LiDAR-based object detection method and apparatus

A LiDAR-based object detection method includes clustering a point cloud acquired from LiDAR, selecting a to-be-divided cluster among clusters generated in the clustering, and selecting division points according to a geometrical feature formed with adjacent points from among points belonging to the to-be-divided cluster, and dividing the to-be-divided cluster based on a representative point determined by at least some of the division points.

PRIORITY

This application claims under 35 U.S.C. § 119(a) the benefit of Korean Patent Application No. 10-2022-0006746, filed on Jan. 17, 2022, which is hereby incorporated by reference as if fully set forth herein.

BACKGROUND OF THE DISCLOSURE

Field of the Disclosure

The present disclosure relates to a method and apparatus for detecting an object based on Light Detection And Ranging (LiDAR).

Discussion of the Related Art

Recently, as interest in autonomous driving technology has increased, a LiDAR-based object detection apparatus, which may be an essential object detection apparatus for autonomous driving, has actively been developed.

In general, the LiDAR-based object detection apparatus generally transmits laser beams to the surroundings, acquires laser beams reflected from an object as point cloud data, and detects the object using the acquired LiDAR data.

For object detection, point cloud data may be first preprocessed and then clustered according to a predetermined manner, and shapes may be extracted from the cluster data thus obtained.

Here, as one clustering method, grid-based clustering generates a grid map for preprocessed point cloud data, compares features of adjacent grids, and groups grids having similar features into one cluster.

This grid-based method may be a method of generating grid features using several points present in the respective grids, and comparing these features to determine whether the grids should be clustered into the same cluster. In this method, since Lidar points may be processed by grid to generate an output, there may be an advantage in real-time compared to a by-point-level processing method.

However, there may be points or feature information thereof not utilized to generate the grid features, and there may be a possibility of occurrence of information loss and mis-clustering.

That is, even though there may be actually two separate objects, points detected from the respective objects may be treated as close enough according to a grid resolution, and thus a problem may arise that the two objects may be grouped into one cluster.

For example, as illustrated inFIG.1, when a vehicle may be located close to a stationary object of a road boundary, such as a guard rail, even though a first grid G1may be for stationary object points and a second grid G2may be for vehicle points, the two grids may be grouped into the same cluster to cause mis-clustering.

SUMMARY OF THE DISCLOSURE

Accordingly, the present disclosure may be directed to a LiDAR-based object detection method and apparatus that substantially obviate one or more problems due to limitations and disadvantages of the related art.

An object of the present disclosure may be to improve object detection capability through improved clustering for LiDAR point data.

Another object of the present disclosure may be to perform such improved clustering without significantly increasing computational cost.

To achieve these objectives and other advantages and in accordance with the purpose of the disclosure, as embodied and broadly described herein, a LiDAR-based object detection method includes clustering points of a point cloud acquired from LiDAR, selecting a to-be-divided cluster among clusters generated in the clustering, and selecting division points according to a geometrical feature formed with adjacent points belonging to the to-be-divided cluster, and dividing the to-be-divided cluster based on a representative point determined by at least some of the division points.

The dividing may include selecting division points according to the geometrical feature among points belonging to the to-be-divided cluster, clustering the division points, and selecting a final division point cluster among clusters of the division points, and dividing the to-be-divided cluster using a representative point of the final division point cluster.

The dividing may be performed based on a straight line passing through the representative point.

The straight line may be obtained by connecting the representative point and an origin.

The dividing may be performed when a ratio of the number of points in at least one cluster divided from the to-be-divided cluster to the total number of points in the to-be-divided cluster may be less than or equal to a reference value.

One of two division point clusters may be selected as the final division point cluster, the two division point clusters having representative points forming maximum and minimum angles, respectively, with a reference line passing through an origin.

The final division point cluster may be selected based on a difference in the number of points between the two divided clusters to be generated after division.

After mapping the division points to a 2D grid map, the clustering of the division points may be performed based on a degree of proximity of grids.

Grids located as directly connected to each other among the grids including the division points may be determined as the same division point cluster.

The representative point may be determined by an average coordinate value of at least some of the division points.

The to-be-divided cluster may be selected according to the number of division points therein.

The geometrical feature may include an angle θ formed with the adjacent points.

The division points may be selected according to whether the angle θ may be an acute angle.

Whether the angle θ may be an acute angle may be determined using “1−cos θ”.

The adjacent points may be points on the same layer as the one of the division points.

In another embodiment of the present disclosure, a LiDAR-based object detection apparatus includes a microprocessor, a memory, and an input/output device, in which the microprocessor executes the above-described detection method.

In another embodiment, a vehicle may comprise the LiDAR-based object detection apparatus as described herein.

It may be to be understood that both the foregoing general description and the following detailed description of the present disclosure may be exemplary and explanatory and may be intended to provide further explanation of the disclosure as claimed.

DETAILED DESCRIPTION OF THE DISCLOSURE

Further, in describing the embodiments disclosed in the present specification, when it may be determined that a detailed description of related publicly known technology may obscure the gist of the embodiments disclosed in the present specification, the detailed description thereof will be omitted. The accompanying drawings may be used to help easily explain various technical features and it should be understood that the embodiments presented herein may not be limited by the accompanying drawings. As such, the present disclosure should be construed to extend to any alterations, equivalents and substitutes in addition to those which may be particularly set out in the accompanying drawings.

Although terms including ordinal numbers, such as “first”, “second”, etc., may be used herein only to differentiate elements, the elements may not be construed to be limited by these terms. These terms may be generally only used to distinguish one element from another.

When an element may be referred to as being “coupled” or “connected” to another element, the element may be directly coupled or connected to the other element. However, it should be understood that another element may be present therebetween. In contrast, when an element may be referred to as being “directly coupled” or “directly connected” to another element, it should be understood that there may be no other elements therebetween.

A singular expression includes the plural expression unless the context clearly dictates otherwise.

In the present specification, it should be understood that a term such as “include” or “have” may be intended to designate that the features, numbers, steps, operations, elements, parts, or combinations thereof described in the specification may be present, and does not preclude the possibility of addition or presence of one or more other features, numbers, steps, operations, elements, parts, or combinations thereof.

In addition, the term “unit” or “control unit” may be only a widely used term for a name of a controller for controlling a specific function of a vehicle, and does not mean a generic function unit. For example, each unit or control unit may include a communication device configured to communicate with another control device or sensor to control a function assigned thereto, a memory configured to store an operating system or logic command and input/output information, and one or more processors configured to perform determination, calculation, decision, etc. necessary for controlling the function assigned thereto.

First,FIG.2is a flowchart of clustering in an object detection method according to an embodiment of the present disclosure, andFIGS.3A to3Bare an example of a to-be-divided cluster.FIG.4illustrates an angle formed by a division point and adjacent points, andFIG.5is a conceptual diagram illustrating division points identified in one cluster.FIGS.6A to6Dillustrate a process of clustering division points and a representative point of the clustering, andFIG.7is a flowchart illustrating a process of dividing the to-be-divided cluster.FIGS.8A to8Care diagrams conceptually illustrating a process of dividing the to-be-divided cluster

The object detection method of the present embodiment includes a step S100of clustering points of a point cloud obtained from LiDAR, a step S200of selecting a to-be-divided candidate from among clusters generated in the step S100, and a step S300of dividing a to-be-divided cluster.

In addition, the step S300of dividing the to-be-divided cluster includes a step S310of selecting division points (break points) from among LiDAR points belonging to the to-be-divided cluster, a step S320of clustering the division points, and a step S330of dividing the to-be-divided cluster using a division point cluster generated in the step S320.

The point cloud acquired from LiDAR first undergoes a preprocessing to delete points having low signal strength or reflectivity and points reflected by the vehicle body, thereby extracting only valid data, and calibration may be performed to match LiDAR points to a reference coordinate system of the vehicle.

Here, the present embodiment relates to a case where the point cloud acquired from LiDAR may be 3D data acquired by multi-layered scans. However, the present disclosure may not be limited thereto.

When the preprocessing for the point cloud ends, the step S100of clustering the points of the point cloud based on grids may be performed.

For clustering, the preprocessed point cloud may be mapped to the grids, features of the grids may be generated using points included in each grid, and the features may be compared between grids in terms of similarity to determine whether to process the grids as the same cluster.

Subsequently, a to-be-divided cluster may be selected from among the clusters generated through the clustering step (S200).

Mis-clustering may be mainly caused by a dynamic object (for example, moving vehicle) and a temporary wall or a guardrail being grouped into one cluster. In addition, when there may be a motorcycle inbetween vehicles, mis-clustering may occur by these objects grouped into one cluster or the vehicle and a person grouped into one cluster.

The to-be-divided cluster may be preferably selected so that such mis-clusters may be targets.

In the present embodiment, the to-be-divided cluster may be selected using a division point to be described later.

For example, a cluster in which the number of division points may be equal to or greater than a reference value may be selected as the to-be-divided cluster.

For example,FIGS.3A to3Care an example of a mis-cluster C in which a temporary wall and a vehicle may be grouped into one cluster (shown as a box), and illustrates the case where the cluster C may be selected as a to-be-divided cluster C based on a division point criterion. For reference,FIG.3Ais an actual photograph of an object detection target,FIG.3Bis a point cloud acquired by LiDAR, andFIG.3Cillustrates an example of a state in which clustering may be performed on the point cloud.

In the present embodiment, a division point may be selected based on a geometrical feature formed by adjacent points (S310).

For example, as illustrated inFIG.4, when an angle θ formed by two front and back points Pn−1and Pn+1with reference to the index may be an acute angle, the corresponding point Pnmay be selected as a division point.

Such angle detection described above may be performed for points in the same layer, and when there may be no valid point among three consecutive indices, a point of an index immediately after the invalid point index may be used. For example, when there may be no valid point for an index ‘Pn+1’ inFIG.4, although not illustrated, a point of an index ‘Pn+2’, which may be a subsequent index, may be used.

In addition, in determining whether the angle θ ofFIG.4may be an acute angle, the following expression may be used.
1−cos θ  [Expression 1]

Here, cos θ may be as follows.

The above Expression 1 has 0 as a minimum value and 2 as a maximum value in a range where θ may be greater than 0 and less than π, and tends to nonlinearly increase. That is, as the angle θ increases, the value of Expression 1 tends to increase.

Accordingly, through Expression 1, an angle corresponding to a center point in a triangle formed by three points may be digitized and used.

Expression 1 may be significantly useful in that it may be possible to check an acute angle condition for an angle without a trigonometric operation, the computational cost of which may be high.

Meanwhile, instead of the above-mentioned acute angle criterion, an optimal reference value may be determined based on data in an actual driving environment according to a sensor configuration and mounting position, and a point greater than or equal to the reference value may be selected as a division point.

FIG.5is an example of selecting a division point by applying the acute angle criterion.

First, a point cloud cluster ofFIG.5is a simplified simulation of the to-be-divided cluster illustrated inFIGS.3A to3C. In the point cloud cluster, points of indices ‘Pk+1’ to ‘Pk+7’ may be data related to the vehicle, and indices ‘Pk−7’ to ‘Pk’ may be data related to the temporary wall.

When the acute angle criterion may be applied to the points ofFIG.5, three points from ‘Pk−1’ to ‘Pk+1’ may be selected as division points. Even though three division points may be illustrated inFIG.5, this illustration may be merely an example. Division points may be present for each layer, and also more than three division points may be present on the same layer.

Once all the division points for the to-be-divided cluster may be selected, next, the step S320of clustering the division points may be performed.

Referring toFIGS.6A to6C, first, division points may be mapped to a 2D grid map as illustrated inFIG.6B. Here, a grid may be a square as an example, and the size of the grid may be preferably determined so that division points detected in the same object may be processed as one cluster by checking data in which division points may be dense.

For the division point clustering, when there may be a grid adjacent to a certain grid as illustrated inFIG.6C, division points of those grids may be labeled as belonging to the same cluster.

In addition, for the division point cluster determined in this way, a representative point may be determined using an average value of the division points belonging thereto as illustrated inFIG.6D.

In the present embodiment, the representative point may be newly created as a point having average (coordinate) values of the corresponding division points. However, the representative point may be only used to represent the corresponding division point cluster, and may not be used as an object detection point.

In addition, determination of the representative point may not be limited to the above case, and for example, unlike the present embodiment, a division point closest to the average-valued coordinates may be determined as the representative point.

A plurality of division point clusters may be present in one to-be-divided cluster C, and it may be necessary to select an optimum division point cluster among these division point clusters.

FIG.7illustrates a process in which a final division point cluster to be used for division of the to-be-divided cluster may be selected, which will be described in detail below with reference toFIG.7.

First, it may be determined whether the number of division points may be equal to or greater than a first reference value for each of the division point clusters (S331and S332).

Determining whether the number of division points may be equal to or greater than the first reference value serves to determine the validity of the corresponding division point cluster, and an invalid cluster may be excluded from the division point clusters.

Here, the first reference value, which may be a criterion for determining validity, may be experimentally obtained through data on actual driving, and an optimal value may be preferably determined through many experiments.

A final cluster may be selected from among the valid clusters remaining. In order to minimize the amount of calculation, candidates may be first selected using feature angles θfof the clusters, and among the candidate clusters, one that satisfies conditions to be described later may be finally selected (S333).

In the present embodiment, among the valid clusters, a cluster having the largest feature angle θfand a cluster having the smallest feature angle θfmay be selected as candidates (S3331). When the to-be-divided cluster may be divided by straight lines of the two feature angle θfSof the two clusters selected as candidates, the one having the smaller difference in the number of points between the two divided clusters may be selected as the final cluster (S3332).

Here, the feature angle θfmay be an angle of a straight line L connecting the representative point of the division point cluster and the origin P0, as illustrated inFIGS.8Ato8C. In addition, here, the origin P0may be determined as coordinates corresponding to a LiDAR position.

In the present embodiment, the amount of calculation may be significantly reduced by selecting two clusters of the maximum and minimum values as final candidates based on the feature angle θf. Since clusters each having the number of division points equal to or greater than the first reference value may be targeted, an effective division point cluster may be obtained with only two candidates, which was confirmed proved to be through actual data.

Once the final division point cluster may be selected, it may be determined whether, when the to-be-divided cluster may be divided by the straight line L of the feature angle θfof the selected division point cluster, a ratio of the number of points of the one of the divided clusters DC1and DC2, which has more points than the other, to the total number of points may be smaller than a second reference value (S334).

Here, when it may be determined that the corresponding ratio may be equal to or greater than the second reference value, it may be determined that the corresponding cluster may be an invalid division point cluster, and a division process may be ended without change in order to prevent additional calculation costs.

When it may be determined that the ratio may be smaller than the second reference value, final division may be performed by the straight line L of the feature angle θfto obtain final two clusters C1and C2from the to-be-divided cluster C (S335). After division, the existing cluster C may be deleted and the final two clusters C1and C2may be registered as new clusters.

Here, the second reference value may be experimentally obtained through actual driving data, and an optimal value may be preferably determined through many experiments.

Meanwhile, a LiDAR-based object detection apparatus20according to an embodiment of the present disclosure may be an apparatus made to execute the method of the above-described embodiment, and may be included in a driving control system together with a driving strategy unit30and a vehicle control unit40as illustrated inFIG.9.

As illustrated inFIG.9, the object detection apparatus20receives point cloud data from a LiDAR 10, executes object detection, and outputs a result to the driving strategy unit30.

As illustrated in the figure, the object detection apparatus20may include a microprocessor, a memory, and an input/output device.

Here, the input/output device may be a device configured to receive data from the LiDAR 10 and output a detection result to the driving strategy unit.

In addition, the microprocessor may be a place for detecting an object by performing necessary data processing on a point cloud, and the method of the above-described embodiment may be loaded thereon as a program.

The memory stores a detection program executed by the microprocessor and related data such as the reference values.

In addition, the driving strategy unit30establishes a driving strategy for the vehicle according to a result detected by the object detection apparatus20, and outputs a result to the vehicle control unit40.

According to the result, the vehicle control unit40transmits a control signal for a steering device and/or a braking device to each corresponding device so that the driving strategy established by the driving strategy unit30may be executed.

According to the present disclosure, it may be possible to obtain improved object detection capability by providing an improved clustering result for LiDAR point data.

In addition, according to at least one embodiment of the present disclosure, it may be possible to perform such improved clustering without significantly increasing computational cost.