WO2021261756A1

WO2021261756A1 - Air conditioner and control method therefor

Info

Publication number: WO2021261756A1
Application number: PCT/KR2021/005602
Authority: WO
Inventors: 임헌정; 강유; 김정훈; 왕소이
Original assignee: 삼성전자주식회사; 서울대학교산학협력단
Priority date: 2020-06-26
Filing date: 2021-05-04
Publication date: 2021-12-30
Also published as: KR20220000724A

Abstract

An air conditioner is disclosed. The present air conditioner comprises a LiDAR sensor and a processor. The processor: acquires a plurality of point data sensed by the LiDAR sensor; groups the plurality of point data into a plurality of groups on the basis of vertical position information of the plurality of point data; generates vertically-augmented point data corresponding to a third vertical position between a first vertical position, corresponding to a first group among the plurality of groups, and a second vertical position corresponding to a second group thereamong; on the basis of horizontal position information of the point data included in the first group and the second group, generates horizontally-augmented point data among the point data; and identifies an object on the basis of the plurality of sensed point data, the generated vertically-augmented point data and the generated horizontally-augmented point data.

Description

Air conditioner and its control method

The present disclosure relates to an air conditioner and a control method thereof, and more particularly, to an air conditioner for recognizing an object using a lidar sensor and a control method thereof.

A lidar sensor is a device that calculates the distance to an object by emitting a laser pulse and measuring the time it takes to reflect and return. Since the lidar sensor transmits the part where the laser pulse hits the object as a three-dimensional coordinate value, the performance can be maintained constant even in a dark environment.

However, the lidar sensor has a problem in that the distance between the pulses increases linearly as the distance between the pulses increases because the laser pulses are radiated radially. This problem causes a decrease in resolution, which ultimately leads to a decrease in the performance of object determination. As the object is positioned further away from the lidar sensor, the density of dots decreases and the original shape of the object may become unclear.

In order to solve the problem of resolution degradation according to the distance of the lidar sensor described above, the prior art focused on improving the quality of the features used in the classification model. For example, when recognizing an object using data generated through a lidar sensor, an attempt was made to increase recognition performance by generating information about the movement state and behavior of the object as a feature.

However, an attempt to increase the quality of a feature has a problem in that a certain degree of performance improvement exists, but when the performance includes only too little information, a recognition rate and accuracy are lowered. Depending on how far it is located, the density of points representing an object and the amount of information may be significantly lowered. Therefore, even if the quality of the features is improved, the fundamental problem cannot be solved.

The present disclosure has been devised to improve the above problems, and an object of the present disclosure is to provide an air conditioner that augments data obtained from a lidar sensor in a vertical direction and a horizontal direction, and a control method thereof.

The air conditioner according to this embodiment for achieving the above object includes a lidar sensor and a processor, wherein the processor acquires a plurality of point data sensed by the lidar sensor, and Group the plurality of point data into a plurality of groups based on location information, and a third vertical position between a first vertical position corresponding to a first group among the plurality of groups and a second vertical position corresponding to a second group Generates vertical augmentation point data corresponding to , and generates horizontal augmentation point data between the point data based on horizontal position information of the point data included in the first group and the second group, and the plurality of sensed The object is identified based on the point data, the generated vertical augmentation point data, and the generated horizontal augmentation point data.

Here, if the distance between the plurality of point data and the lidar sensor is not a predefined standard distance, the processor may change vertical position information of the plurality of point data based on the predefined standard distance.

Here, if the distance between the plurality of point data and the lidar sensor is not a predefined standard distance, the processor sets the vertical position of the plurality of point data included in each group to each corresponding to the predefined standard distance. You can change the vertical position for each group.

Meanwhile, the processor generates a third group including vertical augmentation point data corresponding to the grouped plurality of point data, and points data included in the first group, the second group, and the generated third group. Horizontal augmentation point data may be generated between the point data based on the horizontal position information of the .

On the other hand, if the distance between the point data adjacent in the horizontal direction in the grouped plurality of point data is greater than a predefined threshold, the processor divides the adjacent point data into different segments, and divides the adjacent point data into different segments. Based on the vertical augmentation point data may be generated.

Here, the processor may identify the number of segments divided for each group, compare the number of segments in each of the adjacent groups, and generate the vertical augmentation point data based on the compared number of segments in each of the adjacent groups. have.

Here, if the number of segments of the compared first group and the second group is one, the processor generates a first linear function linking the first point data of the point data for each group, and the last of the point data for each group generating a second linear function connecting the point data, generating a third linear function connecting the middle point data of the group-by-group point data to correspond to a ratio of the number, and generating the first to third linear functions It is possible to generate vertical augmentation point data at a position where a predefined vertical position intersects.

On the other hand, if the number of segments in the first group is one among the compared adjacent groups and the number of segments in the remaining second group is two or more, the number of segments in the first group is equal to the number of segments in the second group A linear function may be generated to connect the point data of the first group and the point data of the second group based on the divided segments of the first group and the segment of the second group.

On the other hand, the processor connects the point data of the first group and the point data of the second group based on the center point of each segment when the compared number of segments of the first group and the second group is two or more You can create linear functions.

On the other hand, if the distance between the identified object and the lidar sensor is not a predefined standard distance, the processor changes the horizontal position information of each group point data based on the predefined standard distance and changes each group point Horizontal augmentation point data may be generated based on the azimuth angle of the data.

The control method of the air conditioner according to an embodiment of the present disclosure includes acquiring a plurality of point data sensed by a lidar sensor, and collecting a plurality of the plurality of point data based on vertical position information of the plurality of point data. grouping into groups of, generating vertical augmentation point data corresponding to a third vertical position between a first vertical position corresponding to a first group and a second vertical position corresponding to a second group among the plurality of groups; , generating horizontal augmentation point data between the point data based on horizontal position information of the point data included in the first group and the second group, the sensed plurality of point data, and the generated vertical augmentation point and identifying an object based on data and the generated horizontal augmentation point data.

Here, if the distance between the plurality of point data and the lidar sensor is not a predefined standard distance, the control method includes changing the vertical position information of the plurality of point data based on the predefined standard distance may include more.

Here, in the step of changing the vertical position information, if the distance between the plurality of point data and the lidar sensor is not a predefined standard distance, the vertical position of the plurality of point data included in each group is set to the predefined distance. It can be changed to a vertical position for each group corresponding to the standard distance.

Here, the generating of the vertical augmentation point data may generate a third group including vertical augmentation point data corresponding to the grouped plurality of point data, and the generating of the horizontal augmentation point data includes the first Horizontal augmentation point data may be generated between point data based on horizontal position information of point data included in the first group, the second group, and the generated third group.

Meanwhile, in the generating of the vertical augmentation point data, when the distance between the horizontally adjacent point data in the grouped plurality of point data is greater than a predefined threshold, the adjacent point data is divided into different segments (segmentaion) and generate the vertical augmentation point data based on the divided segments.

Here, the generating of the vertical augmentation point data may include identifying the number of segments divided for each group, comparing the number of segments in each of the adjacent groups, and based on the compared number of segments in each of the adjacent groups, the vertical Augmented point data may be generated.

Here, in the step of generating the vertical augmentation point data, if the number of segments of the compared first group and the second group is one, a first linear function is generated that connects the first point data among the point data for each group, and , generating a second linear function connecting the last point data of the point data for each group, generating a third linear function connecting the middle point data of the point data for each group to correspond to the number ratio, and the generated first Vertical augmentation point data may be generated at a position where the first to third linear functions intersect the predefined vertical position.

Meanwhile, in the generating of the vertical augmentation point data, if the number of segments in the first group is one among the compared adjacent groups and the number of segments in the remaining second group is two or more, the number of segments in the first group is Divide so as to be the number of segments of the second group, and based on the divided segments of the first group and the segment of the second group, a linear function connecting the point data of the first group and the point data of the second group can create

Meanwhile, in the generating of the vertical augmentation point data, when the compared number of segments in the first group and the second group is two or more, the point data of the first group and the second group are based on the center point of each segment. You can create a linear function that connects the group's point data.

Meanwhile, in the generating of the horizontal augmented point data, if the distance between the identified object and the lidar sensor is not a predefined standard distance, horizontal position information of each group point data based on the predefined standard distance may be changed and horizontal augmented point data may be generated based on the changed azimuth of each group point data.

1 is a block diagram illustrating an air conditioner according to an embodiment of the present disclosure.

FIG. 2 is a block diagram illustrating a specific configuration of the air conditioner of FIG. 1 .

3 is a view for explaining an embodiment of recognizing an object through a lidar sensor included in a stand-type air conditioner.

4 is a view for explaining an embodiment of recognizing an object through a lidar sensor included in the ceiling air conditioner.

5 is a view for explaining a plurality of indoor units and outdoor units included in the multi-type air conditioner.

6 is a diagram for explaining a plurality of point data sensed through a lidar sensor.

7 is a diagram for explaining an operation of additionally generating point data based on a plurality of point data sensed through a lidar sensor.

8 is a view for explaining a standard distance according to the arrangement position of the lidar sensor.

9 is a diagram for explaining vertical position information corresponding to a standard distance.

10 is a diagram for explaining an operation of dividing a plurality of point data included in a group and a linear function for generating a vertical augmentation point.

11 is a diagram for explaining vertical direction augmentation data according to an embodiment.

12 is a diagram for explaining point data according to the embodiment of FIG. 11 .

13 is a view for explaining vertical direction augmentation data according to another embodiment.

14 is a diagram for explaining point data according to the embodiment of FIG. 13 .

15 is a diagram for explaining vertical direction augmentation data according to another embodiment.

16 is a diagram for explaining point data according to the embodiment of FIG. 15 .

17 is a diagram for explaining an operation of generating horizontal direction augmented data.

18 is a diagram for explaining an operation of generating horizontal direction augmented data.

19 is a diagram for explaining a feature used to analyze an object.

20 is a table showing the number of human objects or non-human objects included in each of a plurality of samples.

21 is a diagram for explaining a result of applying a machine learning classification algorithm to a plurality of data processing results according to an embodiment.

22 is a diagram for explaining a result of applying a machine learning classification algorithm according to another embodiment to a plurality of data processing results.

23 is a table for explaining performance comparison according to different embodiments.

24 is a graph for comparing processing times according to a plurality of data processing operations.

25 is a flowchart illustrating an operation of processing data obtained from a lidar sensor according to an embodiment of the present disclosure.

26 is a flowchart illustrating an operation of generating vertical augmentation point data.

FIG. 27 is a flowchart for specifically explaining an operation of generating the vertical augmentation point data of FIG. 26 .

28 is a flowchart illustrating a control operation of the air conditioner according to an embodiment of the present disclosure.

Hereinafter, the present disclosure will be described in detail with reference to the accompanying drawings.

Terms used in the embodiments of the present disclosure are selected as currently widely used general terms as possible while considering the functions in the present disclosure, which may vary depending on the intention or precedent of a person skilled in the art, the emergence of new technology, etc. . In addition, in a specific case, there is a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the corresponding disclosure. Therefore, the terms used in the present disclosure should be defined based on the meaning of the term and the contents of the present disclosure, rather than the simple name of the term.

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

The expression "at least one of A and/or B" is to be understood as indicating either "A" or "B" or "A and B".

As used herein, expressions such as "first," "second," "first," or "second," can modify various elements, regardless of order and/or importance, and refer to one element. It is used only to distinguish it from other components, and does not limit the components.

A component (eg, a first component) is "coupled with/to (operatively or communicatively)" to another component (eg, a second component); When referring to "connected to", it should be understood that an element may be directly connected to another element or may be connected through another element (eg, a third element).

The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as "comprises" or "consisting of" are intended to designate that the features, numbers, steps, operations, components, parts, or combinations thereof described in the specification exist, and are intended to indicate that one or more other It should be understood that this does not preclude the possibility of addition or presence of features or numbers, steps, operations, components, parts, or combinations thereof.

In the present disclosure, a “module” or “unit” performs at least one function or operation, and may be implemented as hardware or software, or a combination of hardware and software. In addition, a plurality of “modules” or a plurality of “units” are integrated into at least one module and implemented with at least one processor (not shown) except for “modules” or “units” that need to be implemented with specific hardware. can be

In this specification, the term user may refer to a person who uses an electronic device or a device (eg, an artificial intelligence electronic device) using the electronic device.

Hereinafter, an embodiment of the present disclosure will be described in more detail with reference to the accompanying drawings.

Referring to FIG. 1 , the air conditioner 100 may include a lidar sensor 110 and a processor 120 .

The air conditioner 100 performs an operation for conditioning the air in the room. Specifically, the air conditioner 100 may be a cooling device that lowers the temperature of indoor air according to an embodiment. According to another embodiment, the air conditioner 100 may perform at least one of heating to increase the temperature of indoor air, blowing to form an airflow in the room, and dehumidification to decrease indoor humidity. The air conditioner 100 may be an electronic device that uses air, such as an air conditioner, a dehumidifier, or an air purifier.

The lidar sensor 110 (Light Detection And Ranging sensor) may include a laser transmitter, a laser detector, and a data processor. The laser transmitter may emit a laser in a predefined direction, the laser detector may detect a laser reflected by an object in front of the emitted laser, and the data processor collects, processes, and transmits/receives the detected laser. can The lidar sensor 110 may refer to a laser detection and ranging (LADAR) sensor.

The method of the lidar sensor 110 may include a time of flight (TOF) method and a phase-shift method. The TOF method may be a method of calculating a distance by emitting a pulse signal and measuring a time at which the reflected pulse signal is received. The phase-shift method may be a method of calculating a distance by emitting a laser that is continuously modulated using a predefined frequency, receiving a reflected laser, and measuring a phase change amount.

Meanwhile, the lidar sensor 110 may be implemented by various methods other than the above-described method.

Meanwhile, although it has been described that the lidar sensor 110 is attached to the air conditioner 100 according to an embodiment of the present disclosure, a robot cleaner, a washing machine, a dryer, It may be implemented as an embodiment in which the lidar sensor 110 is attached to various electronic devices connectable to an Internet of Things (IoT) network, such as a TV or an AI speaker.

The processor 120 may perform an overall control operation of the air conditioner 100 . Specifically, the processor 120 functions to control the overall operation of the air conditioner 100 .

The processor 120 may be implemented as a digital signal processor (DSP), a microprocessor, or a time controller (TCON) that processes digital signals, but is not limited thereto, and the central processing unit ( central processing unit (CPU), micro controller unit (MCU), micro processing unit (MPU), controller, application processor (AP), graphics-processing unit (GPU) or communication processor (CP)), may include one or more of an ARM processor, or may be defined by a corresponding term In addition, the processor 120 is a SoC (System on Chip) or LSI (large scale integration) in which a processing algorithm is embedded. It may be implemented in the form of a field programmable gate array (FPGA), and the processor 120 may perform various functions by executing computer executable instructions stored in a memory.

The processor 120 acquires a plurality of point data sensed by the lidar sensor 110 , groups the plurality of point data into a plurality of groups based on vertical position information of the plurality of point data, and among the plurality of groups Generate vertical augmentation point data corresponding to a third vertical position between the first vertical position corresponding to the first group and the second vertical position corresponding to the second group, and point data included in the first group and the second group The horizontal augmentation point data is generated between the point data based on the horizontal position information of the objects, and the object is identified based on the sensed plurality of point data, the generated vertical augmentation point data, and the generated horizontal augmentation point data.

The processor 120 may obtain data from the lidar sensor 110 . In addition, the processor 120 may acquire object data corresponding to an area estimated to be an object from the acquired data. This is because it is necessary to limit some analysis targets because not all data can be analyzed. The object data may include a plurality of point data corresponding to the object. In various embodiments of the present disclosure, sensing data or point data to be analyzed may mean object data. The processor 120 may acquire a plurality of point data included in the object data.

In addition, the processor 120 may group the obtained point data into a plurality of groups (or channels) based on vertical position information. For example, point data located between 9 cm and 11 cm in the vertical direction may be grouped into one group. That is, a group is determined according to a vertical position (height), and each group may include a plurality of point data having a similar vertical position. The grouping operation will be described later with reference to FIG. 7 .

When the distance between the object and the lidar sensor 110 is long, the object data may have a low density. When machine learning is performed using data with low density, the accuracy may be low. Accordingly, the processor 120 may generate new point data through an augmentation operation.

According to an embodiment, the processor 120 may perform an augmentation operation in a vertical direction. The processor 120 may add vertical augmentation point data to an empty space between each group. Assume that there are a first group and a second group having different vertical positions. The processor 120 may identify that there is an empty area between the first vertical position of the first group and the second vertical position of the second group, and may add vertical augmentation point data to the empty area. The position of the blank area may be a third vertical position between the first vertical position of the first group and the second vertical position of the second group. Here, since each group includes a plurality of point data, the first to third vertical positions may be values representing the plurality of point data. A detailed description of generating the vertical augmentation point data will be described later with reference to FIGS. 10 to 16 .

According to another embodiment, the processor 120 may perform an augmentation operation in a horizontal direction. The processor 120 may add horizontal augmentation point data between horizontal intervals of a plurality of point data. The processor 120 may identify a blank area between a plurality of point data included in one group. In addition, the processor 120 may add horizontal augmentation point data to the blank area. An operation of generating the horizontal augmentation point data will be described later with reference to FIGS. 17 to 18 .

The processor 120 may perform at least one of a vertical augmentation operation and a horizontal augmentation operation. Here, when both the augmentation operation in the vertical direction and the augmentation operation in the horizontal direction are performed, the processor 120 may perform the vertical direction first, and may perform the horizontal direction first according to an implementation example.

The processor 120 may add vertical augmentation point data and horizontal augmentation point data to object data including a plurality of point data corresponding to the object among the data received from the lidar sensor 110 . Data to which new point data is added according to the augmentation operation may be described as analysis data. Meanwhile, the analysis data may be described as changed data because existing object data is modified by an augmentation operation.

The processor 120 may acquire a predefined standard distance corresponding to the lidar sensor 110 . It may mean a maximum distance that can be sensed by the laser 801-1 radiated in the lowermost direction or a horizontal distance in which the laser 801-1 radiated in the lowermost direction is in contact with the bottom surface.

Also, the processor 120 may identify distance information between the plurality of point data and the lidar sensor 110 .

In addition, the processor 120 may identify whether distance information between the plurality of point data and the lidar sensor 110 is the same as a predefined standard distance corresponding to the lidar sensor 110 .

Here, if the distance between the plurality of point data and the lidar sensor 110 is not a predefined standard distance, the processor 120 may change vertical position information of the plurality of point data based on the predefined standard distance. .

The acquired plurality of point data may include location information identified based on an operation in which a laser emitted by the lidar sensor 110 is reflected. However, the processor 120 may change the vertical position of the plurality of point data for each group to a vertical position for each group corresponding to the standard distance so that it can be easily used for analyzing the density.

Here, if the distance between the plurality of point data and the lidar sensor 110 is not a predefined standard distance, the processor 120 corresponds to the vertical position of the plurality of point data included in each group to the predefined standard distance. It can be changed to the vertical position for each group being

Meanwhile, a detailed description of the standard distance will be described later with reference to FIGS. 8 to 9 .

Meanwhile, the processor 120 generates a third group including vertical augmentation point data corresponding to a plurality of grouped point data, and horizontally selects the first group, the second group, and the point data included in the generated third group. Horizontal augmentation point data may be generated between the point data based on the location information.

Here, the third group may mean a plurality of vertical augmentation point data generated by the vertical augmentation operation, and the plurality of vertical augmentation point data included in the third group may be a vertical position within a threshold range of a specific vertical position. have. For example, if the representative height of the first group is 10 cm and the representative height of the second group is 20 cm, the representative height of the third group may be 15 cm.

In addition, the processor 120 may perform a horizontal augmentation operation on the third group generated by the vertical augmentation operation. The reason for performing the vertical augmentation operation first and then the horizontal augmentation operation is to reduce the amount of data computation and increase the processing speed. When the horizontal augmentation operation is performed first and then the vertical augmentation operation is performed, an empty space may be generated due to the radiation angle of the laser emitted from the lidar sensor 110 . Accordingly, an embodiment in which the vertical augmentation operation is performed first and the horizontal augmentation operation is performed later may generate more dense data than an embodiment in which the horizontal augmentation operation is first performed and the vertical augmentation operation is performed later.

Therefore, the air purifier 100 according to an embodiment of the present disclosure may first perform a vertical augmentation operation, and thereafter perform a horizontal augmentation operation on the existing point data and the newly generated vertical augmentation point data thereafter.

On the other hand, if the distance between the horizontally adjacent point data in the grouped plurality of point data is greater than a predefined threshold, the processor 120 divides the adjacent point data into different segments, and based on the segmented segment to generate vertical augmentation point data.

The processor 120 may identify distances between adjacent point data among the plurality of point data based on the horizontal positions of the plurality of point data included in one group. And, when the identified distance is greater than a predefined threshold, point data adjacent to each other may be divided into separate segments. A detailed description related to the operation of segmenting a segment based on a predefined threshold will be described later with reference to FIG. 10 .

Here, the processor 120 may identify the number of segments divided for each group, compare the number of segments in each adjacent group, and generate vertical augmentation point data based on the compared number of segments in each adjacent group.

The processor 120 may identify the number of segments in each of the plurality of groups grouped according to height. In addition, a segment relationship between adjacent groups may be identified by comparing the number of segments of adjacent groups. In the segment relationship, the number of segments may be expressed as 1:1, 1:n (or n:1), or n1:n2 (n1:n2 may be the same according to an implementation example). Here, n, n1, and n2 may mean two or more numbers.

The processor 120 may generate vertical augmentation point data based on a segment relationship of adjacent groups.

Here, if the number of segments of the compared first group and the second group is one, the processor 120 generates a first linear function linking the first point data among the point data for each group, and the last point of the point data for each group A second linear function connecting data is generated, a third linear function is generated in which middle point data among group-specific point data corresponds to a ratio of the number of points, and the generated first to third linear functions and predefined Vertical augmentation point data may be generated at a position where the vertical positions intersect.

If the segment relationship of adjacent groups (the first group and the second group) is 1:1, the processor 120 determines the point data (p11 and p21 in FIG. 11 ) located in the left edge region of each group as the reference point data. can Here, the left edge area may mean an area in which the leftmost point in one segment is located. In addition, the processor 120 may generate a linear function based on reference point data corresponding to the left edge region. A linear function may be described as a linear interpolation function.

In addition, the processor 120 may determine the point data (p17 and p29 of FIG. 11 ) located in the right edge region as the reference point data. Here, the right edge area may mean an area in which the rightmost point in one segment is located. In addition, the processor 120 may generate a linear function based on reference point data corresponding to the left edge region.

In addition, the processor 120 may determine the reference point data based on the number of point data for each group among the point data located in the center region. Here, the middle area may mean an area excluding the left edge area and the right edge area in one segment. The reference point data may be one in each group. Accordingly, when the number of point data located in the center region is different for each group, the processor 120 may generate a linear function based on a ratio of the number of point data for each group. Here, the number of linear functions generated based on the number ratio may be plural according to the number of point data located in the center region.

The processor 120 may generate vertical augmentation point data at a position where the generated linear function and a predefined vertical position (height) intersect. The predefined vertical position may be a height value obtained based on the height values of the first group and the second group. The predefined vertical position may mean a vertical position of a group that does not include point data among vertical positions for each group corresponding to the standard distance. For example, the predefined vertical position may be a height hc3 of c3 and a height hc5 of c5 of FIG. 9 .

On the other hand, since the left or right side may be a relative direction, it may be described as the first data or the last data.

Meanwhile, a detailed description of an embodiment in which the segment relationship is 1:1 will be described later with reference to FIGS. 11 to 12 .

Meanwhile, when the number of segments in the first group is one among the compared adjacent groups and the number of segments in the remaining second group is two or more, the processor 120 divides the number of segments in the first group to be the number of segments in the second group. and a linear function connecting the point data of the first group and the point data of the second group may be generated based on the divided segments of the first group and the segment of the second group.

If the segment relationship of adjacent groups (the first group and the second group) is 1:n (or n:1), the processor 120 may segment a group having one segment number into n segments. .

According to the division operation, the number of segments in adjacent groups becomes the same. Accordingly, the processor 120 may match each segment of the adjacent group 1:1. The matching operation may be performed based on horizontal information. For example, it is assumed that two segments (divided) exist in the first group and two segments exist in the second group. Here, the processor 120 may match segments having similar horizontal positions in consideration of the horizontal positions of the two segments included in the first group and the horizontal positions of the two segments included in the second group. And, after matching the segments for each group 1:1, the processor 120 places the adjacent groups (the first group and the second group) in the edge area and the center area, similar to the embodiment in which the segment relationship is 1:1. A linear function can be created based on

Meanwhile, a detailed description of an embodiment in which the segment relationship is 1:n (or n:1) will be described later with reference to FIGS. 13 to 14 .

On the other hand, if the compared number of segments in the first group and the second group is two or more, the processor 120 linearly connects the point data of the first group and the point data of the second group based on the center point of each segment. You can create functions.

If the segment relationship of adjacent groups (the first group and the second group) is n1: n2, the processor 120 may obtain the center point of a plurality of segments included in each group. Here, the central point may be an average horizontal position of a plurality of point data included in each segment.

In addition, the processor 120 may match the segments for each group based on the horizontal position of the center point corresponding to each segment. If the horizontal positions of the center points corresponding to the segments of each group are similar, the segments for each group may be matched. Here, the matching operation may be performed 1:1 or 1:n (or n:1). If the horizontal position is within the critical range, matching may be performed, and if it is outside the critical range, matching may not be performed.

Here, if the segment relationship for each group is 1:1 after matching, a linear function may be generated based on the edge region and the center region.

In addition, if the segment relationship for each group after matching is 1:n (or n:1), a linear function can be generated by dividing the segment.

Meanwhile, a detailed description of an embodiment in which the segment relationship is n1: n2 will be described later with reference to FIGS. 15 to 16 .

On the other hand, if the distance between the identified object and the lidar sensor 110 is not a predefined standard distance, the processor 120 changes the horizontal position information of each group point data based on the predefined standard distance and changes each Horizontal augmentation point data may be generated based on the azimuth of the group point data.

A detailed description of the horizontal augmentation operation will be described later with reference to FIGS. 17 to 18 .

Meanwhile, sensing data acquired from the lidar sensor 110 may be changed by an augmentation operation according to an embodiment of the present disclosure. Specifically, since the number of point data is larger than the original data obtained from the lidar sensor 110 by the augmentation operation, the density may be increased. When object recognition is performed using high-density data, a recognition rate and accuracy may be increased, and thus performance of recognizing an object may be improved. In addition, since the augmentation operation is performed in consideration of the standard distance and segment relationship while improving the data density, the accuracy of object recognition can be increased.

Meanwhile, although only a simple configuration of the air conditioner has been illustrated and described above, various configurations may be additionally provided when implemented. This will be described below with reference to FIG. 2 .

Referring to FIG. 2 , the air conditioning system may include an air conditioner 100 and an outdoor unit 200 . Here, the air conditioner 100 may mean an indoor unit. Accordingly, in the description of FIG. 2 , the air conditioner 100 will be referred to as an indoor unit 100 .

In addition, the indoor unit 100 includes an indoor unit temperature sensor 110 , a processor 120 , a memory 130 , a communication interface 140 , a cooling unit 150 , a user interface 160 , a display 170 , and a speaker 180 . ) may be included.

Meanwhile, redundant descriptions of the same operations as those described above among the operations of the lidar sensor 110 and the processor 120 will be omitted.

Specifically, the indoor unit 100 may include the indoor unit 100 for exchanging heat with external air using a refrigerant and for performing an indoor air conditioning operation by exchanging a refrigerant with an outdoor unit.

The indoor unit 100 may include an indoor heat exchanger that receives a refrigerant and exchanges heat with indoor air. In addition, the indoor unit 100 may include an indoor fan that forcibly discharges indoor air by an indoor fan motor so that heat is exchanged in the indoor heat exchanger.

The memory 130 is implemented as an internal memory such as a ROM (eg, electrically erasable programmable read-only memory (EEPROM)) included in the processor 120, a RAM, or the like, or with the processor 120 It may be implemented as a separate memory. In this case, the memory 130 may be implemented in the form of a memory embedded in the indoor unit 100 or may be implemented in the form of a memory detachable to the indoor unit 100 according to the purpose of data storage. For example, data for driving the indoor unit 100 is stored in a memory embedded in the indoor unit 100 , and data for an extension function of the indoor unit 100 is stored in a memory detachable from the indoor unit 100 . can be

On the other hand, in the case of the memory embedded in the indoor unit 100, a volatile memory (eg, dynamic RAM (DRAM), static RAM (SRAM), or synchronous dynamic RAM (SDRAM), etc.), non-volatile memory (eg, : OTPROM (one time programmable ROM), PROM (programmable ROM), EPROM (erasable and programmable ROM), EEPROM (electrically erasable and programmable ROM), mask ROM, flash ROM, flash memory (such as NAND flash or NOR flash) , a hard drive, or a solid state drive (SSD), and in the case of a memory that is detachable from the indoor unit 100, a memory card (eg, compact flash (CF), secure digital (SD)) ), Micro-SD (micro secure digital), Mini-SD (mini secure digital), xD (extreme digital), MMC (multi-media card), etc.), external memory that can be connected to the USB port (e.g. USB memory) ) can be implemented in a form such as

The communication interface 140 may receive audio content including an audio signal. For example, the communication interface 140 is AP-based Wi-Fi (Wi-Fi, Wireless LAN network), Bluetooth (Bluetooth), Zigbee (Zigbee), wired / wireless LAN (Local Area Network), WAN, Ethernet, IEEE 1394, External device (eg source device), external storage medium (eg USB), external server via communication method such as HDMI, USB, MHL, AES/EBU, Optical, Coaxial, etc. Audio content including an audio signal may be received from (eg, a web hard drive) in a streaming or download manner.

The communication interface 140 is configured to communicate with various types of external devices according to various types of communication methods. The communication interface 140 includes a Wi-Fi module, a Bluetooth module, an infrared communication module, and a wireless communication module. Here, each communication module may be implemented in the form of at least one hardware chip.

The processor 120 may communicate with various external devices using the communication interface 140 .

The Wi-Fi module and the Bluetooth module perform communication using a WiFi method and a Bluetooth method, respectively. In the case of using a Wi-Fi module or a Bluetooth module, various types of connection information such as an SSID and a session key are first transmitted and received, and various types of information can be transmitted/received after communication connection using this.

The infrared communication module communicates according to the infrared data association (IrDA) technology, which wirelessly transmits data in a short distance using infrared that is between visible light and millimeter waves.

In addition to the above-described communication methods, the wireless communication module includes Zigbee, 3rd Generation (3G), 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), LTE Advanced (LTE-A), 4th Generation (4G), 5G It may include at least one communication chip that performs communication according to various wireless communication standards such as (5th Generation).

In addition, the communication interface 140 may include at least one of a local area network (LAN) module, an Ethernet module, or a wired communication module for performing communication using a pair cable, a coaxial cable, or an optical fiber cable.

According to an example, the communication interface 140 may use the same communication module (eg, Wi-Fi module) to communicate with an external device such as a remote control and an external server.

According to another example, the communication interface 140 may use a different communication module (eg, a Wi-Fi module) to communicate with an external device such as a remote control and an external server. For example, the communication interface 140 may use at least one of an Ethernet module or a WiFi module to communicate with an external server, and may use a BT module to communicate with an external device such as a remote control. However, this is only an embodiment, and when communicating with a plurality of external devices or external servers, the communication interface 140 may use at least one communication module among various communication modules.

The cooling unit 150 is configured to discharge the temperature-controlled air to adjust the indoor air. Specifically, the cooling unit 150 may include an indoor heat exchanger, an expansion valve, a blowing fan, and the like.

Here, the indoor heat exchanger may exchange heat between the air introduced into the indoor unit 100 and the refrigerant provided from the outdoor unit. Specifically, the indoor heat exchanger may serve as an evaporator during cooling. That is, the indoor heat exchanger may absorb latent heat required for a phase transition in which the low-pressure, low-temperature, foggy refrigerant evaporates into gas from the air introduced into the indoor unit 100 . Conversely, the indoor heat exchanger may serve as a condenser during heating. That is, when the flow of the refrigerant is reversed as opposed to cooling, the heat of the refrigerant passing through the indoor heat exchanger may be discharged to the air introduced into the indoor unit 100 .

The expansion valve regulates the pressure of the refrigerant. Specifically, the expansion valve can lower the pressure by expanding the high-pressure and low-temperature refrigerant that has passed through the outdoor heat exchanger during cooling. In addition, the amount of refrigerant flowing into the indoor heat exchanger may be adjusted. Conversely, the expansion valve may lower the pressure by expanding the low-pressure, high-temperature refrigerant before transferring the refrigerant that has passed through the indoor heat exchanger to the outdoor heat exchanger during heating. In addition, the amount of refrigerant flowing into the outdoor heat exchanger can be adjusted.

The blower fan introduces external air into the indoor unit 100 and discharges air whose temperature has changed due to heat exchange to the outside of the indoor unit 100 .

In addition, the air conditioner 150 may adjust the temperature of the air discharged into the indoor space, the strength of the wind, etc. according to the control of the processor 120 .

Meanwhile, for convenience of explanation, the configuration for controlling the temperature of the air is referred to as the cooling unit 150, but it is not limited to cooling, and heating to increase the temperature of the indoor air, blowing to form an airflow in the room, and lowering the indoor humidity At least one air conditioning of dehumidification may be performed.

The user interface 160 may be implemented as a device such as a button, a touch pad, a mouse, and a keyboard, or may be implemented as a touch screen capable of performing the above-described display function and manipulation input function together. Here, the button may be various types of buttons such as a mechanical button, a touch pad, a wheel, etc. formed in an arbitrary area such as the front, side, or rear of the exterior of the main body of the indoor unit 100 .

The display 170 may be implemented as various types of displays, such as a liquid crystal display (LCD), an organic light emitting diode (OLED) display, a plasma display panel (PDP), and the like. The display 170 may also include a driving circuit, a backlight unit, and the like, which may be implemented in the form of an a-si TFT, a low temperature poly silicon (LTPS) TFT, or an organic TFT (OTFT). Meanwhile, the display 170 may be implemented as a touch screen combined with a touch sensor, a flexible display, a three-dimensional display, or the like.

The speaker 180 may be a component that outputs not only various audio data but also various notification sounds or voice messages. Specifically, the speaker 180 may output notification information about the abnormal environment by voice.

Meanwhile, the outdoor unit 200 may include an outdoor unit heat exchanger 210 , an outdoor fan 220 , a compressor 230 , and a memory 240 .

The outdoor unit 200 may include a compressor 230 that compresses the refrigerant into a high-temperature and high-pressure gaseous state. In addition, the outdoor unit 200 may include an outdoor heat exchanger 210 that receives the high-temperature and high-pressure gas refrigerant compressed by the compressor 230 and exchanges heat with outdoor air. In addition, the outdoor unit 200 may include an outdoor fan 220 that forcibly blows outdoor air by the motor of the outdoor fan 220 so that heat exchange is performed in the outdoor heat exchanger 210 .

According to an embodiment, the outdoor unit 200 may be installed in an outdoor space. Meanwhile, according to another embodiment, the outdoor unit 200 may be installed in an indoor space. Specifically, the outdoor unit 200 may be implemented in a form arranged in an indoor space called an outdoor unit room (air-conditioning plant room).

The outdoor unit heat exchanger 210 may be disposed inside the outdoor unit 200 , and may be a condenser that liquefies the gaseous refrigerant based on the operation of the indoor unit 100 . The outdoor unit heat exchanger 210 may liquefy the high-temperature and high-pressure gas discharged through the compressor 230 through air sucked from the outside.

The outdoor fan 220 may be configured to forcibly discharge outdoor air by an outdoor fan motor so that heat exchange is performed in the outdoor heat exchanger 210 . Also, the rotation speed of the outdoor fan 220 may be changed based on a control signal transmitted from the processor 120 .

The compressor 230 may be configured to compress the refrigerant into a gaseous state of high temperature and high pressure.

The memory 240 may be configured to store setting information, control information, or various information related to the outdoor unit.

Referring to FIG. 3 , the air conditioner 100 may include a lidar sensor 110 . Specifically, the lidar sensor 110 may be attached to the front panel of the air conditioner 100 . The lidar sensor 110 may acquire sensing data corresponding to an object in front. Here, the sensing data may mean a plurality of point data. Based on the sensing data obtained by the lidar sensor 110 , the air conditioner 100 may analyze an object in front. Specifically, the analysis module may analyze at least one of whether the front object is a human, movement, size, shape, and distance.

According to an embodiment, the analysis module may be included in the air conditioner 100 , and the air conditioner 100 may directly acquire sensing data obtained from the lidar sensor 110 .

According to another embodiment, the analysis module may be included in an external server. The air conditioner 100 may transmit the sensing data obtained from the lidar sensor 110 to an external server, and the external server generates an analysis result based on the analysis module based on the sensing data received from the air conditioner 100 . can do. In addition, the external server may transmit the generated analysis result to the air conditioner 100 .

According to another embodiment, the analysis module may be included in a host device of an Internet of Things (IoT) network. The IoT network may include a plurality of in-house electronic devices. In addition, the IoT network may determine a host device that is connected to a plurality of electronic devices to perform management and control operations. Here, the host device may include an analysis module. The air conditioner 100 may transmit the sensing data obtained from the lidar sensor 110 to the host device of the IoT network. In addition, the host device of the IoT network may generate an analysis result based on the analysis module based on the received sensing data. In addition, the host device of the IoT network may transmit the generated analysis result to the air conditioner 100 .

Referring to FIG. 4 , the air conditioner 100 may be installed on the ceiling. Here, the lidar sensor 110 may be attached to the lower panel of the air conditioner 100 . In addition, the lidar sensor 110 may acquire sensing data corresponding to an object located below the air conditioner 100 (in front of the lidar sensor 110 ). Since the other operations are the same as those of FIG. 3 , a redundant description will be omitted.

Referring to FIG. 5 , there may be a plurality of air conditioners 100 . The multi-type air conditioner may mean a system air conditioner. For example, the air conditioner 100 corresponds to the stand-type air conditioner 100-1, the first ceiling-type air conditioner 100-2, and the second ceiling-type air conditioner 100-3. It may be composed of an indoor unit 200 connected to the plurality of air conditioners 100 - 1 , 100 - 2 and 100 - 3 .

Meanwhile, although the air conditioner has been described as an indoor unit, the air conditioner may be a concept including both an indoor unit and an outdoor unit according to the expression.

Referring to FIG. 6 , the lidar sensor 110 may acquire sensing data corresponding to an object located in front of the lidar sensor 110 .

According to an embodiment 601 , when the object is at a first distance (eg, 2.98 m), the lidar sensor 110 may acquire sensing data corresponding to the object.

According to another embodiment 602, when the object is at the second distance (eg, 5.36m), the lidar sensor 110 may acquire sensing data corresponding to the object.

According to another embodiment 603 , when the object is at a third distance (eg, 9.34m), the lidar sensor 110 may acquire sensing data corresponding to the object.

According to another embodiment 604 , when the object is at the fourth distance (eg, 13.71 m), the lidar sensor 110 may acquire sensing data corresponding to the object.

The plurality of

embodiments

601 , 602 , 603 , and 604 may be a situation in which the same object is at different distances, and sensing data obtained by the lidar sensor 110 according to the distance may be different. Sensing data acquired at a first distance (eg, 2.98 m) may have a relatively larger number of point data and a relatively high density of point data. However, the sensed data acquired at the fourth distance (eg, 13.71 m) may have a relatively small number of point data and a relatively low density of point data.

Accordingly, depending on the sensing method of the lidar sensor 110 , the recognition rate or accuracy of an object having a relatively long distance may be decreased. Since the number of point data used to analyze an object is small and the density is low, it may take longer to analyze the object, resulting in a longer processing time and poor accuracy.

Therefore, it is necessary to perform an operation to improve the quality of the sensing data acquired by the lidar sensor 110 . Here, good data quality may mean that the recognition rate or accuracy of object analysis is high because there are relatively many point data. Poor data quality may mean that the recognition rate or accuracy of object analysis is low because point data is relatively small. Operations for improving data quality will be described in the drawings below.

Referring to FIG. 7 , a plurality of human objects and one human object may be expressed as point data by the lidar sensor 110 . Data for each step corresponds to 701 to 704. Data 701 sensed by the lidar sensor 110 may have poor data quality. Specifically, the air conditioner 100 may increase the object recognition rate by changing the data 701 sensed by the lidar sensor 110 . The air conditioner 100 may group the data 701 sensed by the lidar sensor 110 based on height information. Here, the height information may mean vertical position information.

The air conditioner 100 may obtain height information of point data from the data 701 sensed by the lidar sensor 110 and group a plurality of point data based on the height information. The air conditioner 100 may group the point data within a threshold ratio of a specific height into one group by synthesizing the heights of a plurality of points. For example, assuming that the height of the first group is 10 cm and the critical ratio is 10%, point data between 9 cm and 11 cm may be determined as one group. Here, the height of each group and the total number of groups may be determined based on the data 701 sensed by the lidar sensor 110 . The air conditioner 100 may acquire data 702 in which data 701 sensed by the lidar sensor 110 is grouped based on height information.

The point data may be position information on a Cartesian coordinate system, and the position information of the Cartesian coordinate system may be converted into a spherical or polar coordinate system to confirm a vertical angle between two points. Through the converted value, the air conditioner 100 may check the group position (or channel position) of each point and group each point according to each channel.

Specifically, the air conditioner 100 may perform an augmentation operation to increase the density of point data. When point data is added through the augmentation operation, data obtained from the lidar sensor 110 with a low density and unclear shape can be supplemented due to a distance from an object.

The air conditioner 100 may additionally generate point data (vertical augmentation point data) in a vertical direction based on the grouped data 702 . Here, the operation of additionally generating the point data may be a data augmentation operation. Since the quality of data is not good only with the grouped data 702 , the air conditioner 100 may generate and add point data to an empty space (vertical direction) of the grouped data 702 . The air conditioner 100 may acquire the first change data 703 by adding the point data generated in the vertical direction to the grouped data 702 .

Also, the air conditioner 100 may additionally generate point data (horizontal augmentation point data) in a horizontal direction based on the first change data 703 . The air conditioner 100 may generate and add point data to an empty space (horizontal direction) of the first change data 703 . The air conditioner 100 may acquire the second change data 704 by adding the point data generated in the horizontal direction to the first change data 703 .

The second change data 704 obtained by the air conditioner 100 may have a greater number of point data and a higher density than the data 701 sensed by the lidar sensor 110 . Accordingly, when the air conditioner 100 performs object recognition using the second change data 704 , the recognition rate is higher than that of object recognition using the data 701 sensed by the lidar sensor 110 . Alternatively, accuracy can be obtained.

On the other hand, when the analysis module implements the operation of FIG. 7 in the form of an algorithm, the analysis module may receive information about a channel mapped to a standard distance as an input value in the form of a list. Also, the analysis module may perform an augmentation operation on an empty group (or channel). In addition, the analysis module may create a list-type variable for storing the final result after the augmentation operation, and may add information about the existing channel list entered as an input value. In addition, the analysis module may sequentially check two adjacent groups (or channels) and check whether an empty group (or channel) exists between them. If there is an empty group (or channel), information on the group (or channel) added to the variable created after performing the augmentation operation may be added.

Referring to FIG. 8 , a standard distance of the lidar sensor 110 may be determined according to an arrangement position. The height of the lidar sensor 110 may be h, and the angle between the laser 801-1 and the vertical axis 807 of the lidar sensor 110 emitted in the lowest direction may be a1. In addition, the angle of the front axis 806 of the lidar sensor 110 and the laser 801-1 radiated in the lowest direction may be a2. In addition, the angle between the front axis 806 of the lidar sensor 110 and the laser 801 - 16 emitted in the uppermost direction may be a2. That is, the maximum radiation angle in the vertical direction of the lidar sensor 110 may be 2*a2. In addition, the lidar sensor 110 may radiate a laser in any direction 360 degrees.

The above-mentioned height and radiation angle may be different for each lidar sensor, and the height of the lidar sensor 110 of the present disclosure may be 0.8 m, and the laser 801-1 and the lidar sensor ( The angle of the vertical axis 907 of 110 may be 75 degrees. In addition, the angle of the front of the laser (801-16) and the lidar sensor 110 emitted in the uppermost direction may be +15 degrees. In addition, the angle of the front of the laser 801-1 and the lidar sensor 110 that is emitted in the downward direction may be -15 degrees. On the other hand, the expression -15 degrees is a mark to distinguish the upper (+) and the lower (-), and may be described as +15 degrees depending on the use.

The standard distance corresponding to the lidar sensor 110 may be calculated by Equation (810). The standard distance may mean a maximum distance that can be sensed by the laser 801-1 radiated in the lowermost direction. Also, the standard distance may mean a horizontal distance at which the laser 801-1 emitted in the lowest direction contacts the bottom surface. Here, the laser 801-1 radiated in the lowest direction may correspond to the last channel (or the lowest channel). The reason that the standard distance is determined as described above is that when the object is sensed by all lasers (or all channels) emitted by the lidar sensor 110, the amount of information of the object may be the greatest, but the last channel (the lowest channel) is the bottom. This is because the distance in contact with the surface can be sensed by as many lasers (or as many channels) as possible.

For example, when an object of a certain size is located closer than a standard distance, the object is sensed by the laser 801-1 emitted in the lowest direction, but all parts of the object cannot be sensed. Conversely, when an object of a certain size is located farther than a standard distance, all parts of the object are sensed, but the laser 801-1 radiated in the lowermost direction cannot sense the object. If the object is located at a standard distance, the size of the object may be large enough to deviate from the maximum radiation angle. This part may require a separate data change operation.

Meanwhile, based on Equation 810, the standard distance SD corresponding to the lidar sensor 110 may be h*tan(a1). Here, SD may mean a standard distance, h means the arrangement height (or vertical position) of the lidar sensor 110 , and a1 is the vertical axis 907 and the lowest direction of the lidar sensor 110 . It may mean an angle between the emitted lasers 801-1.

Referring to FIG. 9 , in the air conditioner 100 , the standard distance vertical axis 908 of the lidar sensor and the laser emitted from the lidar sensor 110 are aligned with the front axis 906 of the lidar sensor 110 . Height information of the intersecting positions c1 to c16 may be calculated.

The lidar sensor 110 assumes an embodiment 905 in which eight lasers are radiated in a downward direction with respect to the front axis 906 of the lidar sensor 110 . A total of eight lasers may be radiated downward of the lidar sensor 110 , starting with the first laser 901-1 radiated in the lowermost direction.

The standard distance SD may be a distance to the bottom surface that can be recognized by the first laser 901-1 radiated in the lowest direction. Accordingly, a point (or intersection) that intersects the vertical axis 908 of the standard distance with the first laser 901-1 may be c1, and intersects the vertical axis 908 of the standard distance with the second laser 901-2 The point to be may be c2. Similarly, a point where the third laser 901-3 to the eighth laser and the vertical axis 908 of the standard distance intersect may be c3 to c8.

Similarly, the lidar sensor 110 may radiate eight lasers in an upward direction with respect to the front axis 906, and the point where the eight lasers and the vertical axis 908 of the standard distance intersect may be c9 to c16. have.

Meanwhile, each of the plurality of lasers emitted by the lidar sensor 110 may be described as a channel. For example, the first laser 901-1 may be described as a first channel, and each laser may be described as a respective channel.

Meanwhile, the air conditioner 100 may acquire the vertical midpoints M1, M2, M3, M4, M5, ... of the point where the vertical axis 908 of the standard distance and each laser intersect. The vertical midpoint may mean a midpoint between adjacent intersections.

Then, the air conditioner 100 may obtain the heights for the intersections c1 to c16 of the standard distances based on Equation 910 . The height hc1 for the intersection point c1 may be -h with respect to the front axis 906 . In addition, the height hc2 of the intersection point c2 may be hc1+SD*(tan(ac1)-tan(ac2)) with respect to the front axis 906 . In addition, the height hc3 of the intersection point c3 may be hc2+SD*(tan(ac2)-tan(ac3)) with respect to the front axis 906 . Here, SD may mean a standard distance of the lidar sensor 110 . ac1 may mean an angle between the front axis 906 of the lidar sensor 110 and the first laser 901-1, and ac2 is the front axis 906 of the lidar sensor 110 and the second laser ( 901-2), and ac3 may mean an angle between the front axis 906 of the lidar sensor 110 and the third laser 901-3. Meanwhile, the difference angle between ac1 and ac2 may be a unit vertical angle (or vertical resolution) of the lidar sensor 110 .

Meanwhile, the object may be located at an actual distance (AD) instead of a standard distance. Here, the laser capable of sensing an object may be the fourth laser 901-4 to the thirteenth laser 901-13. In addition, the intersection point of the fourth laser 901-4 and the vertical axis 909 on which the object A' is located may be c4' to c13'.

When the object A' is located at the measured distance AD, the air conditioner 100 may change the vertical position of the point data based on the standard distance. The air conditioner 100 may change the sensing data obtained from the object A' located at the measured distance to correspond to the standard distance. Specifically, the air conditioner 100 determines the vertical position of the vertical axis 909 where the object A' is located and the intersection points c4' to c13' of the laser with the vertical axis 908 of the closest standard distance and the intersection point of the laser ( It can be changed to the vertical position of c1 to c16). In this process, the air conditioner 100 may use the vertical midpoints M1, M2, M3, M4, M5, ... .

If the vertical positions of the crossing points c4' to c13' and the vertical positions of the crossing points c1 to c16 are the same, the air conditioner 100 may not change the vertical positions of the crossing points c4' to c13'. For example, since c4' has the same vertical position as c1, the vertical position may not change.

When the vertical position of the intersection points c4' to c13' and the vertical positions of the intersection points c1 to c16 are different, the air conditioner 100 sets the vertical midpoints M1, M2, M3, M4, M5, ... ) It is possible to change the intersecting point c4' to c13' between

For example, the air conditioner 100 may change the vertical position of c5' between M1 and M2 to the vertical position of c2 between M1 and M2. The point of the changed position may be c5'^adj. Also, the intersection points c4' to c13' may not exist between M2 and M3. In this case, the air conditioner 100 may identify that data corresponding to c3 between M2 and M3 does not exist. Also, the air conditioner 100 may change the vertical position of c6' between M3 and M4 to the vertical position of c4 between M3 and M4. The changed point may be c6'^adj. Also, the air conditioner 100 may identify that data corresponding to c5 does not exist. Also, the air conditioner 100 may change c7' between M5 and M6 (not shown) to a vertical position of c6 between M5 and M6 (not shown). The changed point may be c7'^adj.

As described above, the reason for changing the vertical position of the point data is to easily identify which part of the object is a low-density group (or channel) when performing the augmentation operation.

Meanwhile, the operation of changing the vertical position performed in FIG. 9 may be described as an operation of mapping data.

Meanwhile, a group without corresponding point data, such as c3 and c5, may be described as an empty group (or channel).

Meanwhile, according to the operation of changing the vertical position during the operation of FIG. 9 , all vertical positions of a plurality of point data included in the same channel may be changed in the same manner. However, depending on implementation, each point data may be changed to a different vertical position based on the vertical position of each point data.

Referring to FIG. 10 , the air conditioner 100 is expressed by Equation 1005 ((x-x1)/(x2-x1)=(y-y1)/(y2-y1)=(z-z1)/( z2-z1)) can be used to create a linear function. In Equation (1005), x, y, and z may mean a three-dimensional axis. For example, z may mean height information, and when the point data is viewed from the top to the bottom of the z-axis, x may indicate a horizontal axis and y may indicate a vertical axis. In addition, the linear function may be a function connecting two reference points in a three-dimensional space. Here, the two reference points may be (x1, y1, z1) and (x2, y2, z2).

Meanwhile, the air conditioner 100 may group some point data according to height information based on the sensed data. The air conditioner 100 may divide the plurality of point data 1010 included in the group into a plurality of segments based on the threshold distance. If the horizontal distance of the adjacent point data is equal to or greater than (or exceeds) the threshold distance, the air conditioner 100 may divide the data into separate segments. The plurality of point data 1010 included in the group may be divided into three segments. Here, the threshold distance may be obtained by Equation (1015). The threshold distance τ may be [2*dij*tan(θ/2)]*(1+λ). Here, dij may mean a distance between the j-th point located in the i-th channel and the lidar sensor 110 . And, θ may mean a horizontal angle of the lidar sensor 110 . λ may mean a constant, and may vary depending on the type or environment of the lidar sensor 110 . The horizontal angle θ of the lidar sensor 110 according to an embodiment of the present disclosure may be 2 degrees, and the constant λ may be 2.4.

Referring to FIG. 11 , each adjacent group (or channel) may consist of one segment. Assume an embodiment 1105 in which there are 7 points in the first group (or first channel) and 9 points in the second group (or second channel).

The air conditioner 100 may generate vertical augmentation point data to be added between the first group and the second group in order to augment the point data in the vertical direction. Here, a linear function may be used to determine the generated position. The air conditioner 100 may generate a linear function that connects one point data among a plurality of point data included in the first group and one point data among a plurality of point data included in the second group. The linear function may be obtained through Equation 1005 of FIG. The air conditioner 100 may identify a reference point for a linear function.

In determining the reference point used for the linear function, the air conditioner 100 may identify a point located at the edge of each group. For example, p11 at the left edge and p17 at the right edge of the first group may be reference points of the first group. In addition, p21 at the left edge and p29 at the right edge of the second group may be reference points of the second group. The air conditioner 100 may determine the point data of the first group and the point data of the second group as reference points.

Here, the air conditioner 100 may generate a reference point in consideration of a horizontal position. The air conditioner 100 may determine p11 of the first group and p21 of the second group as reference points, and generate a linear function based on the determined reference points. In addition, the air conditioner 100 may generate vertical augmentation point data based on the generated linear function.

Also, the air conditioner 100 may determine p17 of the first group and p29 of the second group as reference points, and generate a linear function based on the determined reference points. In addition, the air conditioner 100 may generate vertical augmentation point data based on the generated linear function.

Meanwhile, the air conditioner 100 may generate vertical augmentation point data using point data located in the center region after generating vertical augmentation point data using point data located at the edge of each group. When the point data of each group is the same, the air conditioner 100 may generate a linear function by matching the point data corresponding to each horizontal position, and may generate vertical augmentation point data based on the generated linear function. However, the number of point data included in the middle region of the first group may be different from the number of point data included in the middle region of the second group. Here, the air conditioner 100 may generate a linear function in consideration of ratio information of point data of each group.

For example, there are 5 point data except for edge point data in the first group. And, in the second group, except for the edge point data, there are 7 point data. Here, if the number of point data (7) of a group containing more center area point data is divided by the number of point data points (5) of a group containing less center area point data, ratio information (1.4) can be obtained . The air conditioner 100 may generate a linear function as much as the number of point data 5 of a group including less center area point data. Accordingly, the air conditioner 100 determines all of the point data of the group including less center area point data as reference points, and some points among the number of point data 7 of the group including more center area point data. Data should be determined as reference points.

Here, the air conditioner 100 may calculate [1,2,3,4,5]*1.4=[1.4,2.7,4.2,5.6,7.0] in consideration of the ratio information 1.4. Then, rounding is performed at the first decimal place, and the air conditioner 100 may obtain [1,3,4,6,7]. Here, the air conditioner 100 is the point data (p22, p24, p25, p27, p28) can be determined.

Here, the air conditioner 100 may determine p12 of the first group and p22 of the second group as reference points, and generate a linear function based on the determined reference points. In addition, the air conditioner 100 may generate vertical augmentation point data between p12 of the first group and p22 of the second group based on the generated linear function.

Similarly, the air conditioner 100 may generate a linear function connecting p13 and p24, a linear function connecting p14 and p25, a linear function connecting p15 and p27, and a linear function connecting p16 and p28.

Meanwhile, data corresponding to the embodiment 1105 may be data obtained from the head region 1111 of the human object 1105 .

12 , data 1205 may be data obtained by grouping sensing data obtained from the lidar sensor 110 according to height information and changed based on a standard distance. As described with reference to FIG. 9 , point data corresponding to a group of standard distances may not be sensed for objects not located at the standard distance. For example, the object A' of FIG. 9 may not have sensing data corresponding to c3 and c5 of the standard distance. Accordingly, sensing data corresponding to the group including c3 and the group including c5 may not exist. That is, c3 may exist immediately after c1, and the air conditioner 100 may generate vertical augmentation point data at a height at which point data corresponding to c2 should exist.

In the embodiment of FIG. 11 , the segment of an adjacent channel is 1:1, and when vertical augmentation point data is generated by the operation of FIG. 11 and combined with existing point data, changed data 1210 can be obtained. Although the vertical augmentation operation corresponding to one group is described in FIG. 11 , the vertical augmentation operation may be applied to all groups (or channels). The changed data 1210 may be a result of performing a vertical augmentation operation on all groups.

Referring to FIG. 13 , it is assumed that there are 16 point data in the first group and 11 point data in the second group 1305 . It is assumed that the point data of the first group consists of one segment seg1 and the point data of the second group consists of two segments seg2-1 and seg2-2.

The air conditioner 100 may divide the point data of a group (first group) having a smaller number of segments into the number of segments (two) of a group (second group) having a larger number of segments. The reference point at which the first group is divided may use a horizontal position or an azimuth angle. The air conditioner 100 determines the horizontal position (or azimuth) of the point data p25 located at the right edge of the second group and the horizontal position (or azimuth) of the point data p26 located at the left edge of the second group. The center line 1306 may be determined in consideration of , and the first group may be divided into two segments seg1-1 and seg1-2 based on the center line 1306 .

The air conditioner 100 may generate a linear function using seg1-1 and seg2-1, and may generate a linear function using seg1-2 and seg2-2. Since the description of the embodiment in which the number of segments in each group is 1:1 has been described with reference to FIGS. 11 and 12 , a redundant description will be omitted.

Meanwhile, data corresponding to the embodiment 1305 may be data obtained from the waist region 1311 of the human object 1310 .

Referring to FIG. 14 , when the number of segments of adjacent groups is 1:n, the air conditioner 100 may generate vertical augmentation point data by dividing a group including one segment into n segments.

The data 1405 may be data before generating the vertical augmentation point data, and the data 1410 may be data after additionally generating the vertical augmentation point data. Since descriptions other than those have been described with reference to FIGS. 11 to 12 , redundant descriptions are omitted.

Referring to FIG. 15 , an embodiment 1505 in which 16 point data exists in a first group and 10 point data in a second group is assumed. Here, it is assumed that the first group consists of three segments seg1-1, seg1-2, and seg1-3, and the second group consists of two segments seg2-1 and seg2-2.

The air conditioner 100 may identify the average center points (cp1-1, cp1-2, cp1-3, cp2-1, cp2-2) based on the horizontal and vertical positions of the point data included in each segment. have. Based on the average center points (cp1-1, cp1-2, cp1-3) corresponding to the first group and the average center points (cp2-1, cp2-2) corresponding to the first group, the air conditioner 100 is A segment of the first group may be matched with a segment of the second group. The air conditioner 100 may match adjacent segments 1:1, 1:n, or n:1 in consideration of the horizontal position and vertical position of each average center point.

Here, when the segments are matched 1:1, the air conditioner 100 generates a linear function using point data located in the edge area and then generates a linear function using point data located in the center area. Vertical augmentation point data may be obtained. Since the operation related thereto has been described in FIG. 11 , a redundant description thereof will be omitted.

Meanwhile, when the segments are matched by 1:n or n:1, the air conditioner 100 may generate vertical augmentation point data by dividing a group consisting of one segment into n segments. Since the related operation has been described with reference to FIG. 13 , a redundant description thereof will be omitted.

Meanwhile, data corresponding to the embodiment 1505 may be data obtained from the leg region 1511 of the human object 1510 .

Referring to FIG. 16 , when the number of segments in adjacent groups is n1:n2, the air conditioner 100 may generate vertical augmentation point data using the average center point of each segment.

The data 1605 may be data before generating the vertical augmentation point data, and the data 1610 may be data after additionally generating the vertical augmentation point data. Since descriptions other than those have been described with reference to FIGS. 11 to 12 , redundant descriptions are omitted.

Referring to FIG. 17 , a graph 1705 may be 2D data viewed from the z-axis among 3D sensing data obtained from the lidar sensor 110 . The two-dimensional data may be expressed by the x-axis and the y-axis, and may mean horizontal position information.

The embodiment 905 described in FIG. 9 is a side view, and the embodiment described in FIG. 17 is a plan view. G' may indicate data corresponding to a measured distance at which an object is recognized, and G may indicate data corresponding to a standard distance. O may mean the position of the lidar sensor 110 .

Here, in the description of FIG. 9 , the air conditioner 100 changed the vertical position of the point data based on the standard distance. However, in FIG. 17 , the air conditioner 100 may change the horizontal position of the point data based on the standard distance. That is, changing the vertical position may mean changing the z-axis coordinates, and changing the horizontal position may mean changing the x-axis and y-axis coordinates.

The air conditioner 100 may use the average center point 1711 of the object data 1710 in changing the horizontal position information. Here, the object data 1710 may refer to data of an area estimated as an object among sensing data obtained from the lidar sensor 110 .

The average center point 1711 may be a value calculated in consideration of a horizontal position and a vertical position of a plurality of point data included in the object data 1710 . Although the average center point 1711 considers the vertical position, according to an embodiment, the air conditioner 100 may obtain the average center point by using only the average of the horizontal positions in order to reduce the calculation process.

A portion of the plan view of the object data 1710 may include point data p1', p2', p3', p4', p5', and p6'. Here, the average center point 1711 may correspond to pco of the graph 1705 . In addition, the air conditioner 100 may change the point data p1', p2', p3', p4', p5', and p6' based on the center point (pcs) of the standard distance. The changed point data p1, p2, p3, p4, p5, and p6 may be moved by the difference between the average center point pos of the object and the center point pcs of the standard distance. Here, the center point (pcs) of the standard distance may be a point at which a straight line between the origin (o) of the lidar sensor 110 and the average center point (pos) of the object and the standard distance line 1706 intersect. The standard distance line 1706 may be a line through which the laser emitted in the lowest direction from the lidar sensor 110 is in contact with the bottom surface. In addition, the object distance line 1707 may mean a line through which an actual object is sensed, and may be determined by the average center point pos of the object.

The change calculation operation may be performed by Equation (1715). Δx may mean a difference value between the x-coordinate value of the center point (pcs) of the standard distance and the x-coordinate value of the average center point (pos) of the object. And, Δy may mean a difference value between the y-coordinate value of the center point (pcs) of the standard distance and the y-coordinate value of the average center point (pos) of the object.

The coordinate value of p1 may be (x1-Δx, y1-Δy), the coordinate value of p5 may be (x5-Δx, y5-Δy), and the coordinate value of p6 is (x6-Δx) , y6-Δy). The calculation operations of p2 to p4 are omitted.

Referring to FIG. 18 , the air conditioner 100 may acquire horizontally augmented point data after horizontally moving point data based on a standard distance.

Referring to FIG. 18 , the air conditioner 100 may determine how many horizontal augmentation point data to add in the horizontal direction based on Equation 1805. The number of points to be added (N12) may be “[(ap1-ap2)/ahu] -1”. ap1 may mean an angle between the reference axis and p1, and ap2 may mean an angle between the reference axis and p2. ahu may mean a horizontal unit angle, and the horizontal unit angle may mean a horizontal angle difference between a plurality of lasers emitted by the lidar sensor 110 . Here, the air conditioner 100 may perform a discard operation with respect to the number N12. This is because the added point data is an integer. On the other hand, the air conditioner 100 may apply a rounding operation or a rounding operation in addition to discarding.

In addition, the air conditioner 100 may determine at which position to add the horizontal augmentation point data based on Equations 1810 and 1815 .

Based on Equation (1810), the air conditioner 100 uses p1x+(p2x-p1x)*i/(N12+1) for the x-coordinate value of the horizontal augmentation point data to be newly added between p1 and p2. can be obtained p1x means the x-coordinate value of p1, p2x means the x-coordinate value of p2, i means the index of the point to be added, and N12 is the total number of points to be added calculated in Equation (1805) can mean

In addition, based on Equation (1815), the air conditioner 100 calculates the y-coordinate value of the horizontal augmentation point data to be newly added between p1 and p2 by p1y+(p2y-p1y)*i/(N12+1). It can be obtained using p1y means the y-coordinate value of p1, p2y means the y-coordinate value of p2, i means the index of the point to be added, and N12 is the total number of points to be added calculated in Equation (1805) can mean

If p1=62.4 degrees, p2=61.9 degrees, p3=61.5 degrees, p4=61.1 degrees, p5=60.8 degrees, p6=60.2 degrees, it is assumed that the horizontal unit angle is 0.2 degrees. The angle difference between p1 and p2 is 0.5 degrees. Here, when Equation 1805 is applied, the number of points N12 to be added may be 1.5. If decimal point truncation is used, the air conditioner 100 may add one horizontal augmentation point data between p1 and p2, and add horizontal augmentation point data at an intermediate position bisecting between p1 and p2. have.

Also, the angle difference between p5 and p6 is 0.6 degrees. Here, when Equation 1805 is applied, the number of points N56 to be added may be two. Here, the air conditioner 100 may identify two positions dividing between p5 and p6 into thirds, and add horizontal augmentation point data to each of the two positions.

19 is a diagram for explaining a feature used to analyze an object.

Referring to FIG. 19 , the air conditioner 100 may perform a vertical augmentation operation and a horizontal augmentation operation on sensing data obtained from the lidar sensor 110 . However, the initially acquired sensing data may be changed by an augmentation operation. The air conditioner 100 may identify the object based on the changed data. Here, when the air conditioner 100 identifies an object, a plurality of features may be considered. Here, the feature may mean an attribute found in data of the target. For example, in a human face, shapes such as eyes, nose, mouth, and ears may be repeatedly found. Accordingly, the eyes, nose, mouth, ears, and the like may be features to identify a human face.

In order to extract the features, the air conditioner 100 may use various mathematical methods. For example, the air conditioner 100 may extract the first feature using a 3D covariance matrix of the cluster. Also, the air conditioner 100 may extract the second feature by using a 2D covariance matrix in three regions (upper half, left, right lower haves). In addition, the third feature may be extracted using a normalized 2D histogram for the main plane of 14*7 bins generated through principal component analysis (PCA). Also, the air conditioner 100 may extract the fourth feature using a normalized 2D histogram for the second plane of 9*5 bins generated through principal component analysis (PCA). Also, the air conditioner 100 may extract the fifth feature by using a feature slice for the cluster. Also, the air conditioner 100 may extract the sixth feature using a histogram of normal vector information that is a vertical straight line of a point in a three-dimensional space based on the shape of an object included in the changed data. Also, the air conditioner 100 may extract the sixth feature by using the size information of the three-dimensional bounding box through the changed data.

Here, the extracted expression may be changed to the obtained expression.

And, after extracting the feature, the air conditioner 100 may identify the object by applying the extracted feature to the machine learning model.

Referring to FIG. 20 , a table 2005 includes test information according to a plurality of embodiments. As for the data set used for the test, a total of four (first sample to fourth sample) are used as shown in FIG. 20 . The first sample is data obtained in a stationary state in which the electronic device to which the lidar sensor 110 is attached does not move in the first space. The second sample is data obtained by moving the electronic device to which the lidar sensor 110 is attached in the first space. The third sample is data acquired in the second space. The fourth sample is data acquired in the third space. The positive sample shown in FIG. 20 means the number of instances labeled as human, and the negative sample means the number of instances for all non-human objects.

Referring to FIG. 21 ,

graphs

2101 , 2102 , 2103 , and 2104 show original data, data using the first method, and data using the second method. Accuracy when learning with an analysis model such as Support Vector Machines (SVM) Results may be included. Here, the SVM analysis model may be a machine learning model.

Here, the original data is the sensing data itself acquired from the lidar sensor 110 and may refer to raw data in which data is not changed, such as an augmentation operation.

The first method may be indicated as “invention” in the graph, and may mean performing an augmentation operation according to an embodiment of the present disclosure.

The second method may be indicated as “other” in the graph, and may mean an operation of transforming data using a resampling technique of a 2D grid resample technique and a Radial Basis Function (RBF)-based interpolation technique.

The air conditioner 100 may use a Support Vector Machines (SVM) classification model (or analysis model) to determine whether an object included in data is a human or not. In the case of an SVM model, a Radial Basis Function (RBF) kernel can be used for training.

The

graphs

2101,2102,2103,2104 show original data obtained by each sample (first sample, second sample, third sample, and fourth sample), data using the first method, and data using the second method. Each may be an accuracy result tested on the SVM classification model.

Here, each graph may include Area Under the Curve-Receiver Operating Characteristics (AUC-ROC) information for each sample. In addition, the true positive rate may mean a ratio in which an analysis result is a human with respect to data corresponding to a real human object, and a false positive rate may mean a ratio in which an analysis result is not a human being with respect to data corresponding to a real human object.

Combining the

graphs

2101,2102,2103,2104, the data using the second method has a higher true positive rate than the original data and the data using the first method. Accordingly, the object recognition method according to an embodiment of the present disclosure using the first method may have higher accuracy.

Referring to FIG. 22 ,

graphs

2101 , 2102 , 2103 , and 2104 include accuracy results when learning original data, data using the first method, and data using the second method using a random forest (RF) classification model. can do. Here, the RF classification model may be a machine learning model.

The air conditioner 100 may use a random forest (RF) classification model to determine whether an object included in data is a human or not. When learning with a random forest (RF) model, the number of tree estimators can be set to 20.

The

graphs

2201,2202,2203,2204 show original data obtained by each sample (first sample, second sample, third sample, and fourth sample), data using the first method, and data using the second method. Each may be an accuracy result tested on an RF classification model.

Combining the

graphs

2201,2202,2203,2204, the data using the second method has a higher true positive rate than the original data and the data using the first method. Accordingly, the object recognition method according to an embodiment of the present disclosure using the first method may have higher accuracy.

Since other descriptions are given in FIG. 21 , duplicate descriptions will be omitted.

Referring to FIG. 23 , a table 2305 includes Area Under the Curve-Receiver Operating Characteristics (AUC-ROC) scores of each sample based on different methods. In addition, table 2310 includes the F1 scores of each sample based on different methods. Here, the F1 score may be a score reflecting precision and recall. The higher the two scores, the better the performance. Good performance may mean high recognition rate or accuracy.

In both the table 2305 and the table 2310, the data using the first method (invention) acquires a higher score than the original data (raw) and the data using the second method (other). Accordingly, the object recognition method according to an embodiment of the present disclosure using the first method may have higher accuracy.

Referring to FIG. 24 , a graph 2405 may include a relationship between processing times according to the number of points of an object. The time to process data using the second method (other) increases as the number of points of the object increases. In the second method, the number of points subject to resampling may be increased in a quadratically form. However, the time for processing data using the first method (invention) may be constant even as the number of points of the object increases, like the time for processing raw data. Accordingly, the data using the first method may have an advantage in that the processing time is not long even though the accuracy is increased.

Referring to FIG. 25 , the air conditioner 100 may acquire sensing data including a plurality of point data from the lidar sensor 110 ( S2505 ). Here, the sensing data may mean object data corresponding to an object among data obtained from the lidar sensor 110 . The object data may include only a plurality of point data corresponding to the object. In addition, the air conditioner 100 may group a plurality of groups based on vertical position information of a plurality of point data ( S2510 ). Then, the air conditioner 100 may change the vertical position of the grouped plurality of point data based on the standard distance (S2515).

Then, the air conditioner 100 may generate vertical augmentation point data based on the changed vertical position information (S2520). In addition, the air conditioner 100 may generate horizontal augmentation point data based on the horizontal position information for each group ( S2525 ). Then, the air conditioner 100 performs object recognition based on the plurality of point data acquired from the lidar sensor 110, the vertical augmentation point data generated in step S2520, and the horizontal augmentation point data generated in step S2525. It can be (S2530).

Referring to FIG. 26 , the air conditioner 100 may divide a segment for each group based on the changed vertical position information to perform step S2520 of FIG. 25 ( S2605 ). Then, the air conditioner 100 may identify the number of segments divided for each group (S2610). Then, the air conditioner 100 may compare the number of segments for each adjacent group (S2615). Then, the air conditioner 100 may identify whether each segment of the adjacent group is one (S2620).

When there is one segment in each adjacent group, the air conditioner 100 connects the points included in the edge area of each group to generate a linear function, and connects the points included in the center area to generate a linear function It can be done (S2625). In generating a linear function, points of the same group are not connected, and only one point in one group can be determined as a reference point. As a result, a linear function can be created by connecting different groups of points.

If each of the segments of the adjacent groups is not one, the air conditioner 100 may identify whether one of the adjacent groups is one segment and the remaining groups are two or more segments ( S2630 ). If one group is one segment and the remaining groups are two or more segments, the air conditioner 100 divides the one-segment group by the number of segments in the remaining group to generate a linear function connecting the points for each group. There is (S2635). Here, the air conditioner 100 may perform a division operation so that the relationship between the segments of each group becomes 1:1. Then, when the segment relationship becomes 1:1, step S2625 may be performed. Here, the air conditioner 100 may determine one point in one group as a reference point, and may generate a linear function by connecting reference points belonging to different groups.

If one group is one segment and the other group is not two or more segments, the air conditioner 100 may generate a linear function connecting points for each group based on the center point of each segment (S2640). That is, when there are two or more segments in each group, the air conditioner 100 may identify center points of a plurality of segments and match segments of different groups based on the center points. And, if the relationship between the matched segments is a 1:1 relationship, the air conditioner 100 may perform step S2625, and if the relationship between the matched segments is a 1:n relationship (or an n:1 relationship), the air conditioner ( 100) may perform step S2635.

Then, the air conditioner 100 may generate a vertical augmentation point based on the generated linear function after generating the linear function through S2625, S2635, and S2640 (S2645).

Referring to FIG. 27 , the air conditioner 100 may perform operations S2605 , S2610 , and S2615 of FIG. 26 . After that, the air conditioner 100 may identify whether each segment of the adjacent group is one (S2705). Here, when there is one segment of each adjacent group, the air conditioner 100 may generate a linear function by connecting the edge points of each group ( S2710 ). Then, the air conditioner 100 may generate a linear function connecting the center points based on the ratio of the number of points (S2715).

If each of the segments of the adjacent groups is not one, the air conditioner 100 may identify whether one of the adjacent groups is one segment and the remaining groups are two or more segments (S2720). Here, when one group is one segment and the remaining groups are two or more segments, the air conditioner 100 may divide the one-segment group by the number of segments in the remaining group ( S2725 ). In addition, the air conditioner 100 may match the segments for each group based on the segments ( S2730 ).

The air conditioner 100 may identify whether there is one matched segment for each group (S2735). If there is one matched segment for each group, the air conditioner 100 may perform step S2710. If there is not one matched segment for each group, step S2725 may be performed again.

Meanwhile, if one group is one segment and the other group is not two or more segments, the air conditioner 100 may acquire the center point of each segment ( S2740 ). Then, the air conditioner 100 may match the segments for each group based on the obtained center point (S2745). Then, the air conditioner 100 may perform step S2735.

The air conditioner 100 may generate vertical augmentation point data based on the linear function generated after steps S2710 and S2715 are performed and add it to the existing point data. Data to which vertical augmentation point data is added may be data 703 of FIG. 7 .

Referring to FIG. 28 , the control method of the air conditioner according to an embodiment of the present disclosure includes obtaining a plurality of point data sensed by the lidar sensor 110 ( S2805 ), vertical positions of the plurality of point data Grouping a plurality of point data into a plurality of groups based on the information (S2810), a third vertical position between a first vertical position corresponding to the first group among the plurality of groups and a second vertical position corresponding to the second group Generating the vertical augmentation point data corresponding to the position (S2815), generating the horizontal augmentation point data between the point data based on the horizontal position information of the point data included in the first group and the second group (S2820) ), and identifying an object based on the sensed plurality of point data, the generated vertical augmentation point data, and the generated horizontal augmentation point data (S2825).

Here, if the distance between the plurality of point data and the lidar sensor 110 is not a predefined standard distance, the control method further includes the step of changing the vertical position information of the plurality of point data based on the predefined standard distance may include

Here, in the step of changing the vertical position information, if the distance between the plurality of point data and the lidar sensor 110 is not a predefined standard distance, the vertical position of the plurality of point data included in each group is set to a predefined standard. It can be changed to a vertical position for each group corresponding to the distance.

Here, the generating of the vertical augmentation point data ( S2815 ) may generate a third group including vertical augmentation point data corresponding to a plurality of grouped point data, and the generating of the horizontal augmentation point data includes the first Horizontal augmentation point data may be generated between the point data based on horizontal position information of the point data included in the group, the second group, and the generated third group.

Meanwhile, in the step of generating the vertical augmentation point data (S2815), when the distance between the point data adjacent in the horizontal direction in the grouped plurality of point data is greater than a predefined threshold, the adjacent point data is divided into different segments (segmentaion) and may generate vertical augmentation point data based on the divided segments.

Here, in the step of generating the vertical augmentation point data (S2815), the number of segments divided for each group is identified, the number of segments of each adjacent group is compared, and the vertical augmentation point data is based on the compared number of segments of each of the adjacent groups. can create

Here, in the step of generating the vertical augmentation point data (S2815), if the number of segments of the compared first group and the second group is one, a first linear function is generated that connects the first point data among the point data for each group, and , generating a second linear function connecting the last point data of the point data for each group, generating a third linear function connecting the middle point data of the point data for each group to correspond to the number ratio, and the generated first linear function to the third linear function may generate vertical augmentation point data at the intersection of the predefined vertical position.

Meanwhile, in the step of generating the vertical augmentation point data ( S2815 ), when the number of segments in the first group is one among the compared adjacent groups and the number of segments in the remaining second group is two or more, the number of segments in the first group is the second It is possible to divide the group to be the number of segments, and to generate a linear function linking the point data of the first group and the point data of the second group based on the divided segments of the first group and the second group.

Meanwhile, in the step of generating the vertical augmentation point data ( S2815 ), when the compared number of segments in the first group and the second group is two or more, the point data of the first group and the point data of the second group are based on the center point of each segment. A linear function can be created that connects the point data of

On the other hand, in the step of generating the horizontal augmented point data (S2820), if the distance between the identified object and the lidar sensor 110 is not a predefined standard distance, the horizontal level of each group point data based on the predefined standard distance The position information may be changed and horizontal augmented point data may be generated based on the changed azimuth of each group point data.

Meanwhile, the control method of the air conditioner 100 as shown in FIG. 28 may be executed on the air conditioner 100 having the configuration of FIG. 1 or FIG. 2 , and may also be executed on the air conditioner 100 having other configurations. have.

Meanwhile, the above-described methods according to various embodiments of the present disclosure may be implemented in the form of an application that can be installed in an existing electronic device.

In addition, the above-described methods according to various embodiments of the present disclosure may be implemented only by software upgrade or hardware upgrade of an existing electronic device.

In addition, various embodiments of the present disclosure described above may be performed through an embedded server provided in an electronic device or an external server of at least one of an electronic device and a display device.

Meanwhile, according to a temporary example of the present disclosure, the various embodiments described above may be implemented as software including instructions stored in a machine-readable storage media readable by a machine (eg, a computer). can The device is a device capable of calling a stored command from a storage medium and operating according to the called command, and may include the electronic device according to the disclosed embodiments. When the instruction is executed by the processor, the processor may perform a function corresponding to the instruction by using other components directly or under the control of the processor. Instructions may include code generated or executed by a compiler or interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' means that the storage medium does not include a signal and is tangible, and does not distinguish that data is semi-permanently or temporarily stored in the storage medium.

Also, according to an embodiment of the present disclosure, the method according to the various embodiments described above may be included in a computer program product and provided. Computer program products may be traded between sellers and buyers as commodities. The computer program product may be distributed in the form of a machine-readable storage medium (eg, compact disc read only memory (CD-ROM)) or online through an application store (eg, Play Store™). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a storage medium such as a memory of a server of a manufacturer, a server of an application store, or a relay server.

In addition, each of the components (eg, a module or a program) according to the above-described various embodiments may be composed of a single or a plurality of entities, and some sub-components of the aforementioned sub-components may be omitted, or other sub-components may be omitted. Components may be further included in various embodiments. Alternatively or additionally, some components (eg, a module or a program) may be integrated into a single entity to perform the same or similar functions performed by each corresponding component prior to integration. According to various embodiments, operations performed by a module, program, or other component may be sequentially, parallel, repetitively or heuristically executed, or at least some operations may be executed in a different order, omitted, or other operations may be added. can

In the above, preferred embodiments of the present disclosure have been illustrated and described, but the present disclosure is not limited to the specific embodiments described above, and it is common in the technical field pertaining to the present disclosure without departing from the gist of the present disclosure as claimed in the claims. Various modifications may be made by those having the knowledge of

Claims

In the air conditioner,

lidar sensor; and

processor; including;

The processor is

Acquire a plurality of point data sensed by the lidar sensor,

Grouping the plurality of point data into a plurality of groups based on the vertical position information of the plurality of point data,

generating vertical augmentation point data corresponding to a third vertical position between a first vertical position corresponding to a first group among the plurality of groups and a second vertical position corresponding to a second group;

generating horizontal augmentation point data between the point data based on horizontal position information of the point data included in the first group and the second group;

An air conditioner that identifies an object based on the sensed plurality of point data, the generated vertical augmentation point data, and the generated horizontal augmentation point data.
According to claim 1,

The processor is

If the distance between the plurality of point data and the lidar sensor is not a predefined standard distance, the air conditioner changes vertical position information of the plurality of point data based on the predefined standard distance.
3. The method of claim 2,

The processor is

If the distance between the plurality of point data and the lidar sensor is not a predefined standard distance, the vertical position of the plurality of point data included in each group is set to a vertical position for each group corresponding to the predefined standard distance. Modified, air conditioner.
According to claim 1,

The processor is

generating a third group including vertical augmentation point data corresponding to the grouped plurality of point data;

An air conditioner that generates horizontal augmentation point data between point data based on horizontal position information of point data included in the first group, the second group, and the generated third group.
According to claim 1,

The processor is

If the distance between the point data adjacent in the horizontal direction in the grouped plurality of point data is greater than a predefined threshold, the adjacent point data is divided into different segments,

and generating the vertical augmentation point data based on the segmented segment.
6. The method of claim 5,

The processor is

Identifies the number of segments divided for each group,

comparing the number of segments of each of the adjacent groups,

and generating the vertical augmentation point data based on the compared number of segments of each of the adjacent groups.
7. The method of claim 6,

The processor is

If the number of segments of the compared first group and the second group is one each,

generating a first linear function linking the first point data among the group-by-group point data,

generating a second linear function linking the last point data among the group-by-group point data;

A third linear function is generated in which the middle point data of the group-by-group point data corresponds to the number ratio,

An air conditioner that generates vertical augmentation point data at a position where the generated first to third linear functions and a predefined vertical position intersect.
7. The method of claim 6,

The processor is

If the number of segments in the first group is one among the compared adjacent groups and the number of segments in the remaining second group is two or more, dividing the number of segments in the first group to be the number of segments in the second group;

and generating a linear function linking the first group point data and the second group point data based on the divided first group segment and the second group segment.
7. The method of claim 6,

The processor is

When the compared number of segments of the first group and the second group is two or more, generating a linear function linking the point data of the first group and the point data of the second group based on the center point of each segment , air conditioner.
3. The method of claim 2,

The processor is

If the distance between the identified object and the lidar sensor is not a predefined standard distance, the horizontal position information of each group point data is changed based on the predefined standard distance and based on the changed azimuth of each group point data to generate horizontal augmentation point data, the air conditioner.
A method for controlling an air conditioner, comprising:

acquiring a plurality of point data sensed by a lidar sensor;

grouping the plurality of point data into a plurality of groups based on vertical position information of the plurality of point data;

generating vertical augmentation point data corresponding to a third vertical position between a first vertical position corresponding to a first group among the plurality of groups and a second vertical position corresponding to a second group;

generating horizontal augmented point data between the point data based on horizontal position information of the point data included in the first group and the second group;

Identifying an object based on the sensed plurality of point data, the generated vertical augmentation point data, and the generated horizontal augmentation point data; Containing, the control method of the air conditioner.
12. The method of claim 11,

If the distance between the plurality of point data and the lidar sensor is not a predefined standard distance, changing the vertical position information of the plurality of point data based on the predefined standard distance; further comprising, How to control the air conditioner.
13. The method of claim 12,

Changing the vertical position information comprises:

If the distance between the plurality of point data and the lidar sensor is not a predefined standard distance, the vertical position of the plurality of point data included in each group is set to a vertical position for each group corresponding to the predefined standard distance. To change, the control method of the air conditioner.
12. The method of claim 11,

The step of generating the vertical augmentation point data comprises:

generating a third group including vertical augmentation point data corresponding to the grouped plurality of point data;

The step of generating the horizontal augmentation point data comprises:

The control method of the air conditioner, generating horizontal augmentation point data between the point data based on the horizontal position information of the point data included in the first group, the second group, and the generated third group.
12. The method of claim 11,

The step of generating the vertical augmentation point data comprises:

If the distance between the point data adjacent in the horizontal direction in the grouped plurality of point data is greater than a predefined threshold, the adjacent point data is divided into different segments,

A control method of an air conditioner that generates the vertical augmentation point data based on the divided segment.