KR20220064913A - Apparatus and for detecting and removing dynamic obstacle of robot and operating method thereof - Google Patents

Abstract

An apparatus for detecting and removing a dynamic obstacle of a robot by an operation method thereof according to a preferred embodiment of the present invention detects and removes a dynamic obstacle while simultaneously generating a map and estimating a location using a simultaneous localization and mapping (SLAM) technology, to efficiently detect and remove the dynamic obstacle even in a situation with large dynamic changes in surrounding environments and large size of the environments in which location estimation is to be performed. The apparatus comprises a storage unit, a tracking management unit, a dynamic obstacle detection unit, and a dynamic obstacle removal unit.

Description

Apparatus and for detecting and removing dynamic obstacle of robot and operating method thereof

The present invention relates to an apparatus for detecting and deleting a dynamic obstacle for a robot and an operating method thereof, and more particularly, to a dynamic obstacle detection and deletion method while simultaneously performing map preparation and location estimation using SLAM (Simultaneous Localization And Mapping) technology. , an apparatus and a method of operation thereof.

If the dynamic obstacles are not removed, the robot will have difficulty in planning an unnecessary detour or estimating its location. Conventionally, in order to remove dynamic obstacles, either location recognition and map preparation cannot be performed at the same time, or a method of manually deleting dynamic obstacles from the map after map preparation is used.

This method increases the user's time and cost, and as it is done manually, there may be side effects such as damage to the map due to the user's mistake. In the same complex environment, it is difficult to manually distinguish between a dynamic obstacle and a static obstacle.

Accordingly, it is necessary to develop a technology to detect and delete dynamic obstacles while simultaneously performing location recognition and map creation (Simultaneous Localization And Mapping, SLAM).

An object of the present invention is to provide a device for detecting and deleting a dynamic obstacle for a robot and an operating method thereof, which detects and deletes a dynamic obstacle while simultaneously performing map preparation and location estimation using SLAM (Simultaneous Localization And Mapping) technology is to do

Other objects not specified in the present invention may be additionally considered within the scope that can be easily inferred from the following detailed description and effects thereof.

According to a preferred embodiment of the present invention for achieving the above object, there is provided an apparatus for detecting and deleting a dynamic obstacle for a robot, comprising: a storage unit for storing a map composed of cells of a preset size and composed of a three-dimensional coordinate space; Upon receiving sensor data from a sensor mounted on the robot, the sensor data is input to the map, and accumulated detection time information of a cell is updated for each cell of the map based on the cell detected through the sensor data, and the sensor a tracking management unit that updates cell tracking information for each cell of the map based on whether a laser obtained from data passes through the cell; a dynamic obstacle detection unit for obtaining whether a cell is a dynamic obstacle based on the accumulated detection time information and tracking information for each cell of the map, updated through the tracking management unit; and a dynamic obstacle deletion unit that deletes data corresponding to a cell detected as a dynamic obstacle through the dynamic obstacle detection unit from the map.

Here, the tracking management unit acquires initial time information of a cell for a first detected cell among the cells detected through the sensor data, and for the remaining cells, cumulative detection time information of a cell based on the initial time information of the cell can be updated.

Here, the tracking information includes first tracking information indicating the number of times the laser is formed on the cell and second tracking information indicating the number of times the laser passes through the cell, and the tracking management unit, based on the sensor data, For a cell through which is passed, the second tracking information of the cell may be increased by a preset unit, and for a cell on which a laser is focused, the first tracking information of the cell may be increased by a preset unit.

Here, the tracking management unit may obtain a weight based on a distance between the cell and the sensor and a resolution of the sensor, and obtain the first tracking information and the second tracking information of the cell by using the weight. there is.

Here, the dynamic obstacle detecting unit detects whether the cell is a dynamic obstacle based on the obstacle existence probability and the first tracking information for a cell in which the accumulated detection time information of the cell is equal to or longer than a preset reference time, and the obstacle existence probability may be obtained based on the first tracking information and the second tracking information.

Here, the dynamic obstacle detection unit may detect a cell in which the obstacle existence probability is less than or equal to a preset first reference value and the first tracking information is less than or equal to a preset second reference value as a dynamic obstacle.

Here, the obstacle existence probability may be obtained by dividing the first tracking information by a value obtained by adding the first tracking information and the second tracking information.

Here, the tracking management unit, upon receiving the sensor data from the sensor mounted on the robot, corrects the position of the sensor data, inputs the corrected position of the sensor data to the map, and the position of the corrected sensor The accumulated detection time information of the cell may be updated based on the data, and the tracking information of the cell may be updated based on the sensor data whose position is corrected.

Here, the data structure of the cell of the map may be formed of an octree data structure.

In a method of operating a device for detecting and deleting a dynamic obstacle for a robot according to a preferred embodiment of the present invention for achieving the above object, when sensor data is provided from a sensor mounted on the robot, the sensor data is input to a map, and the Based on the cell detected through the sensor data, the cumulative detection time information of the cell is updated for each cell of the map, and the tracking information of the cell is obtained based on whether the laser obtained from the sensor data passes through the cell. updating the map for each cell; obtaining whether a cell is a dynamic obstacle based on the accumulated detection time information and tracking information for each cell of the map; and deleting data corresponding to a cell detected as a dynamic obstacle from the map, wherein the map includes cells of a preset size and a three-dimensional coordinate space.

Here, the tracking information includes first tracking information indicating the number of times the laser is formed on the cell and second tracking information indicating the number of times the laser passes through the cell, and the updating step includes: For a cell through which is passed, the second tracking information of the cell is increased by a preset unit, and for a cell on which a laser is focused, the first tracking information of the cell is increased by a preset unit.

In this case, the step of obtaining whether the dynamic obstacle is a dynamic obstacle consists of detecting whether the cell is a dynamic obstacle based on the obstacle existence probability and the first tracking information for a cell in which the accumulated detection time information of the cell is equal to or longer than a preset reference time. and the obstacle existence probability may be obtained based on the first tracking information and the second tracking information.

A computer program according to a preferred embodiment of the present invention for achieving the above technical problem is stored in a computer-readable recording medium, and any one of the operating methods of the apparatus for detecting and deleting a dynamic obstacle of the robot is executed in the computer .

According to the apparatus for detecting and deleting a dynamic obstacle for a robot and an operation method thereof according to a preferred embodiment of the present invention, a dynamic obstacle is detected and deleted while simultaneously performing map preparation and location estimation using SLAM (Simultaneous Localization And Mapping) technology. , it is possible to efficiently detect and delete a dynamic obstacle even in a situation where the dynamic change of the surrounding environment is severe and the size of the environment to be estimated is large.

Effects of the present invention are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a block diagram illustrating an apparatus for detecting and deleting a dynamic obstacle of a robot according to a preferred embodiment of the present invention.
2 is a diagram for explaining an example of a tracking information update operation according to a preferred embodiment of the present invention.
3 is a diagram for explaining another example of a tracking information update operation according to a preferred embodiment of the present invention.
4 is a view for explaining an example of application of a dynamic obstacle detection and deletion operation according to a preferred embodiment of the present invention.
5 is a view for explaining the effect of the dynamic obstacle detection and deletion operation according to a preferred embodiment of the present invention.
6 is a flowchart for explaining a method of operating a device for detecting and deleting a dynamic obstacle of a robot according to a preferred embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. Advantages and features of the present invention, and a method for achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments published below, but may be implemented in various different forms, and only these embodiments make the publication of the present invention complete, and common knowledge in the art to which the present invention pertains It is provided to fully inform those who have the scope of the invention, and the present invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout.

Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used with the meaning commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless clearly defined in particular.

In the present specification, terms such as “first” and “second” are for distinguishing one component from other components, and the scope of rights should not be limited by these terms. For example, a first component may be termed a second component, and similarly, a second component may also be termed a first component.

In the present specification, identification symbols (eg, a, b, c, etc.) in each step are used for convenience of description, and identification symbols do not describe the order of each step, and each step is clearly Unless a specific order is specified, the order may differ from the specified order. That is, each step may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.

In this specification, expressions such as “have”, “may have”, “include” or “may include” indicate the existence of a corresponding feature (eg, a numerical value, function, operation, or component such as a part). and does not exclude the presence of additional features.

In addition, the term '~ unit' as used herein means software or a hardware component such as a field-programmable gate array (FPGA) or ASIC, and '~ unit' performs certain roles. However, '-part' is not limited to software or hardware. '~' may be configured to reside on an addressable storage medium or may be configured to refresh one or more processors. Accordingly, as an example, '~' indicates components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, and procedures. , subroutines, segments of program code, drivers, firmware, microcode, circuitry, data structures and variables. The functions provided in the components and '~ units' may be combined into a smaller number of components and '~ units' or further separated into additional components and '~ units'.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a preferred embodiment of an apparatus for detecting and deleting a robot and an operating method thereof according to the present invention will be described in detail with reference to the accompanying drawings.

First, an apparatus for detecting and deleting a dynamic obstacle of a robot according to a preferred embodiment of the present invention will be described with reference to FIGS. 1 to 3 .

1 is a block diagram for explaining a device for detecting and deleting a dynamic obstacle of a robot according to a preferred embodiment of the present invention, and FIG. 2 is a view for explaining an example of a tracking information update operation according to a preferred embodiment of the present invention. , Figure 3 is a view for explaining another example of the tracking information update operation according to a preferred embodiment of the present invention.

Referring to FIG. 1 , a robot dynamic obstacle detection and deletion apparatus (hereinafter referred to as a 'dynamic obstacle detection and deletion apparatus') 100 according to a preferred embodiment of the present invention uses SLAM (Simultaneous Localization And Mapping) technology. It detects and deletes dynamic obstacles while mapping and estimating at the same time.

Here, the present invention may be used in household cleaning robots, cleaning robots for public buildings, logistics robots, service robots, and industrial robots.

To this end, the dynamic obstacle detection and deletion apparatus 100 may include a storage unit 110 , a tracking management unit 130 , a dynamic obstacle detection unit 150 , and a dynamic obstacle deletion unit 170 .

The storage 110 serves to store programs and data necessary for the operation of the dynamic obstacle detection and deletion apparatus 100 , and may be divided into a program area and a data area. The program area includes a program for controlling the overall operation of the dynamic obstacle detection and deletion apparatus 100 , an operating system (OS) for booting the dynamic obstacle detection and deletion apparatus 100 , and accumulated detection time information of cells. An application program necessary for the operation of the apparatus 100 for detecting and deleting a dynamic obstacle such as update, update of tracking information of a cell, detection of a dynamic obstacle, and deletion of a dynamic obstacle may be stored. The data area is an area in which data generated according to the use of the dynamic obstacle detection and deletion apparatus 100 is stored, and may store a map, initial time information of a cell, information about a cumulative detection time of a cell, tracking information of a cell, and the like.

Here, the map may be formed of cells having a preset size (such as '3 cm') and may be formed in a three-dimensional coordinate space.

In this case, the data structure of the cell of the map may be formed of an octree data structure. Due to the nature of SLAM technology that needs to operate in real time, a series of object detection and deletion methods with a large number of data accesses require optimized data access. To this end, the cell according to the present invention uses an octree method as a data structure.

That is, the octree data structure is a tree-type data structure in which one intermediate node has 8 child nodes. By expressing the cells of the map as an octree data structure, the access speed to a three-dimensional space can be improved . In the case of a naive implementation using KD-tree, there is a problem that uses too much memory or takes a long time to search.

The tracking management unit 130 manages the tracking information of cells used for dynamic obstacle detection based on sensor data acquired from a sensor mounted on the robot for each cell of the map stored in the storage unit 110 .

Here, the tracking information may include first tracking information indicating the number of times a laser is formed on the cell and second tracking information indicating the number of times the laser passes through the cell.

That is, when the tracking management unit 130 receives sensor data from a sensor mounted on the robot, it inputs the sensor data to the map, and updates the accumulated detection time information of the cells for each cell of the map based on the cells detected through the sensor data. And, based on whether the laser obtained from the sensor data passes through the cell, the tracking information of the cell may be updated for each cell of the map. At this time, when the tracking management unit 130 receives sensor data from a sensor mounted on the robot, it corrects the position using the existing map of the sensor data, inputs the corrected position sensor data to the map, and the position corrected sensor The accumulated detection time information of the cell may be updated based on the data, and the tracking information of the cell may be updated based on the sensor data whose position is corrected.

More specifically, the tracking management unit 130 acquires the first time information of the cell for the first detected cell among the cells detected through the sensor data, and for the remaining cells, the accumulation of cells based on the first time information of the cell The detection time information can be updated.

Then, as shown in FIG. 2 , the tracking management unit 130 increases the second tracking information of the cell by a preset unit ('1', etc.) for a cell through which the laser passes, based on the sensor data, and the laser For a cell in which , the first tracking information of the cell may be increased by a preset unit ('1', etc.).

For example, in the case of a cell through which a laser passes, the first tracking information may be maintained at the same value as before, and the second tracking information may be updated with "the existing value of the second tracking information + a preset unit". On the other hand, in the case of a laser-focused cell, the first tracking information may be updated to "the existing value of the first tracking information + a preset unit", and the second tracking information may be maintained at the same value as the existing value.

At this time, as shown in FIG. 3 , the tracking management unit 130 obtains a weight based on the distance between the cell and the sensor and the resolution of the sensor, and uses the weight to obtain the first tracking information and the second tracking information of the cell. It is also possible to obtain tracking information.

That is, the weight can be calculated through [Equation 1] below.

을 통해 계산되며, (x,y,z)는 셀의 좌표값을 나타낸다. sensor frequency는 센서의 분해능을 나타낸다. 예컨대, 3D 라이다(LiDAR)의 경우, 1개의 레이저 센서가 최상단 45도부터 최하단 -45도까지 1.6초동안 순차적으로 스윕(sweep)하며 움직이고, 최상단에서 다시 최상단에 도달하기까지 3.2초가 소요된다. 이 경우, 센서의 분해능은

를 통해 계산되며, tuning factor는 해당 센서에 대해 미리 설정된 값을 나타낸다.Here, weight represents a weight. distance represents the distance between the cell and the sensor,

It is calculated through , and (x, y, z) represents the coordinate value of the cell. The sensor frequency indicates the resolution of the sensor. For example, in the case of 3D LiDAR, one laser sensor sequentially sweeps from the top 45 degrees to the bottom -45 degrees for 1.6 seconds and moves, and it takes 3.2 seconds to reach the top again from the top. In this case, the resolution of the sensor is

It is calculated through , and the tuning factor represents a preset value for the corresponding sensor.

For example, in the case of a cell through which the laser passes, the first tracking information is maintained at the same value as before, and the second tracking information is updated to "(existing value of the second tracking information + preset unit) * weight of the corresponding cell" can do.

That is, the second tracking information may be calculated through [Equation 2] below.

Here, miss(t) represents the second tracking information (miss) at t. miss(t-1) represents the second tracking information (miss) at t-1. The number of miss indicates a preset unit such as the number of cells that the laser has passed through. weight represents the weight.

On the other hand, in the case of a laser-focused cell, the first tracking information is updated to "(existing value of the first tracking information + preset unit) * weight of the corresponding cell", and the second tracking information can be maintained at the same value as before. there is.

That is, the first tracking information may be calculated through [Equation 3] below.

Here, hit(t) represents the first tracking information (hit) at t. hit(t-1) represents the first tracking information (hit) at t-1. The number of hit indicates a preset unit such as the number of cells on which the laser is focused. weight represents the weight.

That is, due to the characteristics of LiDAR, the density of sensor data changes according to the distance from the sensor. In order to detect and delete a moving object at a distance, the present invention may multiply the distance from the sensor and a weight obtained using the sensor resolution when calculating the first tracking information and the second tracking information.

The dynamic obstacle detection unit 150 acquires whether a cell is a dynamic obstacle based on the tracking information and the accumulated detection time information for each cell of the map updated through the tracking management unit 130 .

That is, the dynamic obstacle detection unit 150 may detect whether the cell is a dynamic obstacle based on the obstacle existence probability and the first tracking information for a cell in which the accumulated detection time information of the cell is equal to or longer than a preset reference time.

For example, if it is detected at t=0, not detected at t=1, and not detected even at t=3, the existence of an obstacle should be suspected. That is, in order to delete the corresponding content when it cannot be continuously detected, a reference time can be set, which is preset by the administrator.

Here, the obstacle existence probability may be obtained based on the first tracking information and the second tracking information. For example, the obstacle presence probability (p) can be obtained by dividing the first tracking information (hit) by a value obtained by adding the first tracking information (hit) and the second tracking information (miss) as shown in [Equation 4] below. there is.

For example, the dynamic obstacle detecting unit 150 may detect a cell having an obstacle existence probability equal to or less than a preset first reference value and having first tracking information equal to or less than a preset second reference value as a dynamic obstacle.

Here, the first reference value may be '50%'. The second reference value may be calculated through [Equation 5] below.

The dynamic obstacle deletion unit 170 deletes data corresponding to a cell detected as a dynamic obstacle through the dynamic obstacle detection unit 150 from the map.

Then, the effect of the robot's dynamic obstacle detection and deletion operation according to a preferred embodiment of the present invention will be described with reference to FIGS. 4 and 5 .

4 is a view for explaining an example of application of the dynamic obstacle detection and deletion operation according to the preferred embodiment of the present invention, Figure 5 is for explaining the effect of the dynamic obstacle detection and deletion operation according to the preferred embodiment of the present invention It is a drawing.

The result of performing the dynamic obstacle detection and deletion operation according to the present invention during SLAM for simultaneously recognizing location and creating a map is shown in FIG. 4 . Referring to FIG. 4 , colored cells represent cells registered on a map through dynamic obstacle detection and deletion operations according to the present invention.

Referring to FIG. 5 , according to the present invention, an object approaching toward the robot can be detected and deleted by using the cell tracking information and the cell's cumulative detection time information together. In other words, it is possible to solve the problem that the previous movement trajectory of the object cannot be detected because it is obscured by an object approaching the robot.

Then, a description will be given of a method of operating the apparatus for detecting and deleting a dynamic obstacle of a robot according to a preferred embodiment of the present invention with reference to FIG. 6 .

6 is a flowchart for explaining a method of operating a device for detecting and deleting a dynamic obstacle of a robot according to a preferred embodiment of the present invention.

Referring to FIG. 6 , the dynamic obstacle detection and deletion apparatus 100 receives sensor data from a sensor mounted on the robot, inputs the sensor data to a map, and cumulatively detects cells based on the cells detected through the sensor data. Time information is updated for each cell of the map, and tracking information of the cell is updated for each cell of the map based on whether the laser obtained from the sensor data passes through the cell (S110). At this time, when the dynamic obstacle detection and deletion apparatus 100 receives sensor data from a sensor mounted on the robot, it corrects the position using the existing map of the sensor data, inputs the corrected position sensor data to the map, and the position may update information on the accumulated detection time of the cell based on the corrected sensor data, and update the tracking information of the cell based on the corrected sensor data.

More specifically, the dynamic obstacle detection and deletion apparatus 100 acquires the first time information of the cell for the first detected cell among the cells detected through the sensor data, and for the remaining cells, based on the first time information of the cell to update the cell's accumulated detection time information.

And, the dynamic obstacle detection and deletion apparatus 100 increases the second tracking information of the cell by a preset unit for the cell through which the laser passes, based on the sensor data, and the first tracking of the cell for the cell on which the laser is focused Information can be incremented in a preset unit.

In this case, the dynamic obstacle detection and deletion apparatus 100 obtains a weight based on the distance between the cell and the sensor and the resolution of the sensor, and may obtain the first tracking information and the second tracking information of the cell by using the weight. .

Thereafter, the dynamic obstacle detection and deletion apparatus 100 acquires whether a cell is a dynamic obstacle based on the accumulated detection time information and tracking information for each cell of the map ( S130 ).

That is, the dynamic obstacle detection and deletion apparatus 100 may detect whether the cell is a dynamic obstacle based on the obstacle existence probability and the first tracking information for a cell in which the accumulated detection time information of the cell is equal to or longer than a preset reference time. . Here, the obstacle existence probability may be obtained based on the first tracking information and the second tracking information.

For example, the apparatus 100 for detecting and deleting a dynamic obstacle may detect a cell having an obstacle existence probability equal to or less than a preset first reference value and having first tracking information equal to or less than a preset second reference value as a dynamic obstacle.

Then, the dynamic obstacle detection and deletion apparatus 100 deletes data corresponding to a cell detected as a dynamic obstacle from the map (S150).

Even though all the components constituting the embodiment of the present invention described above are described as being combined or operated in combination, the present invention is not necessarily limited to this embodiment. That is, within the scope of the object of the present invention, all the components may operate by selectively combining one or more. In addition, all of the components may be implemented as one independent hardware, but a part or all of each component is selectively combined to perform some or all of the functions combined in one or a plurality of hardware program modules It may be implemented as a computer program having In addition, such a computer program is stored in a computer readable media such as a USB memory, a CD disk, a flash memory, etc., read and executed by a computer, thereby implementing an embodiment of the present invention. The recording medium of the computer program may include a magnetic recording medium, an optical recording medium, and the like.

The above description is merely illustrative of the technical idea of the present invention, and those of ordinary skill in the art to which the present invention pertains may make various modifications, changes and substitutions within the scope without departing from the essential characteristics of the present invention. will be. Accordingly, the embodiments disclosed in the present invention and the accompanying drawings are for explaining, not limiting, the technical spirit of the present invention, and the scope of the technical spirit of the present invention is not limited by these embodiments and the accompanying drawings . The protection scope of the present invention should be construed by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present invention.

100: dynamic obstacle detection and deletion device;
110: storage unit;
130: tracking management unit;
150: dynamic obstacle detection unit,
170: dynamic obstacle removal unit

Claims (13)

a storage unit for storing a map composed of cells of a preset size and composed of a three-dimensional coordinate space;
Upon receiving sensor data from a sensor mounted on the robot, the sensor data is input to the map, and accumulated detection time information of a cell is updated for each cell of the map based on the cell detected through the sensor data, and the sensor a tracking management unit for updating cell tracking information for each cell of the map based on whether a laser obtained from data passes through the cell;
a dynamic obstacle detection unit for obtaining whether a cell is a dynamic obstacle based on the cumulative detection time information and tracking information for each cell of the map, updated through the tracking management unit; and
a dynamic obstacle deletion unit for deleting data corresponding to a cell detected as a dynamic obstacle from the map through the dynamic obstacle detection unit;
A robot's dynamic obstacle detection and deletion device comprising a. In claim 1,
The tracking management unit,
Obtaining initial time information of a cell for the first detected cell among the cells detected through the sensor data, and updating the accumulated detection time information of the cell based on the initial time information of the cell for the remaining cells,
Robot's dynamic obstacle detection and deletion device. In claim 1,
The tracking information is
It includes first tracking information indicating the number of times the laser has hit the cell and second tracking information indicating the number of times the laser has passed through the cell,
The tracking management unit,
Based on the sensor data, increasing the second tracking information of the cell by a preset unit for a cell through which the laser passes, and increasing the first tracking information of the cell by a preset unit for a cell on which the laser is focused,
Robot's dynamic obstacle detection and deletion device. In claim 3,
The tracking management unit,
Obtaining a weight based on the distance between the cell and the sensor and the resolution of the sensor, and obtaining the first tracking information and the second tracking information of the cell by using the weight,
Robot's dynamic obstacle detection and deletion device. In claim 3,
The dynamic obstacle detection unit,
For a cell in which the accumulated detection time information of the cell is equal to or longer than a preset reference time, it is detected whether the cell is a dynamic obstacle based on the obstacle existence probability and the first tracking information,
The probability of the existence of the obstacle is,
Obtained based on the first tracking information and the second tracking information,
Robot's dynamic obstacle detection and deletion device. In claim 5,
The dynamic obstacle detection unit,
Detecting a cell in which the obstacle existence probability is less than or equal to a preset first reference value and the first tracking information is less than or equal to a preset second reference value as a dynamic obstacle,
Robot's dynamic obstacle detection and deletion device. In claim 6,
The probability of the existence of the obstacle is
Obtained by dividing the first tracking information by a value obtained by adding the first tracking information and the second tracking information,
Robot's dynamic obstacle detection and deletion device. In claim 1,
The tracking management unit,
Upon receiving the sensor data from the sensor mounted on the robot, the position of the sensor data is corrected, the position corrected sensor data is input to the map, and the cell is accumulated based on the corrected position of the sensor data. Updating the detection time information and updating the tracking information of the cell based on the sensor data whose position is corrected,
Robot's dynamic obstacle detection and deletion device. In claim 1,
The data structure of the cell of the map is,
Consists of an octree data structure,
Robot's dynamic obstacle detection and deletion device. When sensor data is provided from a sensor mounted on the robot, the sensor data is input to the map, and accumulated detection time information of the cell is updated for each cell of the map based on the cell detected through the sensor data, updating cell tracking information for each cell of the map based on whether a laser obtained from the sensor data passes through the cell;
obtaining whether a cell is a dynamic obstacle based on the accumulated detection time information and tracking information for each cell of the map; and
deleting data corresponding to a cell detected as a dynamic obstacle from the map;
includes,
The map is
Consisting of cells of a preset size and consisting of a three-dimensional coordinate space,
The operation method of the robot's dynamic obstacle detection and deletion device. In claim 10,
The tracking information is
It includes first tracking information indicating the number of times the laser has hit the cell and second tracking information indicating the number of times the laser has passed through the cell,
The update step is
Based on the sensor data, increasing the second tracking information of the cell by a preset unit for a cell through which the laser passes, and increasing the first tracking information of the cell by a preset unit for a cell on which the laser is focused,
The operation method of the robot's dynamic obstacle detection and deletion device. In claim 11,
The step of acquiring whether the dynamic obstacle is
With respect to a cell in which the accumulated detection time information of the cell is equal to or longer than a preset reference time, detecting whether the cell is a dynamic obstacle based on the obstacle existence probability and the first tracking information,
The probability of the existence of the obstacle is
Obtained based on the first tracking information and the second tracking information,
The operation method of the robot's dynamic obstacle detection and deletion device. A computer program stored in a computer-readable recording medium in order to execute the operating method of the apparatus for detecting and deleting a dynamic obstacle of a robot according to any one of claims 10 to 12.

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