Object detection device, object detection system, object detection method, and non-transitory computer-readable medium storing program

Provided is an object detection device capable of accurately calculating a movement parameter related to the movement of an object. An object detection device (1) includes a feature extraction unit (2) and a calculation unit (4). When an object passes each of a plurality of irradiation areas of irradiation light from a first sensor and a second sensor, which are configured to detect a feature of a part of a surface of an object by applying irradiation light, the feature extraction unit (2) extracts features of the object in the plurality of irradiation areas. The calculation unit (4) calculates a movement parameter of the object between the plurality of irradiation areas when a difference between the features respectively extracted in the plurality of irradiation areas falls below a predetermined first threshold.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application is a National Stage Entry of International Application No. PCT/JP2018/010422, filed Mar. 16, 2018. The entire contents of the above-referenced application is expressly incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to an object detection device, an object detection system, an object detection method, and a program, and, particularly, relates to an object detection device, an object detection system, an object detection method, and a program that detect an object by using a sensor.

BACKGROUND ART

A technique that detects an object by using a sensor is known. In regard to this technique. Patent Literature 1 discloses a pedestrian trajectory extraction device that detects position coordinates of a pedestrian by a plurality of laser sensors, integrates the detected data by coordinate transformation in a server, and thereby extracts the trajectory of the pedestrian in real time and over a wide range. The pedestrian trajectory extraction device according to Patent Literature 1 controls a plurality of clients provided in correspondence with a plurality of laser sensors that detect the position of a pedestrian or an object. The pedestrian trajectory extraction device includes a synchronization means, an integration means, and a trajectory extraction means. The synchronization means synchronizes detection time of the plurality of laser sensors. The integration means integrates the position coordinates of a pedestrian extracted by the plurality of clients into one coordinate system from data detected by the plurality of laser sensors. The trajectory extraction means extracts the movement trajectory of the pedestrian from the position coordinates of the pedestrian obtained by the integration means.

Patent Literature 2 discloses an image measurement device that measures an image of an object. The image measurement device according to Patent Literature 2 includes first and second imaging means placed in at least different viewing locations, a three-dimensional position detection means, a movement model data storage means, and an action recognition means. The three-dimensional position detection means detects a three-dimensional position of a feature point in an object from output images of the first and second imaging means. The movement model data storage means stores data related to each action and posture of a movement model of an object. The action recognition means compares, in time series, the detected three-dimensional position or its temporal change of the feature point in an object with data related to the action and posture of the object stored in the movement model data storage means, and thereby recognizes the action and posture of the object.

CITATION LIST

Patent Literature

SUMMARY OF INVENTION

Technical Problem

In the above-described patent literatures, the action (movement trajectory) of an object is detected by using a plurality of sensors (imaging means). In the case of detecting the action of an object by using a plurality of sensors, the object to be detected by the plurality of sensors needs to be the same. If objects detected by the plurality of sensors are possibly different, there is a possibility that the action of an object is wrongly detected due to a mix-up between objects. The above-described patent literatures disclose nothing about detecting whether objects detected by a plurality of sensors are the same or not. Thus, according to the above-described patent literatures, there is a possibility that the action of an object is wrongly detected in the environment where a plurality of objects can exist.

The present disclosure has been accomplished to solve the above problem, and an object of the present disclosure is thus to provide an object detection device, an object detection system, an object detection method, and a program capable of accurately calculating a movement parameter related to the movement of an object.

Solution to Problem

An object detection device according to the present disclosure includes a feature extraction means for extracting features of an object in a plurality of irradiation areas of irradiation light from at least one sensor when the object passes each of the plurality of irradiation areas, the at least one sensor being configured to detect a feature of a part of a surface of an object by applying irradiation light, and a calculation means for calculating a movement parameter related to movement of the object between the plurality of irradiation areas when a difference between the features respectively extracted in the plurality of irradiation areas falls below a predetermined first threshold.

An object detection system according to the present disclosure includes at least one sensor configured to detect a feature of a part of a surface of an object by applying irradiation light, and an object detection device, wherein the object detection device includes a feature extraction means for extracting features of an object in a plurality of irradiation areas of irradiation light from at least one sensor when the object passes each of the plurality of irradiation areas, the at least one sensor being configured to detect a feature of a part of a surface of an object by applying irradiation light, and a calculation means for calculating a movement parameter related to movement of the object between the plurality of irradiation areas when a difference between the features respectively extracted in the plurality of irradiation areas falls below a predetermined first threshold.

An object detection method according to the present disclosure includes extracting features of an object in a plurality of irradiation areas of irradiation light from at least one sensor when the object passes each of the plurality of irradiation areas, the at least one sensor being configured to detect a feature of a part of a surface of an object by applying irradiation light, and calculating a movement parameter related to movement of the object between the plurality of irradiation areas when a difference between the features respectively extracted in the plurality of irradiation areas falls below a predetermined first threshold.

A program according to the present disclosure causes a computer to perform a step of extracting features of an object in a plurality of irradiation areas of irradiation light from at least one sensor when the object passes each of the plurality of irradiation areas, the at least one sensor being configured to detect a feature of a part of a surface of an object by applying irradiation light, and a step of calculating a movement parameter related to movement of the object between the plurality of irradiation areas when a difference between the features respectively extracted in the plurality of irradiation areas falls below a predetermined first threshold.

Advantageous Effects of Invention

According to the present disclosure, there are provided an object detection device, an object detection system, an object detection method, and a program capable of accurately calculating a movement parameter related to the movement of an object.

DESCRIPTION OF EMBODIMENTS

Overview of Example Embodiment According to Present Disclosure

Prior to describing example embodiments of the present disclosure, the overview of an example embodiment according to the present disclosure is described.FIG. 1is a view showing the overview of an object detection device1according to an example embodiment of the present disclosure.

The object detection device1includes a feature extraction unit2that functions as a feature extraction means, and a calculation unit4that functions as a calculation means. The feature extraction unit2extracts features of an object in a plurality of irradiation areas of irradiation light from a first sensor and a second sensor, which are configured to detect a feature of a part of the surface of an object by applying irradiation light, when the object passes each of the irradiation areas. The calculation unit4calculates a movement parameter of the object between the plurality of irradiation areas when a difference between the features respectively extracted in the plurality of irradiation areas falls below a predetermined first threshold.

The irradiation light is laser light, for example, though not limited thereto. Further, the movement parameter is a parameter related to the movement of an object. The movement parameter is the moving direction and the moving speed of an object, for example, though not limited thereto. Further, although two sensors (the first sensor and the second sensor) are shown inFIG. 1, the number of sensors may be one, or three or more. In the case where the number of sensors is one, one sensor may have a plurality of irradiation areas. For example, one sensor may form a first irradiation area at time t1and form a second irradiation area at time2.

Since the object detection device1according to this example embodiment calculates the movement parameter by using features of an object, it is able to calculate the movement parameter when the same position on the same object passes a plurality of irradiation areas. Specifically, the object detection device1according to this example embodiment is able to identify an object by using features of the object when calculating the movement parameter. The object detection device1according to this example embodiment is thereby capable of accurately calculating the movement parameter without mixing up between objects.

It should be noted that use of an object detection method performed by the object detection device1also enables accurate calculation of the movement parameter without mixing up between objects. Further, use of a program capable of implementing the object detection method also enables accurate calculation of the movement parameter without mixing up between objects. Furthermore, use of an object detection system that includes the object detection device1and at least one sensor also enables accurate calculation of the movement parameter without mixing up between objects.

First Example Embodiment

A first example embodiment is described hereinafter.

FIG. 2is a view showing the configuration of an object detection system10according to the first example embodiment. The object detection system10according to the first example embodiment includes a first sensor20, a second sensor40, and an object detection device100. The first sensor20and the second sensor40are respectively equivalent of the first sensor and the second sensor shown inFIG. 1. The first sensor20and the second sensor40are three-dimensional sensors such as a 3D scanner, a range sensor, a depth sensor, a distance sensor, and a 3D camera (stereo camera) capable of measuring the distance to an object. The first sensor20and the second sensor40are LIDAR (Light Detection and Ranging) or the like, for example. Further, the first sensor20and the second sensor40are able to recognize the three-dimensional coordinates in the three-dimensional space where the object detection system10is located. Note that the three-dimensional space may be represented by the Cartesian coordinate system or represented by the polar coordinate system. The following description shows an example where the three-dimensional space is represented by the (X, Y, Z) Cartesian coordinate system.

Further, although the first sensor20and the second sensor40emit laser light in the upward direction from below an object90in this example embodiment, the present invention is not limited to this structure. The direction of emitting laser light from the first sensor20and the second sensor40is not limited to upward, and it is arbitrary. Further, although the number of the first sensor20and the second sensor40is one each in this example embodiment, the present invention is not limited to this structure. A plurality of first sensors20and a plurality of second sensors40may be provided. When a plurality of first sensors20are provided, laser light may be applied both from above and below the object90to enable detection of not only the lower shape of the object90but also the upper shape of the object90. The same applies to the second sensor40.

The first sensor20is configured to detect a feature of a part of the surface of an object90by applying irradiation light. To be specific, the first sensor20measures the distance from the first sensor20to each point on the object90. Then, the first sensor20generates distance data indicating the measured distance. The first sensor20generates distance image data indicating a distance image (point cloud) as the distance data. Specifically, the distance data represents, three-dimensionally, a point group on the surface of the object90indicating the distance from the first sensor20.

The first sensor20scans irradiation light such as laser light over a first irradiation area22(first irradiation area), which is a certain range, and receives reflected light of the irradiation light that has been reflected on the object90. The first sensor20then calculates the distance to the object90from a difference between the time of transmission and the time of reception. After that, the first sensor20calculates the three-dimensional coordinates (X, Y, Z) of the reflected position of laser light on the object90from the three-dimensional position coordinates of the first sensor20, the irradiation direction of laser light, and the distance to the object90.

The first irradiation area22of the first sensor20may have a planar shape (or a pyramid shape). In the following description, it is assumed that the first irradiation area22is formed on a plane perpendicular to the X-axis. In other words, the axis perpendicular to the first irradiation area22is the X-axis. The vertical direction is the Z-axis, and the axis perpendicular to the X-axis and the Z-axis is the Y-axis. In this manner, the first sensor20forms a laser wall capable of detecting the object90that has passed the first irradiation area22and entered on the side of the second sensor40.

The first sensor20detects the three-dimensional coordinates (X, Y, Z) of the position where the surface of the object90is irradiated with laser light in the first irradiation area22when the object90passes the first irradiation area22. Thus, a coordinate data group (point group) corresponding to the positions on the object90irradiated by the first sensor20can form a curved line on a plane perpendicular to the X-axis as shown by the arrow C1.

The second sensor40is capable of detecting the shape of the object90that has passed a second irradiation area42(second irradiation area) of the second sensor40by a method similar to that of the first sensor20. Specifically, the second sensor40scans irradiation light such as laser light over the second irradiation area42, which is a certain range, and receives reflected light of the irradiation light that has been reflected on the object90. The second sensor40then calculates the distance to the object90from a difference between the time of transmission and the time of reception. The second sensor40thereby calculates the three-dimensional coordinates (X, Y, Z) of the reflected position of laser light on the object90. The second irradiation area42of the second sensor40may have a planar shape (or a pyramid shape). Note that, although the second irradiation area42is formed on a plane perpendicular to the X-axis, the present invention is not limited to this structure. The second irradiation area42is not necessarily parallel to the first irradiation area22.

The object detection device100is a computer, for example. The object detection device100is connected for communication with the first sensor20and the second sensor40by wired or wireless connection. As described later, the object detection device100extracts feature data indicating a feature of the object90in the first irradiation area22when the object90passes the first irradiation area22of the first sensor20. Further, the object detection device100extracts feature data indicating a feature of the object90in the second irradiation area42when the object90passes the second irradiation area42of the second sensor40. Then, when a difference between the feature data extracted in the first irradiation area22and the feature data extracted in the second irradiation area42falls below a predetermined threshold, the object detection device100calculates the movement parameter (moving direction and moving speed) of the object90. Note that the case where the object90passes the first irradiation area22first and then passes the second irradiation area42is described below. However, the object90may pass the second irradiation area42first and then pass the first irradiation area22.

The object detection device100includes, as main hardware components, a CPU (Central Processing Unit)102, a ROM (Read Only Memory)104, a RAM (Random Access Memory)106, and an interface unit108(IF; Interface). The CPU102, the ROM104, the RAM106and the interface unit108are connected with each other through a data bus or the like.

The CPU102has a function as an arithmetic device that performs control processing, arithmetic processing and so on. The ROM104has a function for storing a control program, an arithmetic program and so on to be executed by the CPU102. The RAM106has a function for temporarily storing processing data or the like. The interface unit108inputs and outputs signals from and to the outside by wired or wireless connection. Further, the interface unit108receives a data input operation by a user and displays information to the user.

FIG. 3is a functional block diagram showing the object detection system10according to the first example embodiment. The object detection device100includes a first feature extraction unit110, a feature storage unit112, a second feature extraction unit120, a feature comparison unit122, a direction calculation unit130, and a speed calculation unit132(which are referred to hereinafter as “each element”). The first feature extraction unit110and the second feature extraction unit120function as a feature extraction means. Further, the feature storage unit112, the feature comparison unit122, the direction calculation unit130, and the speed calculation unit132function as a feature storage means, a feature comparison means, a direction calculation means, and a speed calculation means, respectively.

Each element can be implemented when the CPU102executes a program stored in the ROM104, for example. Further, a necessary program may be recorded on an arbitrary nonvolatile recording medium and installed according to need. Note that each element is not limited to be implemented by software as described above, and it may be implemented by hardware such as some sort of circuit element. Further, one or more of the above-described elements may be implemented by physically separate hardware. Specific functions of each element are described later.

FIG. 4is a flowchart showing an object detection method performed by the object detection device100according to the first example embodiment. The first feature extraction unit110extracts feature data indicating a feature of the object90that has passed the first irradiation area22of the first sensor20(Step S12). The “feature data” corresponds to data related to the surface shape of the object90which is detected by the first sensor20in the first irradiation area22. Further, the “feature data” relates to position information indicating the three-dimensional shape of the object90which is detected by the first sensor20. A specific example of the “feature data” is described later. The first feature extraction unit110may generate the feature data from the position coordinates of each point group acquired by the first sensor20.

Further, the first feature extraction unit110stores the extracted feature data in association with time when the feature data is extracted and a position where the object90has passed the first irradiation area22into the feature storage unit112(S14). Specifically, when “time t1” is associated with certain feature data, this feature data relates to the shape of the position of the object90that has been located in the first irradiation area22at time t1. Further, the “position where the object90has passed the first irradiation area22” is a position that defines the shape of the object90in the first irradiation area22. For example, the object detection device100(the first feature extraction unit110etc.) may detect a position of the object90in the first irradiation area22from a group of coordinate data in the first irradiation area22when the object90passes the first irradiation area22. For example, the object detection device100(the first feature extraction unit110etc.) may calculate the center of mass (i.e., median point) of the coordinate data group of the object90in the first irradiation area22.

Then, when the object90moves from the first irradiation area22to the second irradiation area42, the second feature extraction unit120extracts feature data indicating a feature of the object90that has passed the second irradiation area42of the second sensor40(Step S16). The “feature data” corresponds to data related to the surface shape of the object90which is detected by the second sensor40in the second irradiation area42. The other description of the feature data is the same as in the case of S12and is therefore omitted.

The feature comparison unit122calculates a difference between the feature data extracted by the second feature extraction unit120and the feature data extracted by the first feature extraction unit110and stored in the feature storage unit112(Step S18). A specific example of a method of calculating a difference in feature is described later. Note that the feature comparison unit122may generate the feature data from the position coordinates of point groups respectively acquired by the first sensor20and the second sensor40. Further, the first feature extraction unit110and the second feature extraction unit120may generate the feature data.

Then, the feature comparison unit122determines whether the feature data whose difference from the feature data extracted by the second feature extraction unit120falls below a predetermined threshold ThA exists among one or more feature data stored in the feature storage unit112(Step S20). Specifically, the feature comparison unit122determines whether a difference between the feature extracted by the first feature extraction unit110in the first irradiation area22and the feature extracted by the second feature extraction unit120in the second irradiation area42is less than the threshold ThA or not. The threshold ThA may be set to an appropriate value to determine that the features of the object90are substantially the same. When it is determined that the feature data whose difference from the feature data extracted by the second feature extraction unit120is less than the threshold ThA is not stored in the feature storage unit112, i.e., a difference in feature data is equal to or more than the threshold ThA (NO in S20), the process returns to S12.

On the other hand, when it is determined that a difference in feature data is less than the threshold ThA (YES in S20), the feature comparison unit122determines that the same position on the same object90has passed the first irradiation area22and the second irradiation area42. In this case, the direction calculation unit130calculates the moving direction of the object90between the first irradiation area22and the second irradiation area42based on the position where the object90has passed the first irradiation area22and the position where the object90has passed the second irradiation area42(Step S22). Further, the speed calculation unit132calculates the moving speed of the object90between the first irradiation area22and the second irradiation area42based on the time when and the position where the object90has passed the first irradiation area22and the time when and the position where the object90has passed the second irradiation area42(Step S24).

Thus, when the feature data whose difference from the feature data extracted by the second feature extraction unit120falls below the threshold ThA is stored in the feature storage unit112, the feature comparison unit122determines that the object90having passed the first irradiation area22has passed the second irradiation area42. In other words, the feature comparison unit122determines that a certain position on the object90having passed the first irradiation area22has passed the second irradiation area42. In this case, the direction calculation unit130calculates the moving direction of the object90, and the speed calculation unit132calculates the moving speed of the object90.

FIG. 5is a view illustrating a method of calculating a moving direction and a moving speed according to the first example embodiment. The first sensor20scans laser light in the vertically upward direction (in the positive direction of the Z-axis) at the position of X=Xs1. Thus, the first irradiation area22is formed at the position of X=Xs1. Likewise, the second sensor40scans laser light in the vertically upward direction (in the positive direction of the Z-axis) at the position of X=Xs2. Thus, the second irradiation area42is formed at the position of X=Xs2. Although the distance between Xs1and Xs2is larger than the size of the object90inFIG. 5to clarify the description, the distance between Xs1and Xs2may be significantly smaller than the size of the object90in practice. Thus, a change (rotation) of the posture of the object90between the first irradiation area22and the second irradiation area42is negligible.

Then, the object90passes the first irradiation area22at time t1, and a feature f1(first feature) of the object90in the first irradiation area22is extracted. After that, the object90moves to the second irradiation area42, and the object90passes the second irradiation area42at time t2. At this time, a feature f2(second feature) of the object90in the second irradiation area42is extracted.

In this case, the feature comparison unit122determines that a difference between the feature f1and the feature f2is less than the threshold ThA (i.e., the feature f1and the feature f2are substantially the same). Then, the direction calculation unit130calculates coordinates (Xg1,Yg1,Zg1) of the center of mass G1of a coordinate data group (point group) indicating the feature f1. Likewise, the direction calculation unit130calculates coordinates (Xg2,Yg2,Zg2) of the center of mass G2of a coordinate data group (point group) indicating the feature f2. The direction calculation unit130then calculates the moving direction (indicated by the arrow A1) of the object90from a difference between the coordinates (Xg1,Yg1,Zg1) of the center of mass G1and the coordinates (Xg2,Yg2,Zg2) of the center of mass G2.

Further, the speed calculation unit132calculates the distance D between the center of mass G1and the center of mass G2. The speed calculation unit132then divides the distance D by a time difference (t2−t1) and thereby calculates a speed v of the object90. Thus, the speed calculation unit132calculates v=D/(t2−t1).

As described above, the object detection device100according to the first example embodiment calculates the movement parameter of the object90when a difference in feature data is less than the threshold ThA, i.e., when the feature data in the first irradiation area22and the feature data in the second irradiation area42are substantially the same. The fact that a difference in feature is less than the threshold ThA (i.e. the feature f1and the feature f2are substantially the same) means that the object90that has passed the second irradiation area42is the same as the object90that has passed the first irradiation area22. Therefore, by calculating the movement parameter (the moving direction and the moving speed) of the object90at this time, the movement parameter is accurately calculated without mixing up between objects. Since the object detection device100according to the first example embodiment calculates the movement parameter by using features of an object, it is capable of accurately calculating the movement parameter of the object90.

In the case where the object90is a flight vehicle, the moving direction and the moving speed of the object90are not necessarily constant, and it is not easy to detect the moving direction and the moving speed of the object90by using one radar or the like. On the other hand, the object detection device100according to the first example embodiment calculates the moving direction and the moving speed by using the coordinate data extracted in two irradiation areas (the first irradiation area22and the second irradiation area42) by the first sensor20and the second sensor40, which are three-dimensional sensors. By using features of the object90, it is easily determined that a reference position (e.g., the front of the object90) for calculating the movement parameter in the object90has passed the first irradiation area22at time t1and then passed the second irradiation area42at time t2. This allows easy determination as to whether the same position on the same object90has passed the first irradiation area22and the second irradiation area42. Further, three-dimensional positions where the object90has passed in each of the first irradiation area22and the second irradiation area42can be easily calculated from the extracted coordinate data. Thus, the object detection device100according to the first example embodiment is capable of easily calculating the moving direction and the moving speed of the object90.

Example of Feature Data and Difference

Specific examples of the feature data of the object90and its difference are described hereinafter. First to fourth examples are described below. In the following description, it is assumed that the object90is a flight vehicle.

FIG. 6is a flowchart showing a method of determining whether the same position on the same object90has passed the first irradiation area22and the second irradiation area42in the first example.FIG. 7is a view illustrating the first example. In the first example, the feature data corresponds to the number of data indicating each position on the object90in the first irradiation area22and the second irradiation area42.

As shown by the arrow Fg1inFIG. 7, it is assumed that the first sensor20scans laser light in the positive direction of the Y-axis at the position of X=Xs1at time t1. In this case, (X11,Y11,Z11) corresponds to a position P1where the value of the Y-coordinate is the smallest among the positions where the object90and the first irradiation area22intersect. Further, (X1m,cY1m,Z1m) corresponds to a position Pm where the value of the Y-coordinate is the greatest among the positions where the object90and the first irradiation area22intersect.

As shown by the arrow Fg2inFIG. 7, when the object90moves as indicated by the arrow A, the second sensor40scans laser light in the positive direction of the Y-axis at the position of X=Xs2at time t2. In this case, (X21,Y21,Z21) corresponds to a position P2where the value of the Y coordinate is the smallest among the positions where the object90and the second irradiation area42intersect. Further, (X2n,Y2n,Z2n) corresponds to a position Pn where the value of the Y coordinate is the greatest among the positions where the object90and the second irradiation area42intersect.

The feature comparison unit122determines whether a difference between the number of data acquired in the first irradiation area22and the number of data acquired in the second irradiation area42is less than a predetermined threshold Th1or not (Step S104). Specifically, the feature comparison unit122determines whether |m−n|<Th1is satisfied or not. Th1corresponds to ThA shown inFIG. 4. When it is determined that |m−n|≥Th1is satisfied, i.e., a difference between the number of data in the first irradiation area22and the number of data in the second irradiation area42is equal to or more than the threshold Th1(NO in S104), the feature comparison unit122determines that the feature f1and the feature f2are not the same. Therefore, the feature comparison unit122determines that the same position on the same object90has not passed each irradiation area (Step S106). On the other hand, when it is determined that |m−n|<Th1is satisfied, i.e., a difference between the number of data in the first irradiation area22and the number of data in the second irradiation area42is less than the threshold Th1(YES in S104), the feature comparison unit122determines that the feature f1and the feature f2are the same. Therefore, the feature comparison unit122determines that the same position on the same object90has passed each irradiation area (Step S108). Thus, the direction calculation unit130and the speed calculation unit132calculate the movement parameter of the object90at this time.

The surface shape of the object90is irregular. Thus, a feature of the shape can vary by object90or at different positions on the same object90. Accordingly, if the object90(or a position on the same object90) that has passed each irradiation area is different, the number m of data acquired when the object90has passed the first irradiation area22and the number n of data acquired when the object90has passed the second irradiation area42can be different. In contrast, when the same position on the same object90has passed each irradiation area, the number m of data and the number n of data can be substantially the same. It is therefore possible to determine whether the same position on the same object90has passed the first irradiation area22and the second irradiation area42or not by using a difference between the number m of data obtained in the first irradiation area22and the number n of data obtained in the second irradiation area42.

Further, in the first example, a difference between the number of data acquired in the first irradiation area22and the number of data acquired in the second irradiation area42is a difference in the feature of the object90. Note that the number of data is easily and immediately determined. Thus, the method according to the first example enables easy and immediate calculation of a difference in the feature of the object90.

FIG. 8is a flowchart showing a method of determining whether the same position on the same object90has passed the first irradiation area22and the second irradiation area42in the second example.FIG. 9is a view illustrating the second example. In the second example, the feature data corresponds to coordinate data indicating each position on the object90in the first irradiation area22and the second irradiation area42. The first feature extraction unit110extracts a data group #1 (X11,Y11,Z11), (X12,Y12,Z12), . . . , (X1k,Y1k,Z1k), . . . , and (X1m,Y1m,Z1m) in the first irradiation area22at time t1in the same manner as in the processing of S100(Step S110). Next, the second feature extraction unit120extracts a data group #2 (X21,Y21,Z21), (X22,Y22,Z22), . . . , (X2k,Y2k,Z2k), . . . , and (X2n,Y2n,Z2n) in the second irradiation area42at time t2in the same manner as in the processing of S102(Step S112).

The feature comparison unit122determines whether a difference between the number of data acquired in the first irradiation area22and the number of data acquired in the second irradiation area42is less than a predetermined threshold Th21or not in the same manner as in the processing of S104(Step S114). Th21corresponds to Th1shown inFIG. 6. When it is determined that a difference between the number of data in the first irradiation area22and the number of data in the second irradiation area42is equal to or more than the threshold Th21(NO in S114), the feature comparison unit122determines that the same position on the same object90has not passed each irradiation area (Step S116).

On the other hand, when it is determined that a difference between the number of data in the first irradiation area22and the number of data in the second irradiation area42is less than the threshold Th21(YES in S114), the feature comparison unit122calculates a correlation coefficient c between the data group #1 and the data group #2 (Step S120). For example, the feature comparison unit122calculates the variation between adjacent coordinate data in the data group #1. Likewise, the feature comparison unit122calculates the variation between adjacent coordinate data in the data group #2.

The “variation” is the slope, distance or the like between coordinate data, for example. For example, for the data group #1, the feature comparison unit122calculates the slope between (X11,Y11,Z11) and (X12,Y12,Z12). Further, the feature comparison unit122calculates the slope between (X1k,Y1k,Z1k) and (X1(k+1),Y1(k+1),Z1(k+1)). Then, the feature comparison unit122calculates the slope between (X1(m-1),Y1(m-1),Z1(m-1)) and (X1m,Y1m,Z1m). Likewise, for the data group #2, the feature comparison unit122calculates the slope between adjacent coordinate data.FIG. 9is a graph showing the relationship between the data number (the second index k of coordinate data) and the slope for each of the data group #1 and the data group #2. The feature comparison unit122calculates the correlation coefficient c between those two graphs.

The feature comparison unit122determines whether a value Δ2=1−c indicating a difference between the data group #1 and the data group #2 is less than a predetermined threshold Th22or not (Step S122). Specifically, the feature comparison unit122determines whether 1−c<Th22is satisfied or not. Th22(0<Th22<1) corresponds to ThA shown inFIG. 4. When it is determined that 1−c≥Th22is satisfied, i.e., 1−c is equal to or more than the threshold Th22(NO in S122), the feature comparison unit122determines that the feature f1and the feature f2are not the same. Thus, the feature comparison unit122determines that the same position on the same object90has not passed each irradiation area (Step S116). On the other hand, when it is determined that 1−c<Th22is satisfied, i.e., 1−c is less than the threshold Th22(YES in S122), the feature comparison unit122determines that the feature f1and the feature f2are the same. Thus, the feature comparison unit122determines that the same position on the same object90has passed each irradiation area (Step S124). Thus, the direction calculation unit130and the speed calculation unit132calculate the movement parameter of the object90at this time.

The surface shape of the object90is irregular. Thus, a feature of the shape can vary by object90or at different positions on the same object90. Accordingly, if the object90(or a position on the same object90) that has passed each irradiation area is different, the correlation between the data group #1 acquired when the object90has passed the first irradiation area22and the data group #2 acquired when the object90has passed the second irradiation area42is low. In contrast, when the same position on the same object90has passed each irradiation area, the correlation between the data group #1 and the data group #2 can be high. It is therefore possible to determine whether the same position on the same object90has passed the first irradiation area22and the second irradiation area42or not by using Δ2=1−c indicating a difference between the data group #1 obtained in the first irradiation area22and the data group #2 obtained in the second irradiation area42.

In the second example, a difference (1−c) between the data group #1 acquired in the first irradiation area22and the data group #2 acquired in the second irradiation area42is a difference in the feature of the object90. It is relatively easy to extract the data group #1 and the data group #2 respectively in the first irradiation area22and the second irradiation area42and calculate the correlation coefficient between them. Thus, the method according to the second example enables easy calculation of a difference in the feature of the object90.

Although the feature comparison unit122calculates the variation (the slope etc.) between adjacent coordinate data in each of the data group #1 and the data group #2 and calculates the correlation coefficient between the graphs of the variation, the present invention is not limited to this structure. A method of calculating the correlation coefficient between the data group #1 and the data group #2 is arbitrary. For example, the feature comparison unit122may calculate the correlation coefficient between a curved line formed by the data group #1 and a curved line formed by the data group #2. Further, the feature comparison unit122may calculate a difference between coordinate elements with the same second index k in a data group #1′ and the data group #2, where the data group #1′ is obtained by projection (coordinate transformation) of the data group #1 on the plane of X=Xs2in such a way that the center of mass of the data group #1 and the center of mass of the data group #2 coincide with each other. Then, the feature comparison unit122may calculate the sum of the square values of differences of each element (coordinate data) as a difference in the feature data, and determine whether this difference is less than the predetermined threshold ThA or not. Note that, in this case, the feature comparison unit122may perform coordinate transformation in such a way that the first data in the data group #1 and the first data in the data group #2 coincide with each other.

FIG. 10is a flowchart showing a method of determining whether the same position on the same object90has passed the first irradiation area22and the second irradiation area42in the third example.FIG. 11is a view illustrating the third example. In the third example, the feature data corresponds to the size of the object90in the first irradiation area22and the second irradiation area42. The first feature extraction unit110extracts a data group (X11,Y11,Z11), (X12,Y12,Z12), . . . , (X1k,Y1k,Z1k), . . . , and (X1m,Y1m,Z1m) in the first irradiation area22at time t1in the same manner as in the processing of S100(Step S130).

The feature comparison unit122calculates a coordinate Pmax (Xmax,Ymax,Zmax) of a point where the Y-coordinate is the greatest, and a coordinate Pmin (Xmin,Ymin,Zmin) of a point where the Y coordinate is the smallest among the extracted data (Step S132). As shown by the arrow Fg1inFIG. 11, the first sensor20scans laser light in the positive direction of the Y-axis at the position of X=Xs1at time t1. In this case, (X11,Y11,Z11) corresponds to a position Pmim1where the value of the Y-coordinate is the smallest among the positions where the object90and the first irradiation area22intersect. Further, (X1m,Y1m,Z1m) corresponds to a position Pmax1where the value of the Y-coordinate is the greatest among the positions where the object90and the first irradiation area22intersect.

The feature comparison unit122calculates a distance D1between Pmax and Pmin (Step S134). To be specific, the feature comparison unit122calculates the distance D1by calculating D1=√{(Xmax−Xmin)2+(Ymax−Ymin)2+(Zmax−Zmin)2}. This distance D1corresponds to the size of the object90in the first irradiation area22.

Then, the same processing as in S130to S134is performed for the second irradiation area42, and the distance D2is calculated (Step S136). To be specific, the second feature extraction unit120extracts a data group (X21,Y21,Z21), (X22,Y22,Z22), . . . , (X2k,Y2k,Z2k), . . . , and (X2n,Y2n,Z2n) in the second irradiation area42at time t2. The feature comparison unit122calculates a coordinate Pmax (Xmax,Ymax,Zmax) of a point where the Y-coordinate is the greatest, and a coordinate Pmin (Xmin,Ymin,Zmin) of a point where the Y coordinate is the smallest among the extracted data.

As shown by the arrow Fg2inFIG. 11, when the object90moves as indicated by the arrow A, the second sensor40scans laser light in the positive direction of the Y-axis at the position of X=Xs2at time t2. In this case, (X21,Y21,Z21) corresponds to a position Pmim2where the value of the Y-coordinate is the smallest among the positions where the object90and the second irradiation area42intersect. Further, (X2n,Y2n,Z2n) corresponds to a position Pmax2where the value of the Y-coordinate is the greatest among the positions where the object90and the second irradiation area42intersect. Further, the feature comparison unit122calculates the distance D2between Pmax and Pmin in the same manner as in the case of the first irradiation area22.

After that, the feature comparison unit122determines whether a difference between the distance D1in the first irradiation area22and the distance D2in the second irradiation area42is less than a predetermined threshold Th3or not (Step S137). Specifically, the feature comparison unit122determines whether |D1−D2|<Th3is satisfied or not. Th3corresponds to ThA shown inFIG. 4. When it is determined that |D1−D2|≥Th3is satisfied (NO in S137), the feature comparison unit122determines that the feature f1and the feature f2are not the same. Thus, the feature comparison unit122determines that the same position on the same object90has not passed each irradiation area (Step S138). On the other hand, when it is determined that |D1−D2|<Th3is satisfied (YES in S137), the feature comparison unit122determines that the feature f1and the feature f2are the same. Thus, the feature comparison unit122determines that the same position on the same object90has passed each irradiation area (Step S139). Thus, the direction calculation unit130and the speed calculation unit132calculate the movement parameter of the object90at this time.

The surface shape of the object90is irregular, and its width is not uniform. Thus, the size (width) of the object90can vary by object90or at different positions on the same object90. Accordingly, if the object90(or a position on the same object90) that has passed each irradiation area is different, the size (distance D1) of the object90in the first irradiation area22and the size (distance D2) of the object90in the second irradiation area42can be different. In contrast, when the same position on the same object90has passed each irradiation area, the distance D1and the distance D2can be substantially the same. It is therefore possible to determine whether the same position on the same object90has passed the first irradiation area22and the second irradiation area42or not by using a difference between the size in the first irradiation area22and the size in the second irradiation area42. Note that the distance may be normalized when calculating a difference in distance.

In the third example, a difference between the size of the object90in the first irradiation area22and the size of the object90in the second irradiation area42is a difference in the feature of the object90. It is relatively easy to acquire the coordinate data in each of the first irradiation area22and the second irradiation area42, calculate the sizes of the object90, and calculate a difference in the size of the object90in each of the first irradiation area22and the second irradiation area42. Thus, the method according to the third example enables easy calculation of a difference in the feature of the object90.

FIG. 12is a flowchart showing a method of determining whether the same position on the same object90has passed the first irradiation area22and the second irradiation area42in the fourth example.FIG. 13is a view illustrating the fourth example. In the fourth example, the feature data corresponds to the normal vector of the object90in the first irradiation area22and the second irradiation area42. Note that, in the fourth example, the first irradiation area22and the second irradiation area42are not planar, and they have a width in the X-axis direction.

The first feature extraction unit110extracts data at three measurement points A(Xa,Ya,Za), B(Xb,Yb,Zb), and C(Xc,Yc,Zc) in the first irradiation area22as shown by the arrow Fg1inFIG. 13(Step S140). The measurement points A. B and C may be points whose intervals (angular difference) in the irradiation direction of laser light are constant. Further, A may be a point near the center of the object90in the first irradiation area22.

Next, the feature comparison unit122calculates the cross product [AB,AC] (cross product vector) between the vector AB and the vector AC (Step S142). The cross product [AB,AC] corresponds to the normal vector Vn1of the object90in the first irradiation area22at time t1. When the X, Y, Z components of the cross product [AB,AC] are (a,b,c), the cross product [AB,AC] is calculated geometrically as below.
a=(Yb−Ya)*(Zc−Za)−(Yc−Ya)*(Zb−Za)
b=(Zb−Za)*(Xc−Xa)−(Zc−Za)*(Xb−Xa)
c=(Xb−Xa)*(Yc−Ya)−(Xc−Xa)*(Yb−Ya)

Then, the feature comparison unit122performs the processing of S140to S142for the second irradiation area42, and calculates the cross product [A′B′,A′C′] (cross product vector) (Step S144). Specifically, the second feature extraction unit120extracts data at three measurement points A′(Xa′,Ya′,Za′), B′(Xb′,Yb′,Zb′), and C′(Xc′,Yc′,Zc′) in the second irradiation area42as shown by the arrow Fg2inFIG. 13. The feature comparison unit122then calculates the cross product [A′B′,A′C′] between the vector A′B′ and the vector A′C′. The cross product [A′B′,A′C′] corresponds to the normal vector Vn2of the object90in the second irradiation area42at time t2. The measurement points A′, B′ and C′ may be points whose intervals (angular difference) in the irradiation direction of laser light are constant. Thus, the X-coordinate can be constant for each of the measurement points A′, B′ and C′. Further, A′ may be a point near the center of the object90in the second irradiation area42. The intervals of the measurement points A′, B′ and C′ in the irradiation direction may be the same as the intervals of the measurement points A, B and C in the irradiation direction in the first irradiation area22.

The feature comparison unit122calculates an angle θ between the normal vector Vn1(i.e., the cross product vector [AB,AC]) and the normal vector Vn2(i.e., the cross product vector [A′B′,A′C′]), and determines whether 1−cos θ<Th41is satisfied or not (Step S146). Note that Th41is a predetermined threshold, which corresponds to ThA shown inFIG. 4. Further, cos θ can be calculated by the inner product between the normal vector Vn1and the normal vector Vn2. When 1−cos θ≥Th41is satisfied (NO in S146), a difference in direction between the normal vector Vn1and the normal vector Vn2is large, and the feature comparison unit122determines that the feature f1and the feature f2are not the same. Thus, the feature comparison unit122determines that the same position on the same object90has not passed each irradiation area (Step S148).

On the other hand, when 1−cos θ<Th41is satisfied (YES in S146), a difference in direction between the normal vector Vn1and the normal vector Vn2is small. In this case, the feature comparison unit122determines whether a difference between the size |Vn1| of the normal vector Vn1and the size |Vn2| of the normal vector Vn2is less than a predetermined threshold Th42or not (Step S147). Specifically, the feature comparison unit122determines whether ∥Vn1|−|Vn2∥<Th42is satisfied or not. Th42corresponds to ThA shown inFIG. 4.

When it is determined that ∥Vn1|−|Vn2∥≥Th42is satisfied (NO in S147), the feature comparison unit122determines that the feature f1and the feature f2are not the same. Thus, the feature comparison unit122determines that the same position on the same object90has not passed each irradiation area (Step S148). Therefore, the feature comparison unit122determines that the same position on the same object90has not passed each irradiation area when a difference between the size |Vn1| of the normal vector in the first irradiation area22and the size |Vn2| of the normal vector in the second irradiation area42is equal to or more than the threshold Th42. On the other hand, when it is determined that ∥Vn1|−|Vn2∥<Th42is satisfied (YES in S147), the feature comparison unit122determines that the feature f1and the feature f2are the same. Thus, the feature comparison unit122determines that the same position on the same object90has passed each irradiation area (Step S149). Therefore, the feature comparison unit122determines that the same position on the same object90has passed each irradiation area when a difference between the size |Vn1| of the normal vector in the first irradiation area22and the size |Vn2| of the normal vector in the second irradiation area42is less than the threshold Th42. Thus, the direction calculation unit130and the speed calculation unit132calculate the movement parameter of the object90at this time.

The surface shape of the object90is irregular, and the orientation of the surface is also not uniform when the surface shape has a streamlined shape or the like. Thus, the normal vector can vary by object90or at different positions on the same object90. Accordingly, if the object90(or a position on the same object90) that has passed each irradiation area is different, the normal vector Vn1of the object90in the first irradiation area22and the normal vector Vn2of the object90in the second irradiation area42can be different. In contrast, when the same position on the same object90has passed each irradiation area, the normal vector Vn1and the normal vector Vn2can be substantially the same. It is therefore possible to determine whether the same position on the same object90has passed the first irradiation area22and the second irradiation area42or not by using a difference between the normal vectors (a difference in each of the direction and the size of the normal vectors) in the first irradiation area22and the second irradiation area42. Note that the size of the normal vector may be normalized when calculating a difference in the size of the normal vectors (S147).

In the fourth example, a difference between the normal vector of the object90in the first irradiation area22and the normal vector of the object90in the second irradiation area42is a difference in the feature of the object90. Since the normal vector is uniquely defined if a plane is determined, it appropriately represents the surface shape of the object90. Thus, the method according to the fourth example enables more appropriate calculation of a difference in the feature of the object90.

Modified Example

It should be noted that the present invention is not restricted to the above-described example embodiments, and various changes and modifications may be made without departing from the scope of the invention. For example, the order of process steps in the flowcharts shown inFIG. 4and so on may be altered as appropriate. Further, one or more process steps in the flowcharts shown inFIG. 4and so on may be omitted. For example, one of S22and S24inFIG. 4may be omitted. Further, although the object90passes the first irradiation area22and then passes the second irradiation area42in the above-described example embodiments, the present invention is not limited to this structure. The object90may pass the second irradiation area42and then pass the first irradiation area22. In this case, the feature storage unit112may store the feature data extracted by the second feature extraction unit120. The feature comparison unit122may then compare the feature data extracted by the first feature extraction unit110with the feature data stored in the feature storage unit112.

Further, although the object detection device100according to the first example embodiment includes the first feature extraction unit110and the second feature extraction unit120in the above description, the present invention is not limited to this structure. The first feature extraction unit110and the second feature extraction unit120may be implemented by one element. Specifically, one feature extraction unit may extract a feature of the object90in the first irradiation area22and a feature of the object90in the second irradiation area42.

Further, although the first sensor20and the second sensor40are three-dimensional sensors in the above-described example embodiments, the present invention is not limited to this structure. The first sensor20and the second sensor40may be two-dimensional sensors. However, if the first sensor20and the second sensor40are two-dimensional sensors, it is necessary to perform complicated image processing such as image recognition in order to detect the shape of the object90. Further, use of a three-dimensional sensor enables accurate detection of the three-dimensional shape of the object90compared with use of a two-dimensional sensor. Thus, use of a three-dimensional sensor enables easy and accurate detection of the shape of the object90. Furthermore, use of a three-dimensional sensor enables accurate detection of a position where the object90has passed the irradiation area compared with use of a two-dimensional sensor. Thus, use of a three-dimensional sensor enables accurate calculation of the movement parameter of the object90.

Further, although the object detection system10includes two sensors in the above-described example embodiments, the present invention is not limited to this structure. The number of sensors may be three. Then, the acceleration of the object90may be calculated by using the position and time where object90passes three irradiation areas formed by the three sensors. For example, a third irradiation area may be provided between the first irradiation area22and the second irradiation area42, and the acceleration can be calculated from a difference between the speed between the first irradiation area22and the third irradiation area and the speed between the third irradiation area and the second irradiation area42. Thus, the object detection device100may calculate the acceleration of the object90as the movement parameter.

Further, although the first sensor20forms the first irradiation area22and the second sensor40forms the second irradiation area42in the above-described example embodiments, the present invention is not limited to this structure. One sensor may form both of the first irradiation area22and the second irradiation area42. In this case, the first irradiation area22and the second irradiation area42may be formed by using a scan mirror such as a polygon mirror for laser light from a sensor. Further, one sensor may form three or more irradiation areas.

Further, in the above-described first example embodiment, rotation of the object90between the first irradiation area22and the second irradiation area42is not taken into consideration. However, the feature comparison unit122may compare coordinate data groups extracted respectively in the first irradiation area22and the second irradiation area42in consideration of the case where the object90rotates between the first irradiation area22and the second irradiation area42.

FIG. 14is a view illustrating coordinate transformation of a data group in consideration of the rotation of the object90.FIG. 14shows the case where the object90has rotated clockwise (in the direction indicated by the arrow A2) about the roll axis (the axis parallel to the X-axis). In this case, the feature comparison unit122may determine whether the similarity (correlation coefficient c′) between the shape of a curved line formed by the data group #1 and the shape of a curved line formed by the data group #2 is equal to or more than a predetermined threshold when the data group #1 or the data group #2 is rotated. The similarity (correlation coefficient c′) of the shapes may be calculated by the method described earlier in the second example, for example. For the rotation of a data group, the feature comparison unit122calculates a slope a1of the straight line connecting the first coordinate data (X1,Y11,Z11) and the last coordinate data (X1m,Y1m,Z1m) in the data group #1. Likewise, the feature comparison unit122calculates a slope a2of the straight line connecting the first coordinate data (X21,Y21,Z21) and the last coordinate data (X2n,Y2n,Z2n) in the data group #2. Then, the feature comparison unit122may determine the degree of rotation when rotating the data group #1 or the data group #2 from a difference between the slope a1and the slope a2.

Note that, when the object90rotates, there is a case where a position that has been detected in the first irradiation area22is not detected in the second irradiation area42, and a position that has not been detected in the first irradiation area22is detected in the second irradiation area42. For example, inFIG. 14, there is a case where a position corresponding to the left part (on the negative side of the Y-axis) of the data group #1 (for example, the left part of the bottom surface of the object90) is not irradiated with laser light in the second irradiation area42and does not appear in the data group #2. In contrast, there is a case where a position corresponding to the right part (on the positive side of the Y-axis) of the data group #2 (for example, the right side surface of the object90) is not irradiated with laser light in the first irradiation area22and does not appear in the data group #1. Thus, the feature comparison unit122may determine whether the correlation coefficient c′ between at least part of the shape of a curved line formed by the data group #1 and at least part of the shape of a curved line formed by the data group #2 is equal to or more than a predetermined threshold. The feature comparison unit122therefore does not need to compare the whole of the data group. The same applies to the case with no consideration of the rotation of the object90.

Further, in the above-described example embodiments, it is determined to be the same position on the same object when a difference in feature is less than a predetermined threshold (S20inFIG. 4). On the other hand, the case where, despite that a certain object has passed the first irradiation area22and the second irradiation area42, a difference in feature does not fall below a threshold due to some reasons even if the feature data extracted in the second irradiation area42is compared with all the feature data stored in the feature storage unit112may be taken into consideration. In this case, the movement parameter of the object90may be calculated from the passing position and time where the feature data whose difference from the feature data extracted in the second irradiation area42is the smallest among the feature data stored in the feature storage unit112is extracted.

Further, the case where there are a plurality of feature data whose difference from the feature data extracted in the second irradiation area42is less than a threshold among the feature data stored in the feature storage unit112may be taken into consideration. In this case, the movement parameter of the object90may be calculated from the passing position and time where the feature data whose difference from the feature data extracted in the second irradiation area42is the smallest among the plurality of feature data is extracted.

Further, the case where objects in similar shapes have entered in a group may be taken into consideration. In this case also, there is a possibility that there are a plurality of feature data whose difference from the feature data extracted in the second irradiation area42is less than a threshold among the feature data stored in the feature storage unit112. In this case, the movement parameter of the object90may be calculated from the passing position and time where the feature data with the minimum distance between positions where each feature data is extracted in each irradiation area is extracted.

An object detection device comprising:

a feature extraction means for extracting features of an object in a plurality of irradiation areas of irradiation light from at least one sensor when the object passes each of the plurality of irradiation areas, the at least one sensor being configured to detect a feature of a part of a surface of the object by applying irradiation light; and

a calculation means for calculating a movement parameter related to movement of the object between the plurality of irradiation areas when a difference between the features respectively extracted in the plurality of irradiation areas falls below a predetermined first threshold.

The object detection device according to Supplementary Note 1, further comprising:

a feature comparison means for comparing the features respectively extracted in the plurality of irradiation areas, and determining that the same position on the same object has passed each of the irradiation areas when a difference between the extracted features falls below the predetermined first threshold.

The object detection device according to Supplementary Note 1 or 2, wherein, when a difference between a first feature extracted in a first irradiation area among the plurality of irradiation areas and a second feature extracted in a second irradiation area among the plurality of irradiation areas falls below the first threshold, the calculation means calculates a moving direction of the object between the first irradiation area and the second irradiation area based on a position where the object has passed the first irradiation area when the first feature is extracted and a position where the object has passed the second irradiation area when the second feature is extracted.

The object detection device according to any one of Supplementary Notes 1 to 3, wherein, when a difference between a first feature extracted in a first irradiation area among the plurality of irradiation areas and a second feature extracted in a second irradiation area among the plurality of irradiation areas falls below the first threshold, the calculation means calculates a moving speed of the object between the first irradiation area and the second irradiation area based on a time when and a position where the object has passed the first irradiation area when the first feature is extracted and a time when and a position where the object has passed the second irradiation area when the second feature is extracted.

The object detection device according to any one of Supplementary Notes 1 to 4, wherein

the sensor is a three-dimensional sensor, and

the extracted feature relates to a shape of the object.

The object detection device according to Supplementary Note 5, wherein

the extracted feature corresponds to the number of data indicating each position of the object in the irradiation areas of the sensor, and

the calculation means calculates the movement parameter when a difference in the number of data in each of the plurality of irradiation areas falls below the first threshold.

The object detection device according to Supplementary Note 5, wherein

the extracted feature corresponds to coordinate data indicating each position of the object in the irradiation areas of the sensor, and

the calculation means calculates the movement parameter when a difference in the coordinate data in each of the plurality of irradiation areas falls below the first threshold.

The object detection device according to Supplementary Note 5, wherein

the extracted feature corresponds to a size of the object in the irradiation areas of the sensor, and

the calculation means calculates the movement parameter when a difference in the size of the object in each of the plurality of irradiation areas falls below the first threshold.

The object detection device according to Supplementary Note 5, wherein

the extracted feature corresponds to a normal vector of the object in the irradiation areas of the sensor, and

the calculation means calculates the movement parameter when a difference in the normal vector of the object in each of the plurality of irradiation areas falls below the first threshold.

An object detection system comprising:

at least one sensor configured to detect a feature of a part of a surface of an object by applying irradiation light; and

the object detection device according to any one of Supplementary Notes 1 to 9.

An object detection method comprising:

extracting features of an object in a plurality of irradiation areas of irradiation light from at least one sensor when the object passes each of the plurality of irradiation areas, the at least one sensor being configured to detect a feature of a part of a surface of the object by applying irradiation light; and

calculating a movement parameter related to movement of the object between the plurality of irradiation areas when a difference between the features respectively extracted in the plurality of irradiation areas falls below a predetermined first threshold.

The object detection method according to Supplementary Note 11, comprising:

comparing the features respectively extracted in the plurality of irradiation areas, and determining that the same position on the same object has passed each of the irradiation areas when a difference between the extracted features falls below the predetermined first threshold.

The object detection method according to Supplementary Note 11 or 12, wherein when a difference between a first feature extracted in a first irradiation area among the plurality of irradiation areas and a second feature extracted in a second irradiation area among the plurality of irradiation areas falls below the predetermined first threshold, a moving direction of the object between the first irradiation area and the second irradiation area is calculated based on a position where the object has passed the first irradiation area when the first feature is extracted and a position where the object has passed the second irradiation area when the second feature is extracted.

The object detection method according to any one of Supplementary Notes 11 to 13, wherein, when a difference between a first feature extracted in a first irradiation area among the plurality of irradiation areas and a second feature extracted in a second irradiation area among the plurality of irradiation areas falls below the predetermined first threshold, a moving speed of the object between the first irradiation area and the second irradiation area is calculated based on a time when and a position where the object has passed the first irradiation area when the first feature is extracted and a time when and a position where the object has passed the second irradiation area when the second feature is extracted.

The object detection method according to any one of Supplementary Notes 11 to 14, wherein

the sensor is a three-dimensional sensor, and

the extracted feature relates to a shape of the object.

The object detection method according to Supplementary Note 15, wherein

the extracted feature corresponds to the number of data indicating each position of the object in the irradiation areas of the sensor, and

the movement parameter is calculated when a difference in the number of data in each of the plurality of irradiation areas falls below the first threshold.

The object detection method according to Supplementary Note 15, wherein

the extracted feature corresponds to coordinate data indicating each position of the object in the irradiation areas of the sensor, and

the movement parameter is calculated when a difference in the coordinate data in each of the plurality of irradiation areas falls below the first threshold.

The object detection method according to Supplementary Note 15, wherein

the extracted feature corresponds to a size of the object in the irradiation areas of the sensor, and

the movement parameter is calculated when a difference in the size of the object in each of the plurality of irradiation areas falls below the first threshold.

The object detection method according to Supplementary Note 15, wherein

the extracted feature corresponds to a normal vector of the object in the irradiation areas of the sensor, and

the movement parameter is calculated when a difference in the normal vector of the object in each of the plurality of irradiation areas falls below the first threshold.

A non-transitory computer-readable medium storing a program causing a computer to perform:

a step of extracting features of an object in a plurality of irradiation areas of irradiation light from at least one sensor when the object passes each of the plurality of irradiation areas, the at least one sensor being configured to detect a feature of a part of a surface of the object by applying irradiation light; and

a step of calculating a movement parameter related to movement of the object between the plurality of irradiation areas when a difference between the features respectively extracted in the plurality of irradiation areas falls below a predetermined first threshold.

REFERENCE SIGNS LIST