) when the interval measured by the interval measuring unit (220) exceeds a preset reference value. The straight line generation unit (240) can generate straight line data in the driving direction (forward direction) and the reverse direction of the driving direction based on the point data (p1, p2) of the section determined to be an abnormal section by the abnormal section detection unit (230). Based on p1, since it is an abnormal section in the driving direction, a first line, which is a straight line connecting it to point data p0 in the opposite direction of the driving direction, is created. Based on p2, since the reverse direction of the driving direction is an abnormal section, a second line, which is a straight line connecting it to point data p3 in the driving direction, can be created.

[0057] The parallel judgment unit (250) determines whether the first line and the second line are parallel, and when the first line and the second line are parallel as shown in FIGS. 3 and 4, the interpolation point insertion unit (260) calculates the midpoint between p1, the starting point of the abnormal section, and p2, the ending point, and can insert an interpolation point on the straight line created between p1 and p2. Here, there may be one or more interpolation points depending on the interval of the abnormal section, and one or more interpolation points can be inserted by repeatedly performing the process until no abnormal section appears.

[0058] FIG. 5 is a drawing illustrating an example of determining whether a first line and a second line are parallel when they are not parallel from a parallel detection unit by a road linear object correction system and method using a point cloud automatic analysis result according to one embodiment of the present invention.

[0059] FIG. 6 is a diagram schematically illustrating a process of calculating a second point and inserting an interpolation point when the first line and the second line are not parallel according to one embodiment of the present invention.

[0060] Referring to FIGS. 5 and 6, in one embodiment, the point cloud data storage unit (210) repeatedly collects point cloud data (point cloud data of p0, p1, p2, p3, ..., pn), and the interval measuring unit (220) can measure intervals, such as the interval between p0 and p1, the interval between p1 and p2, and the interval between p2 and p3. The interval measuring unit (220) measures each data interval, and the abnormal section detection unit (230) can determine that the interval exceeding the preset reference value is an abnormal section (sections p1 and p2) when the interval measured by the interval measuring unit (220) exceeds a preset reference value. The straight line generation unit (240) can generate straight line data in the driving direction (forward direction) and the reverse direction of the driving direction based on the point data (p1, p2) of the section determined to be an abnormal section by the abnormal section detection unit (230). Based on p1, since it is an abnormal section in the driving direction, a first line, which is a straight line connecting it to point data p0 in the opposite direction of the driving direction, is created. Based on p2, since the reverse direction of the driving direction is an abnormal section, a second line, which is a straight line connecting it to point data p3 in the driving direction, can be created.

[0061] The parallel judgment unit (250) determines whether the first line and the second line are parallel, and when the first line and the second line are not parallel as shown in FIGS. 5 and 6, the interpolation point insertion unit (260) calculates the extended intersection point (pK) with the first line and the second line, generates a Bezier curve connecting p1 and p2 using pK, and calculates the midpoint of p1, which is the starting point of the abnormal section, and p2, which is the ending point, to insert an interpolation point on the Bezier curve generated between p1 and p2. Here, there may be one or more interpolation points depending on the interval of the abnormal section, and one or more interpolation points may be inserted by repeatedly performing the process until no abnormal section appears.

[0062] FIG. 7 is a block diagram showing a computer system for implementing a road linear object correction method using point cloud automatic analysis results according to one embodiment of the present invention.

[0063] As illustrated in FIG. 7, a road linear object correction method using a point cloud automatic analysis result according to one embodiment of the present invention may include steps S710 to S760.

[0064] Step S710 is a step of storing point cloud (PCL) data collected on the road in the point cloud data storage unit (210).

[0065] Step S720 is a step of measuring the interval between point cloud data based on information collected from the point cloud data storage unit (210) in the interval measuring unit (220). In one embodiment, when collecting point cloud data, any one point data can be p0, and data can be repeatedly collected at intervals of a certain distance along the driving direction (forward direction), such as p1, p2, and p3, and the interval between previously collected cloud data can also be measured in the forward and reverse directions based on any reference point.

[0066] Step S730 is performed in the abnormal interval search unit (230) and is a step for finding abnormal intervals where the interval between point cloud data exceeds a preset reference value. Here, the preset reference value can be directly input and separately specified, or set as a threshold value through an average measurement of the entire distance value.

[0067] Step S740 is performed in the straight line generation unit (240) and is a step of generating a first line and a second line. Here, the first line may refer to the first line connecting the first point (p1), which is the starting point of the ideal section, the second point (p2), which is the ending point, the first point, and the 0th point (p0) which has a distance of a preset reference value in the reverse direction of the driving direction from the first point, and may refer to the second line connecting the second point (p2) and the third point (p3) which has a distance of a preset reference value in the forward direction from the second point (p2).

[0068] Step S750 is performed in the parallel determination unit (250) and is a step for determining whether the first line and the second line are parallel. Here, it can be determined whether the first line formed by the 0th point (p0) and the first point (p1) and the second line formed by the second point (p2) and the third point (p3) are parallel. The first line and the second line refer to lines formed at the same distance, and the first line and the second line can be determined to be parallel if they are formed horizontally in the driving direction (forward direction). In addition, the first line and the second line can be determined to be not parallel if they are not formed horizontally in the driving direction (forward direction).

[0069] Step S760 is performed in the interpolation point insertion unit (260), and is a step of inserting an interpolation point by generating one or more intermediate data on the abnormal section searched by the abnormal section search unit. Here, the abnormal section may mean any point that is composed of a straight line and passes through the straight line if the first and second lines are determined to be parallel, and may mean any point that is composed of a curve and passes through the curve if the first and second lines are determined to be not parallel.

[0070] Step S760 is performed in the interpolation point insertion unit (260), and if the parallel verification unit determines that the first line and the second line are parallel, the abnormal section is determined to be a straight section, and one or more intermediate data points of the first point and the second point can be calculated to insert an interpolation point into the abnormal section. In addition, if the parallel verification unit determines that the first line and the second line are not parallel, the interpolation point insertion unit (260) determines that the abnormal section is a curved section, and calculates an extended intersection point (pK) of the first line and the second line, and can generate a Bezier curve connecting the first point and the second point using the first point, the second point, and the extended intersection point, and can insert an interpolation point by calculating one or more intermediate data points of the first point and the second point on the Bezier curve.

[0071] The aforementioned method for correcting road linear objects using automatic point cloud analysis results according to one embodiment of the present invention has been described with reference to the flowchart presented in the drawings. For simplicity, the method has been illustrated and described as a series of blocks; however, the present invention is not limited to the order of the blocks, and some blocks may occur in a different order or simultaneously with other blocks than illustrated and described herein, and various other branches, flow paths, and block orders that achieve the same or similar results may be implemented. Furthermore, not all illustrated blocks may be required to implement the method described herein.

[0072] In the description with reference to FIG. 7, each step may be further divided into additional steps or combined into fewer steps, depending on the implementation of the present invention. Furthermore, some steps may be omitted as needed, and the order of the steps may be changed. Furthermore, even if other omitted content is included, the content of FIGS. 1 through 6 may be applied to the content of FIG. 7. Furthermore, the content of FIG. 7 may be applied to the content of FIGS. 1 through 6.

[0073] FIG. 8 is a block diagram showing a computer system for implementing a road linear object correction method using point cloud automatic analysis results according to one embodiment of the present invention.

[0074] Referring to FIG. 8, a computer system (1000) may include at least one of a processor (1010), a memory (1030), an input interface device (1050), an output interface device (1060), and a storage device (1040) that communicate via a bus (1070). The computer system (1000) may further include a communication device (1020) coupled to a network. The processor (1010) may be a central processing unit (CPU), or a semiconductor device that executes instructions stored in the memory (1030) or the storage device (1040). The memory (1030) and the storage device (1040) may include various forms of volatile or non-volatile storage media. For example, the memory may include a read-only memory (ROM) and a random access memory (RAM). In embodiments of the present disclosure, the memory may be located internally or externally to the processor, and the memory may be connected to the processor via various known means. Memory is a variety of volatile or non-volatile storage media, and may include, for example, read-only memory (ROM) or random access memory (RAM).

[0075] Accordingly, embodiments of the present invention may be implemented as a computer-implemented method or as a non-transitory computer-readable medium storing computer-executable instructions. In one embodiment, when executed by a processor, the computer-readable instructions may perform a method according to at least one aspect of the present disclosure.

[0076] The communication device (1020) can transmit or receive wired or wireless signals.

[0077] In addition, the method according to the embodiment of the present invention may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium.

[0078] The computer-readable medium may include program commands, data files, data structures, etc., either singly or in combination. The program commands recorded on the computer-readable medium may be specially designed and configured for embodiments of the present invention, or may be known and usable by those skilled in the art of computer software. The computer-readable recording medium may include a hardware device configured to store and execute the program commands. For example, the computer-readable recording medium may be a magnetic medium such as a hard disk, a floppy disk, and a magnetic tape, an optical medium such as a CD-ROM or a DVD, a magneto-optical medium such as a floptical disk, a ROM, a RAM, a flash memory, etc. The program commands may include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer through an interpreter, etc.

[0079] For reference, components according to embodiments of the present invention may be implemented in the form of software or hardware such as a digital signal processor (DSP), a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC), and may perform certain roles.

[0080] However, 'components' are not limited to software or hardware, and each component may be configured to reside on an addressable storage medium or configured to trigger one or more processors.

[0081] Thus, as an example, components include components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.

[0082] Components and the functionality provided within those components may be combined into a smaller number of components or further separated into additional components.

[0083] Meanwhile, it will be understood that each block of the flowchart drawings and combinations of the flowchart drawings can be performed by computer program instructions. These computer program instructions can be installed on a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, so that the instructions executed by the processor of the computer or other programmable data processing equipment create a means for performing the functions described in the flowchart block(s). The computer program instructions can also be installed on a computer or other programmable data processing equipment, so that a series of operational steps are performed on the computer or other programmable data processing equipment to create a computer-executable process, so that the instructions executed by the computer or other programmable data processing equipment can also provide steps for performing the functions described in the flowchart block(s).

[0084] Additionally, each block may represent a module, segment, or portion of code that contains one or more executable instructions for performing a specific logical function(s). It should also be noted that in some alternative implementation examples, the functions described in the blocks may occur out of order. For example, two blocks depicted in succession may actually be executed substantially concurrently, or the blocks may sometimes be executed in reverse order, depending on their respective functions.

[0085] The term 'part' or 'module' used in this embodiment means a software or hardware component such as an FPGA or ASIC, and the 'part' or 'module' performs certain roles. However, the 'part' or 'module' is not limited to software or hardware. The 'part' or 'module' may be configured to be on an addressable storage medium and may be configured to play one or more processors. Thus, as an example, the 'part' or 'module' includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within the components and '~sub-parts' or '~modules' may be combined into a smaller number of components and '~sub-parts' or '~modules' or further separated into additional components and '~sub-parts' or '~modules'. In addition, the components and '~sub-parts' or '~modules' may be implemented to regenerate one or more CPUs within the device or secure multimedia card.

[0086] Although the present invention has been described above with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below.

Claims

1. A point cloud data storage unit that stores point cloud (PCL) data collected on the road; An interval measuring unit for measuring the interval between the above point cloud data; An abnormal interval search unit that searches for an abnormal interval in which the above interval exceeds a preset reference value; Straight line components forming the first and second lines; A parallel judgment unit for checking whether the first line and the second line are parallel; and A road alignment object correction system using the results of automatic point cloud analysis, characterized in that it includes an interpolation point insertion unit that inserts an interpolation point by calculating one or more intermediate data between the above-mentioned abnormal sections.

2. In paragraph 1, The above straight line component is, The first line is formed by connecting the first point, which is the starting point of the above-mentioned ideal section, the second point, which is the ending point, the first point, and the 0 point, which is spaced apart from the first point by the preset reference value in the opposite direction of the driving direction. A road alignment object correction system using the results of automatic point cloud analysis, characterized in that the second line is formed by connecting the second point and a third point spaced apart from the second point by the preset reference value in the positive direction of the driving direction.

3. In paragraph 2, The above interpolation point insertion part is, A road alignment object correction system using the results of automatic point cloud analysis, characterized in that if the first line and the second line are judged to be parallel in the parallel judgment unit, the abnormal section is judged to be a straight section, and at least one intermediate data of the first point and the second point is calculated in the abnormal section and the interpolation point is inserted.

4. In paragraph 2, The above interpolation point insertion part is, A road alignment object correction system using the results of automatic point cloud analysis, characterized in that if the parallel judgment unit determines that the first line and the second line are not parallel, the abnormal section is determined to be a curved section, an extended intersection point (pK) of the first line and the second line is calculated, and a Bezier curve connecting the first point and the second point is generated using the first point, the second point, and the extended intersection point.

5. In paragraph 4, The above interpolation point insertion part is, A road alignment object correction system using the results of automatic point cloud analysis, characterized in that it inserts the interpolation point by calculating at least one intermediate data of the first point and the second point on the Bezier curve.

6. A point cloud data storage step that is performed in the point cloud data storage unit and stores point cloud (PCL) data collected on the road; An interval measurement step performed in an interval measurement unit, wherein the interval between the point cloud data is measured; An abnormal interval search step performed in the abnormal interval search section, which searches for an abnormal interval in which the interval exceeds a preset reference value; A straight line configuration step performed in a straight line configuration section, which constitutes the first line and the second line; A parallel confirmation step performed in a parallel judgment unit to confirm whether the first line and the second line are parallel; and A method for correcting a road linear object using the results of automatic point cloud analysis, characterized in that it includes an interpolation point insertion step, which is performed in an interpolation point insertion section and inserts an interpolation point by calculating one or more intermediate data between the above-described abnormal sections.

7. In paragraph 6, The above straight line configuration steps are: A step of forming the first line connecting the first point, which is the starting point of the above-mentioned ideal section, the second point, which is the ending point, the first point, and the 0 point, which is spaced apart from the first point by the preset reference value in the opposite direction of the driving direction; A method for correcting a road linear object using the results of automatic point cloud analysis, characterized in that it comprises a step of forming a second line connecting the second point and a third point having an interval equal to the preset reference value in the positive direction of the driving direction from the second point.

8. In paragraph 7, The above interpolation point insertion step is, A method for correcting a road linear object using the results of automatic point cloud analysis, characterized in that it comprises the steps of: if the first line and the second line are determined to be parallel in the parallel judgment unit, the abnormal section is determined to be a straight section, and calculating at least one intermediate data of the first point and the second point in the abnormal section to insert the interpolation point; 9. In paragraph 7, The above interpolation point insertion step is, A method for correcting a road alignment object using the results of automatic point cloud analysis, characterized in that it comprises the steps of: if the parallel judgment unit determines that the first line and the second line are not parallel, determining the abnormal section as a curved section, calculating the extended intersection point (pK) of the first line and the second line, and generating a Bezier curve connecting the first point and the second point using the first point, the second point, and the extended intersection point; 10. In paragraph 9, The above interpolation point insertion step is, A method for correcting a road linear object using the results of automatic point cloud analysis, characterized in that it comprises a step of inserting the interpolation point by calculating at least one intermediate data of the first point and the second point on the Bezier curve.

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