KR101997531B1 - Obstacle avoiding system for a personal mobility and a method for avoiding an obstacle using the same - Google Patents

Abstract

In the obstacle avoidance system of the personal moving mechanism and the obstacle avoiding method using the same, the obstacle avoidance system includes a scanning unit, a floor surface extracting unit, an obstacle map generating unit, an obstacle information extracting unit, a avoiding direction calculating unit, and a driving control unit. The scanning unit acquires depth information on the front of the personal moving mechanism and converts the acquired depth information into three-dimensional point data. The bottom surface extracting unit extracts bottom surface data from the three-dimensional point data. The obstacle map generation unit generates a two-dimensional obstacle map from the floor data. The obstacle information extracting unit extracts obstacle information from the two-dimensional obstacle map. The avoidance direction calculation unit calculates a direction for avoiding the obstacle when an obstacle is detected from the obstacle information. And the drive control unit drives the personal moving mechanism in the avoidance direction.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an OBM

The present invention relates to an obstacle avoidance system and an obstacle avoiding method using the obstacle avoidance system. More particularly, the present invention relates to an obstacle avoidance system that is mounted on a personal moving apparatus such as an electric wheelchair mounted on a disabled person or an elderly person, To an obstacle avoidance system and an obstacle avoidance method using the same.

In recent years, the use of personal mobile devices such as electric wheelchairs and electric scooters has been increasing as the need for improved quality of life for disabled people or elderly people with mobility problems is increasing. However, in the case of the handicapped or the elderly, the judgment ability or the steering ability is relatively lowered in comparison with the normal person.

Accordingly, techniques for reducing the occurrence of accidents during driving are being developed. For example, Korean Patent Registration No. 10-1440054 discloses a technique for detecting an inclination of a traveling direction of an electric wheelchair or detecting an obstacle on the basis of a tilt sensor, and in Korean Patent Publication No. 10-2015-0027987, Discloses a technique of setting a traveling route based on image information of a ground on which the vehicle travels and informing the occupant of the traveling route.

However, there is a problem in that the safety of the personal moving mechanism is insufficient with only the tilt sensor, and in the case of using the image information, expensive sensor and processing system for photographing and analyzing the image information must be mounted.

Furthermore, there is a technique of using ultrasonic waves as a method of detecting an obstacle. However, although the processing speed of the ultrasonic using technique is advantageous, the resolution of the detected image is very low, which makes it difficult to identify obstacles.

Korean Patent No. 10-1440054 Korean Patent Publication No. 10-2015-0027987

SUMMARY OF THE INVENTION Accordingly, the present invention has been made keeping in mind the above problems occurring in the prior art, and an object of the present invention is to provide an obstacle avoidance system for a personal moving mechanism capable of enhancing safety with a relatively high processing speed and improved accuracy of detected images.

Another object of the present invention is to provide an obstacle avoiding method using the obstacle avoidance system.

According to an embodiment of the present invention, an obstacle avoidance system includes a scanning unit, a floor surface extracting unit, an obstacle map generating unit, an obstacle information extracting unit, a avoiding direction calculating unit, and a driving control unit. The scanning unit acquires depth information on the front of the personal moving mechanism and converts the acquired depth information into three-dimensional point data. The bottom surface extracting unit extracts bottom surface data from the three-dimensional point data. The obstacle map generation unit generates a two-dimensional obstacle map from the floor data. The obstacle information extracting unit extracts obstacle information from the two-dimensional obstacle map. The avoidance direction calculation unit calculates a direction for avoiding the obstacle when an obstacle is detected from the obstacle information. And the drive control unit drives the personal moving mechanism in the avoidance direction.

In one embodiment, the scanning unit may include a depth camera attached to the personal moving mechanism.

In one embodiment, the bottom surface extracting unit selects a combination of three points selected from the three-dimensional point data, which is the closest normal vector to the initial normal vector of the bottom surface, Can be defined.

In one embodiment, the obstacle map generator may calculate a normal distance to the defined floor for each of the combinations, select a combination having a normal distance greater than a threshold value for the obstacle size, Can be changed.

In one embodiment, the obstacle information extracting unit may determine an obstacle by removing noise based on the number of data constituting the two-dimensional coordinate.

In one embodiment, if no obstacle is detected from the obstacle information, the obstacle detecting unit may further include a cliff detecting unit for detecting cliffs from the floor data.

In one embodiment, the cliff detection unit calculates a ratio of a width of a virtual band region defined in front of the personal moving mechanism and a width of an area formed by the three-dimensional point data obtained in the virtual band region The cliff can be detected.

In one embodiment, the drive control section may stop the traveling of the personal moving mechanism when the cliff detection section detects a cliff.

In the obstacle avoidance method according to an embodiment of the present invention for realizing the above-described object, the depth information of the forward movement of the personal moving mechanism is acquired and converted into three-dimensional point data. And extracts bottom surface data from the three-dimensional point data. And generates a two-dimensional obstacle map from the floor surface data. Obstacle information is extracted from the two-dimensional obstacle map. And calculates a direction for avoiding the obstacle when the obstacle is detected from the obstacle information. And drives the personal moving mechanism in the avoidance direction.

In one embodiment, the step of extracting the floor surface data comprises the steps of: sampling N points at regular intervals of the three-dimensional point data; selecting a combination of three points out of the N points; Selecting a combination having a normal vector most similar to an initial normal vector of the normal vectors, computing a center point in the normal vector of the selected combination, And defining bottom surface data as a center point.

In one embodiment, the step of generating the two-dimensional obstacle map comprises computing a normal distance to the floor for each combination, selecting a combination having a normal distance greater than a threshold value for the size of the obstacle And changing the selected combination to two-dimensional coordinates.

In one embodiment, the step of extracting the obstacle information may determine an obstacle by removing noise based on the number of data constituting the two-dimensional coordinate.

In one embodiment, if an obstacle is not detected from the obstacle information, the step of detecting a cliff from the floor data may further include detecting the obstacle.

In one embodiment, the step of detecting the cliff may include detecting a ratio of a width of a virtual band region defined in front of the personal moving mechanism to a width of an area formed by the three-dimensional point data obtained in the virtual band region ) Can be calculated to detect the cliff.

In one embodiment, the ratio of the width of the imaginary band region to the width of the region formed by the three-dimensional point data is calculated by the following equation,

인 경우, 절벽으로 검출할 수 있다.

, It can be detected as a cliff.

In one embodiment, the step of stopping the traveling of the personal moving mechanism may be further included when the cliff is detected.

According to the embodiments of the present invention, a two-dimensional obstacle map is generated from the floor surface data and the obstacle information is extracted from the two-dimensional obstacle map, so that the resolution is higher than the obstacle detection result using the conventional ultrasonic wave, It is cheaper and more practical than the system, it can increase data processing speed and it can extract accurate obstacle information.

In particular, since the scanning unit acquires depth information using a depth camera, it can acquire relatively inexpensive but relatively accurate data as compared with a laser or a stereo camera, and information about cliffs as well as obstacles can be acquired.

In addition, since the bottom surface extractor uses the initial normal vector, it is possible to extract the bottom surface more accurately than the conventional RANSAC algorithm. In the case of extracting arbitrary point data combination for bottom surface extraction, However, since the combination of three points is used, it is possible to calculate the floor from a smaller number of sampling points.

In addition, since the obstacle map generation unit determines obstacles based on the number of data constituting the two-dimensional coordinates, it is possible to perform real-time calculation by simple calculation for extracting obstacle information, to easily distinguish noise, It is possible to change the grid size, which is a criterion for determining the number of data, to perform an optimized operation.

In addition, even when a protruding obstacle is not detected, since there are many cliffs such as a staircase in the room, it is possible to detect the cliff from the floor surface data, thereby further improving the safety of the personal transportation means.

In this case, the cliff detection algorithm can improve the accuracy of judgment while performing the calculation relatively easily by applying the concept of the virtual band region.

Particularly, in the case of a cliff, the risk of the obstacle is higher than that of the obstacle. Therefore, the driving control unit performs the stop driving unlike the avoidance driving of the obstacle.

1 is a block diagram illustrating an obstacle avoidance system according to an embodiment of the present invention.
2 is a flowchart illustrating an obstacle avoidance method using the obstacle avoidance system of FIG.
Fig. 3 is a flowchart showing a step of extracting the floor surface data of Fig.
4 is a flow chart illustrating the step of generating the two-dimensional obstacle map of FIG.
FIGS. 5A and 5B are images obtained by obtaining the depth information of FIG. 2 and converted into three-dimensional point data.
FIG. 6 is an image showing an example of generating the two-dimensional obstacle map of FIG.
FIG. 7 is an image showing an example of determining whether or not the obstacle exists in FIG.
FIG. 8 is a schematic diagram illustrating a step of calculating a direction for avoiding the obstacle in FIG. 2; FIG.
FIGS. 9A to 9D are graphical images illustrating experimental results of avoiding an obstacle through the obstacle avoidance method of FIG.

While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments. It is to be understood, however, that the invention is not intended to be limited to the particular forms disclosed, but on the contrary, is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention. Like reference numerals are used for like elements in describing each drawing. The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms.

The terms are used only for the purpose of distinguishing one component from another. The terminology used in this application is used only to describe a specific embodiment and is not intended to limit the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise.

In the present application, the term "comprises" or "comprising ", etc. is intended to specify that there is a stated feature, figure, step, operation, component, But do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as either ideal or overly formal in the sense of the present application Do not.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram illustrating an obstacle avoidance system according to an embodiment of the present invention.

Referring to FIG. 1, the obstacle avoidance system 10 according to the present embodiment includes a scanning unit 100, a floor surface extracting unit 200, an obstacle map generating unit 300, an obstacle information extracting unit 400, An avoidance direction calculation unit 600, and a drive control unit 700. [

Hereinafter, the obstacle avoidance system 10 and the obstacle avoidance method using the obstacle avoidance system according to the present embodiment will be described concretely with reference to FIG. 1 and the following figures.

2 is a flowchart illustrating an obstacle avoidance method using the obstacle avoidance system of FIG. Fig. 3 is a flowchart showing a step of extracting the floor surface data of Fig. 4 is a flow chart illustrating the step of generating the two-dimensional obstacle map of FIG. FIGS. 5A and 5B are images obtained by obtaining the depth information of FIG. 2 and converted into three-dimensional point data. FIG. 6 is an image showing an example of generating the two-dimensional obstacle map of FIG. FIG. 7 is an image showing an example of determining whether or not the obstacle exists in FIG. FIG. 8 is a schematic diagram illustrating a step of calculating a direction for avoiding the obstacle in FIG. 2; FIG. FIGS. 9A to 9D are graphical images illustrating experimental results of avoiding an obstacle through the obstacle avoidance method of FIG.

Referring to FIGS. 1 and 2, in the obstacle avoidance method according to the present embodiment, the scanning unit 100 acquires the depth information of the forward movement of the personal moving mechanism 50 and converts it into three-dimensional point data (Step S10).

The scanning unit 100 may be a depth camera attached to the personal moving mechanism 50. Accordingly, the data acquired by the scanning unit 100 includes depth information, and the depth information may be three-dimensional point data Conversion.

More specifically, the scanning unit 100 includes, for example, one infrared laser projector and a monochrome CMOS sensor, and can mainly acquire depth information in front of the room irrespective of intensity of illumination.

In this case, the obstacle 20 is scanned through the scanning unit 100 and the depth map is the same as the image of FIG. 5A. In the scanning unit 100, the depth map is converted into three- 5B.

1 and 2, in the obstacle avoidance method, the bottom surface detecting unit 200 extracts bottom surface data from the three-dimensional point data (step S20).

More specifically, referring to FIG. 3, in the step of extracting the bottom surface data (step S20), N points of a predetermined interval are sampled from the obtained three-dimensional point data (step S21) .

개가 된다. Thereafter, a combination of three points among the N sampled points is selected (step S22). That is, the combination of the three points

Dogs.

개 각각의 조합은 3개의 포인트를 포함하므로 평면을 형성하게 되며, 이러한 각각의 조합에 대하여 법선벡터를 연산한다(단계 S23). 이렇게 연산된 각각의 법선벡터의 집합을

로 정의할 수 있다. In this case,

Each combination of the dogs includes three points, so that a plane is formed, and a normal vector is calculated for each of these combinations (step S23). Each set of normal vectors

.

와 가장 유사한 법선벡터

를 갖는 조합을 선택한다(단계 S24). Thereafter, the deviation of the normal distances from other points in the set of normal vectors is the smallest, and the initial normal vectors

The most similar normal vector to

(Step S24).

인

로 선택될 수 있으며, 이는 곧 초기 법선벡터와 가장 유사한 법선벡터에 해당된다. In this case, the normal vector most similar to the initial normal vector is

sign

, Which corresponds to the normal vector most similar to the initial normal vector.

는 개인 이동기구(50)에 부착된 깊이 카메라의 방향에 의해 예측할 수 있으며, 현재 연산되는 조합에서의 법선벡터는 다음 연산되는 조합에서의 초기 법선벡터로 활용될 수 있다. Also, the initial normal vector

Can be predicted by the direction of the depth camera attached to the personal moving mechanism 50 and the normal vector in the currently operated combination can be utilized as the initial normal vector in the next calculated combination.

는 개인 이동기구(50)에 부착된 깊이 카메라의 방향으로부터도 예측할 수 있으며, 상기 깊이 카메라가 정면을 향하고 있다면 상기 초기 법선벡터

으로 정의될 수 있다. On the other hand, the initial normal vector

Can also be predicted from the direction of the depth camera attached to the personal moving mechanism 50, and if the depth camera is facing front, the initial normal vector

. ≪ / RTI >

를 구성하는 3개의 포인트로 형성되는 바닥면의 중심점

를 연산하게 된다(단계 S25). Thereafter, when the normal vector is selected, the normal vector of the selected combination

Of the bottom surface formed by the three points constituting the center point

(Step S25).

The bottom surface data is defined through the selected normal vector and the center point (step S26).

As described above, the bottom surface extracting unit 200 extracts bottom surface data from the three-dimensional point data. In the case of the extraction method, since sampling N points at regular intervals, The extraction of the bottom surface is possible with a smaller number of sampling points while more accurate bottom surface detection is possible because the initial normal vector is utilized.

Referring to FIGS. 1 and 2 again, in the obstacle avoidance method, the obstacle map generation unit 300 generates a two-dimensional obstacle map from the floor data (step S30).

개 각각에 대하여 상기에서 정의된 바닥면까지의 법선거리를 연산한다(단계 S31). More specifically, referring to FIG. 4, in the step of generating the two-dimensional obstacle map, first,

The normal distance from the bottom surface to the bottom surface defined above is calculated (step S31).

, 상기 정의된 바닥면의 중심점을

라 하면, 상기 각각의 조합에서의 중심점간의 벡터는

이며, 상기 바닥면까지의 법선거리는

로 연산된다. For example, if the normal vector of the floor defined above is

, The center point of the above defined floor

, The vector between the center points in each combination is

, And the normal distance to the bottom surface is

.

Thereafter, a combination having a normal distance larger than a threshold value for the size of the obstacle 20 is selected (step S32).

That is, a threshold value for the size of the obstacle 20 is predefined as a minimum obstacle size, and a combination having a normal distance larger than the minimum obstacle size is selected. Thus, the selected combinations are selected as obstacle candidates.

Thereafter, the selected combination is changed to two-dimensional coordinates (step S33).

와 같이 평면으로 투영하여 2차원 좌표로 변경하고, 장애물 후보가 되는 2차원 장애물 맵을 생성하게 된다. That is, for the selected combinations

Dimensional obstacle map, which is a candidate for the obstacle, is generated.

As described above, the two-dimensional obstacle map generation using the obstacle map generation unit 300 in the present embodiment can be easily performed through a relatively simple vector operation.

1 and 2, in the obstacle avoidance method, the obstacle information extraction unit 400 extracts obstacle information from the one-dimensional obstacle map (step S40), and extracts obstacle information (Step S50).

6 and 7, the obstacle information extracting unit 400 constructs a grid as shown in FIG. 7, and determines whether the number of point data projected in the grid is smaller than a predetermined number If the number exceeds a certain number, it is judged whether or not the obstacle is determined by discretizing the obstacle.

In the case of the above-mentioned obstacle determination method, there is an advantage that the calculation amount is small and the noise can be easily determined and removed.

However, since the detection accuracy can be changed according to the size of the grating, the size of the grating can be selected in consideration of the traveling speed and the detection precision of the personal moving mechanism 50, so that an optimum grating can be selected And the detection accuracy can be optimized.

In addition, the obstacle information extracting unit 400 may determine the obstacle as described above, and determine whether the obstacle 20 closest to the personal moving mechanism 50 among the extracted obstacle candidates within the range of the personal moving mechanism 50 Position and distance, the position of the leftmost and rightmost obstacle, and the obstacle information to be encountered in the progress.

1 and 2, in the obstacle avoidance method, the avoidance direction calculation unit 600 calculates a direction for avoiding the obstacle 20 (step S60).

를 이용하여 상기 장애물(20)의 척방향(repulsive vector,

)을 연산한다. 8, among the information on the obstacle extracted by the obstacle information extracting unit 400, position information of the closest, leftmost, or rightmost obstacle within the travel range of the personal moving mechanism 50

A repulsive vector of the obstacle 20,

).

과 상기 개인 이동기구(50)의 진행방향

과의 벡터 합

을 이용하여 좌측 또는 우측의 회피 방향을 결정하고, 상기 결과를 상기 구동 제어부(700)로 전달한다. Further,

And the moving direction of the personal moving mechanism (50)

Vector sum of

And transmits the result to the drive control unit 700. The drive control unit 700 controls the drive control unit 700 to determine the avoidance direction.

1 and 2, the drive control unit 700 drives the personal moving mechanism 50 to avoid the obstacle 20 based on the avoidance direction, thereby performing obstacle avoidance (Step S91).

1 and 2, when the obstacle 20 is not determined to be the obstacle 20 in the obstacle information extraction unit 400 (step S50), that is, when the obstacle 20 is not detected, the cliff detection unit 500 Detects the cliff 30 from the floor surface data (step S70).

In the cliff detecting unit 500, a virtual band region is defined in front of the personal moving mechanism 50 and applied to the detection of the cliff 30. FIG.

를 산출한다. More specifically, the virtual band region is defined to have a certain thickness at a predetermined distance forward of the personal moving mechanism 50, and the width of the region defined by the virtual band region

.

를 산출한다. The width of the region formed by the three-dimensional point data obtained in the virtual band region

.

와 상기 3차원 점 데이터가 형성하는 영역의 넓이

의 비(比)(

)를 연산하여,

인 경우, 상기 절벽 검출부(500)는 절벽(30)을 검출한다. Thus, the width of the imaginary band region

And the width of the area formed by the three-dimensional point data

(Ratio) of

),

, The cliff detection unit 500 detects the cliff 30. The cliff detection unit 500 detects the cliff 30 as shown in FIG.

If the cliff 30 is detected by the cliff detection unit 500 in step S80 and the cliff 30 is more dangerous than the obstacle 20 in the cliff detection unit 500, (Step S93).

Of course, if the cliff detection unit 500 does not detect the cliff 30, the drive control unit 700 maintains the travel of the personal moving mechanism 50 without any other avoidance or stopping operation (step S92 ).

9A to 9D illustrate a process for avoiding an actual obstacle through the obstacle avoidance system and the obstacle avoiding method using the same according to the present embodiment. In the process of avoiding a front obstacle, Dimensional obstacle map.

As shown above, the personal moving mechanism 50 has been determined to avoid to the right from the detected obstacle 20 information when it approaches a threshold distance to the obstacle 20, and accordingly the personal moving mechanism 50 Can observe that the detected obstacle 20 gradually disappears out of the camera as the obstacle 20 is driven and avoided.

According to the embodiments of the present invention, a two-dimensional obstacle map is generated from the floor surface data and the obstacle information is extracted from the two-dimensional obstacle map, so that the resolution is higher than the obstacle detection result using the conventional ultrasonic wave, It is cheaper and more practical than the system, it can increase data processing speed and it can extract accurate obstacle information.

In particular, since the scanning unit acquires depth information using a depth camera, it can acquire relatively inexpensive but relatively accurate data as compared with a laser or a stereo camera, and information about cliffs as well as obstacles can be acquired.

In addition, since the bottom surface extractor uses the initial normal vector, it is possible to extract the bottom surface more accurately than the conventional RANSAC algorithm. In the case of extracting arbitrary point data combination for bottom surface extraction, However, since the combination of three points is used, it is possible to calculate the floor from a smaller number of sampling points.

In addition, since the obstacle map generation unit determines obstacles based on the number of data constituting the two-dimensional coordinates, it is possible to perform real-time calculation by simple calculation for extracting obstacle information, to easily distinguish noise, It is possible to change the grid size, which is a criterion for determining the number of data, to perform an optimized operation.

In addition, even when a protruding obstacle is not detected, since there are many cliffs such as a staircase in the room, it is possible to detect the cliff from the floor surface data, thereby further improving the safety of the personal transportation means.

In this case, the cliff detection algorithm can improve the accuracy of judgment while performing the calculation relatively easily by applying the concept of the virtual band region.

Particularly, in the case of a cliff, the risk of the obstacle is higher than that of the obstacle. Therefore, the driving control unit performs the stop driving unlike the avoidance driving of the obstacle.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the present invention as defined by the following claims. It can be understood that it is possible.

INDUSTRIAL APPLICABILITY The obstacle avoiding system and the obstacle avoiding method using the personal moving mechanism according to the present invention have industrial applicability that can be used in a personal moving mechanism such as an electric wheelchair or an electric scooter.

10: Obstacle avoidance system
100: scanning unit 200: bottom surface extracting unit
300: an obstacle map generating unit 400: an obstacle information extracting unit
500: cliff detection unit 600: avoidance direction calculation unit
700:

Claims (16)

A scanning unit which acquires depth information of a front of the personal moving mechanism and converts the acquired depth information into three-dimensional point data;
A floor surface extracting unit for extracting floor surface data from the three-dimensional point data;
An obstacle map generating unit for generating a two-dimensional obstacle map from the floor surface data;
An obstacle information extracting unit for extracting obstacle information from the two-dimensional obstacle map;
A avoidance direction calculation unit for calculating a direction for avoiding the obstacle when an obstacle is detected from the obstacle information; And
And a drive control section for driving the personal moving mechanism in the avoidance direction,
Wherein the bottom surface extracting unit selects a combination of the point combinations selected from the three-dimensional point data that is the closest to the initial normal vector of the bottom surface as the bottom surface data. The apparatus according to claim 1,
And a depth camera attached to the personal moving mechanism. The method according to claim 1,
Wherein the combination of points is a combination of three points selected from the three-dimensional point data. 4. The obstacle detection apparatus according to claim 3,
Calculating a normal distance to the defined floor for each of the combinations, and selecting a combination having a normal distance greater than a threshold value for the obstacle size to change the two-dimensional coordinates. The method of claim 4, wherein the obstacle information extracting unit
Wherein an obstacle is determined by removing noise based on the number of data constituting the two-dimensional coordinate. The method according to claim 1,
Further comprising a cliff detection unit for detecting a cliff from the floor data if no obstacle is detected from the obstacle information. 7. The apparatus according to claim 6, wherein the cliff-
Wherein a cliff is detected by calculating a ratio of a width of a virtual band region defined in front of the personal moving mechanism and a width of a region formed by the three-dimensional point data obtained in the virtual band region, Avoidance system. 8. The apparatus as claimed in claim 7,
And stops the traveling of the personal moving mechanism when a cliff is detected in the cliff detection unit. Acquiring depth information on the front of the personal moving mechanism and converting the acquired depth information into three-dimensional point data;
Extracting bottom surface data from the three-dimensional point data;
Generating a two-dimensional obstacle map from the floor surface data;
Extracting obstacle information from the two-dimensional obstacle map;
Calculating a direction for avoiding the obstacle when the obstacle is detected from the obstacle information; And
And driving the personal moving mechanism in the avoiding direction,
Wherein a combination of points selected from the three-dimensional point data and forming a normal vector most similar to the initial normal vector of the bottom surface is selected as floor surface data in the step of extracting the bottom surface data, Avoidance method. 10. The method of claim 9, wherein extracting the bottom-
Sampling N points at regular intervals among the three-dimensional point data;
Selecting three point combinations of the N points;
Computing a normal vector in each of the combinations;
Selecting a combination of the normal vectors having a normal vector most similar to the initial normal vector;
Computing a center point in the normal vector of the selected combination; And
And defining bottom plane data as a normal vector and a center point of the selected combination. 11. The method of claim 10, wherein generating the two-
Calculating a normal distance to a bottom surface for each combination;
Selecting a combination having a normal distance greater than a threshold value for the size of the obstacle; And
And changing the selected combination to two-dimensional coordinates. The method of claim 11, wherein the extracting of the obstacle information comprises:
Wherein an obstacle is determined by removing noise based on the number of data constituting the two-dimensional coordinate. 10. The method of claim 9,
And if no obstacle is detected from the obstacle information, detecting a cliff from the floor data. 14. The method of claim 13, wherein detecting the cliff comprises:
Wherein a cliff is detected by calculating a ratio of a width of a virtual band region defined in front of the personal moving mechanism and a width of a region formed by the three-dimensional point data obtained in the virtual band region, Avoidance method. 15. The method of claim 14,
The ratio of the width of the imaginary band region and the width of the region formed by the three-dimensional point data is calculated by the following equation,

The obstacle avoidance method is characterized by detecting the obstacle as a cliff. 14. The method of claim 13,
And stopping the traveling of the personal moving mechanism when the cliff is detected.

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