KR102559196B1 - Unstructured object detection system and method using 3D LiDAR - Google Patents

Abstract

The present invention relates to a system and method for detecting an atypical object using a 3D lidar, and more particularly, a data acquisition step (S100) of acquiring at least one 3D point cloud for an area scanned using at least one 3D lidar sensor in a lidar acquisition unit, and a clustering step (S100) of performing clustering into at least one object candidate group by analyzing the 3D point cloud obtained by the data acquisition step (S100) in a clustering unit. 200), in a feature extraction unit, a feature extraction step (S300) of extracting a feature image of a preset item using a 3D point cloud constituting each object candidate group clustered by the clustering step (S200), and an irregular object detection step (S400) of detecting an atypical object by applying features for each object candidate group extracted by the feature extraction unit (S300) to a pre-stored object detection algorithm in the detection unit. It relates to an atypical object detection method using a 3D lidar, characterized in that.

Description

Unstructured object detection system and method using 3D LiDAR

The present invention relates to a system and method for detecting an irregular object using a 3D lidar, and more particularly, to a system and method for detecting an irregular object using a 3D lidar capable of reducing erroneous detection of an irregular object in consideration of noise when detecting an object using 3D LiDAR data.

In particular, when applied to an autonomous vehicle, it relates to an atypical object detection system and method using a 3D lidar capable of accurately recognizing atypical obstacles on the road and accurately removing noise by determining whether or not there is noise.

3D LiDAR (Light Detection and Ranging) refers to a plurality of points obtained by scanning a surrounding object with a laser as a point cloud, and each point includes a 3D position coordinate value and a reflectance value for the surface of the scanned object.

When an object detection system using such 3D lidar data is applied to an autonomous vehicle or assisted driving vehicle, required detection objects are lanes, traffic lights, pedestrians, surrounding vehicles, and objects that interfere with driving.

However, due to the nature of 3D LiDAR data obtained by scanning surrounding objects with a laser, in a rainy situation or an environment in which a large amount of water droplets can be scanned by a nearby vehicle stepping on a water puddle, there is a problem of false detection in which water droplets that do not affect vehicle driving are detected as objects that interfere with driving (see FIG. 1 a)).

As another example of such a false detection problem, the degree of condensation of high-concentration water vapor discharged from the exhaust vents of surrounding vehicles varies according to seasonal characteristics, so in an environment where water vapor can be scanned, even water vapor that does not affect vehicle driving at all is detected as an obstacle to driving (see b) in FIG. 1).

As such, when performing ambient environment recognition using 3D lidar data, it is necessary to perform more robust environment recognition.

In this regard, Korean Patent Registration No. 10-2083482 (“LIDAR-Based Vehicle Driving Area Detection Apparatus and Method”) only discloses a technology for detecting a road boundary object, which is a location in which a vehicle cannot drive, and does not mention noise determination of an atypical object detected on a road on which a vehicle can drive.

Domestic Patent Registration No. 10-2083482 (Registration date 2020.02.25.)

The present invention has been made to solve the problems of the prior art as described above, and an object of the present invention is to provide an irregular object detection system and method using 3D LiDAR that can accurately recognize an irregular object by using a 3D clustering method for 3D LiDAR data, and can reduce false detection by determining whether or not the recognized irregular object has noise.

An atypical object detection system using 3D lidar according to an embodiment of the present invention includes at least one 3D lidar sensor, and includes a lidar acquisition unit 100 that acquires at least one 3D point cloud for an area scanned through the 3D lidar sensor, and a clustering unit 200 that performs clustering into at least one object candidate group using the 3D point cloud received from the lidar acquisition unit 100 It is preferable to include a feature extraction unit 300 that generates a top-view image using a 3D point cloud of the object candidate group clustered by the clustering unit 200 and extracts features of a preset item for the generated top-view image, and a detection unit 400 that detects an atypical object by applying features for each object candidate group extracted from the feature extraction unit 300 to a pre-stored object detection algorithm.

Furthermore, the clustering unit 200 uses the representative point extraction unit 210 that sets representative points based on the position coordinates of each 3D point cloud using at least one 3D point cloud for each lidar channel received from the lidar acquisition unit 100 and an object separation unit that separates at least one object candidate group data by performing clustering on the representative points set by the representative point extraction unit 210 using a pre-stored 3D clustering algorithm. It is preferable to further include (220).

Furthermore, the feature extractor 300 preferably extracts at least one feature of a maximum height value, an average height value, a maximum reflectance value, an average reflectance value, the number of clustered points, a density value, and a distribution value of the 3D point cloud clustered into the object candidate group, based on the top-view image generated by the clustering unit 200 based on the 3D point cloud of the object candidate group.

Furthermore, the detection unit 400 preferably detects an atypical object among the object candidate groups, which are estimated objects, by using a result value obtained by applying the features of each object candidate group extracted from the feature extraction unit 300 to the object detection algorithm.

The atypical object detection method using 3D lidar according to an embodiment of the present invention includes a data acquisition step (S100) of acquiring at least one 3D point cloud for an area scanned using at least one 3D lidar sensor in a lidar acquisition unit, a clustering step (S200) of performing clustering into at least one object candidate group by analyzing the 3D point cloud acquired by the data acquisition step (S100) in a clustering unit, Preferably, the extraction unit includes a feature extraction step (S300) of extracting a feature image of a predetermined item using the 3D point cloud constituting each object candidate group clustered in the clustering step (S200), and an irregular object detection step (S400) of detecting an atypical object by applying features of each object candidate group extracted by the feature extraction unit (S300) to a pre-stored object detection algorithm in the detection unit.

Furthermore, in the clustering step (S200), the representative point extraction step (S210) of setting a representative point based on the position coordinates of each 3D point cloud using at least one 3D point cloud for each LiDAR channel acquired in the data acquisition step (S100) and a pre-stored 3D clustering algorithm are used to perform clustering on the representative point set by the representative point extraction step (S210), and at least one object candidate group data It is preferable to further include a candidate group separation step (S220) of separating the.

Furthermore, the feature extraction step (S300) includes an image generating step (S310) of generating a top-view image for the 3D point clouds constituting each object candidate group clustered by the clustering step (S200) and a maximum height of the clustered 3D point cloud based on the top-view image generated based on the 3D point cloud of the clustered object candidate group based on the top-view image generated by the image generating step (S310). It is preferable to further include a detailed analysis step ( S320 ) of extracting at least one feature among value, average height value, maximum reflectance value, average reflectance value, number of clustered points, density value, and distribution value.

Furthermore, the object detection step (S400) preferably further includes a learning step (S410) of learning standardized object-related information input from the object detection algorithm and a detailed detection step (S420) of detecting an atypical object from the object candidate group, which is an estimated object, by applying the features of each object candidate group extracted in the feature extraction step (S300) to the learning model through learning in the learning step (S410).

The atypical object detection system and method using the 3D LiDAR of the present invention according to the above configuration has the advantage of being able to accurately recognize atypical objects by utilizing the 3D clustering method for 3D LiDAR data, and reducing false detection by determining whether or not the recognized atypical object is noisy.

In particular, when applied to an autonomous vehicle, it is possible to accurately recognize an atypical obstacle on the road, accurately determine whether or not the recognized atypical obstacle is noisy, in other words, to provide an autonomous driving service that accurately considers the surrounding environment through atypical object recognition in consideration of noise that does not interfere with driving.

In addition, unlike the environment recognition technology using a camera, only the LIDAR sensor can be used alone, so it can be used even in dark night situations, and it has the advantage of being able to recognize the surrounding environment more robustly through the detection of atypical objects with noise removed.

1 is an exemplary view of erroneous detection of an atypical object by a conventional object detection technique using lidar.
2 is an exemplary configuration diagram illustrating an atypical object detection system using a 3D lidar according to an embodiment of the present invention.
3 is an exemplary diagram illustrating a process of setting a representative point based on location coordinates for each 3D point cloud in the clustering unit 200 of the atypical object detection system using 3D lidar according to an embodiment of the present invention.
4 is an exemplary diagram illustrating separated object candidate group data in the clustering unit 200 of the atypical object detection system using 3D lidar according to an embodiment of the present invention.
5 is an exemplary view showing extracted feature items in the feature extraction unit 300 of the atypical object detection system using 3D lidar according to an embodiment of the present invention.
6 is an exemplary view illustrating a method of detecting an atypical object using a 3D lidar according to an embodiment of the present invention.

Hereinafter, an atypical object detection system and method using a 3D lidar of the present invention will be described in detail with reference to the accompanying drawings. The drawings introduced below are provided as examples to sufficiently convey the spirit of the present invention to those skilled in the art. Accordingly, the present invention may be embodied in other forms without being limited to the drawings presented below. Also, like reference numerals denote like elements throughout the specification.

At this time, unless there is another definition in the technical terms and scientific terms used, they have meanings commonly understood by those of ordinary skill in the art to which this invention belongs, and descriptions of known functions and configurations that may unnecessarily obscure the subject matter of the present invention will be omitted in the following description and accompanying drawings.

In addition, a system refers to a set of components including devices, mechanisms, and means that are organized and regularly interact to perform necessary functions.

A system and method for detecting an atypical object using 3D lidar according to an embodiment of the present invention extracts object candidate groups through 3D clustering using 3D lidar data, generates various characteristic items from the extracted object candidate groups, and detects an atypical object using the generated characteristic items. Different from conventional object detection techniques, by extracting and applying density characteristics and distribution characteristics, there are technical features capable of detecting atypical objects considering noise. .

FIG. 2 is an exemplary configuration diagram showing an irregular object detection system using a 3D lidar according to an embodiment of the present invention. Referring to FIG. 2, the irregular object detection system using a 3D lidar according to an embodiment of the present invention will be described in detail.

As shown in FIG. 2, the atypical object detection system using a 3D lidar according to an embodiment of the present invention preferably includes a lidar acquisition unit 100, a clustering unit 200, a feature extraction unit 300, and a detection unit 400. It is preferable to be configured to further include a database unit (not shown) that receives data generated from each of the components, stores and manages the data into a database. In addition, each component is configured in one arithmetic processing unit or each arithmetic processing unit to perform an operation.

In addition, an atypical object detection system using a 3D lidar according to an embodiment of the present invention will be described by taking an example when applied to an autonomous vehicle or assisted driving vehicle for easy explanation.

For a detailed look at each component,

Preferably, the lidar acquisition unit 100 includes at least one 3D lidar sensor, and acquires at least one 3D point cloud for an area scanned through the 3D lidar sensor.

In detail, the 3D lidar sensor is a type of environment recognition sensor, which shoots lasers in all directions while rotating and measures the location coordinates of a reflector based on the reflected return time in a data format called a point cloud. It is a sensor.

That is, the 3D point cloud refers to an aggregate formed by gathering points including positional coordinate values and reflectivity values of reflectors.

Preferably, the clustering unit 200 performs clustering with at least one object candidate group using the 3D point cloud transmitted from the LIDAR acquisition unit 100 .

In detail, as shown in FIG. 2 , the clustering unit 200 preferably includes a representative point extraction unit 210 and an object separation unit 220 .

It is preferable that the representative point extraction unit 210 sets a representative point based on the location coordinates of each 3D point cloud using at least one 3D point cloud for each lidar channel received from the lidar acquisition unit 100.

That is, as shown in FIG. 3, the representative point extractor 210 receives at least one 3D point cloud for each lidar channel and sets each of the representative points based on the positional coordinates of each 3D point cloud. In FIG. 3, the center point is set as the representative point, but this is only one embodiment of the present invention.

Preferably, the object separator 220 performs clustering on the representative points set by the representative point extractor 210 using a pre-stored 3D clustering algorithm to separate at least one object candidate group data.

Clustering means a group of data having similar characteristics, and the object separation unit 220 preferably separates at least one object candidate group data by calculating the distance between points located closest to each other by using a 3D Euclidean clustering algorithm, which is a pre-stored 3D clustering algorithm, and when the intervening value is less than or equal to a predetermined value, by considering it as the same cluster and forming the group.

At this time, it is preferable to use the Euclidean distance calculation method as a method of calculating the interval value, and the predetermined value is set differently depending on the system, means, etc. applied to the atypical object detection system using 3D lidar according to an embodiment of the present invention. It is preferable.

In other words, in the case of the conventional lidar object detection technology through top-view or polar-view conversion, when the noise is above or behind the object, the object separation unit 220 does not erroneously detect or even detect the object. To prevent this, it is preferable to use the 3D Euclidean clustering algorithm.

However, when the Euclidean clustering algorithm is applied to all points constituting the point cloud, a lot of time and cost is incurred. In order to solve this problem, it is preferable to set and utilize each representative point for each 3D point cloud through the representative point extractor 210.

Through this, as shown in FIG. 4 , the object separation unit 220 separates at least one object candidate group data while clustering representative points located close to each other into the same group.

Preferably, the feature extraction unit 300 generates a top-view image using a 3D point cloud of the object candidate group clustered by the clustering unit 200, and extracts features of preset items for the generated top-view image.

In detail, the feature extraction unit 300 receives 3D point cloud (Raw data) that matches at least one object candidate group data separated by the clustering unit 200 from the lidar acquisition unit 100, and converts it into the top-view image.

As shown in FIG. 5, the feature extraction unit 300 preferably extracts at least one or more of a maximum height value, an average height value, a maximum reflectance value, and an average reflectance value of the transformed top-view image, a number value of clustered points, a density value, and a distribution value, using a pre-stored feature extraction algorithm.

For the features extracted by the feature extraction unit 300, density values or distribution values cannot be analyzed through a top-view image using a 2D lidar sensor.

In detecting atypical objects (objects of unknown shape existing on the road, etc.) that need to be considered for vehicle driving, these technical features determine atypical objects that do not affect vehicle driving, such as steam, raindrops, or grass, as noise, and in order to recognize the surrounding environment more robustly. It is desirable to perform analysis through top-view images using 3D lidar sensors.

Preferably, the detection unit 400 detects an atypical object by applying features of each clustered object candidate group extracted from the feature extraction unit 300 to a pre-stored object detection algorithm.

To this end, it is preferable that the detection unit 400 learns standardized object-related information using the object detection algorithm in advance and receives a learning model according to a learning result.

That is, it is preferable to detect an atypical object by applying the features of each clustered object candidate group extracted from the feature extraction unit 300 to the learning model, but considering the density value or distribution value among the features extracted by the feature extraction unit 300, the object candidate group having a discontinuous distribution and low density is judged as 'noise' and excluded, and the atypical object is detected. In detail, the detection unit 400 preferably receives the center offset (coordinates of the center of the estimated object), object class (class of the estimated object), object score (accuracy of the class of the estimated object), and object height (height of the estimated object) of each object candidate group clustered as the result value by the object detection algorithm, and detects the atypical object using these outputs. Of course, at this time, considering the density value or the distribution value among the features extracted by the feature extractor 300, it is preferable that the object candidate group having a discontinuous distribution and a low density is determined as 'noise' and the result value is output.

At this time, it is preferable to use a Convolution Neural Network (CNN) algorithm as the object detection algorithm, but this is also only one embodiment of the present invention.

6 is a flowchart illustrating a method of detecting an irregular object using a 3D lidar according to an embodiment of the present invention, and the method of detecting an irregular object using a 3D lidar according to an embodiment of the present invention will be described in detail with reference to FIG. 6 .

As shown in FIG. 6, the atypical object detection method using 3D lidar according to an embodiment of the present invention includes a data acquisition step (S100), a clustering step (S200), a feature extraction step (S300), and an atypical object detection step (S400).

In addition, the method for detecting an atypical object using a 3D lidar according to an embodiment of the present invention will be described as an example when it is applied to an autonomous vehicle or assisted driving vehicle for easy explanation.

For a detailed look at each step,

In the data acquisition step (S100), it is preferable that the lidar acquisition unit 100 acquires at least one 3D point cloud for an area scanned through the 3D lidar sensor. Here, the 3D point cloud refers to a group formed by gathering points including position coordinate values and reflectivity values of reflectors.

In the clustering step (S200), it is preferable that the clustering unit 200 performs clustering with at least one object candidate group using the 3D point cloud obtained by the data acquisition step (S100).

To this end, as shown in FIG. 6, the clustering step (S200) preferably includes a representative point extraction step (S210) and a candidate group separation step (S220).

In the representative point extraction step (S210), it is preferable to set a representative point based on the location coordinates of each 3D point cloud by using the 3D point cloud acquired in the data acquisition step (S100), that is, by using at least one 3D point cloud for each LIDAR channel received.

In other words, as shown in FIG. 3, in the representative point extraction step (S210), at least one 3D point cloud is received for each LIDAR channel, and each representative point is set based on the positional coordinates of each 3D point cloud. In FIG. 3, the center point is set as the representative point, but this is only one embodiment of the present invention.

Preferably, in the candidate group separation step (S220), at least one object candidate group data is separated by performing clustering on the representative points set in the representative point extraction step (S210) using a pre-stored 3D clustering algorithm.

The candidate group separation step (S220) is a pre-stored 3D clustering algorithm. It is preferable to separate at least one object candidate group data by using a 3D Euclidean clustering algorithm to calculate the distance between points located closest to each other and to consider them as the same cluster when the interval value is less than or equal to a predetermined value.

At this time, when the Euclidean clustering algorithm is applied to all points constituting the point cloud, a lot of time and cost is incurred. In order to solve this problem, it is preferable to set and utilize each representative point for each 3D point cloud through the representative point extraction step (S210).

Through this, in the candidate group separation step (S220), as shown in FIG. 4, at least one object candidate group data is separated while clustering representative points located close to each other into the same group.

In the feature extraction step (S300), it is preferable that the feature extraction unit 300 extracts features of preset items using the 3D point cloud of the object candidate group clustered by the clustering step (S200).

To this end, as shown in FIG. 6, the feature extraction step (S300) preferably includes an image generation step (S310) and a detailed analysis step (S320).

In the image generation step (S310), the 3D point cloud (Raw data) matching the at least one object candidate group data separated by the a run collection step (S200) is received from the data acquisition step (S100), and converted into the top-view image.

As shown in FIG. 5, the detailed analysis step (S320) preferably extracts at least one of a maximum height value, an average height value, a maximum reflectance value, an average reflectance value, a value of the number of clustered points, a density value, and a distribution value of the top-view image generated by the image generating step (S310) using a pre-stored feature extraction algorithm.

For the features extracted by the detailed analysis step (S320), density or distribution values cannot be analyzed through a top-view image using a 2D lidar sensor.

In detecting atypical objects (objects of unknown shape existing on the road, etc.) that need to be considered for vehicle driving, these technical features determine atypical objects that do not affect vehicle driving, such as steam, raindrops, or grass, as noise, and in order to recognize the surrounding environment more robustly. It is desirable to perform analysis through top-view images using 3D lidar sensors.

At this time, the feature extraction algorithm corresponds to the prior art, and a detailed description thereof will be omitted.

In the object detection step (S400), it is preferable to detect an irregular object by applying the features of each clustered object candidate group extracted by the feature extraction step (S300) to the pre-stored object detection algorithm in the detection unit 400.

To this end, the object detection step (S400) is preferably configured to include a learning step (S410) and a detailed detection step (S420) as shown in FIG.

In the learning step (S410), it is preferable to learn standardized object-related information using the object detection algorithm in advance and output a learning model according to a learning result.

In the detailed detection step (S420), an atypical object is detected by applying the features of each clustered object candidate group extracted in the feature extraction step (S300) to the learning model obtained in the learning step (S410).

At this time, the detailed detection step (S420) considers the density value or the distribution value among the features extracted by the feature extraction step (S300), and determines that the object candidate group having a discontinuous distribution and low density is 'noise'. It is preferable to exclude it and detect an atypical object. In detail, in the detailed detection step (S420), it is preferable to receive the center offset (coordinates of the center of the estimated object), object class (class of the estimated object), object score (accuracy of the class of the estimated object), and object height (height of the estimated object) of each object candidate group clustered as the result value by the object detection algorithm, and detect the irregular object using the output. Of course, at this time, considering the density value or distribution value among the features extracted by the feature extraction step (S300), the object candidate group with discontinuous distribution and low density is judged as 'noise' and the result value is output.

At this time, it is preferable to use a Convolution Neural Network (CNN) algorithm as the object detection algorithm, but this is also only one embodiment of the present invention.

That is, in other words, the unstructured object detection system and method using 3D lidar according to an embodiment of the present invention has the advantage of being able to detect irregular objects considering noise by setting representative point points for each point cloud for each lidar channel and performing 3D clustering to extract features including density values and distribution values.

As described above, the present invention has been described with specific details such as specific components and limited embodiment drawings, but this is provided only to help a more general understanding of the present invention, and the present invention is not limited to the above one embodiment, and those skilled in the art can make various modifications and variations from these descriptions.

Therefore, the spirit of the present invention should not be limited to the described embodiments and should not be determined, and all the modifications equivalent or equivalent to the scope of the claims as well as the claims to be described later will fall within the scope of the spirit of the present invention.

100: lidar acquisition unit
200: clustering department
210: representative point extraction unit 220: object separation unit
300: feature extraction unit
400: detection unit

Claims (8)

It includes at least one 3D lidar sensor and acquires at least one 3D point cloud for an area scanned through the 3D lidar sensor (100);
a clustering unit 200 that performs clustering with at least one object candidate group using the 3D point cloud transmitted from the LIDAR acquisition unit 100;
A feature extraction unit 300 that generates a top-view image using the 3D point cloud of the object candidate group clustered by the clustering unit 200 and extracts at least one feature among a maximum height value, an average height value, a maximum reflectance value, an average reflectance value, the number of clustered points, a density value, and an analysis value of the 3D point cloud clustered into the object candidate group, which is a predetermined item, based on the generated top-view image; and
A detection unit 400 that receives standardized object-related information from the outside in advance, inputs it into a stored object detection algorithm for learning, and applies features for each clustered object candidate group extracted from the feature extraction unit 300 to a learning model by learning, but detects an irregular object judged as noise among the object candidate group, which is an estimated object, in consideration of a density value or a distribution value among the features;
Including,
The clustering unit 200 is
Using at least one 3D point cloud for each lidar channel received from the lidar acquisition unit 100, extracting each center point based on the positional coordinates of each 3D point cloud, and setting it as each representative point Extraction unit 210; and
an object separation unit 220 that separates at least one object candidate group data by performing clustering on each representative point set in the representative point extraction unit 210 using a pre-stored 3D clustering algorithm;
Including more,
The object separation unit 220
An interval value, which is the Euclidean distance between each representative point, is calculated, and when the calculated interval value is less than a predetermined value, the corresponding point representative is regarded as the same cluster and separated into at least one object candidate group data. An atypical object detection system using 3D lidar, characterized in that.
delete delete delete A data acquisition step of acquiring at least one 3D point cloud for an area to be scanned using at least one 3D lidar sensor in a lidar acquisition unit (S100);
a clustering step (S200) of performing clustering into at least one object candidate group by analyzing the 3D point cloud obtained by the data acquisition step (S100) in a clustering unit;
In a feature extraction unit, a feature extraction step (S300) of extracting at least one or more feature images from the maximum height value, average height value, maximum reflectance value, average reflectance value, number of clustered points, density value, and analysis value of the 3D point cloud clustered into the object candidate group, which is a preset item, by using the 3D point cloud constituting each object candidate group clustered in the clustering step (S200); and
An object detection step (S400) of detecting an atypical object by applying the features for each object candidate group extracted by the feature extraction step (S300) to a pre-stored object detection algorithm in the detection unit;
Including,
The clustering step (S200)
A representative point extraction step (S210) of setting each representative point based on the positional coordinates of each 3D point cloud using at least one 3D point cloud for each LIDAR channel obtained in the data acquisition step (S100); and
A candidate group separation step (S220) of separating at least one object candidate group data by performing clustering on each representative point set by the representative point extraction step (S210) using a pre-stored 3D clustering algorithm;
Including more,
The candidate group separation step (S220)
An interval value, which is the Euclidean distance between each representative point, is calculated, and if the calculated interval value is less than a predetermined value, the corresponding point representative is regarded as the same cluster and separated into at least one object candidate group data,
The object detection step (S400)
A learning step (S410) of receiving standardized object-related information from the outside in advance and inputting it into a stored object detection algorithm for learning; and
A detailed detection step (S420) of detecting an atypical object determined to be noise among the object candidate groups, which is an estimated object, by applying the features of each object candidate group extracted by the feature extraction step (S300) to the learning model learned in the learning step (S410), and considering the density value or distribution value among the features;
Atypical object detection method using 3D lidar further comprising.
delete According to claim 5,
The feature extraction step (S300)
an image generation step (S310) of generating a top-view image of the 3D point cloud constituting each object candidate group clustered by the clustering step (S200); and
A detailed analysis step (S320) of extracting the features of the clustered 3D point cloud based on the top-view image generated based on the 3D point cloud of the clustered object candidates based on the top-view image generated by the image generation step (S310);
Atypical object detection method using 3D lidar, characterized in that it further comprises.

delete

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