KR20230041515A - Method for recognizing moving objects by using lidar and server using the same - Google Patents

Abstract

According to the present invention, disclosed is a method which, in a method for determining a moving object by using the LiDAR, comprises: (a) a step in which, if a first Lidar data is acquired, a server performs the filtering by a preset criterion, and acquires residual Lidar data by removing fixed LiDAR data corresponding to a fixed object from the first LiDAR data; (b) a step in which the server tracks the moving object by referring to the residual LiDAR data; and (c) a step in which the server classifies the moving object by referring to the tracking data on the moving object. Therefore, a moving object can be classified.

Description

Method for performing judgment on a moving object using LiDAR and server using the same

The present invention relates to a method for determining a moving object using lidar and a server using the same.

Lida is a device that accurately draws the surroundings by emitting laser pulses, receiving the light reflected from nearby objects and measuring the distance to the object. Lidar is a compound word made by mixing 'light' and 'radar (radio detection and ranging)' in terms of its origin. In other words, the name LIDAR means a radar that uses light instead of radio waves, and the principle is the same as that of traditional radar, but the wavelength of the electromagnetic wave used is different, so the actual use technology and application range are different.

In recent years, various transportation subjects such as self-driving cars, motorcycles, and electric kickboards as well as general cars are emerging, so a more advanced transportation infrastructure system is needed to reflect this reality.

However, there are not many cases in which a system that can obtain traffic data more accurately and quickly by using lidar in such a transportation infrastructure system has been implemented. It was just a skill to acquire. For example, most cases in which lidar is used in transportation infrastructure systems are equipped with lidar in cars to detect objects around them while the car is driving and give a warning to the driver to avoid them. are using

However, there is almost no technology for obtaining and using complex traffic data of intersections by using LIDAR in a fixed location such as an intersection. In other words, LIDAR is installed at intersections to detect dynamic objects such as cars and pedestrians passing around the intersection and static objects such as facilities, and analyze the detected data over time to understand traffic flow, which is used as a transportation infrastructure. How to utilize the system and the situation where the server is needed.

The present invention has as its object to solve all the problems mentioned above.

In addition, the present invention performs filtering on lidar data according to a predetermined criterion to remove fixed lidar data corresponding to a fixed object to obtain residual lidar data, and to obtain a moving object with reference to the remaining lidar data tracking, and another purpose is to classify moving objects.

In addition, another object of the present invention is to more accurately determine fixed lidar data.

According to one aspect of the present invention, in the method for determining a moving object using lidar, (a) when first lidar data is obtained, the server performs filtering according to a predetermined criterion. Obtaining remaining lidar data by removing fixed lidar data corresponding to a fixed object from the first lidar data by performing a; (b) tracking, by the server, the moving object by referring to the remaining lidar data; and (c) classifying, by the server, the moving object with reference to the tracking data of the moving object.

As an example, in the step (a), the server additionally obtains second lidar data in real time, and in the step (b), the server, the remaining lidar data and the second lidar data With reference to, a method characterized by tracking the moving object is disclosed.

As an example, in the step (a), the server divides the area covered by the LIDAR into a virtual 3D grid area, and for at least one observation frame, for each cell in the grid area, the first A method characterized in that counting the number of lidar data detected and determining the first lidar data detected in specific cells in which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data is disclosed.

As an example, a virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first zone to an nth zone, wherein the first zone is a virtual zone closest to the lidar and the nth zone is the farthest virtual zone from the lidar - in a state where is set, the server determines the number of specific observation frames in which the first lidar data is detected among the k cells included in the kth zone during the observation frame. The first lidar data detected in the kth specific cells that are equal to or greater than the threshold is determined as the fixed lidar data, and the (k+1th) neighboring area farther away from the lidar than the kth area during the observation frame. ) The first radar detected in the (k + 1)th specific cells in which the number of specific observation frames in which the first lidar data is detected among the (k + 1)th cells included in the zone is equal to or greater than the (k + 1)th threshold When it is determined that the data is the fixed lidar data, the (k+1)th threshold is set to a value lower than or equal to the kth threshold.

As an example, in the step (a), the server divides the area covered by the LIDAR into a virtual 3D grid area, and for at least one observation frame, for each cell in the grid area, the first The number of observation point data of lidar data is counted and compared with the preset average number of point data of each cell, and the ratio of the difference between the number of observation point data and the average number of point data is detected in specific cells below a threshold ratio. A method characterized in that determining the first lidar data to be the fixed lidar data is disclosed.

As an example, a virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first zone to an nth zone, wherein the first zone is a virtual zone closest to the lidar and the nth zone is the farthest virtual area from the lidar - in a state where is set, the server determines the relationship between the observation point data number and the average point data number of the first lidar data among the kth cells included in the kth area. The first lidar data detected in the k th specific cells in which the ratio of the difference is less than or equal to the k th critical ratio is determined as the fixed lidar data, and the neighboring area farther away from the lidar than the k th area ( Among the (k+1)th cells included in the k+1) zone, the ratio of the difference between the observation point data number of the first LiDAR data and the average point data number is equal to or less than the (k+1)th critical ratio. (k + 1) When determining the first lidar data detected in specific cells as the fixed lidar data, the (k + 1)th critical ratio is set to a value higher than or equal to the kth critical ratio A method characterized by that is disclosed.

As an example, in determining the fixed lidar data before removing the fixed lidar data from the first lidar data, before the step (a), the server is obtained from the first lidar data The traffic volume of at least one reference frame is obtained, and if it is determined that the traffic volume is equal to or less than a preset value, reference logging data corresponding to the reference frame is recorded. Disclosed is a method characterized by determining the fixed lidar data included in the first lidar data during the observation frame using the reference logging data during at least one observation frame following.

As an example, in the step (c), the server classifies the moving object as a vehicle or a pedestrian, but with reference to a feature data set of the moving object, the feature of the moving object A method characterized in obtaining each median and classifying the moving object using the median is disclosed.

ve Bayes Classification), 딥러닝(Deep Learning), 머신러닝(Machine Learning) 중 적어도 하나를 이용하여 획득되는 것을 특징으로 하는 방법이 개시된다.As an example, the characteristic includes at least a part of the size of the moving object, the speed of the moving object, the distance from the lidar, and the number of point data of the first lidar data, and the median value is a support vector machine (Support Vector Machine), Neural Network, Naive Bayes Classification (Na

A method characterized by being obtained using at least one of ve Bayes Classification, Deep Learning, and Machine Learning is disclosed.

As an example, a virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first zone to an nth zone, wherein the first zone is a virtual zone closest to the lidar and the nth zone is the farthest virtual zone from the lidar - in a state in which is set, the server determines the k th reference value for distinguishing the characteristics of the moving object of the first category and the characteristic of the moving object of the second category included in the k th zone is set to be greater than or equal to the (k+1)th reference value for distinguishing the characteristics of the moving object of the first category and the characteristics of the moving object of the second category included in the (k+1)th zone. A method for doing so is disclosed.

As an example, in the step (b), the server determines the moving path point of the moving object in order to track the moving object, but uses the MeanX and MeanY values of the moving object, or sets the moving object to 2D A method characterized by determining the movement path point by fitting a rectangle and using the median value of the fitted 2D rectangle or by fitting the moving object to a 3D rectangle and using the median value of the fitted 3D rectangle is initiated

According to another aspect of the present invention, in a method for determining a moving object using lidar, (a) applying filtering according to a predetermined criterion to obtained first lidar data Acquiring, by a server, second lidar data in a state in which fixed lidar data corresponding to a fixed object is obtained from the first lidar data; (b) obtaining, by the server, second residual lidar data with reference to the second lidar data and the fixed lidar data; (c) tracking, by the server, a moving object by referring to the second residual lidar data; and (d) classifying, by the server, the moving object with reference to the tracking data of the moving object.

As an example, before the step (a), the server divides the area covered by the LIDAR into a virtual 3D grid area, and for at least one observation frame, the server divides the area covered by the lidar for each cell in the grid area. A method characterized by counting the number of detected 1 lidar data and determining the first lidar data detected in specific cells in which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data is disclosed.

As an example, before the step (a), a virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, and the first zone is the most distant from the lidar. In a state where the nearest virtual zone and the n-th zone is the farthest virtual zone from the lidar - is set, the server detects the first lidar data among the k-th cells included in the k-th zone during the observation frame. Determines the first lidar data detected in the kth specific cells in which the number of specific observation frames is equal to or greater than the kth threshold as the fixed lidar data, and further away from the lidar than the kth zone during the observation frame The (k+1)th specific number of observation frames in which the first lidar data is detected among the (k+1)th cells included in the (k+1)th zone, which is a neighboring area, is equal to or greater than the (k+1)th threshold. When the first lidar data detected in cells is determined to be the fixed lidar data, the (k+1)th threshold is set to a value lower than or equal to the kth threshold. .

As an example, before the step (a), the server divides the area covered by the LIDAR into a virtual 3D grid area, and for at least one observation frame, the server divides the area covered by the lidar for each cell in the grid area. In specific cells where the ratio of the difference between the observation point data number and the average point data number is less than the threshold ratio by counting the number of observation point data of 1 lidar data and comparing it with the preset average point data number of each cell. A method characterized in that determining the detected first lidar data as the fixed lidar data is disclosed.

As an example, before the step (a), a virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, and the first zone is the most distant from the lidar. In a state in which a near virtual zone and the n-th zone is a virtual zone farthest from the lidar - is set, the server determines the observation point data number of the first lidar data among the k-th cells included in the k-th zone. Determines the first lidar data detected in the kth specific cells in which the ratio of the difference between the number of points and the average point data number is less than or equal to the kth threshold ratio as the fixed lidar data, The ratio of the difference between the number of observation point data and the average point data number of the first LiDAR data among the (k + 1) th cells included in the (k + 1) th zone, which is a distant nearby area, is th ( When the first lidar data detected in the (k+1)th specific cells that are less than or equal to the k+1) critical ratio is determined to be the fixed lidar data, the (k+1)th critical ratio corresponds to the kth critical ratio A method characterized by setting it to a higher or equal value is disclosed.

As an example, in the step (b), a plurality of first point positions corresponding to the fixed lidar data and a plurality of second point positions obtained from each of the second lidar data are matched, and the first point position And a method characterized by obtaining the second residual lidar data by removing specific lidar data detected from a specific point corresponding to a specific position where the position of the second point overlaps, from the second lidar data. .

According to another aspect of the present invention, a server for determining a moving object using lidar includes at least one memory for storing instructions; and at least one processor configured to execute the instructions, wherein the processor performs filtering according to a predetermined criterion when (I) first lidar data is obtained and fixes the first lidar data from the first lidar data. A process of obtaining residual lidar data by removing fixed lidar data corresponding to the object; (II) a process of tracking the moving object with reference to the residual lidar data; and (III) a process of classifying the moving object with reference to the tracking data of the moving object.

As an example, in the (I) process, the processor additionally acquires second lidar data in real time, and in the (II) process, the processor performs the remaining lidar data and the second lidar data. With reference to, a server characterized in that for tracking the moving object is provided.

As an example, in the (I) process, the processor divides an area covered by the LIDAR into a virtual 3D grid area, and the first observation frame for each cell in the grid area during at least one observation frame. A server is provided that counts the number of lidar data detected and determines the first lidar data detected in specific cells in which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data.

As an example, a virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first zone to an nth zone, wherein the first zone is a virtual zone closest to the lidar and the nth zone is the farthest virtual region from the lidar - in a state where is set, the processor determines the number of specific observation frames in which the first lidar data is detected among the k cells included in the kth region during the observation frame. The first lidar data detected in the kth specific cells that are equal to or greater than the threshold is determined as the fixed lidar data, and the (k+1th) neighboring area farther away from the lidar than the kth area during the observation frame. ) The first radar detected in the (k + 1)th specific cells in which the number of specific observation frames in which the first lidar data is detected among the (k + 1)th cells included in the zone is equal to or greater than the (k + 1)th threshold When it is determined that the data is the fixed lidar data, the (k + 1)th threshold is set to a value lower than or equal to the kth threshold.

As an example, in the (I) process, the processor divides an area covered by the LIDAR into a virtual 3D grid area, and the first observation frame for each cell in the grid area during at least one observation frame. The number of observation point data of lidar data is counted and compared with the preset average number of point data of each cell, and the ratio of the difference between the number of observation point data and the average number of point data is detected in specific cells below a threshold ratio. A server is provided, characterized in that for determining the first lidar data to be the fixed lidar data.

As an example, a virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first zone to an nth zone, wherein the first zone is a virtual zone closest to the lidar and the nth zone is the farthest virtual area from the lidar - in a state where is set, the processor determines the relationship between the observation point data number and the average point data number of the first lidar data among the k th cells included in the k th area. The first lidar data detected in the k th specific cells in which the ratio of the difference is less than or equal to the k th critical ratio is determined as the fixed lidar data, and the neighboring area farther away from the lidar than the k th area ( Among the (k+1)th cells included in the k+1) zone, the ratio of the difference between the observation point data number of the first LiDAR data and the average point data number is equal to or less than the (k+1)th critical ratio. (k + 1) When determining the first lidar data detected in specific cells as the fixed lidar data, the (k + 1)th critical ratio is set to a value higher than or equal to the kth critical ratio A server characterized in that is provided.

As an example, in determining the fixed lidar data before removing the fixed lidar data from the first lidar data, before the (I) process, the processor is obtained from the first lidar data Obtains the traffic volume of at least one reference frame, records reference logging data corresponding to the reference frame when it is determined that the traffic volume is less than or equal to a predetermined value, and in the (I) process, the processor performs a temporally A server is provided, characterized in that for determining the fixed lidar data included in the first lidar data during the observation frame using the reference logging data during at least one observation frame following.

As an example, in the (III) process, the processor classifies the moving object as a vehicle or a pedestrian, and with reference to a feature data set of the moving object, the feature of the moving object A server characterized in that each median is acquired and the moving object is classified using the median.

ve Bayes Classification), 딥러닝(Deep Learning), 머신러닝(Machine Learning) 중 적어도 하나를 이용하여 획득되는 것을 특징으로 하는 서버가 제공된다.As an example, the characteristic includes at least a part of the size of the moving object, the speed of the moving object, the distance from the lidar, and the number of point data of the first lidar data, and the median value is a support vector machine (Support Vector Machine), Neural Network, Naive Bayes Classification (Na

ve Bayes Classification), deep learning (Deep Learning), and machine learning (Machine Learning) characterized in that the obtained server is provided.

As an example, a virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first zone to an nth zone, wherein the first zone is a virtual zone closest to the lidar and the nth zone is the farthest virtual area from the lidar - in a state where is set, the processor determines the k th reference value for distinguishing the characteristics of the moving object of the first category and the moving object of the second category included in the k th area. is set to be greater than or equal to the (k+1)th reference value for distinguishing the characteristics of the moving object of the first category and the characteristics of the moving object of the second category included in the (k+1)th zone. A server is started.

As an example, in the (II) process, in order to track the moving object, the processor determines a moving path point of the moving object, uses MeanX and MeanY values of the moving object, or sets the moving object to 2D A server characterized in that by fitting a rectangle and using the median of the fitted 2D rectangle, or by fitting the moving object to a 3D rectangle and using the median of the fitted 3D rectangle to determine the movement path point Provided.

According to another aspect of the present invention, a server for determining a moving object using lidar includes at least one memory for storing instructions; and at least one processor configured to execute the instructions, wherein the processor is configured to: (I) fix the obtained first lidar data by applying filtering according to a predetermined criterion to the obtained first lidar data; a process of acquiring second lidar data in a state in which fixed lidar data corresponding to the object is acquired; (II) a process of obtaining second residual lidar data with reference to the second lidar data and the fixed lidar data; (III) a process of tracking a moving object with reference to the second residual lidar data; and (IV) a process of classifying the moving object with reference to the tracking data of the moving object.

As an example, prior to the (I) process, the processor divides an area covered by the LIDAR into a virtual 3D grid area, and the first observation frame for each cell in the grid area. A server is provided that counts the number of detected 1 lidar data and determines the first lidar data detected in specific cells in which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data.

As an example, before the (I) process, a virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first zone to an n-th zone, the first zone being the most distant from the lidar In a state in which a near virtual zone and the n-th zone is a virtual zone farthest from the lidar are set, the processor detects the first lidar data among the k-th cells included in the k-th zone during the observation frame. Determines the first lidar data detected in the kth specific cells in which the number of specific observation frames is equal to or greater than the kth threshold as the fixed lidar data, and further away from the lidar than the kth zone during the observation frame The (k+1)th specific number of observation frames in which the first lidar data is detected among the (k+1)th cells included in the (k+1)th zone, which is a neighboring area, is equal to or greater than the (k+1)th threshold. When determining the first lidar data detected in cells as the fixed lidar data, the (k + 1)th threshold is set to a value lower than or equal to the kth threshold. .

As an example, prior to the (I) process, the processor divides an area covered by the LIDAR into a virtual 3D grid area, and the first observation frame for each cell in the grid area. In specific cells where the ratio of the difference between the observation point data number and the average point data number is less than the threshold ratio by counting the number of observation point data of 1 lidar data and comparing it with the preset average point data number of each cell. A server is provided, characterized in that for determining the detected first lidar data as the fixed lidar data.

As an example, before the (I) process, a virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first zone to an n-th zone, the first zone being the most distant from the lidar In a state in which the nearest virtual zone and the n-th zone is the farthest virtual zone from the lidar - is set, the processor determines the number of observation point data of the first lidar data among the k-th cells included in the k-th zone. Determines the first lidar data detected in the kth specific cells in which the ratio of the difference between the number of points and the average point data number is less than or equal to the kth threshold ratio as the fixed lidar data, The ratio of the difference between the number of observation point data and the average point data number of the first LiDAR data among the (k + 1) th cells included in the (k + 1) th zone, which is a distant nearby area, is th ( When the first lidar data detected in the (k+1)th specific cells that are less than or equal to the k+1) critical ratio is determined to be the fixed lidar data, the (k+1)th critical ratio corresponds to the kth critical ratio A server characterized in that it is set to a higher or equal value is provided.

As an example, in the process (II), a plurality of first point positions corresponding to the fixed lidar data and a plurality of second point positions obtained from each of the second lidar data are matched, and the first point position And a server is provided, characterized in that for obtaining the second residual lidar data by removing specific lidar data detected from a specific point corresponding to a specific position where the second point position overlaps, from the second lidar data. .

The present invention performs filtering on lidar data according to a predetermined criterion to obtain residual lidar data by removing fixed lidar data corresponding to a fixed object, tracking a moving object with reference to the remaining lidar data, , has the effect of classifying moving objects.

In addition, the present invention has an effect of more accurately determining fixed lidar data.

1 is a diagram showing a schematic configuration of a server that determines a moving object using lidar according to an embodiment of the present invention.
2 is a flowchart illustrating an overall sequence of a method for determining a moving object using lidar according to an embodiment of the present invention.
3 is a flowchart for explaining the overall sequence of a method for determining a moving object using first lidar data and second lidar data according to an embodiment of the present invention.
4 is a diagram illustrating an example of a virtual zone corresponding to each preset distance based on lidar according to an embodiment of the present invention.
5A is a diagram showing an example of a screen before removing fixed lidar data corresponding to a fixed object according to an embodiment of the present invention.
5B is a diagram showing an example of a screen after removing fixed lidar data corresponding to a fixed object according to an embodiment of the present invention.
6 is a diagram illustrating an example in which a moving object is fitted to a 2D rectangle and a median value is included in the fitted 2D rectangle according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description of the present invention which follows refers to the accompanying drawings which illustrate, by way of illustration, specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable one skilled in the art to practice the present invention. It should be understood that the various embodiments of the present invention are different from each other but are not necessarily mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented in one embodiment in another embodiment without departing from the spirit and scope of the invention. Additionally, it should be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the invention. Accordingly, the detailed description set forth below is not to be taken in a limiting sense, and the scope of the present invention, if properly described, is limited only by the appended claims, along with all equivalents as claimed by those claims. Like reference numbers in the drawings indicate the same or similar function throughout the various aspects.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily practice the present invention.

1 is a diagram showing a schematic configuration of a server that determines a moving object using lidar according to an embodiment of the present invention.

As shown in FIG. 1 , a server 100 that determines a moving object using the lidar of the present invention may include a memory 110 and a processor 120 .

The memory 110 of the server 100 that determines the moving object may store instructions to be executed by the processor 120. Specifically, the instructions are transmitted to the server 100 that determines the moving object. Code that is created for the purpose of causing a computer to function in a particular way and may be stored in a computer usable or computer readable memory that may be directed to a computer or other programmable data processing equipment. Instructions may perform processes for executing functions described in the specification of the present invention.

In addition, the processor 120 of the server 100 that determines the moving object is hardware such as a micro processing unit (MPU) or a central processing unit (CPU), a cache memory, and a data bus. configuration may be included. In addition, it may further include a software configuration of an operating system and an application that performs a specific purpose.

Also, the server 100 that determines the moving object may be linked with a database (not shown). Here, the database (not shown) is a flash memory type, a hard disk type, a multimedia card micro type, and a card type memory (for example, SD or XD memory). , RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (ReadOnly Memory, ROM), EEPROM (Electrically Erasable Programmable ReadOnly Memory), PROM (Programmable ReadOnly Memory), magnetic memory, magnetic disk, optical disk It may include at least one type of storage medium, but is not limited thereto and may include any medium capable of storing data. In addition, the database (not shown) is installed separately from the server 100 that determines the moving object, or is installed inside the server 100 that determines the moving object to transmit or receive data. The data to be used may be recorded, or, unlike shown, may be implemented by being separated into two or more, which may vary depending on the implementation conditions of the present invention.

A method using the server 100 for determining a moving object according to an embodiment of the present invention configured as described above will be described with reference to FIG. 2 .

2 is a flowchart illustrating an overall sequence of a method for determining a moving object using lidar according to an embodiment of the present invention.

First, when first lidar data is obtained (S210), filtering (S221) according to a predetermined criterion is performed to remove fixed lidar data corresponding to a fixed object from the first lidar data (S222), and residual lidar data is obtained. data can be obtained.

Thereafter, the moving object may be tracked with reference to the residual lidar data (S230). After the moving object is tracked, it can be stored (S235) and the moving object can be classified (S240).

In addition, when the moving object classification (S240) is completed, a precise map may be generated or an intersection state may be detected (S245).

Here, the first lidar data may be real-time data or already acquired data.

Meanwhile, according to another embodiment of the present invention, determination of a moving object may be performed. This is explained in FIG. 3 as follows.

3 is a flowchart for explaining the overall sequence of a method for determining a moving object using first lidar data and second lidar data according to an embodiment of the present invention.

First, when first lidar data is obtained (S310), first residual lidar data may be obtained by determining fixed lidar data corresponding to a fixed object from the first lidar data (S320). This operation may be performed in advance before obtaining second LiDAR data to be described later. Next, when the second lidar data is obtained (S330), the second residual lidar data may be obtained (S340) with reference to the first residual lidar data and the second lidar data. The second residual lidar data is data from which fixed lidar data has been removed.

Here, when obtaining the second residual lidar data with reference to the second lidar data and the fixed lidar data, (i) a plurality of first point positions corresponding to the fixed lidar data and (ii) the second lidar data It is possible to match the locations of a plurality of second points obtained with each of the data. That is, the second residual lidar data may be obtained by removing specific lidar data detected from a specific point corresponding to a specific position where the first point location and the second point location overlap from the second lidar data.

In other words, by matching the previously acquired point position of the fixed object to the point position of the object acquired in real time, removing the fixed lidar data for the fixed object from the real-time lidar data, the second lidar data in which only the moving object exists. It is possible to acquire the remaining LIDAR data.

Thereafter, the moving object may be tracked with reference to the second residual lidar data (S350). After the moving object is tracked, it can be stored (S355) and the moving object can be classified (S360).

Similarly, when the moving object classification (S360) is completed, a precision map may be generated or an intersection state may be detected (S365).

Here, the first lidar data may be already acquired data, but the second lidar data may correspond to real-time data.

In the above embodiments, an example of a method of determining the fixed lidar data as fixed lidar data before removing the fixed lidar data corresponding to the fixed object is as follows.

In an example of determining fixed lidar data, first, an area covered by lidar is divided into a virtual 3D grid area. Since LIDAR acquires point data from multiple layers, it can be divided into three-dimensional grid areas included in the X-axis, Y-axis, and Z-axis.

In addition, counting the number of first lidar data detected for each cell in the grid area during at least one observation frame, and converting the first lidar data detected in specific cells in which the counted number of cells is equal to or greater than a threshold value to fixed lidar data can be judged by

That is, it is determined that there is a fixed object in a cell where the first lidar data is continuously detected. For example, when first lidar data is detected in 90 observation frames out of 100 observation frames in the first cell, it is determined that a fixed object exists in the first cell. Conversely, when the first lidar data is detected in 80 observation frames out of 100 observation frames in the second cell, it is determined that the fixed object does not exist in the second cell. Here, the threshold may be changed according to the user's setting, but in the above example, it may be assumed that the threshold is set to "more than 90 observation frames".

Here, the threshold may be uniformly set uniformly, but preferably, the threshold may be set differently depending on the distance from the lidar as follows.

For example, in a state in which a corresponding virtual zone is set for each predetermined distance from lidar, the number of specific observation frames in which the first lidar data is detected among the k cells included in the k th zone during the observation frame is greater than or equal to the k th threshold k First lidar data detected in specific cells may be determined as fixed lidar data.

On the other hand, during the observation frame, the number of specific observation frames in which the first lidar data is detected among the (k+1)th cells included in the (k+1)th zone, which is a nearby area farther from lidar than the kth zone, is First lidar data detected in the (k+1)th specific cells that are equal to or greater than the (k+1)th threshold may be determined as fixed lidar data.

Here, the (k+1)th threshold may be set to a value lower than or equal to the kth threshold, the virtual zone includes the first to nth zones, the first zone being the closest to the lidar and the virtual zone being the closest to the lidar. Zone n is the farthest imaginary zone from LiDAR.

This will be explained through FIG. 4 .

4 is a diagram illustrating an example of a virtual zone corresponding to each predetermined distance from LIDAR according to an embodiment of the present invention.

In FIG. 4, two zones are illustrated for convenience. There is a kth region 410 and a (k+1)th region 420 . The kth area 410 is an area closer to the LiDAR installation point than the (k+1)th area 420 .

Here, it is assumed that the number of cells included in the kth region 410 is 10 and the number of cells included in the (k+1)th region 420 is 15. Also, it is assumed that the threshold is 90%. That is, it is assumed that it is determined that fixed lidar data is detected from the specific cell only when 90% or more lidar data is detected in the specific cell.

At this time, the number of specific observation frames in which the first lidar data is detected among the 10 cells included in the kth region 410 is 90 (ie, the specific observation frame in which the first lidar data is detected among 100 observation frames). If there are two cells that are 90 or more), the first lidar data detected in the two cells can be determined as fixed lidar data. It may be determined that the first lidar data detected in 8 cells in which the number of specific observation frames is less than 90 is not fixed lidar data.

On the other hand, the number of specific observation frames in which the first lidar data is detected among 15 cells included in the (k+1)th region 420 is 90 (ie, the first lidar data is detected among 100 observation frames). If it is desired to determine cells having 90 or more specific observation frames as cells corresponding to fixed lidar data, there may be more errors than the kth region 410 . This is because, if the data is more likely to be inaccurate as the distance from the lidar increases, the probability of not determining it as fixed lidar data increases even if it is actually fixed lidar data if applied with the same threshold criterion. Therefore, for the (k + 1)th region 420, the threshold is slightly lowered so that if the number of specific observation frames in which the first lidar data is detected is 80 or more, cells corresponding to fixed lidar data can be determined. will be.

On the other hand, in another example of determining fixed lidar data, the area covered by lidar is divided into a virtual 3-dimensional grid area, and the first lidar data for each cell in the grid area during at least one observation frame. The number of observation point data is counted and compared with the preset average number of point data of each cell (for this, information has already been acquired and analyzed in the past frame to secure the average number of point data), the number of observation point data and First lidar data detected in specific cells having a difference ratio of the average point data number equal to or less than a threshold ratio may be determined as fixed lidar data.

More specifically, assuming that the average number of point data in cells judged as fixed lidar data is 10 at a distance corresponding to the kth area 410, point data detected in a specific cell in the kth area 410 In a state where is 9, since it corresponds to 90% compared to the average value of 10, the first lidar data detected in the cell can be determined as fixed lidar data. Here, it is assumed that the critical ratio is set to 90%.

However, assuming that the average number of point data of cells judged as fixed lidar data at a distance corresponding to the (k+1)th area 420 was 5, a specific cell in the (k+1)th area 420 In a state where the point data detected in 4 is 4, (if the threshold ratio corresponds to 90% like the critical ratio of the k th region 410), the 4 points detected in a specific cell in the (k+1)th region 420 Since the point data is only 80% of the average value of 5, it is impossible to determine the first lidar data detected in the cell as fixed lidar data. This is because even though a specific cell may be vulnerable to noise due to its long distance, by applying the same threshold ratio, an irrational decision result that is not judged as fixed LIDAR data occurs. For the cells included in the region 420, the threshold ratio for determining whether or not the LIDAR data is fixed may be lowered to 80%.

Remaining lidar data may be obtained by determining fixed lidar data using at least one of the above examples and removing the fixed lidar data.

Next, let's look at Figures 5a and 5b.

5A is a diagram showing an example of a screen before removing fixed lidar data corresponding to a fixed object according to an embodiment of the present invention.

5B is a diagram showing an example of a screen after removing fixed lidar data corresponding to a fixed object according to an embodiment of the present invention.

As shown in FIG. 5A, before removing the fixed lidar data, a number of yellow points are scattered. In this case, it is not easy to distinguish between fixed lidar data and residual lidar data.

However, in the case of FIG. 5B, since the fixed lidar data is removed using the above-mentioned example, it can be seen that the yellow points are significantly reduced compared to FIG. 5A, and the yellow points shown in FIG. 5B move with higher accuracy. It can be judged as an object.

On the other hand, in determining fixed lidar data, as a pre-process, while monitoring the traffic volume from the first lidar data, frames during which it is determined that the traffic volume is equal to or less than a predetermined value are set as reference frames and correspond to the reference frames. Reference logging data can be recorded.

This is because, when determining fixed lidar data corresponding to a fixed object, accuracy can be further increased only when a frame of a time period in which a moving object is as small as possible is set as a reference frame and measurement is performed. Therefore, by analyzing the traffic volume in the area where the lidar is installed, logging data measured in a time zone with few moving objects (eg, early morning) is recorded as reference logging data, and then using the reference locking data to determine fixed lidar data will be.

Thereafter, in order to more accurately determine a moving object during at least one observation frame temporally following the reference frame, the reference logging data recorded as a pre-process is called and the fixed lidar included in the first lidar data during the observation frame. We can help you determine and remove the data.

Meanwhile, a moving object may be tracked with reference to residual lidar data from which fixed lidar data is removed. Here, in order to track the moving object, a moving path point of the moving object may be determined.

The method for determining the moving path point is to use the MeanX and MeanY values of the moving object, or to fit the moving object to a 2D rectangle and use the median of the fitted 2D rectangle, or to fit the moving object to a 3D rectangle and then fit. The movement route point can be determined using the median value of the 3D rectangle.

In addition, as shown in FIG. 4, a virtual zone corresponding to each predetermined distance from the lidar (the virtual zone includes a first to n-th zone, and the first zone is the closest virtual zone from the lidar) zone, and the n-th zone is a virtual zone farthest from the lidar) is set, the server 100 determines the characteristics of the moving object of the first category included in the k-th zone 410 and the second category. The kth reference value for distinguishing the characteristics of the moving object is the first category included in the (k+1)th zone 420 and the second category for distinguishing the characteristics of the moving object from the first category. (k+1) may be set greater than or equal to the reference value. Here, the first category may be a vehicle and the second category may be a pedestrian, but is not limited thereto, and the first category may be a first type of vehicle and the second category may be a second type of vehicle. will be able to assume In addition, the k th reference value and the (k + 1) th reference value may be concepts including reference values for at least some factors of, for example, the size factor of an object and the number of points of an object factor. In some cases, each of the two factors It may mean the standard value for In addition, the k th reference value and the (k+1) th reference value may be median values for each of the factors, but will not be limited thereto.

6 is a diagram illustrating an example in which a moving object is fitted to a 2D rectangle and a median value is included in the fitted 2D rectangle according to an embodiment of the present invention.

As shown in Fig. 6, the moving object was fitted to a 2D rectangle. The center point of a 2D rectangle is the red point, but the median of a 2D rectangle is the yellow point. The moving path point of the moving object can be determined using the median value, which is the yellow dot.

Meanwhile, the previously mentioned moving objects may be classified as vehicles or pedestrians. Here, with reference to the feature data set of the moving object, the median of each feature of the moving object may be obtained, and the moving object may be classified using the median value.

ve Bayes Classification), 딥러닝(Deep Learning), 머신러닝(Machine Learning) 중 적어도 하나를 이용하여 획득될 수 있으며, 이에 한정하는 것은 아니다.Here, the characteristics may include at least some of the size of the moving object, the speed of the moving object, the distance from the lidar, and the number of point data of the first lidar data. In addition, the median value is a support vector machine (Support Vector Machine), a neural network (Neural Network), naive Bayes classification (Na

ve Bayes Classification), deep learning (Deep Learning), and can be obtained using at least one of machine learning (Machine Learning), but is not limited thereto.

Finally, when the classification of the moving object is completed, a precise map can be created or the state of the intersection can be detected using this. However, this is not an essential part, and can be used in a variety of other fields.

In addition, the embodiments according to the present invention described above may be implemented in the form of program instructions that can be executed through various computer components and recorded in a computer readable recording medium. The computer readable recording medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the computer-readable recording medium may be specially designed and configured for the present invention, or may be known and usable to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magnetooptical media such as floptical disks. , and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter or the like as well as machine language codes such as those produced by a compiler. The hardware device may be configured to act as one or more software modules to perform processing according to the present invention and vice versa.

In the above, the present invention has been described by specific details such as specific components and limited embodiments and drawings, but these are provided to help a more general understanding of the present invention, and the present invention is not limited to the above embodiments. , Those skilled in the art to which the present invention pertains may seek various modifications and variations from these descriptions.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and not only the claims described later, but also all modifications equivalent or equivalent to these claims belong to the scope of the spirit of the present invention. will do it

Claims (32)

In the method for making a decision on a moving object using Lidar,
(a) When the first lidar data is obtained, the server performs filtering according to a predetermined criterion to obtain residual lidar data by removing fixed lidar data corresponding to a fixed object from the first lidar data step;
(b) tracking, by the server, the moving object by referring to the remaining lidar data; and
(c) classifying, by the server, the moving object with reference to the tracking data of the moving object;
How to include. According to claim 1,
In step (a),
The server divides the area covered by the lidar into a virtual three-dimensional grid area, and counts the number of detected first lidar data for each cell in the grid area during at least one observation frame, , A method characterized in that determining the first lidar data detected in specific cells in which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data. According to claim 2,
A virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, the first zone is the virtual zone closest to the lidar, and the nth zone is the is the farthest virtual zone from − is set, the server determines that the number of specific observation frames in which the first lidar data is detected among the k th cells included in the k th zone during the observation frame is greater than or equal to the k th threshold. The first lidar data detected in k-specific cells is determined as the fixed lidar data, and during the observation frame, the (k+1)th zone, which is a nearby area farther from the lidar than the kth zone The first lidar data detected in the (k+1)th specific cells in which the number of specific observation frames in which the first lidar data is detected among the included (k+1)th cells is equal to or greater than the (k+1)th threshold When it is determined by the fixed lidar data, the (k + 1)th threshold is set to a value lower than or equal to the kth threshold. According to claim 1,
In step (a),
The server divides the area covered by the lidar into a virtual 3D grid area, and counts the number of observation point data of the first lidar data for each cell in the grid area during at least one observation frame And compared with the preset average point data number of each cell, the first lidar data detected in specific cells in which the ratio of the difference between the observation point data number and the average point data number is less than or equal to a threshold ratio is fixed. A method characterized by judging by LiDAR data. According to claim 4,
A virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, the first zone is the virtual zone closest to the lidar, and the nth zone is the is a virtual area furthest from Ida - in this set state, the server, among the k th cells included in the k th area, the ratio of the difference between the number of observation point data of the first lidar data and the average number of point data The first lidar data detected in the kth specific cells that are less than or equal to the kth critical ratio is determined as the fixed lidar data, and the (k+1th) neighboring area farther away from the lidar than the kth area ) Among the (k+1)th cells included in the zone, the ratio of the difference between the number of observation point data of the first lidar data and the average point data number is less than or equal to the (k+1)th critical ratio (k+th) 1) When the first lidar data detected in specific cells is determined to be the fixed lidar data, the (k + 1)th critical ratio is set to a value higher than or equal to the kth critical ratio. How to. According to claim 1,
In determining the fixed lidar data before removing the fixed lidar data from the first lidar data,
Before step (a),
The server obtains the traffic volume of at least one reference frame obtained from the first lidar data, and records reference logging data corresponding to the reference frame when it is determined that the traffic volume is less than or equal to a predetermined value,
In step (a),
Characterized in that the server determines the fixed lidar data included in the first lidar data during the observation frame using the reference logging data for at least one observation frame temporally following the reference frame How to. According to claim 1,
In step (c),
The server classifies the moving object as a vehicle or a pedestrian, and obtains a median value of each feature of the moving object with reference to a Feature Data Set of the moving object; Classifying the moving object using the median value. According to claim 7,
The feature includes at least a part of the size of the moving object, the speed of the moving object, the distance from the lidar, and the number of point data of the first lidar data, and the median value is a support vector machine (Support Vector Machine). Machine), Neural Network, Naive Bayes Classification (Na

ve Bayes Classification), deep learning (Deep Learning), characterized in that the method is obtained using at least one of machine learning (Machine Learning). According to claim 1,
In step (c),
A virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, the first zone is the virtual zone closest to the lidar, and the nth zone is the is a virtual area furthest from . characterized in that it is set to be greater than or equal to the (k+1)th reference value for distinguishing the characteristics of the moving object of the first category and the characteristics of the moving object of the second category included in the (k+1) zone. . According to claim 1,
In step (b),
The server determines a moving path point of the moving object in order to track the moving object,
MeanX and MeanY values of the moving object are used, the moving object is fitted to a 2D rectangle and the median of the fitted 2D rectangle is used, or the moving object is fitted to a 3D rectangle and the fitted 3D rectangle and determining the moving route point using a median value. In the method for making a decision on a moving object using Lidar,
(a) In a state in which fixed lidar data corresponding to a fixed object is obtained from the first lidar data by applying filtering according to a predetermined criterion to the obtained first lidar data, the server, the second lidar data acquiring data;
(b) obtaining, by the server, second residual lidar data with reference to the second lidar data and the fixed lidar data;
(c) tracking, by the server, a moving object by referring to the second residual lidar data; and
(d) classifying, by the server, the moving object with reference to the tracking data of the moving object;
How to include. According to claim 11,
Before step (a),
The server divides the area covered by the lidar into a virtual three-dimensional grid area, and counts the number of detected first lidar data for each cell in the grid area during at least one observation frame, , A method characterized in that determining the first lidar data detected in specific cells in which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data. According to claim 12,
Before step (a),
A virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, the first zone is the virtual zone closest to the lidar, and the nth zone is the is the farthest virtual zone from − is set, the server determines that the number of specific observation frames in which the first lidar data is detected among the k th cells included in the k th zone during the observation frame is greater than or equal to the k th threshold. The first lidar data detected in k-specific cells is determined as the fixed lidar data, and during the observation frame, the (k+1)th zone, which is a nearby area farther from the lidar than the kth zone The first lidar data detected in the (k+1)th specific cells in which the number of specific observation frames in which the first lidar data is detected among the included (k+1)th cells is equal to or greater than the (k+1)th threshold When it is determined by the fixed lidar data, the (k + 1)th threshold is set to a value lower than or equal to the kth threshold. According to claim 11,
Before step (a),
The server divides the area covered by the lidar into a virtual 3D grid area, and counts the number of observation point data of the first lidar data for each cell in the grid area during at least one observation frame And compared with the preset average point data number of each cell, the first lidar data detected in specific cells in which the ratio of the difference between the observation point data number and the average point data number is less than or equal to a threshold ratio is fixed. A method characterized by judging by LiDAR data. According to claim 14,
Before step (a),
A virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, the first zone is the virtual zone closest to the lidar, and the nth zone is the is a virtual area furthest from Ida - in this set state, the server, among the k th cells included in the k th area, the ratio of the difference between the number of observation point data of the first lidar data and the average number of point data The first lidar data detected in the kth specific cells that are less than or equal to the kth critical ratio is determined as the fixed lidar data, and the (k+1th) neighboring area farther away from the lidar than the kth area ) Among the (k+1)th cells included in the zone, the ratio of the difference between the number of observation point data of the first lidar data and the average point data number is less than or equal to the (k+1)th critical ratio (k+th) 1) When the first lidar data detected in specific cells is determined to be the fixed lidar data, the (k + 1)th critical ratio is set to a value higher than or equal to the kth critical ratio. How to. According to claim 11,
In step (b),
A specific position where the first point position and the second point position overlap by matching a plurality of first point positions corresponding to the fixed lidar data and a plurality of second point positions obtained from each of the second lidar data Characterized in that the second residual lidar data is obtained by removing specific lidar data detected from a specific point corresponding to from the second lidar data. A server for determining a moving object using Lidar,
at least one memory for storing instructions; and
at least one processor configured to execute the instructions;
The processor obtains residual lidar data by removing fixed lidar data corresponding to a fixed object from the first lidar data by performing filtering according to a predetermined criterion when (I) first lidar data is obtained. process; (II) a process of tracking the moving object with reference to the residual lidar data; and (III) a process of classifying the moving object with reference to the tracking data of the moving object. According to claim 17,
In the above (I) process,
The processor divides the area covered by the lidar into a virtual 3-dimensional grid area, and counts the number of times the first lidar data is detected for each cell in the grid area during at least one observation frame, The server, characterized in that for determining the first lidar data detected in specific cells of which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data. According to claim 18,
A virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, the first zone is the virtual zone closest to the lidar, and the nth zone is the is a virtual zone furthest from . The first lidar data detected in k-specific cells is determined as the fixed lidar data, and during the observation frame, the (k+1)th zone, which is a nearby area farther from the lidar than the kth zone The first lidar data detected in the (k+1)th specific cells in which the number of specific observation frames in which the first lidar data is detected among the included (k+1)th cells is equal to or greater than the (k+1)th threshold When it is determined by the fixed lidar data, the (k + 1)th threshold is set to a value lower than or equal to the kth threshold. According to claim 17,
In the above (I) process,
The processor divides the area covered by the lidar into a virtual 3D grid area, and counts the number of observation point data of the first lidar data for each cell in the grid area during at least one observation frame And compared with the preset average point data number of each cell, the first lidar data detected in specific cells in which the ratio of the difference between the observation point data number and the average point data number is less than or equal to a threshold ratio is fixed. A server characterized in that it is judged by lidar data. According to claim 20,
A virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, the first zone is the virtual zone closest to the lidar, and the nth zone is the is a virtual zone farthest from the ida - in this set state, the processor, among the k th cells included in the k th zone, the ratio of the difference between the observation point data number of the first lidar data and the average point data number The first lidar data detected in the kth specific cells that are less than or equal to the kth critical ratio is determined as the fixed lidar data, and the (k+1th) neighboring area farther away from the lidar than the kth area ) Among the (k+1)th cells included in the zone, the ratio of the difference between the number of observation point data of the first lidar data and the average point data number is less than or equal to the (k+1)th critical ratio (k+th) 1) When the first lidar data detected in specific cells is determined to be the fixed lidar data, the (k + 1)th critical ratio is set to a value higher than or equal to the kth critical ratio. server that does. According to claim 17,
In determining the fixed lidar data before removing the fixed lidar data from the first lidar data,
Prior to the (I) process,
The processor obtains the traffic volume of at least one reference frame obtained from the first lidar data, and records reference logging data corresponding to the reference frame when it is determined that the traffic volume is equal to or less than a preset value,
In the above (I) process,
Characterized in that the processor determines the fixed lidar data included in the first lidar data during the observation frame using the reference logging data for at least one observation frame temporally following the reference frame server that does. According to claim 17,
In the above (III) process,
The processor classifies the moving object as a vehicle or a pedestrian, and obtains a median value of each feature of the moving object with reference to a feature data set of the moving object; and classifying the moving object using the median value. According to claim 23,
The feature includes at least a part of the size of the moving object, the speed of the moving object, the distance from the lidar, and the number of point data of the first lidar data, and the median value is a support vector machine (Support Vector Machine). Machine), Neural Network, Naive Bayes Classification (Na

ve Bayes Classification), deep learning (Deep Learning), a server characterized in that obtained using at least one of machine learning (Machine Learning). According to claim 17,
In the above (III) process,
A virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, the first zone is the virtual zone closest to the lidar, and the nth zone is the is a virtual area furthest from . Characterized in that the server is set to be greater than or equal to the (k+1)th reference value for distinguishing the characteristics of the moving object of the first category and the characteristics of the moving object of the second category included in the (k+1) zone . According to claim 17,
In the above (II) process,
The processor determines a movement path point of the moving object in order to track the moving object,
MeanX and MeanY values of the moving object are used, the moving object is fitted to a 2D rectangle and the median of the fitted 2D rectangle is used, or the moving object is fitted to a 3D rectangle and the fitted 3D rectangle The server, characterized in that for determining the movement route point using the median value. A server for determining a moving object using Lidar,
at least one memory for storing instructions; and
at least one processor configured to execute the instructions;
The processor, (I) in a state in which fixed lidar data corresponding to a fixed object is obtained from the first lidar data by applying filtering according to a predetermined criterion to the obtained first lidar data, the second lidar data is obtained. is the process of acquiring data; (II) a process of obtaining second residual lidar data with reference to the second lidar data and the fixed lidar data; (III) a process of tracking a moving object with reference to the second residual lidar data; and (IV) a process of classifying the moving object with reference to the tracking data of the moving object. The method of claim 27,
Prior to the (I) process,
The processor divides the area covered by the lidar into a virtual 3-dimensional grid area, and counts the number of times the first lidar data is detected for each cell in the grid area during at least one observation frame, The server, characterized in that for determining the first lidar data detected in specific cells of which the counted number of the cells is equal to or greater than a threshold value as the fixed lidar data. According to claim 28,
Prior to the (I) process,
A virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, the first zone is the virtual zone closest to the lidar, and the nth zone is the is a virtual zone furthest from . The first lidar data detected in k-specific cells is determined as the fixed lidar data, and during the observation frame, the (k+1)th zone, which is a nearby area farther from the lidar than the kth zone The first lidar data detected in the (k+1)th specific cells in which the number of specific observation frames in which the first lidar data is detected among the included (k+1)th cells is equal to or greater than the (k+1)th threshold When it is determined by the fixed lidar data, the (k + 1)th threshold is set to a value lower than or equal to the kth threshold. The method of claim 27,
Prior to the (I) process,
The processor divides the area covered by the lidar into a virtual 3D grid area, and counts the number of observation point data of the first lidar data for each cell in the grid area during at least one observation frame And compared with the preset average point data number of each cell, the first lidar data detected in specific cells in which the ratio of the difference between the observation point data number and the average point data number is less than or equal to a threshold ratio is fixed. A server characterized in that it is judged by lidar data. 31. The method of claim 30,
Prior to the (I) process,
A virtual zone corresponding to each predetermined distance from the lidar - the virtual zone includes a first to nth zone, the first zone is the virtual zone closest to the lidar, and the nth zone is the is a virtual zone farthest from the ida - in this set state, the processor, among the k th cells included in the k th zone, the ratio of the difference between the observation point data number of the first lidar data and the average point data number The first lidar data detected in the kth specific cells that are less than or equal to the kth critical ratio is determined as the fixed lidar data, and the (k+1th) neighboring area farther away from the lidar than the kth area ) Among the (k+1)th cells included in the zone, the ratio of the difference between the number of observation point data of the first lidar data and the average point data number is less than or equal to the (k+1)th critical ratio (k+th) 1) When the first lidar data detected in specific cells is determined to be the fixed lidar data, the (k + 1)th critical ratio is set to a value higher than or equal to the kth critical ratio. server that does. The method of claim 27,
In the above (II) process,
A specific position where the first point position and the second point position overlap by matching a plurality of first point positions corresponding to the fixed lidar data and a plurality of second point positions obtained from each of the second lidar data The server, characterized in that for obtaining the second residual lidar data by removing specific lidar data detected from a specific point corresponding to the second lidar data.

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