KR102137043B1 - Positioning accuracy improvement system - Google Patents

Abstract

The present invention relates to a low-cost commercial GNSS positioning accuracy increasing system and a method thereof. According to the present invention, the position accuracy can be increased in an autonomous driving vehicle environment by using an environmental sensor scanning around the vehicle and a precision map to enable to correct coordinate information obtained in a low-cost GPS.

Description

Positioning accuracy improvement system using environmental sensors and precision maps

The present invention relates to a system for improving positioning accuracy, and more particularly, to a technology for accurately grasping the position of a vehicle to a lane unit even if a low-cost GPS is used.

In the field of smart cars or autonomous vehicles, the technology to accurately locate (locate) the location of the own vehicle is essential.

GNSS is used as the most widely used positioning system. GNSS (Global Navigation Satelite System) is a system that provides information on the position, altitude, and speed of objects on the ground using satellites, and the United States' Global Positioning System (GPS) is typical.

In the case of the widely used low-cost GPS, it is sufficient to determine the direction or location of the vehicle, and which road it is currently located on. However, in the case of a low-cost GPS, an error of about 2 meters occurs, and when the signal reception environment is poor, such as an urban area or a mountainous area with many high-rise buildings, a large error of 7 meters or more may occur. Therefore, the low-cost GPS is difficult to use in an autonomous driving environment in which the driving lane needs to be classified. To this end, after mounting an expensive GPS module with high positioning accuracy, a technology demonstration of an autonomous vehicle may be demonstrated, but it is difficult to commercialize it because the price of an expensive GPS is equivalent to 30 million to 100 million won.

On the other hand, a related technology for locating a vehicle is Korean Patent Registration No. 10-1752342 (2017.06.23.'Vehicle Positioning Method').

The present invention was devised to solve the problems of the prior art as described above, by enabling the environment information to scan the vehicle surroundings and precision map to correct the coordinate information obtained from the low-cost GPS, autonomous vehicle environment The aim is to provide a technique that can improve the positioning accuracy in.

A system for improving positioning accuracy according to the present invention for achieving the above object includes: an environment sensor that scans a vehicle surroundings and recognizes an object; A GPS module for obtaining GPS coordinate information; Using the first distance information to the curb according to the scan data of the environmental sensor, and the second distance information from the GPS coordinates obtained from the GPS module to the curb extracted from the precision map corresponding to the GPS, the GPS coordinate information It includes; position correction means for updating output.

Here, the position correction means, a map data extraction unit for extracting the precision map data corresponding to the GPS coordinate information obtained from the GPS module from the precision map DB; A first distance information acquisition unit that recognizes a curb from the object recognized by the environmental sensor and acquires first distance information to the curb; A second distance information acquiring unit acquiring second distance information that is a distance from the GPS coordinates obtained by the GPS module to a curb of a location corresponding to the GPS coordinates among the precision maps extracted by the map data extraction unit; A correction information generator configured to calculate coordinate correction information for correcting the GPS coordinate information by using a difference between the first distance information and the second distance information; And a location update unit that updates location information by reflecting the coordinate correction information to the GPS coordinate information.

In addition, the object classification means for determining whether the object is recognized and fixed or moved by the environment sensor; and further comprising, the object classification means, feature point extraction unit for extracting a feature point from the object recognized by the environment sensor; An area calculation unit repeatedly calculating an area of a triangle consisting of three characteristic points with a parallax through a relative coordinate system of the characteristic points extracted from the characteristic point extraction unit; A change rate confirmation unit that checks a rate of change of the triangular area calculated by the area calculation unit; And a determining unit for distinguishing the fixed object and the moving object according to the result of checking the triangular area change rate of the change rate checking unit.

In addition, the object classification means, further comprising a vehicle movement confirmation unit for confirming whether the vehicle is stopped, the determination unit is first fixed object and when the vehicle is confirmed to be stopped through the vehicle movement confirmation unit The moving object is classified, but when the area change rate of the triangle is less than the reference, objects for three feature points are determined as fixed objects. Then, when it is confirmed through the vehicle movement confirmation unit that the vehicle is not stopped, the feature point extraction unit The determination unit extracts two feature points from the feature points of the object determined as fixed objects, and one feature point of the object to be determined, and the area calculation unit determines the area of the triangle consisting of two fixed feature points and one feature point to be determined. If the area change rate is confirmed to be less than the reference by the change rate confirmation unit, the determination unit determines the object to be determined as a fixed object, and when the area change rate is confirmed by the change rate confirmation unit, the determination unit determines the object to be determined. Can be determined as a moving object.

According to the present invention, even if a low-cost GPS module is mounted, it is possible to check errors in GPS coordinates by using riders and precision map data, and correct location data by correcting GPS coordinate information by the identified errors. Therefore, in the field of autonomous vehicles, it is possible to accelerate the commercialization of autonomous vehicles by improving location accuracy at a low price.

In addition, in order to correct the GPS coordinate information including the error, the curbstone included in the precision map data needs to be scanned and analyzed through the rider, and it is possible to accurately determine the curbstone as a fixed object through the object classification means, so it is similar to the curbstone. Errors can be eliminated by applying the correction algorithm through the moving object of.

That is, the position correction means must first accurately identify the shape of the curb among the objects scanned through the lidar. If a moving object having a shape similar to the curb is judged as a curb, an error may occur when generating correction information. Therefore, in the present invention, the possibility of an error when applying a correction algorithm can be prevented by recognizing a curb after excluding a moving object from the scan data of the lidar through the object classification means.

1 is a block diagram illustrating a positioning accuracy improving system according to an embodiment of the present invention.
Figure 2 is a block diagram for explaining the position correction means in the positioning accuracy improvement system shown in Figure 1;
3 is a block diagram for explaining the object classification means in the positioning accuracy improvement system shown in FIG.
4 is a flowchart illustrating a method for improving positioning accuracy according to an embodiment of the present invention.
5 is a view for comparing and explaining a distance between a curb from an actual vehicle location and a distance between a curb on a precision map at a location obtained through a GPS module.
6 is a view for explaining a process of obtaining a correction value through a difference between a distance between a grinding at an actual vehicle position and a distance between a curb on a precision map at a position obtained through a GPS module.
7 is a flowchart for explaining a method of classifying a fixed object and a moving object.
8 is a flowchart for explaining a process of determining a stationary object in a stopped state of the own vehicle.
9 is a flow chart for explaining a process of distinguishing a fixed object from a moving object in a moving state of the own vehicle.
10 is a view for explaining an area change between three fixed objects.
11 is a view for explaining an area change between two fixed objects and one moving object.

Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings. However, some components irrelevant to the subject matter of the present invention will be omitted or compressed, but the omitted components are not necessarily required in the present invention, and are used in combination by those skilled in the art to which the present invention pertains. Can.

1 is a block diagram illustrating a system for improving positioning accuracy according to an embodiment of the present invention. The positioning accuracy improvement system 100 grasps the exact position of the magnetic vehicle 10 and tracks the progressing direction, and also precisely tracks whether the objects around the magnetic vehicle 10 are fixed or moved. The positioning accuracy improvement system 100 includes an environmental sensor, a GPS module 120, a position correction means 130, an object classification means 140, and an object tracking unit 150. In addition, each component of the positioning accuracy improving system 100 is mounted on the vehicle 10, and each component may be integrated into a hardware configuration, or may be composed of individual hardware and interlocked with each other. Also, some components of the positioning accuracy improving system 100 may be designed in software.

The environment sensor is provided to recognize objects by scanning or photographing objects around the vehicle 10 and acquiring and processing data. As an environmental sensor, a laser scanner, a radar, a lidar, a camera, and the like may be used, and in this embodiment, an example in which the lidar 110 is used as an environmental sensor will be illustrated and described.

The GPS module 120 is provided to receive GPS signals through satellites and process data to obtain coordinate information. The coordinate information obtained through the GPS module 120 includes a longitude value and a latitude value, and may further include altitude information according to the implementation.

The GPS module 120 used in the positioning accuracy improving system 100 according to an embodiment of the present invention is a low-cost product, and thus the obtained coordinate information may have an error. Of course, even if there are some errors, it is possible to check on which road it is located, etc., but it is necessary to grasp a more precise position to accurately determine from which lane you are driving.

The position correction means 130 is provided to correct the error of the coordinate information obtained from the GPS module 120 using the data scanned through the lidar 110 to update the coordinate information of the GPS signal.

The object classification means 140 is provided to determine whether an object around the subject vehicle 10 is a fixed object or a moving object.

The object tracking unit 150 uses the coordinate information updated and output from the position correction means 130 to track the exact position change and the moving direction of the own vehicle 10, or to accurately track the objects around the own vehicle 10 It is prepared to.

FIG. 2 is a block diagram illustrating the position correction means 130 in the positioning accuracy improvement system 100 shown in FIG. 1. 2, the location correction means 130 includes a map data extraction unit 131, a first distance information acquisition unit 132, a second distance information acquisition unit 133, and a progress direction determination unit 134, It includes a correction information generating unit 135, a location update unit 136 and a precision map DB 137.

The map data extraction unit 131 is provided to extract map data in the vicinity of the coordinate from the precision map DB 137 based on the coordinate information obtained through the GPS module 120.

The first distance information acquisition unit 132 is provided to obtain first distance information, which is basic data for correcting coordinate information obtained from the GPS module 120. Here, the first distance information is the distance from the position 11R of the actual vehicle 10 to the curb, which can be obtained by utilizing the data scanned through the lidar 110. The process of acquiring the first distance information from the first distance information acquisition unit 132 will be described in detail below.

The second distance information acquisition unit 133 is provided to acquire second distance information, which is a basic data for correcting GPS coordinate information. Here, the second distance information refers to the coordinates obtained from the GPS module 120 and the distance to the curb located near the corresponding GPS coordinates on the precision map. The process of acquiring the second distance information from the second distance information acquisition unit 133 will also be covered again below.

The traveling direction determining unit 134 is provided to determine the direction in which the vehicle 10 is currently traveling. The direction of progress is determined by calculating the previous coordinate information and the subsequent coordinate information obtained from the progress direction determination unit 134, the GPS module 120, or the direction of progress is determined using an azimuth sensor fixed to the vehicle 10. You can also figure it out.

The correction information generating unit 135 is provided to generate coordinate correction information for correcting coordinate information obtained from the GPS module 120. That is, the correction information generating unit 135 includes the first distance information, the second distance information, and the progress obtained from the first distance information acquisition unit 132, the second distance information acquisition unit 133, and the progress direction determination unit 134. A predetermined operation is performed using the direction information, and correction information is calculated as to how much coordinate information obtained by the GPS module 120 should be corrected.

The coordinate information of the GPS signal obtained by the GPS module 120 is updated by using the coordinate correction information generated by the location update unit 136, the correction information generation unit 135. That is, the correct coordinate information is output by reflecting the coordinate correction information to the incorrect coordinate information.

When the coordinate information corrected by the location update unit 136 is output, the object tracking unit 150 may track the state of the own vehicle 10 using a Kalman filter.

The precision map DB 137 is a database that stores the precision map. The precision map can be expressed in detail up to the lane unit, and further includes detailed information such as traffic lights, signs, road markings, road facilities, buildings, curbs, and the like. Precise image images stored in the precision map DB are in the form of numerous points, and each point has three-dimensional absolute coordinates such as latitude and longitude.

The position correction means 130 described above will be further embodied from the description of the method for improving positioning accuracy, which will be described with reference to FIG. 4 below.

3 is a block diagram for explaining the object classification means 140 in the positioning accuracy improvement system 100 shown in FIG. 1. 3, the object classification means 140 includes a vehicle movement confirmation unit 141, a feature point extraction unit 142, an area calculation unit 143, a change rate confirmation unit 144, and a determination unit 145. . The object classification means 140 determines whether a specific object is fixed and moved through the scan data of the lidar 110, and outputs the determination result to the object tracking unit 150 to accurately track the fixed object or the moving object. To make.

The vehicle movement confirmation unit 141 is provided to check whether the own vehicle 10 is in a stopped state or a moved state. For example, the vehicle movement confirmation unit 141 analyzes coordinate information obtained from the GPS module 120 to check whether the vehicle 10 is moving, or obtains relevant information from an ECU, etc. of the vehicle 10, and checks the movement. Can.

The feature point extracting unit 142 is provided to extract feature points such as corners or vertices of the corresponding object when the object is recognized through the lidar 110.

The area calculation unit 143 is provided to calculate the area of the triangle consisting of three selected feature points among the feature points extracted from the feature point extraction unit 142.

The change rate checking unit 144 is provided to check the change rate of how much the area of the triangle calculated according to the time difference changes when the area calculation unit 143 calculates the triangle area of the same feature points with a time difference.

The determination unit 145 is provided to determine whether a specific object is a fixed object or a moving object according to the confirmation result of the change rate confirmation unit 144 and output the result.

The object classification means 140 described above will be further clarified by a method of classifying a fixed object and a moving object, which will be described with reference to FIGS. 7 to 11 below.

4 is a flowchart illustrating a method for improving positioning accuracy according to an embodiment of the present invention. That is, in the positioning accuracy improvement system 100 shown in FIGS. 1 and 2, the position correction means 130 updates the coordinate information including the error obtained from the GPS module 120 with correct coordinate information, and then the vehicle 10 ) Is about the process of tracking the status.

First, to correct the GPS coordinate information, the vehicle is scanned around the vehicle through the lidar 110, and the first distance information acquisition unit 132 recognizes the curb 162 through the scan data of the lidar 110 <S405> Then, the distance from the position 11R of the actual vehicle 10 to the curb 162, that is, the first distance information is acquired <S410>.

Lidar is a device that can accurately measure the distance and direction to a target object by firing a high-power pulse laser with high linearity instead of radio waves and receiving the light reflected from the target object. The lidar 110 may include an optical unit such as a lens, a laser emitting/receiving unit, a laser driving unit, a processor that processes a laser signal, and the like. Using the lidar 110, high-precision data in the form of a point cloud (point cloud), which is a set of points, can be secured, and three-dimensional points reflecting width, distance, and height are gathered together to extract shape data of objects. I can do it. Due to this characteristic of the lidar 110, the lidar 110 is essentially mounted for object recognition in an autonomous vehicle.

Referring to FIG. 5, a curb 162 is provided at the boundary of the road to distinguish the road from the sidewalk. The curbstone is continuously installed along the road boundary at a height of approximately 25 cm. Using these features, the first distance information acquisition unit 132 analyzes the scan data of the lidar 110 to recognize the shape of the curbstone 162, , It is possible to obtain a distance between the vehicle 10 and the curb 162.

Here, since various types of curb 162 can be identified on the road at various locations, the present invention acquires a distance from the curb 162 with a specific rule.

First, the first distance information acquisition unit 132 takes only the curb 162 located in a specific direction in the traveling direction of the vehicle 10 as a measurement target. That is, although the curb 162 may be detected on both the left and right sides of the vehicle 10, in this embodiment, only the curb 162 located on the right side with respect to the vehicle traveling direction determined by the traveling direction determining unit 134 is subject to detection. To make.

In addition, the first distance information acquisition unit 132 takes only the curb 162 in the form of a straight line as a measurement object. That is, if the curve is mainly, the shape of the curb 162 will also be a curve, but when the curve is mainly progressed, the first distance information is not obtained, and only the curb 162 in the form of a straight line is detected when driving the line mainly.

In addition, the first distance information acquiring unit 132 acquires, as the first distance information, the closest distance from the straight curb 162, that is, the vertical distance from the lidar 110 to the straight line formed by the curb 162. Anyway, from the data scanned by the lidar 110, the curbs will be made up of a set of numerous points, and since there will be information about distances and directions in the information of each point, the first distance information acquisition unit 132 determines the closest distance. It can be obtained with the first distance information.

Also, the first distance information acquisition unit 132 acquires the first distance information only when the vehicle 10 is driving straight in parallel with the curb 162, even if the curb 162 forms a straight line. That is, even if the road is mainly straight, the distance from the vehicle 10 to the curb 162 continuously changes when the vehicle 10 travels across the road for reasons such as changing lanes. Cannot be measured correctly. Accordingly, the first distance information acquisition unit 132 continuously stores the distance of the curb 162, but determines the value when the standard deviation is equal to or less than a preset value as the first distance information.

In addition, the first distance information acquisition unit 132 acquires the first distance information in linear driving in different directions, respectively. Referring to FIG. 5, when the vehicle is traveling at 10 o'clock and when traveling at 1 o'clock, the distance from the curb 162 located on the right side of the vehicle 10 is obtained as first distance information.

Meanwhile, at the same time as the first distance information acquisition process, the GPS module 120 receives GPS signals in real time to acquire GPS coordinate information <S415>. Since the GPS module 120 applied in the present invention is a low-cost type, the acquired GPS coordinate information may have an error. That is, referring to FIG. 5, the location according to the obtained GPS coordinate information is indicated by reference numeral 11G, which is adjacent to the location 11R of the actual vehicle 10, but has a different location due to an error.

When the GPS module 120 obtains the coordinate information, the map data extracting unit 131 extracts map data of a nearby area corresponding to the GPS coordinate information from the precision map DB 137 <S420>. The precision map data stored in the precision map DB 137 is very large, and the map data extraction unit 131 extracts only map data in a referenced area using GPS coordinate information.

Thereafter, the second distance information acquisition unit 133 acquires information about the curb from the precise map data extracted by the map data extraction unit 131 <S425>.

That is, although the coordinate information obtained from the GPS module 120 is different from the actual vehicle location, it is certain that it is adjacent coordinates. In addition, it is possible to obtain the direction of progress through the direction of progress determination unit 134. Therefore, the second distance information acquisition unit 133 can check the curb information located to the right of the direction of travel from the extracted precision map data, and thereafter, the shortest distance from the current GPS coordinates to the curb obtained from the precision map (second distance information). ) Can be calculated <S430>.

The second distance information acquisition unit 133 may calculate a second distance from the GPS coordinates to the curb on the precision map. For example, in the precision map data, the information of the curbs can also be expressed as a set of coordinate information, through which the linear equations of the curbs can be obtained, and by obtaining the length of the water line from a point corresponding to GPS coordinates to a straight line consisting of curbs The second distance information can be obtained.

In addition, the second distance information acquisition unit 133, like the first distance information acquisition unit 132, acquires the second distance information under specific conditions. That is, only the curb 162 located on the right side in the traveling direction of the vehicle 10 is considered, and only the curb 162 in a linear form is detected, and when the vehicle is driving parallel to the curb 162, and straight lines in different directions Each second distance information is obtained in driving.

When the first distance information acquisition unit 132 and the second distance information acquisition unit 133 acquire the first distance information and the second distance information, respectively, the correction information generation unit 135 first and second distance information Using the difference of information (rel_error_y, rel_error_x) and direction information toward the curb 162, correction information to correct the GPS coordinates is generated <S435>.

Meanwhile, in FIG. 5, reference numeral 11G denotes a location according to GPS coordinate information, and reference numeral 11R denotes an actual vehicle 10 location. However, in more detail, FIG. 5 shows the direction and distance from the GPS coordinates 11G to the curb 162 (second distance), and the direction and distance from the actual vehicle 11R to the curb 162 (first distance). ), and since it is a conceptual diagram for intuitively showing the difference between the first distance and the second distance (rel_error_y, rel_error_x), reference numerals 11G and 11R do not indicate the GPS coordinates at a specific time point and the actual vehicle 10 position. . For example, even if the driving vehicle 11R at 10 o'clock drawn at the lower right in FIG. 5 is positioned further forward or rearward in the traveling direction, the straight line distance and direction to the curb 162 will be the same, so the first distance and the first The difference between the two distances (rel_error_y) and its direction will also be measured equally, and is only conceptually illustrated in the drawings.

FIG. 6 is a view for explaining a process of obtaining a correction value using the first distance information and the second distance information obtained according to the progress of the vehicle 10 of the example shown in FIG. 5.

First, as shown in the lower right of FIG. 5, when the vehicle 11R is moving from 4 to 10 o'clock, the first distance information acquisition unit 132 and the second distance information acquisition unit 133 remove each. When the 1st distance information and the 2nd distance information are measured, the correction information generation unit 135 obtains a difference (rel_error_y) between the first distance information and the second distance information. In addition, since the traveling direction determining unit 134 always grasps the traveling direction of the vehicle 11R, the first distance information and the second distance information also include direction information, and accordingly rel_error_y also includes direction information. have.

The correction information generating unit 135 extracts the latitude and longitude components from the difference information (rel_error_y) according to the first driving direction thus obtained. Also, the correction information generating unit 135 extracts the latitude component and the longitude component from the difference information (rel_error_x) according to the second driving direction traveling from 7 to 1 o'clock, and calculates the latitude correction information and the longitude correction information Saves

The difference between the first distance information and the second distance information is a meter value. However, in order to correct GPS coordinates, the values of latitude and longitude must be corrected. This will be described with reference to the coordinate system shown in FIG. 6.

As shown in Fig. 6, difference information rel_error_y and rel_error_x are displayed on the azimuth coordinate system, respectively. Since the difference information has measured length and direction information, a vector of a specific length can be displayed in the corresponding direction on the azimuth coordinate system. In FIG. 6, rel_error_x is illustrated starting from the origin and pointing toward the second quadrant, and rel_error_y is illustrated starting from the end of rel_error_x and pointing toward the first quadrant. Marking rel_error_y to start at the end of rel_error_x is for convenience of explanation, and rel_error_y may also be illustrated to start at the origin.

Each difference information, rel_error_x and rel_error_y, includes both latitude and longitude components. That is, the difference information rel_error_x and rel_error_y are initial length information, but when plotted on the azimuth coordinates, they are converted into latitude and longitude values corresponding to the corresponding length and illustrated. Of course, all of these processes are performed by the algorithm set in advance by the correction information generating unit 135.

Thereafter, the correction information generating unit 135 adds the latitude component (com_lat_rel_error_x) of rel_error_x and the latitude component (com_lat_rel_error_y) of rel_error_y to generate the final latitude correction information (com_lat). In addition, the correction information generating unit 135 adds the hardness component (com_lon_rel_error_x) of rel_error_x and the hardness component (com_lon_rel_error_y) of rel_error_y to generate the final hardness correction information (com_lon).

In the embodiment of Fig. 5, since the first driving direction (4 to 10 o'clock) and the second driving direction (from 7 to 1 o'clock) are perpendicular to each other, the difference information (rel_error_y, rel_error_x) thus obtained is obtained. After displaying as it is on the azimuth coordinates of FIG. 6, the latitude and longitude components may be obtained. However, if the first driving direction and the second driving direction are not perpendicular to each other, the correction information generating unit 135 uses the trigonometric function to obtain the rel_error_x value obtained through the second driving direction through the first driving direction. When it is perpendicular to rel_error_y, it is converted to a corresponding value, and then the final correction information is calculated.

When the correction information generating unit 135 generates correction information including latitude correction information (com_lat) and longitude correction information (com_lon), the location update unit 136 reflects the correction information in the GPS coordinate information. The coordinate information of the GPS signal can be updated <S440>. That is, the coordinate information obtained from the GPS module 120 was the location of reference numeral 11G, but when the coordinate correction information is reflected, the location information of the actual vehicle 10 is updated and output. This may correct the coordinate error in real time by reflecting the received signal of the GPS module 120 that is subsequently received. Of course, the error rate can also be changed from time to time, so the previous process is repeated over a period of time so that the latest correction information is always reflected.

When the GPS coordinate information corrected by the location update unit is output, the object tracking unit 150 tracks the object (here, the object refers to the own vehicle 10) using the corrected coordinate information (S445). At this time, the object tracking unit 150 tracks the state of the vehicle 10 using a Kalman Filter. The Kalman filter repeatedly performs state prediction and measurement update to estimate the current state of the vehicle 10 and subsequent motion.

As described above, according to the present invention, even if the low-cost GPS module 120 is mounted, it is possible to check the error of the GPS coordinates by using the rider 110 and the precision map data, and correct the GPS coordinate information by the identified error. Accurate location data can be secured. Therefore, the positioning accuracy improving system 100 can be applied at a low price in the field of autonomous vehicles, which can accelerate the commercialization of autonomous vehicles.

On the other hand, looking at the process of generating the correction information of the position correction means 130, it is necessary to accurately recognize the curb from the data scanned through the lidar 110. However, when a moving object similar to a curb shape is located around the vehicle 10, there is a possibility that a moving object other than the curb is mistaken for a curb.

To this end, the first distance information acquiring unit 132 of the position correction means 13 analyzes the scan data, and even if it is an object in the form of a curb, the height is the standard (the height of the curb is usually 25 cm, so it can be caught based on 50 cm) ), it can be determined that the object is not a curb.

In addition, even if the height does not exceed the standard, there may be cases in which other objects of a continuous shape are recognized. This is the case, for example, when numerous bicycles are grouping along the road. In this case, there is a possibility that the straight line connecting the same points on the bicycle is mistaken for a curb shape. Therefore, when the positioning accuracy improving system 100 according to the present invention recognizes the curb shape in the first distance information acquisition unit 132 of the position correction means 130, it completely excludes the moving object and recognizes the curb shape among the fixed objects. Make it possible. The determination of the distinction between the moving object and the fixed object is made in the object classification means 140.

Hereinafter, the process of classifying the fixed object and the moving object through FIGS. 7 to 11 will be described in detail.

7 is a flowchart illustrating a method of classifying a fixed object and a moving object according to an embodiment of the present invention. That is, the object classification means 140 of the positioning accuracy improvement system 100 shown in FIGS. 1 and 3 is for a process of recognizing an object and determining whether it is moved/fixed.

First, in order to determine whether it is fixed or moved, an object is recognized through the rider 110 <S705>. After recognizing the object through the lidar 110, the feature point extracting unit 142 extracts the feature points of each object <S710>. The data acquired through the lidar 110 is a set of points having information on distance and direction. By filtering the set of these points, you can distinguish the face or edge of a specific object, and also the vertices where the edges meet. The feature point may be a point that can be used as a reference in the object, for example, a vertex where specific edges meet.

After the feature points are extracted from the feature point extracting unit 142 through the data scanned through the lidar 110, the feature point extracting unit 142 determines three feature points among the multiple feature points, The area calculation unit 143 calculates the area of the triangle consisting of three feature points (S715).

That is, each feature point has distance and direction information based on the own vehicle 10. Therefore, the feature points can be placed on the relative coordinate system based on the own vehicle 10, and the area calculating unit 143 can calculate the area of the triangle through the coordinate information. If the coordinate information of the three points is known in plane coordinates or spatial coordinates, the area of the triangle having three points as the vertices can be obtained by a predetermined formula.

At this time, the area calculation unit 143 does not calculate the area of the triangle only once, but repeats several times (for example, 10 times) for the scanned data over a time difference to calculate the triangle area of the same feature points. When the triangular area for the same feature points is calculated with the time difference in this way, the area change rate can be checked by the rate-of-change section 144, and the determination unit 145 is determined according to the area change rate of the triangle checked by the rate-of-change section 144. The fixed object and the moving object are separated <S720>.

That is, if the area change rate of the triangle is less than a predetermined criterion, the determination unit 145 determines that all objects corresponding to the three feature points are fixed objects, and if it is more than the reference object, the object corresponding to at least one feature point is a moving object. It is judged.

Of course, in order to determine the correct fixation/movement, it is necessary to calculate the different objects, not the area change rate between the same objects, and thus, it is possible to verify whether the fixation/movement object is accurate through a cross check <S725>.

In the present invention, in order to accurately determine a fixed/moving object, a fixed object is first determined in a state where the vehicle 10 is stopped, and then, when the vehicle 10 is moved, it is verified whether a specific object is in a fixed state or a moving state. This will be described with reference to FIGS. 8 and 9.

8 is a flowchart for explaining a process of determining a stationary object in a stopped state of the own vehicle. The vehicle movement confirmation unit 141 receives the speed information of the vehicle 10 in real time in connection with an ECU, etc., and checks whether the vehicle 10 is stopped through this (S805). Of course, whether the vehicle 10 is stopped may be checked through a GPS signal.

If it is confirmed that the vehicle 10 is stationary, <S810>, it scans the surroundings of the vehicle 10 through the lidar 110 and recognizes a plurality of objects <S815>. Also, the feature point extraction unit 142 extracts the feature points from a plurality of objects recognized by the lidar 110 <S820>.

Thereafter, the feature point extracting unit 142 randomly extracts three feature points from the plurality of feature points, and the area calculating unit 143 calculates an area of a triangle consisting of three feature points. At this time, the area calculation unit 143 repeats several times (for example, 10 times) for the scanned data over a time difference to calculate a triangular area of the same feature points, and the change rate confirmation unit 144 is calculated with the time difference from the area calculation unit 143 Check the area change rate of the triangle <S825>.

That is, it is checked whether the area change rate of the triangle shows a reference change rate, for example, a change rate of less than 10%, and if it shows a change rate of less than 10%, the determination unit 145 fixes all objects corresponding to the three feature points Confirm as an object.

If the area change rate of the triangle shows more than the reference (10%), it means that at least one of the objects corresponding to the three feature points is a moving object. However, since it is impossible to accurately determine which object is currently moving, the process of scanning the new lidar 110 data and extracting new feature points is repeated.

That is, in the process of FIG. 8, rather than confirming what the moving object is, the surrounding area is scanned while the vehicle 10 is stopped, and the area change rate of the triangle consisting of the feature points of the three randomly extracted objects is checked to determine the fixed object. It is a process to determine what is. Therefore, in the process of FIG. 8, it is not meaningful to determine what the moving object is.

Through the process of FIG. 8, if it is determined whether the fixed object for various objects is stopped while the vehicle 10 is stopped, it is possible to accurately determine whether a specific object is fixed/moved when the vehicle 10 is driving. . The process for this will be described with reference to FIG. 9 as follows.

The vehicle movement confirmation unit 141 checks whether or not the vehicle 10 is stopped <S905>, and if it is stopped, performs the process of determining a fixed object again through the process of FIG. 8, and if it is not moving, If it is confirmed <S910>, the object is recognized through the scan of the lidar 110<S915>, and then the feature point extraction unit 142 extracts the feature point.

At this time, the feature point extracting unit 142 does not extract feature points for three random objects, but is determined in a stationary state through the process of FIG. 8, that is, a new object to determine whether it is fixed object or fixed object or moving. One feature point of the object is extracted <S920>. Here, the object to be determined may be a newly recognized object in this process, or an object that is not accurately identified as a fixed object or a moving object in the process of FIG. 8. That is, it is intended to make a judgment by matching an object with a fixed/moved object with two objects with a fixed fixed object.

After the three feature points are extracted (fixed 2, the object to be judged 1), the area calculating unit 143 calculates the area of the triangle consisting of the three characteristic points several times over a time difference, and the rate of change checking unit 144 is the area calculating unit 143 Check the rate of change of the triangle area calculated in) (S925).

If it is confirmed that the area change rate of the triangle is less than the reference (10%) <S930>, the determination unit 145 determines the object to be determined as a fixed object <S935>.

That is, if the magnetic vehicle 10 is in a moving state, even if it is a fixed object, the relative coordinates of the magnetic vehicle 10 standard are different. However, the area of the triangle formed by three fixed points on the relative coordinate system will not change. Accordingly, if there is no change in the area of the triangle formed by the two feature points that have been confirmed to be fixed and one feature point to be determined regardless of whether the own vehicle 10 is moving, the object corresponding to one of the feature points to be determined is a fixed object. Im sure.

On the other hand, if it is determined that the area change rate of the two feature points of the fixed object and one feature point of the object to be judged is greater than or equal to the reference, the determination unit 145 determines the object to be determined as the moving object <S940>. When the moving object is determined in this way, the object tracking unit 150 may intensively track the object. In addition, when the fixed object is determined by the determination unit 145, the determined result is provided to the location correction means 130, and accordingly, the road information extraction unit of the location correction means 130 extracts a feature point of a specific object for generating correction information. At this time, only the feature points for the fixed object included in both the scan data and the precision map data of the lidar 110 can be extracted.

10 and 11 are conceptual diagrams for explaining a change in area of a triangle formed by objects. First, referring to (a) of FIG. 10, the first object 210, the second object 220, and the third object 230 can be recognized by the scan of the lidar 110 of the own vehicle 10. The feature point extraction unit 142 extracts the feature points 1-1 (211 ), the feature points 2-1 (221 ), and the feature points 3-1 (231) from each object, and then the area calculation unit (143) has three feature points (211,221,231) You can check the area of the triangle with a) as the vertex.

If the magnetic vehicle 10 moves as shown in FIG. 10(b), the relative coordinates for the three feature points 211,221,231 are also changed. However, if the three feature points 211, 221, and 231 are fixed, the vehicle 10 can be confirmed that there is no change in the area of the triangle formed by the three feature points 211, 221, 231 even though it is moving.

On the other hand, in the case of a triangular area connecting two feature points 211 and 221 which are fixed or not and a feature point 4-1 (241) of the fourth object 240 to be determined as shown in FIG. As shown in b), it can be seen that the area of the triangle changes as the fourth object 240 moves. Here, if the two feature points 211 and 221 are fixed or not, the fourth object 240 corresponding to the feature point 4-1 241 can be determined to be a moving object regardless of whether the own vehicle 10 moves. .

According to the object classification means 140 and the object classification method of the positioning accuracy improving system 100 according to the present invention, the feature points are extracted from three different objects through the lidar 110 and the magnetic vehicle 10 is referenced. The accuracy is high by classifying the fixed object and the moving object according to the area change rate of the triangle formed by the three feature points according to the relative coordinate system.

That is, in the conventional method of classifying moving objects according to the relative speed, when the magnetic vehicle 10 and the relative object move at the same speed in the same direction, there is a case that the moving object is mistaken for a fixed object even though it is a clear moving object. Since the fixed object is determined, and it is checked whether the moving object is a moving object according to the area change rate of the triangle formed by the two fixed objects and the object to be determined, the fixed object is fixed even when the own vehicle 10 is moving. You can accurately determine whether or not you are moving.

On the other hand, the determination unit 145 has been described through the process of FIG. 8, when the area change rate of the triangle consisting of three feature points is less than the reference, all the objects are determined as fixed objects. At this time, additional conditions for more accurately determining the fixed objects You can also add more. That is, it is determined whether the object is a fixed object by considering height information.

In general, fixed objects such as signs, traffic lights, street trees, buildings, etc. often have a top point of 3 meters or more from the ground. On the other hand, moving objects such as automobiles (except for some vehicles 10), bicycles, and pedestrians are often less than 3 meters.

Therefore, even if the area change rate is less than the reference in the process of FIG. 8, it is determined as a fixed object only when the height of the object, more specifically, the height of the feature point extracted from the object is greater than or equal to the reference height (for example, 3 meters). If it is, it is desirable to go directly to the process of calculating the triangle area using the feature points of another object.

For example, if the height of the feature point is 3 meters or more and the area change rate is less than the standard, the object having the feature point is very likely to be a fixed object. Therefore, if the area of change between the three feature points is monitored by extracting two fixed objects with a height of 3 meters or more from the feature points of the target object to be determined and the fixed objects that have already been determined, the determination of whether to move or fix will be more reliable. Can be. That is, even if the area change rate between the three feature points is less than the reference, the possibility that these three objects are objects moving in the same direction cannot be excluded. Therefore, in this case, it is necessary to check the area change rate of the triangle consisting of the feature points of two predetermined fixed objects having a feature point and a height of 3 meters or more of any one of these objects, and through this, whether the object to be judged is a fixed object or not Can be determined more reliably.

Of course, when the feature point extracting unit 142 extracts three feature points for area calculation, if only feature points higher than a reference height are selected as the area calculation target, the operation speed for determining a fixed object may be faster.

Here, the height of the feature point can be calculated through a simple trigonometric formula through the relative coordinate system. That is, the point coordinates obtained through the scan data of the lidar 110 include distance and direction information, and thus can be expressed as a relative coordinate system based on the own vehicle 10. The angle between the straight line connecting the points and the ground is also known. Multiplying the sine value for this angle by the distance to that point yields the height of that point.

The above-described preferred embodiments of the present invention are disclosed for the purpose of illustration, and those skilled in the art having ordinary knowledge of the present invention, various modifications, changes, and additions will be possible within the spirit and scope of the present invention. And additions should be considered to fall within the scope of the claims of the present invention.

10: vehicle
11G: GPS coordinates
11R: actual vehicle location
100: positioning accuracy improvement system
110: Lida
120: GPS module
130: location correction means
131: Map data extraction unit
132: First street information acquisition department
133: Second Street Information Acquisition Department
134: Progress direction determination unit
135: correction information generation unit
136: location update unit
137: Precision map DB
140: object classification means
141: Vehicle movement confirmation unit
142: feature point extraction unit
143: Area computation department
144: Change rate confirmation unit
145: judgment unit
150: object tracking unit
162: curb
210: first object
211: Feature point 1-1
220: second object
221: Feature point 2-1
230: third object
231: Feature point 3-1
240: fourth object
241: feature point 4-1

Claims (4)

An environment sensor for recognizing objects by scanning around the vehicle;
A GPS module for obtaining GPS coordinate information;
Using the first distance information to the curb according to the scan data of the environmental sensor, and the second distance information from the GPS coordinates obtained from the GPS module to the curb extracted from the precision map corresponding to the GPS, the GPS coordinate information Includes; position correction means for updating output;
Further comprising; object classification means for determining whether the object is recognized and fixed or moved by the environmental sensor,
The object classification means,
A feature point extraction unit for extracting a feature point from the object recognized by the environmental sensor;
An area calculation unit repeatedly calculating an area of a triangle consisting of three characteristic points with a parallax through a relative coordinate system of the characteristic points extracted from the characteristic point extraction unit;
A change rate confirmation unit that checks a rate of change of the triangular area calculated by the area calculation unit; And
It includes; a determination unit for distinguishing the fixed object and the moving object according to the change result of the triangular area change rate check unit
The object classification means,
Further comprising; a vehicle movement confirmation unit for confirming whether the vehicle is stopped,
When it is confirmed that the vehicle is in a stopped state through the vehicle movement confirmation unit, the determination unit first distinguishes the fixed object and the moving object, but when the area change rate of the triangle is less than the reference, objects for three feature points are determined as fixed objects. And
Subsequently, when it is confirmed through the vehicle movement confirmation unit that the vehicle is not in a stopped state, the feature point extraction unit extracts two feature points from the feature points of the object determined by the determination unit as a fixed object and one feature point of the object to be determined. Then, the area calculation unit calculates the area of the triangle consisting of two fixed feature points and one feature point to be judged. When it is confirmed that the area change rate is less than the standard, the determination unit determines the object to be determined as a fixed object. And, when it is confirmed that the area change rate is greater than or equal to the reference in the change rate checking unit, the determination unit determines the object to be determined as a moving object.

According to claim 1,
The position correction means,
A map data extraction unit extracting precision map data corresponding to the GPS coordinate information obtained from the GPS module from the precision map DB;
A first distance information acquisition unit that recognizes a curb from the object recognized by the environmental sensor and acquires first distance information to the curb;
A second distance information acquiring unit acquiring second distance information that is a distance from the GPS coordinates obtained by the GPS module to a curb of a location corresponding to the GPS coordinates among the precision maps extracted by the map data extraction unit;
A correction information generator configured to calculate coordinate correction information for correcting the GPS coordinate information by using a difference between the first distance information and the second distance information; And
And a location updating unit that updates the location information by reflecting the coordinate correction information to the GPS coordinate information.
According to claim 1,
The environmental sensor is a lidar, laser scanner, camera or laser positioning accuracy improvement system, characterized in that.
According to claim 1,
The stationary object includes a sign, traffic lights, street trees, buildings, the moving object is a positioning accuracy improvement system, characterized in that it comprises a car, bicycle, pedestrian.

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