JP2023028449A - Road surface condition detection device, three-dimensional object detection device, vehicle and three-dimensional object detection method - Google Patents

Abstract

To provide a technology for accurately detecting the condition of a road surface extending forward of a vehicle, a technology for accurately detecting a three-dimensional object existing on the road surface in consideration of the condition of the road surface, and a vehicle equipped with the three-dimensional object detection technology.SOLUTION: A distance sensor attached to a vehicle traveling on a road surface receives the electromagnetic waves reflected in front of the vehicle while scanning the electromagnetic waves emitted from above and diagonally toward the road surface in a direction perpendicular to the traveling direction of the vehicle, thereby acquiring point cloud data indicating the distance from the vehicle to the reflection point and the direction of the reflection point relative to the vehicle. When the point cloud corresponding to the multiple reflection points can draw an arc-shaped or elliptical-arc-shaped road surface identification trajectory based on the point cloud data, it is determined that there is no solid object on the road surface.SELECTED DRAWING: Figure 5C

Description

TECHNICAL FIELD The present invention relates to a detection technique for detecting the condition of a road surface on which a vehicle travels and a three-dimensional object existing on the road surface, and a vehicle equipped with the same.

In order to detect a three-dimensional object existing on the road surface, for example, in Patent Document 1, a grid map is generated by accumulating three-dimensional distance data point groups measured by a laser radar. Then, the road surface and solid objects are determined from the grid map.

JP 2013-140515 A

In the conventional technology described above, three-dimensional objects are detected on the assumption that the road surface extending in the vehicle's running direction is at the same height as the vehicle's current running position, that is, the road surface is flat. More specifically, based on the three-dimensional coordinates of a point obtained from a laser radar fixed to the vehicle, it is determined whether or not the point is a point of a three-dimensional object with a certain threshold in the height direction from the known road surface coordinates. is discriminating. Therefore, for example, when the front road surface is an upwardly sloping surface, this upwardly sloping surface exceeds the threshold value. As a result, an upsloping surface is mistaken for a three-dimensional object. As described above, in the prior art, the three-dimensional object is detected without accurately grasping the condition of the road ahead, so it is difficult to accurately detect the three-dimensional object.

The present invention has been made in view of the above problems, and includes a technique for accurately detecting the condition of the road surface extending in front of the vehicle, a technique for accurately detecting a three-dimensional object existing on the road surface in consideration of the condition of the road surface, and to provide a vehicle equipped with the three-dimensional object detection technology.

A first aspect of the present invention is a road surface condition detection device, which is attached to a vehicle running on a road surface, and emits electromagnetic waves emitted obliquely from above to the road surface in a direction orthogonal to the running direction of the vehicle. By receiving the electromagnetic waves reflected in front of the vehicle while scanning the vehicle, we acquire point cloud data that indicates the distance from the vehicle to the reflection points and the direction of the reflection points with respect to the vehicle. a distance sensor, a point cloud trajectory determination unit that determines whether the point cloud corresponding to the plurality of reflection points based on the point cloud data can draw an arc-shaped or elliptical road surface discrimination trajectory, and a point cloud trajectory determination unit that determines the points a road surface determination unit that determines that a three-dimensional object does not exist on the road surface when it is determined that the group can draw the road surface determination locus.

A second aspect of the present invention is a three-dimensional object detection device, which is attached to a vehicle running on a road surface, and emits electromagnetic waves emitted obliquely from above to the road surface. Point cloud data showing the distance from the vehicle to the reflection point and the direction of the reflection point with respect to the vehicle for multiple reflection points that reflected the electromagnetic waves by receiving the electromagnetic waves reflected in front of the vehicle while scanning in the direction of the vehicle. and the point cloud trajectory drawn by the point cloud corresponding to multiple reflection points based on the point cloud data, and the arc-shaped or elliptical arc-shaped road surface discrimination trajectory. A deviation point identification unit that identifies deviation points where the point cloud trajectory deviates from the road surface determination trajectory, and a solid object identification unit that identifies the presence of a solid object on the road surface based on the deviation points identified by the deviation point identification unit. and

A third aspect of the present invention is a vehicle comprising the three-dimensional object detection device described above, wherein running on a road surface is controlled based on the presence of a three-dimensional object identified by the three-dimensional object detection device. and

Furthermore, a fourth aspect of the present invention is a three-dimensional object detection method, which is attached to a vehicle traveling on a road surface, and emits electromagnetic waves emitted obliquely from above to the road surface, perpendicular to the running direction of the vehicle. Point cloud data showing the distance from the vehicle to the reflection point and the direction of the reflection point with respect to the vehicle for multiple reflection points that reflected the electromagnetic waves by receiving the electromagnetic waves reflected in front of the vehicle while scanning in the direction of the vehicle. a step of comparing a point cloud trajectory drawn by a point cloud corresponding to a plurality of reflection points based on the point cloud data with an arc-shaped or elliptical arc-shaped road surface discrimination trajectory; The method is characterized by comprising a step of identifying a deviation point where the point cloud locus deviates from the road surface discrimination locus, and a step of identifying the presence of a three-dimensional object on the road surface based on the deviation point.

In the invention configured as described above, the distance sensor attached to the vehicle running on the road surface scans the road surface with electromagnetic waves emitted obliquely from above and reflects them in front of the vehicle while scanning in the direction perpendicular to the running direction of the vehicle. receive electromagnetic waves. As a result, point cloud data indicating the distance from the vehicle to the reflection point and the direction of the reflection point with respect to the vehicle is acquired. Then, when the point group corresponding to the plurality of reflection points can draw an arc-shaped or elliptical arc-shaped road surface determination trajectory based on the point group data, it is determined that a three-dimensional object does not exist on the road surface. Therefore, regardless of whether the road surface in front of the vehicle is flat or sloped, and even when the vehicle is stationary, it is possible to accurately detect whether or not there is a three-dimensional object on the road surface. can. In this specification, the term "three-dimensional object" includes objects that exist above the road surface, as well as depressions and grooves that exist below the road surface.

Here, when it is determined that the point cloud can draw an arc-shaped road surface discrimination trajectory, it is determined that the road surface is a flat surface, and when it is determined that an elliptical arc-shaped road surface discrimination trajectory can be drawn, the road surface is determined to It may be configured to determine that the surface is tilted to . In this case, it is possible to accurately detect not only the absence of a three-dimensional object on the road surface, but also whether the road surface is flat or inclined.

Further, when the road surface is inclined in the direction of travel, the inclination angle of the inclined surface may be further obtained based on the curvature of the elliptical arc-shaped road surface discrimination trajectory, whereby the road surface condition can be detected in more detail. .

Further, in determining whether or not the point group can draw the road surface discrimination trajectory, it may be further taken into consideration whether or not the points located on the road surface discrimination trajectory out of the plurality of points constituting the point group are a certain percentage or more. . In other words, it is determined that the point group can draw the road surface discrimination trajectory when the number of points located on the road surface discrimination trajectory is greater than or equal to a certain percentage, while the points located on the road surface discrimination trajectory among the plurality of points constituting the point group are constant. When the ratio is less than the ratio, it may be determined that the point group cannot draw the road surface determination trajectory and that a three-dimensional object exists on the road surface. As a result, even if the point cloud data is affected by disturbance, noise, or the like, it is possible to suppress the influence and stably detect the road surface condition and the presence or absence of a three-dimensional object.

Also, when the point cloud trajectory drawn by the point cloud does not include discontinuous points, it is determined that the point cloud can draw the road surface discrimination trajectory. It may be determined that a three-dimensional object exists on the road surface because the point group cannot draw the road surface determination trajectory. This makes it possible to accurately detect not only the road surface condition but also the presence or absence of a three-dimensional object.

As for the distance sensor, either a two-dimensional sensor that acquires point cloud data by scanning electromagnetic waves at a single irradiation angle or a three-dimensional sensor that can scan irradiation angles in multiple stages may be used. . However, by using a three-dimensional sensor, it is possible to determine whether the point cloud can draw a road surface discrimination trajectory for each different irradiation angle, and to detect the road surface conditions and the presence of three-dimensional objects with even higher accuracy. . More specifically, when it is determined by the point cloud trajectory determination unit that the point cloud can draw the road surface discrimination trajectory for all of the plurality of irradiation angles, it is determined that there is no three-dimensional object on the road surface. When the point cloud trajectory determination unit determines that the point cloud cannot draw the road surface determination trajectory for at least one of the above, it may be determined that a three-dimensional object exists on the road surface. As a result, three-dimensional objects of various sizes can be detected in the vertical direction, and the detectable range is widened.

Further, when the three-dimensional object identifying unit identifies a three-dimensional object based on the deviating points, the three-dimensional object identifying unit clusters the deviating points identified by the deviating point identifying unit and classifies them into one or more deviating point groups. and an individual identification unit for identifying the presence of a three-dimensional object on the road surface for each deviation point group. Accordingly, it is possible to accurately detect a three-dimensional object not only when one three-dimensional object exists on the road surface, but also when a plurality of three-dimensional objects exist. It should be noted that the clustering unit is preferably configured to classify a plurality of deviation points, which are equal to or more than a certain number and are adjacent to each other at a distance within a threshold value, into deviation point groups. In other words, by adopting the above-described classification method, it is possible to accurately acquire the deviation point group corresponding to the three-dimensional object while suppressing the influence of noise and the like, thereby further improving the detection accuracy of the three-dimensional object.

As described above, according to the present invention, the condition of the road surface in front of the vehicle and the three-dimensional object on the road surface can be accurately detected.

1 is a diagram showing a golf cart as an example of a vehicle equipped with a road surface condition detection device according to a first embodiment of the present invention; FIG. 2 is a block diagram showing an electrical configuration for driving and controlling the golf cart shown in FIG. 1; FIG. 2 is a functional block diagram showing the configuration of a road surface condition detection device incorporated in the golf cart shown in FIG. 1; FIG. 1 is a schematic diagram showing a representative example of a running environment of a golf cart running on a road surface; FIG. It is the figure which plotted on the two-dimensional plane the position coordinate of the reflection point calculated from the point cloud data acquired by the distance sensor in driving|running|working environment (A) for every irradiation angle. FIG. 4 is a diagram in which position coordinates of reflection points calculated from point cloud data acquired by a distance sensor in a driving environment (B) are plotted on a two-dimensional plane for each irradiation angle; FIG. 4 is a diagram in which position coordinates of reflection points calculated from point cloud data acquired by a distance sensor in a driving environment (C) are plotted on a two-dimensional plane for each irradiation angle; FIG. 4 is a diagram in which position coordinates of reflection points calculated from point cloud data acquired by a distance sensor in a driving environment (D) are plotted on a two-dimensional plane for each irradiation angle. 2 is a flow chart showing the operation of the golf cart shown in FIG. 1; It is a flowchart which shows the three-dimensional object detection process performed during operation|movement of a golf cart. 9 is a flow chart showing a three-dimensional object detection process executed by the second embodiment of the road surface condition detection device according to the present invention;

FIG. 1 is a diagram showing a golf cart, which is an example of a vehicle equipped with a road surface condition detection device according to a first embodiment of the present invention. In the following description, front and rear, left and right, and up and down mean front and rear, left and right, and up and down with reference to the state in which the passenger is seated on the front seat portion 18 of the golf cart 10 facing the steering wheel 22 .

The golf cart 10 has a frame portion 12 as shown in FIG. In the frame portion 12, a pair of left and right front wheels 14 are rotatably supported at the front portion, and a pair of left and right rear wheels 16 are rotatably supported at the rear portion. In the golf cart 10, a front seat portion 18 and a rear seat portion 20 on which a passenger sits are divided into front and rear portions, and are supported by the frame portion 12 via connecting members (not shown) or the like. A steering wheel 22 is provided in front of the front seat portion 18 . A pair of left and right front pillars 24 are provided in front of the steering wheel 22 , and their lower ends are supported by the frame portion 12 . A pair of left and right rear pillars 26 are provided behind the rear seat portion 20 , and their lower ends are supported by the frame portion 12 . A roof portion 28 is supported by the front pillars 24 and the rear pillars 26 so as to cover the front seat portion 18, the rear seat portion 20 and the steering wheel 22 configured in this manner from above.

2 is a block diagram showing an electrical configuration for driving and controlling the golf cart shown in FIG. 1. FIG. Inside the golf cart 10, a drive motor 30 for driving wheels (=front wheels 14+rear wheels 16) is provided and functions as a drive source. Of course, instead of the drive motor 30, an engine may be installed as a drive source, or a hybrid drive source combining a motor and an engine may be used. A controller 32 for controlling the drive motor 30 is provided inside the golf cart 10 .

The control unit 32 includes an autonomous travel control unit 34 that controls autonomous travel of the golf cart 10 along a guide line (not shown), a road surface condition detection device 36 corresponding to the first embodiment of the present invention, and a road surface A warning output unit 38 that issues a warning to the occupant and surroundings in response to the detection of a three-dimensional object by the condition detection device 36, and the golf cart 10 according to the road surface condition (including the presence or absence of a three-dimensional object) detected by the road surface condition detection device 36. and a traveling speed control unit 40 that controls the traveling speed of the. Among the elements constituting the control unit 32, the configuration other than the road surface condition detection device 36 is the same as that employed in the prior art. On the other hand, the road surface condition detection device 36 is a characteristic configuration of the present invention, and includes an example of the "three-dimensional object detection device" of the present invention as described below. Therefore, while the configuration and operation of the road surface condition detection device 36 will be described in detail below, description of other configurations will be omitted.

FIG. 3 is a functional block diagram showing the configuration of the road surface condition detection device incorporated in the golf cart shown in FIG. The road surface condition detection device 36 has a microprocessor, a storage unit, and the like. and detect three-dimensional objects on the road surface.

The distance sensor 42 is a three-dimensional sensor attached to the roof 28 of the golf cart 10, as shown in FIG. sensor. In this embodiment, the distance sensor 42 irradiates a road surface (see FIG. 4) with a laser beam as an example of an electromagnetic wave from obliquely above. The distance sensor 42 receives the laser beam reflected in front of the golf cart 10 while scanning the laser beam in the left-right direction, that is, in the direction Y orthogonal to the traveling direction X of the golf cart 10 . The irradiation angle of the laser beam to be scanned (symbol θ in FIG. 4) is switched in multiple stages. As a result, the distance sensor 42 determines the distance from the golf cart 10 to the reflection point and the direction of the reflection point with respect to the golf cart 10 for each of the irradiation angles θ, for a plurality of reflection points (road surface, three-dimensional object, etc.) that reflected the laser beam. and is output to the point cloud trajectory determination unit 44 as shown in FIG.

The point cloud trajectory determination unit 44 determines whether or not the point cloud corresponding to the plurality of reflection points can draw an arc-shaped or elliptical arc-shaped road surface determination trajectory based on the point cloud data from the distance sensor 42, and the determination result is used as the road surface. It is given to the determination unit 46 . When it is determined that the road surface determination trajectory can be drawn, the road surface determination unit 46 determines that there is no three-dimensional object on the road surface, and provides the determination result to the output unit 48 . Then, the output unit 48 outputs the determination result to the autonomous driving control unit 34 . Although the road surface condition is detected in this way, the operations of the point cloud trajectory determination unit 44 and the road surface determination unit 46 will be described later in FIG. , 5A-5D, 6 and 7. FIG.

The point cloud data from the distance sensor 42 is given to the point cloud trajectory determination unit 44 and also to the deviating point identification unit 50 at the same time. In addition to the point cloud data, the deviation point identification unit 50 is provided with the determination result of the point cloud trajectory determination unit 44 (whether or not the road surface determination trajectory can be drawn). The deviation point identifying unit 50 has a function of identifying a deviation point from among the points forming the point group based on the above information, where the point cloud trajectory deviates from the road surface discrimination trajectory. data corresponding to the deviation point) is provided to the three-dimensional object identification unit 52 . The three-dimensional object identification unit 52 clusters the deviation points and classifies them into one or more deviation point groups. More specifically, as shown in FIGS. 5C and 5D, a plurality of deviating points DP adjacent to each other at a distance equal to or greater than a certain number and within a threshold value are defined as one deviating point group DG. Then, the three-dimensional object identification unit 52 identifies three-dimensional object-related information such as the position of the three-dimensional object OB existing on the road surface RS for each deviation point group DG, and provides the identification result to the output unit 48 . Then, the output unit 48 outputs the identification result to the autonomous travel control unit 34 , the warning output unit 38 and the travel speed control unit 40 . In this manner, the three-dimensional object identifying section 52 functions as the clustering section 521 and the individual identifying section 522 .

Next, FIG. 4 and FIG. 5A show representative driving environments of the golf cart 10 having the above configuration when the golf cart 10 is driving on the road surface and point groups acquired by the distance sensor 42 in each driving environment. , 5B, 5C and 5D. After the explanation, the operation of detecting the road surface condition and the three-dimensional object by the control unit 32 will be explained.

FIG. 4 is a schematic diagram showing a representative example of the running environment of a golf cart running on a road surface, and four representative running environments (A) to (D).
(A) Flat road surface RS with no three-dimensional object OB on the road surface RS (B) Uphill road surface RS with no three-dimensional object OB on the road surface RS (C) Flat road surface RS and a three-dimensional object OB exists on the road surface RS.
is illustrated.

5A to 5D plot the position coordinates of the reflection points calculated from the point cloud data acquired by the distance sensor in the driving environments (A) to (D), respectively, for each irradiation angle θ on a two-dimensional plane. It is a diagram of 5A to 5D, in order to clarify the positional relationship between the golf cart 10, the distance sensor 42, the (+Y) direction side edge EG+ of the road surface RS and the (−Y) direction side edge EG− of the road surface RS, They are supplementarily illustrated by dotted lines.

In the driving environment (A), the road surface RS extending in front of the golf cart 10 is a flat road surface RS, and the three-dimensional object OB does not exist on the road surface RS. Therefore, when the laser beam is scanned at the irradiation angle θ0, for example, the distance from the distance sensor 42 to the road surface RS is substantially constant, and as shown in FIG. have. This point is the same for the other irradiation angles θ1, θ2, . . . , θm.

The driving environment (B) is the same as the driving environment (A) in that there is no three-dimensional object OB on the road surface RS, but the road surface RS is an uphill inclined surface. In this case, for example, when the laser beam is scanned at the irradiation angle θ0, as shown in FIG. The trajectory of the portion PG2 has an arc shape as in the driving environment (A), and the elliptical arc portion and the arc portion are continuous at the edges EG+ and EG- of the road surface RS. This point also applies to the other irradiation angles θ1, θ2, . . . , θm. When the scanning range of the laser light is within the width of the road surface RS, the PG2 is not included, and the trajectory of the point group PG becomes an elliptical arc. Further, although the road surface RS that is an upward slope is described here, on the road surface RS that is a downward slope, the trajectory of the point group PG is an elliptical arc portion as in the case of the upward slope. and a circular arc portion (hereinafter referred to as a "composite shape") or an elliptical arc shape. However, the curvatures of the elliptical arc portions are different.

When the three-dimensional object OB does not exist on the road surface RS in this way, the trajectory of the point group PG of the reflection points P has a circular arc shape, an elliptical arc shape, or a composite shape combining the two. Moreover, the locus shape of the point group PG differs depending on whether the road surface RS is a flat surface or an inclined surface. Therefore, by determining whether the point group corresponding to the plurality of reflection points P can draw an arc-shaped or elliptical arc-shaped trajectory (hereinafter referred to as "road surface determination trajectory T") based on the point cloud data, the road surface condition can be accurately determined. can be determined.

Further, when the road surface RS is an inclined surface, the locus of the portion PG1 corresponding to the road surface RS has an elliptical arc shape as described above, and the curvature has a value corresponding to the inclination angle of the inclined surface. Therefore, the inclination angle of the inclined surface can be further obtained based on the curvature of the elliptical arc-shaped road surface discrimination locus T. FIG.

In the above description, the case where the three-dimensional object OB does not exist on the road surface RS is considered. Next, the case where the three-dimensional object OB exists on the road surface RS will be considered. The driving environments (C) and (D) match the driving environments (A) and (B) in that the road surface RS is flat and sloped, respectively, but there is a three-dimensional object OB on the road surface RS. It is different from the driving environments (A) and (B) in one point. In either case, for example, when the laser beam is scanned at the irradiation angle θ0, as shown in columns (C) and (D) of FIG. is reflected by These reflection points P are on the golf cart 10 side, that is, on the (-X) direction side, from the reflection point P on the road surface RS. As a result, as shown in FIGS. A portion PG3 corresponding to OB is located deviating from the other portions in the (-X) direction. That is, the trajectory of the point group PG includes discontinuous points and is neither circular nor elliptical. This point is the same when the three-dimensional object OB is a depression, a groove, or the like. Therefore, by determining whether or not the point group corresponding to the plurality of reflection points P can draw the road surface discrimination trajectory T in the form of an arc or an elliptical arc based on the point group data, the presence or absence of the three-dimensional object OB on the road surface RS can be accurately determined. It is possible to judge. Further, when it is determined that the trajectory of the point group PG includes a discontinuous portion, it is also possible to determine that a three-dimensional object exists on the road surface RS.

FIG. 6 is a flow chart showing the operation of the golf cart shown in FIG. The control unit 32 controls each part of the golf cart 10 according to a program stored in advance in a storage unit (not shown) while being guided by electromagnetic waves emitted from the guide wire embedded in the road surface RS, thereby making the golf cart 10 autonomous. let it run. While the vehicle is traveling or temporarily stopped, the road surface condition detection device 36 performs the road surface condition detection and three-dimensional object detection shown in FIGS. 6 and 7 according to the above program. An example of the operation of the golf cart 10 will now be described with reference to FIGS. 5A-5D, 6 and 7. FIG.

While the golf cart 10 is autonomously traveling or is temporarily stopped, the distance sensor 42 in the road surface condition detection device 36 repeatedly performs three-dimensional spatial imaging in front of the golf cart 10 at regular intervals. In each spatial imaging, the irradiation angle θ is switched in multiple stages while the laser light is scanned in the Y direction from obliquely above the road surface RS at the irradiation angle θ. In the present embodiment, the point group data acquired while scanning the laser light in the Y direction at the irradiation angle θm is analyzed, and the position coordinates of the reflection point P are plotted on a two-dimensional plane. A spatial image is composed of a plurality of layers 0, 1, . . . , m. However, in this embodiment, since the three-dimensional LiDAR is used as the distance sensor 42, "m" is a natural number.

In this embodiment, the variable k is set to an initial value of 0 (step S1). Then, the road surface condition detection device 36 analyzes the point cloud data acquired while scanning the laser light in the Y direction at the irradiation angle θk, and plots the position coordinates of the layer k (reflection point P on a two-dimensional plane plotting). That is, a point group PG as shown in FIGS. 5A to 5D, for example, is acquired (step S2). Note that the layer k acquired in this way is temporarily stored in a storage unit (not shown) in order to use it for a three-dimensional object detection process that will be described later.

The road surface condition detection device 36 calculates the trajectory of the point group PG, that is, the road surface discrimination trajectory T, based on the position coordinates of the point group PG included in the layer k (step S3). For this calculation, a conventionally well-known approximation method such as the method of least squares can be used.

In the next step S4, the road surface condition detection device 36 calculates the ratio of points in the point group PG that match the road surface discrimination trajectory T obtained in step S3 (hereinafter referred to as "matching ratio"). Here, for example, as shown in FIGS. 5A and 5B, when the three-dimensional object OB does not exist on the road surface RS, the matching rate shows a high value. On the other hand, as shown in FIGS. 5C and 5D, for example, when the solid object OB exists on the road surface RS, the road surface determination trajectory T includes discontinuous portions, and the matching rate decreases.

Subsequently, the road surface condition detection device 36 determines whether or not the analysis for these layers k (calculation of the road surface discrimination locus T and matching ratio) has been executed for all layers, that is, whether or not k=m. It judges (step S5). While there is an unanalyzed layer ("NO" in step S5), the road surface condition detection device 36 increments the variable k by 1 (step S6), and then returns to step S2.

When the matching ratio is calculated for all layers in this way, the road surface condition detection device 36 determines that all of the matching ratios acquired in step S4 are equal to or greater than a preset threshold value (equivalent to an example of the "fixed ratio" of the present invention). It is determined whether or not (step S7). That is, when the three-dimensional object OB exists on the road surface RS, the laser light is reflected by the three-dimensional object OB at least at a part of the irradiation angle θ. At the irradiation angle θ, the point indicating the position coordinates of the reflection point P reflected by the three-dimensional object OB deviates from the road surface discrimination trajectory T, and the coincidence rate is reduced. Therefore, when it is determined "YES" in step S7, that is, when it is determined that the three-dimensional object OB does not exist, the road surface condition detection device 36 directly proceeds to step S9. On the other hand, if it is determined as "NO" in step S7, the road surface condition detection device 36 executes a three-dimensional object detection process (step S8), and then proceeds to step S9.

FIG. 7 is a flow chart showing a three-dimensional object detection process that is executed while the golf cart is in motion. In this three-dimensional object detection process, the road surface condition detection device 36 executes steps S81 to S86 to acquire deviation point groups DG corresponding to the three-dimensional object OB for each layer 0, 1, . . . , m. That is, after setting the variable k to an initial value of zero (step S81), the road surface condition detection device 36 reads out the point group PG of the layer k acquired in step S2 from the storage unit (step S82). Then, the road surface condition detection device 36 identifies points (hereinafter referred to as "departure points DP") from the read point group PG that deviate from the road surface discrimination locus T (step S83). For example, in FIG. 5C and FIG. 5D, points separated by a certain distance or more from the road surface discrimination locus T, that is, deviation points DP constitute the deviation point group DG. For example, in the above running environments (C) and (D), there is one solid object OB. Therefore, the point DP corresponds to the reflection point P of the laser beam on the three-dimensional object OB, and only one deviation point group DG also exists on the layer k. Also, when there are a plurality of three-dimensional objects OB, the number of deviating points DP will increase, and a plurality of deviating point groups DG will also exist. Therefore, the road surface condition detection device 36 clusters the deviation points DP and classifies them into one or more deviation point groups (step S84).

When the clustering process for layer k is completed, the road surface condition detection device 36 determines whether the clustering process for all layers is completed, that is, whether k=m (step S85). Here, if it is determined that it is not completed, the road surface condition detection device 36 increments the variable k by 1 (step S86), returns to step S82, and performs a series of processes (steps S82 to S84) for the next layer. Execute.

On the other hand, when it is determined that the clustering process has been completed for all layers ("YES" in step S85), the road surface condition detection device 36 determines whether or not at least one deviation point group DG exists. (Step S87). Here, when confirming the existence of the deviation point group DG ("YES" in step S87), the road surface condition detection device 36 calculates the position coordinates of the deviation point DP included in the deviation point group DG for each deviation point group DG. Based on this, the position of the three-dimensional object OB corresponding to the deviation point group DG is specified (step S88). Subsequently, the road surface condition detection device 36 informs the warning output unit 38 that the three-dimensional object OB exists on the road surface RS in front of the golf cart 10, and the warning output unit 38 notifies the passenger of the golf cart 10 of the three-dimensional object OB. After warning of its existence (step S89), the process proceeds to step S9 in FIG. On the other hand, if the deviation point group DG does not exist at all (“NO” in step S87), the road surface condition detection device 36 proceeds to step S9 in FIG. 6 without issuing the above warning.

Returning to FIG. 6, the description continues. In step S9, the road surface condition detection device 36 determines whether the shape of the road surface determination trajectory T, particularly the shape within the range between both edges EG+ and EG- of the road surface RS, is an arc shape or an elliptical arc shape. This is because the shape of the road surface discrimination locus T corresponds to the condition of the road surface RS as described above. In other words, the road surface condition detection device 36 determines that the road surface RS is flat when determining that it has an arc shape (step S10), and determines that the road surface RS is an inclined surface when determining that it has an elliptical arc shape. (Step S11).

As described above, in the first embodiment, point cloud data is acquired by the distance sensor 42 configured by the three-dimensional LiDAR attached to the golf cart 10 traveling on the road surface RS, and a plurality of reflections are obtained based on the point cloud data. When the point group PG corresponding to the point P can draw an arc-shaped or elliptical arc-shaped road surface discrimination locus T, it is determined that there is no three-dimensional object on the road surface RS. Therefore, regardless of whether the road surface RS in front of the golf cart 10 is a flat surface or an inclined surface, and even if the golf cart 10 is stopped, whether or not the three-dimensional object OB exists on the road surface RS. can be accurately detected.

Further, in the first embodiment, whether the road surface RS is a flat surface extending in the running direction X or an inclined surface can be detected with high accuracy and in real time, depending on the shape of the road surface determination locus T. FIG. In particular, when the road surface RS is an inclined surface, the inclination angle is related to the curvature of the road surface discrimination locus T, so the road surface condition detection device 36 may further obtain the inclination angle of the inclined surface (road surface RS) from the curvature. This function may be included in the road surface condition detection device 36, thereby enabling more detailed detection of the road condition.

Further, in the first embodiment, the deviation point group DG is obtained as a discontinuous point of the road surface discrimination locus T based on the point cloud data, and the three-dimensional object OB is detected. Therefore, as shown in FIGS. 5C and 5D, not only can an object extending upward from the road surface RS be detected with high accuracy, but also depressions and grooves extending downward from the road surface RS can be detected with high accuracy. . In other words, the road surface condition detection device 36 according to the first embodiment has excellent versatility.

Furthermore, in the first embodiment, one or a plurality of deviation point groups DG are acquired to detect three-dimensional objects OB, so all three-dimensional objects OB existing on the road surface RS are accurately detected including position information. can do.

Thus, in the first embodiment, the road surface condition detection device 36 has the function of detecting the road surface condition as described above and also the function of a three-dimensional object detection device that detects the three-dimensional object OB. That is, as shown by the dashed line in FIG. 3, some elements of the road surface condition detection device 36 (the distance sensor 42, the deviation point identification unit 50, and the three-dimensional object identification unit 52) are used as an example of the "three-dimensional object detection device" of the present invention. is configured.

The present invention is not limited to the above-described embodiments, and various modifications other than those described above can be made without departing from the spirit of the present invention. For example, in the first embodiment, the present invention is applied to a golf cart 10 corresponding to an example of the "vehicle" of the present invention. However, vehicles to which the present invention is applied include automatic guided vehicles and robot cleaners used in semiconductor factories, distribution warehouses, and the like. For example, an automated guided vehicle used in a semiconductor factory runs by itself on a road surface RS installed in the semiconductor factory while constantly communicating with a host computer that controls the entire semiconductor factory. The present invention can be applied to this automatic guided vehicle (second embodiment). In the second embodiment, if a three-dimensional object OB exists on the road surface RS, it can be accurately detected in real time. Moreover, since the distance sensor 42 is a three-dimensional sensor and three-dimensional spatial imaging is performed in front of the automatic guided vehicle, the three-dimensional object detection processing in the second embodiment may be configured as follows.

FIG. 8 is a flow chart showing a three-dimensional object detection process executed by the second embodiment of the road surface condition detection device according to the present invention. The major difference between the second embodiment and the three-dimensional object detection process (step S8) of the first embodiment is the process when the deviation point group DG is obtained. Similar to one embodiment. Therefore, the following description will focus on the points of difference, and the same configurations and operations will be given the same reference numerals, and description thereof will be omitted.

In the second embodiment, similarly to the first embodiment, a deviation point group DG corresponding to the three-dimensional object OB is acquired for each layer 0, 1, . . . , m (steps S81 to S86). After that, the road surface condition detection device 36 determines whether or not at least one deviation point group DG exists (step S87). Here, when confirming the existence of the deviation point group DG ("YES" in step S87), the road surface condition detection device 36 calculates the position coordinates of the deviation point DP included in the deviation point group DG for each deviation point group DG. Based on this, the position of the three-dimensional object OB corresponding to the deviation point group DG is specified, and the size and shape of the three-dimensional object OB are specified by taking into account the layer information (laser beam irradiation angle θ) of the deviation point DP ( step S88A). That is, in this step S88A, the road surface condition detection device 36 identifies relevant information indicating the position, size, shape, etc. of the three-dimensional object OB corresponding to the deviation point group DG. Subsequently, the road surface condition detection device 36 outputs the related information of the three-dimensional object OB specified in step S88A to the host computer (step S89A).

As described above, in the second embodiment, the presence of the three-dimensional object OB on the road surface RS is not merely detected, but detailed related information regarding the three-dimensional object OB existing on the road surface RS, that is, the spatial image of the three-dimensional object OB is detected. to semiconductor factories. The host computer that receives this analyzes the three-dimensional object OB and takes measures such as changing the moving route of the automatic guided vehicle in order to avoid the three-dimensional object OB. In this way, by applying the present invention to an automatic guided vehicle that automatically conveys semiconductor wafers and the like in a semiconductor factory, the automatic conveyance can be stably performed.

Further, in the above embodiment, the distance sensor 42 is composed of a three-dimensional LiDAR, but for the purposes of road surface condition detection and three-dimensional object detection, a two-dimensional LiDAR may be used instead of the three-dimensional LiDAR. , detection processing is performed using only layer 0 in the above embodiment. Further, the distance sensor 42 is not limited to LiDAR, and general sensors having the following functions can be used as the distance sensor 42 . That is, by receiving the electromagnetic waves reflected in front of the vehicle while scanning the road surface RS in a direction Y orthogonal to the traveling direction X of the vehicle, the electromagnetic waves are emitted. Any sensor capable of acquiring point cloud data indicating the distance from the vehicle to the reflection point P and the direction of the reflection point P relative to the vehicle can be used for a plurality of reflected reflection points P.

INDUSTRIAL APPLICABILITY The present invention can be applied to general detection techniques for detecting the condition of a road surface on which a vehicle travels and three-dimensional objects existing on the road surface, and to general vehicles equipped with such technologies.

10... golf cart (vehicle)
32... Control part 36... Road surface condition detection device 42... Distance sensor 44... Point cloud trajectory determination part 46... Road surface determination part 50... Deviation point identification part 52... Solid object identification part 521... Clustering part 522... Individual identification part DG... Departure Point group DP... Departure point OB... Three-dimensional object P... Reflection point PG... Point group RS... Road surface T... Road surface discrimination trajectory X... Driving direction Y... Direction (perpendicular to the driving direction) θ... Irradiation angle

Claims (12)

It is attached to a vehicle running on a road surface, and scans the road surface with electromagnetic waves emitted obliquely from above in a direction perpendicular to the running direction of the vehicle, and detects electromagnetic waves reflected in front of the vehicle. a distance sensor that acquires point cloud data indicating distances from the vehicle to the reflection points and directions of the reflection points with respect to the vehicle, for a plurality of reflection points that have reflected the electromagnetic waves, by receiving the data;
A point cloud trajectory determination unit that determines whether the point cloud corresponding to the plurality of reflection points can draw an arc-shaped or elliptical arc-shaped road surface discrimination trajectory based on the point cloud data;
a road surface determination unit that determines that a three-dimensional object does not exist on the road surface when the point cloud trajectory determination unit determines that the point cloud can draw the road surface determination trajectory;
A road surface condition detection device comprising: The road surface condition detection device according to claim 1,
The road surface determination unit
determining that the road surface is a flat surface when the road surface determination unit determines that the point group can draw the arc-shaped road surface determination trajectory;
A road surface condition detection device that determines that the road surface is inclined in the running direction when the road surface determination unit determines that the road surface determination trajectory of the elliptical arc shape can be drawn by the road surface determination unit. The road surface condition detection device according to claim 2,
The road surface condition detecting device further determines the inclination angle of the inclined surface based on the curvature of the elliptical arc-shaped road surface identification trajectory when determining that the road surface is an inclined surface inclined in the running direction. . The road surface condition detection device according to any one of claims 1 to 3,
The point cloud trajectory determination unit
determining that the point group can draw the road surface determination trajectory when a certain percentage or more of the points constituting the point group are located on the road surface determination trajectory;
determining that the point group cannot draw the road surface discrimination trajectory when the number of points located on the road surface discrimination trajectory among the plurality of points constituting the point group is less than a certain percentage;
The road surface condition detection device, wherein the road surface determination unit determines that a three-dimensional object exists on the road surface when the point cloud trajectory determination unit determines that the point group cannot draw the road surface determination trajectory. The road surface condition detection device according to any one of claims 1 to 3,
The point cloud trajectory determination unit
Determining that the point group can draw the road surface discrimination trajectory when the point cloud trajectory drawn by the point group does not include a discontinuous point;
determining that the point group cannot draw the road surface discrimination trajectory when the point cloud trajectory drawn by the point group includes a discontinuous portion;
The road surface condition detection device, wherein the road surface determination unit determines that a three-dimensional object exists on the road surface when the point cloud trajectory determination unit determines that the discontinuous portion is included. The road surface condition detection device according to any one of claims 1 to 5,
The distance sensor is a three-dimensional sensor configured to be able to scan the irradiation angle of the electromagnetic wave in multiple stages,
The road surface condition detection device, wherein the point cloud trajectory determination unit determines whether the point cloud can draw the road surface determination trajectory for each irradiation angle different from each other. The road surface condition detection device according to claim 6,
The road surface determination unit
determining that no three-dimensional object exists on the road surface when the point cloud trajectory determination unit determines that the point cloud can draw the road surface determination trajectory for all of the plurality of irradiation angles;
The road surface condition detecting device determines that a three-dimensional object exists on the road surface when the point cloud trajectory determination unit determines that the point cloud cannot draw the road surface determination trajectory for at least one of the plurality of irradiation angles. . It is attached to a vehicle running on a road surface, and scans the road surface with electromagnetic waves emitted obliquely from above in a direction perpendicular to the running direction of the vehicle, and detects electromagnetic waves reflected in front of the vehicle. a distance sensor that acquires point cloud data indicating distances from the vehicle to the reflection points and directions of the reflection points with respect to the vehicle, for a plurality of reflection points that have reflected the electromagnetic waves, by receiving the data;
Based on the point cloud data, a point cloud trajectory drawn by the point cloud corresponding to the plurality of reflection points is compared with an arc-shaped or elliptical arc-shaped road surface discrimination trajectory, and the point cloud among the points constituting the point cloud is compared. a deviation point identification unit that identifies a deviation point where the trajectory deviates from the road surface discrimination trajectory;
a three-dimensional object identification unit that identifies the presence of a three-dimensional object on the road surface based on data corresponding to the deviation point among the point cloud data acquired by the distance sensor;
A three-dimensional object detection device comprising: The three-dimensional object detection device according to claim 8,
The three-dimensional object identification unit
a clustering unit that clusters the deviation points identified by the deviation point identification unit and classifies them into one or more deviation point groups;
an individual identification unit that identifies the presence of a three-dimensional object on the road surface for each deviation point group;
A three-dimensional object detection device. The three-dimensional object detection device according to claim 9,
The three-dimensional object detection device, wherein the clustering unit classifies a plurality of deviation points, which are equal to or more than a predetermined number and are adjacent to each other at a distance within a threshold value, into the deviation point group. A three-dimensional object detection device according to any one of claims 8 to 10,
A vehicle, wherein running on the road surface is controlled based on the presence of the three-dimensional object identified by the three-dimensional object detection device. It is attached to a vehicle running on a road surface, and scans the road surface with electromagnetic waves emitted obliquely from above in a direction perpendicular to the running direction of the vehicle, and detects electromagnetic waves reflected in front of the vehicle. obtaining point cloud data indicating distances from the vehicle to the reflection points and directions of the reflection points with respect to the vehicle, for a plurality of reflection points that have reflected the electromagnetic waves, by receiving the data;
a step of comparing a point cloud trajectory drawn by the point cloud corresponding to the plurality of reflection points based on the point cloud data with an arc-shaped or elliptical arc-shaped road surface discrimination trajectory;
a step of identifying deviation points at which the point cloud trajectory deviates from the road surface discrimination trajectory among the points constituting the point cloud;
a step of identifying the presence of a three-dimensional object on the road surface based on data corresponding to the deviation point in the point cloud data;
A three-dimensional object detection method, comprising:

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