JP2006234493A - Object recognizing device, and object recognition method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an object recognition method, capable of recognizing stably a shape of an object with a reduced computation amount, even if the data of a detected object are mixed, and to provide an object recognizing device that uses the method. <P>SOLUTION: This object recognizing device, for recognizing the object existing in the periphery of a moving body, is provided with an object detecting means 1 for detecting surface shape information of the object, and a shape-recognizing means 2 for recognizing the outline shape of the object, and the shape recognizing means 2 computes the degree of consistency of a specimen group, with respect to a shape model defined based on a specimen extracted optionally from the specimen group, constituting the surface-shape information to recognize the outline shape. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an object recognition apparatus and an object recognition method for recognizing a contour shape of an object existing around a moving body.

  As such an apparatus, there exists an obstacle detection apparatus as described in the patent document 1 shown below. This device detects an obstacle existing around a vehicle (moving body) and issues an alarm. The present invention is made in contrast to the conventional apparatus configured to issue an alarm only when the distance between the vehicle and the obstacle is simply measured and shorter than a predetermined distance. That is, the warning based on the distance alone is made in view of the problem that it is difficult for the driver to determine which object around the vehicle is an obstacle. According to this, a plurality of obstacle detection sensors are mounted on the vehicle, and the distance to the obstacle is calculated. Whether the shape of the obstacle is a straight line (flat plate shape) or a circular shape (convex surface shape) is estimated from the obtained calculation results and displayed. By comprising in this way, it alert | reports using the distance with an obstruction, and the shape of an obstruction.

JP 2003-194938 A (page 2-3, FIG. 1-7)

  The above known technique is useful for the user in that it also estimates the shape of the obstacle. However, in actual measurement, detection data that detects objects other than the detection target object (obstacle) is often mixed. And since detection data other than a detection target object act as a noise component, it is thought that it becomes a cause of misrecognition at the time of shape estimation of a detection target object. That is, it cannot be said that the stability in detecting an object to be detected such as an obstacle is sufficient. In general, if such a noise removal function is provided, the amount of calculation increases, and accordingly, the processing time increases and the scale of the apparatus increases.

  The present invention has been made in view of the above problems, and uses an object recognition method capable of stably recognizing the shape of an object with a small amount of calculation even when data to be detected is mixed, and this method. The object is to provide an object recognition device.

In order to achieve this object, the feature configuration of the object recognition apparatus according to the present invention is as follows:
Recognizing an object existing around a moving body, comprising: object detecting means for detecting surface shape information of the object; and shape recognizing means for recognizing the contour shape of the object,
The shape recognizing means is such that the contour shape is recognized by calculating the degree of coincidence of the sample group with respect to a shape model determined based on a sample arbitrarily extracted from the sample group constituting the surface shape information.

  According to this feature configuration, the object detection unit detects the surface shape information of the object, and the shape recognition unit recognizes the contour shape of the object based on the surface shape information. Here, the surface shape information is information indicating the shape of the surface of the object viewed from the moving body. A reflective sensor using radio waves or ultrasonic waves may be used, or an image sensor or camera (regardless of moving images or still images) that obtains image data using visible light, infrared light, or the like. Also good.

  The shape recognition means recognizes the contour shape from the sample group obtained by the various object detection means. Here, the sample group refers to a collection of individual data constituting the surface shape information. The individual data is information corresponding to each location obtained by receiving a signal reflected at each location of the obstacle when, for example, a reflective sensor is used. When image data is used, data obtained by various image processing such as edge extraction and three-dimensional conversion can be used. In this way, data representing the surface shape of an object is treated as a sample regardless of the type of shape recognition means, and this collection of samples is referred to as a sample group.

  The shape recognition means arbitrarily (randomly) extracts some samples from the sample group, and determines a shape model based on the extracted samples. When determining this shape model, it may be calculated geometrically from the extracted sample, or a method in which a plurality of templates are prepared in advance and applied to the optimum one may be used. Then, the degree of coincidence of how much the entire sample group matches the shape model is calculated. Based on the calculation result, it is determined whether or not the embodied shape model is suitable for the sample group.

  That is, when a sample with noise characteristics is included in an arbitrarily extracted sample, the degree of coincidence between the determined shape model and the sample group is low. Therefore, it can be determined that this shape model does not match the sample group. When the shape model is determined without including a noise sample, the degree of coincidence increases. Therefore, it can be determined that the shape model matches the sample group. In this way, the contour shape of the target object can be recognized by removing a noisy sample with a small amount of calculation.

  The shape recognition means defines a shape model from arbitrarily extracted samples, which is a much smaller number of samples than the sample group. Therefore, the amount of calculation required when extracting a sample or determining a shape model is small. Therefore, the calculation time is short and the apparatus is not scaled up. In addition, the degree of coincidence of the sample group with respect to the shape model can be calculated geometrically using the coordinates in the space of each sample. Therefore, the degree of coincidence can be calculated with a small amount of calculation. Furthermore, since these calculation amounts are small, an increase in the total calculation amount can be suppressed even if different shape models are repeatedly determined and the degree of coincidence is calculated. As a result, the contour shape can be recognized with high accuracy.

  As described above, according to this feature configuration, it is possible to perform object recognition that can stably recognize the shape of an object with a small amount of calculation even when non-detection target data (noise) is mixed.

  Here, it is preferable that the object detection means detects the surface shape information based on a distance between the surface of the object and the moving body.

  When the contour shape of the target object is a shape related to the perspective from the moving body, for example, a so-called depth, it is preferable to detect the surface shape information based on the distance between the object and the moving body. In such a case, when a noise-like sample is not included, the surface shape information detected based on the distance becomes a sample group representing a contour shape to be substantially recognized. Even if a noisy sample is included in this, if the noisy sample can be removed, the remaining sample group shows a contour shape to be substantially recognized. As described above, according to the present invention, a noisy sample can be satisfactorily removed by calculating the degree of coincidence between a shape model and a sample group. Therefore, when the object detection means detects the surface shape state based on the distance between the surface of the object and the moving object, stable and accurate object recognition can be performed.

  Further, it is preferable that the surface shape information represents an outer contour of the object.

  An object to be recognized is not limited to a flat object such as a wall, and may have a step. The steps are the bumper part of the vehicle, the front and rear window parts, the stairs and the like. The outer contour is a surface shape showing the outer side, that is, the outer shape, including such a step of the object. If only the closest distance between the object and the object detection means, that is, only the part where the object protrudes toward the object detection means can be detected, only the bumper part and the lowermost step of the stairs are detected. However, the portion where the moving body protrudes from the object does not necessarily coincide with the portion where the object protrudes toward the moving body. A user (supervisor) of a mobile object desires to operate or supervise a part of the mobile object and a part of the object so as not to approach too much. Therefore, depending on the case, if the object is a vehicle, the contour shape to be recognized is not limited to the bumper part but may be a window part. The same applies to stairs and the like. Therefore, it is preferable that the surface shape information is not intended only for the portion where the target object protrudes most toward the moving body, but for various places of the target object. It is preferable to recognize the contour shape for various places according to the application by obtaining the surface shape information representing the outer contour of the target object.

  Furthermore, the object recognition apparatus according to the present invention includes a relative arrangement calculation unit that calculates a relative arrangement relationship between the moving body and the object based on the detection and recognition results of the object detection unit and the shape recognition unit. It is preferable to provide.

  As described above, according to the present invention, the contour shape of the target object can be stably obtained. In obtaining the surface shape information, the distance and positional relationship between the object detection means and the object can be acquired as information. The location of the object detection means on the moving body is known, and the outer shape of the moving body is also known. Therefore, it is possible to calculate the relative arrangement relationship between each location of the moving object and each location of the object using the recognized contour shape or the like. From the relative arrangement relationship, it is possible to easily know which part of the moving body is close to which part of the object.

  Furthermore, it is preferable that a moving state detecting unit that detects a moving state of the moving body is provided, and the relative arrangement calculating unit calculates the arrangement relation based on the moving state.

  When the moving state detecting unit detects the moving state of the moving body, the position of the moving body in the near future can be estimated. Therefore, based on the detection result of the moving state detecting means, it is possible to calculate the future arrangement relationship between the object and the moving body, not just the present. Since the degree of approach of each part of the object and the moving body is already known from the arrangement relationship between them, the change in the degree of approach can be calculated by the movement of the moving body. As a result, it is possible to predict in advance the degree of approach of each part of the moving body and the object.

  It is preferable that the object detection means of the object recognition apparatus according to the present invention detects the surface shape information of the object as the moving body moves.

  If the surface shape information of the object is detected along with the movement of the moving body, the object is detected according to the moving direction of the moving body, and efficient detection becomes possible. Further, for example, it may be configured by a fixed sensor (for example, a single beam sensor) in which the object detection means is directed in one direction. That is, even if the object detecting means can detect only one direction in a fixed manner, a wide range can be scanned by moving the moving body.

  Further, the object detection means includes scanning means for scanning a wide-angle area with respect to the object regardless of movement of the moving body, and detects the surface shape information of the object based on the obtained scanning information. It is preferable.

  According to this configuration, an object can be detected by scanning a wide range even when the moving body is in a stopped state. As a result, it is possible to consider surrounding conditions such as the presence or absence of an object when a stationary moving body starts moving, for example.

Further, the object recognition method for recognizing an object existing around a moving body according to the present invention includes an object detection step for detecting surface shape information of the object, a shape recognition step for recognizing the contour shape of the object, This shape recognition process comprises
A sample extraction step of extracting an arbitrary sample from a sample group constituting the surface shape information;
A shape model setting step for determining a shape model based on the extracted sample;
A degree of coincidence calculation step of calculating the degree of coincidence of the sample group with respect to the shape model;
The point is that the contour shape is recognized.

  As with the object recognition apparatus according to the present invention, it is possible to provide an object recognition method capable of stably recognizing the shape of an object with a small amount of calculation even if data to be detected is not mixed. Naturally, the object recognition method can include the functions and effects described for the object recognition apparatus, and all the additional features and effects.

[First embodiment]
DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, a preferred embodiment of the invention will be described with reference to the drawings, taking as an example a case where a vehicle recognizes another vehicle. As shown in FIG. 1, a distance sensor 1 (object detection means) directed to the side is mounted on a vehicle 10 as a moving body. The distance sensor 1 is, for example, a point sensor, that is, a single beam sensor or a sonar using ultrasonic waves. The vehicle 10 measures the distance to the parked vehicle 20 with the distance sensor 1 when passing by another vehicle 20 parked and stopped (hereinafter referred to as a parked vehicle) in the X direction in the figure. The parked vehicle 20 corresponds to the object of the present invention. In FIG. 1, for the sake of simplicity, the distance sensor 1 is provided only on the left side of the vehicle 10, but it may be provided on both sides as a matter of course.

  The distance sensor 1 measures the distance from the parked vehicle 20 according to the movement of the vehicle 10. The surface shape information of the parked vehicle 20 obtained in this way is discrete data corresponding to the moving distance of the vehicle 10. Note that “according to the moving distance” of the vehicle 10 also includes the meaning of “according to a predetermined time interval”. For example, when the vehicle 10 moves at a constant speed, if it is measured according to a predetermined time interval, it is measured according to the moving distance. The moving speed, moving distance, and moving time of the moving body 10 are determined linearly. Therefore, any method may be used as long as the surface shape information can be obtained almost uniformly as a result. Thus, the vehicle 10 acquires the surface shape information of the object (object detection step).

  The distance sensor 1 may include an accompanying sensor such as a timer that measures the movement time, an encoder that measures the movement distance, and a rotation sensor that measures the movement speed. In addition, these sensors may be provided separately to obtain information.

  FIG. 2 is a schematic block diagram of the object recognition apparatus according to the first embodiment of the present invention. In FIG. 2, the shape recognition unit 2 (shape recognition means) is configured by an electronic circuit such as a microcomputer 2A. Each processing unit in the shape recognition unit 2 does not necessarily indicate a physically different electronic circuit, but indicates a processing unit as a function. For example, the case where different functions are obtained by executing different programs by the same CPU is also included.

  As shown in FIG. 2, the surface shape information measured by the distance sensor 1 is input to the shape recognition unit 2. The input surface shape information is mapped to a two-dimensional plane with the X and Y directions shown in FIG. 1 as axes, and is stored in the specimen storage unit 2a. The sample storage unit 2a is composed of a memory. In the present embodiment, a form incorporated in the microcomputer 2A is shown. Of course, a so-called external form may be used by using a memory separate from the microcomputer 2A. Also, other storage media such as a register and a hard disk may be used regardless of whether they are internal or external.

  Hereinafter, the details of the object detection step and the shape recognition step for recognizing the contour shape of the object will be described.

  First, the object detection process will be described. As shown in FIG. 3, the surface shape information S on the parked vehicle 20 is measured by the distance sensor 1. The surface shape information is measurement data obtained discretely in a form along the outer shape of the bumper portion of the parked vehicle 20 in the present embodiment. Here, a group of these discretely obtained data is referred to as a sample group S (Large S). The sample group S is a data set that is a recognition target of the contour shape. Moreover, the data of each point which comprises a data set is called the sample s (small es).

  The sample group S is mapped on XY two-dimensional orthogonal coordinates in the sample storage unit 2a as shown in FIG. For ease of explanation, not all samples s are shown in the figure. Among the samples shown in FIG. 4, a sample s indicated by a black dot is referred to as an inlier, and a sample s indicated by a white dot is referred to as an outlier. In the figure, samples s1, s13, etc. are inliers, and samples s2, s7, s10 are outliers. Although details will be described later, the inlier is a sample constituting the contour shape of the parked vehicle 20. The outlier is a so-called noise sample that deviates from the contour shape of the parked vehicle 20.

  Hereinafter, the procedure (shape recognition process) for recognizing the contour shape of the parked vehicle 20 from the obtained sample group S will be described using the flowchart shown in FIG.

  The sample extraction unit 2b extracts several arbitrary samples si (i is a sample number) from the sample group S (samples s1 to s13) (sample extraction process, FIG. 7 # 1). Which sample s is extracted is determined at random. Preferably random numbers are used. For example, a random number generator (not shown) is provided in the microcomputer 2A, and a sample si having the generated random number as a sample number is extracted. Alternatively, the sample number may be determined by a random number generation program executed by the microcomputer 2A.

  Further, the number of samples to be extracted varies depending on the target shape to be recognized. For example, it is 2 points when recognizing a straight line, and 5 points if it is a quadratic curve. In the present embodiment, the bumper shape of the parked vehicle 20 is approximated to a quadratic curve, and five points are extracted. The collection of individual data and the sample s extracted in this way is a subset as a concept corresponding to the data set.

  Subsequently, the shape model setting unit 2c determines a shape model based on this subset (a collection of randomly extracted samples s) (shape model setting step, FIG. 7 # 2).

  FIG. 5 is an explanatory diagram for calculating the degree of coincidence between the shape model L (first shape model L1) and the sample group S determined from the sample si arbitrarily extracted from the sample group S shown in the scatter diagram of FIG. . This first shape model L1 is determined based on the five samples s1, s1, s5, s8, s11, and s13. The shape model L can be easily obtained by linear calculation with a light calculation load. Alternatively, several types of template shapes may be prepared in advance, and an optimum one may be selected from these template shapes.

  Further, as shown in FIG. 5, points separated by a predetermined distance in both directions orthogonal to the tangent line of the shape model L are connected along the shape model L to define dotted lines B1 and B2. A portion sandwiched between the dotted lines B1 and B2 is an effective range W.

  Next, the coincidence calculation unit 2d calculates the coincidence between the determined shape model L and the sample group S. Specifically, the degree of coincidence is calculated depending on how much each sample si constituting the sample group S is included in the effective range W determined as described above (the degree of coincidence calculation step, FIG. 7 # 3). ).

  The effective range W for the first curve model L1 shown in FIG. 5 includes all the samples s except for the outliers of the samples s2, s7, and s10. Therefore, the degree of coincidence of the first shape model L1 with the sample group S is 77% (10/13). That is, it can be said that the first shape model L1 obtained an agreement (consensus) with a high support rate (77%) by each sample s constituting the sample group S.

  Next, the main arithmetic unit 2e determines whether or not the degree of coincidence exceeds a predetermined threshold (determination step, FIG. 7 # 4). When the threshold value is exceeded, the shape model (first shape model L1) determined from the extracted subset is recognized as a recognition result (authorization process, FIG. 7 # 5). That is, the first shape model L1 is the contour shape. For example, when the threshold is set to 75%, the first shape model L1 is set as the contour shape. When the threshold value is not exceeded, the process returns to the process # 1 in the flowchart of FIG. 7, and another sample s is extracted again to form a new subset, and the same process is performed. If the threshold value is not exceeded even if the processes # 1 to # 4 are repeated a plurality of times, it is determined that there is no target object (such as the parked vehicle 20). This number may be defined in advance.

  In the present embodiment, the total number of samples s constituting the sample group S is 13 in order to facilitate understanding. The threshold value (75%) is also a value for facilitating the description of the present embodiment. Accordingly, neither the number of samples nor the determination threshold value for the degree of coincidence is a value that limits the present invention. For example, if the number of samples is large, the number of inliers relative to the outlier is relatively large, and a threshold value higher than the above example can be set.

  In the shape model L (second shape model L2) shown in FIG. 6, samples s2, s4, s7, s10, and s13 are extracted as subsets. As described above, the samples s2, s7, and s10 are so-called noise samples that deviate from the contour shape of the parked vehicle 20. Therefore, when viewed from the contour shape of the parked vehicle 20, it is a specimen that should be an outlier. Therefore, as shown in FIG. 6, there are many samples s that are out of the effective range W for the second shape model L2 determined based on the subset including these samples s2, s7, and s10. When the degree of coincidence is calculated by the same method as that for the first shape model L1, the degree of coincidence is 38% (5/13). That is, the second shape model L2 does not have an agreement (consensus) with a high support rate by the samples s constituting the sample group S.

  When the two shape models L1 and L2 are extracted, the contour shape that is the recognition result is the first shape model L1. In determining the first shape model L1, the samples s2, s7, and s10, which are noise samples s, are unused. These noise samples are treated as outliers and removed. That is, with the small amount of calculation as described above, even if non-detection target data (outlier) is mixed, it can be removed and the shape of the object can be recognized stably.

  Various methods for calculating the contour shape from the specimen S without using such a method have been conventionally proposed. One of them is the least square method. In the least square method, the shape is calculated by using all the samples s in the data set, with each sample s having the same weight. As a result, a contour shape different from the original shape is recognized under the influence of the above-described outlier (sample s2 and the like). It is also possible to reconfirm the degree of coincidence with the entire data set after recognizing the contour shape. However, the calculation load of the least square method itself is relatively heavy, and if the shape recognition by the repeated least square method is repeatedly performed based on the result of this reconfirmation, the calculation load is further increased.

As another method, there is a method of using a Hough transform suitable for straight line recognition. As is well known, the Hough transform utilizes the property that a straight line existing on an orthogonal coordinate (for example, an XY plane) intersects at one point on a polar coordinate (ρ-θ space). The conversion formula is
ρ = X · cosθ + Y · sinθ
It is. As can be understood from the above formula, if the range of ρ and θ is expanded or a fine resolution is obtained in the polar coordinate space, the amount of calculation increases accordingly. That is, the memory as the primary storage means is required to have a large capacity and the number of calculations is increased.

  Compared to these conventional calculations, the method of “calculating the degree of coincidence of the sample group S with the shape model L determined based on the sample s arbitrarily extracted from the sample group S constituting the surface shape information” of the present invention is The amount is small and the required memory capacity is also small.

[Second Embodiment]
In the above description, the degree of coincidence between the shape model L and the sample group S is examined. If the degree of coincidence exceeds a predetermined threshold value, the shape model L is used as a recognition result. That is, the shape model L that first exceeds the threshold value becomes the recognition result as it is. However, the shape model L may not be immediately recognized as a recognition result when the threshold value is simply exceeded, and a plurality of shape models L may be evaluated. A specific procedure will be described below.

  FIG. 8 is a flowchart for explaining a second method for recognizing the contour shape from the sample group shown in the scatter diagram of FIG. In this second method, the shape model L is determined by extracting a subset a plurality of times, and the shape model L having the highest degree of coincidence among them is used as the recognition result. Hereinafter, the second method will be described with reference to FIG. However, the processing # 1 to # 4 is the same as the flowchart shown in FIG.

  In the second method, since the subset is extracted repeatedly a plurality of times, the number of repetitions is temporarily stored. At the start of the shape recognition process, first, the number of repetitions temporarily stored is cleared (initialization process, FIG. 8 # 0). Hereinafter, similarly to the first embodiment, a sample s is randomly extracted from the sample group S in the sample extraction step (# 1) to create a subset. Next, in the shape model setting step (# 2), the shape model L is determined based on this subset. Then, the coincidence degree between the shape model L and the sample group S is calculated in the coincidence degree calculating step (# 3), and whether or not the coincidence degree exceeds a predetermined threshold value in the determining step (# 4). Determine whether.

  As a result of the determination, if the threshold value is exceeded, the previously determined shape model L and the degree of coincidence with the shape model L are stored in a temporary storage unit (not shown) (storage step, # 41). Since the evaluation for one shape model L is completed, the number of repetitions is incremented (counting step, # 42). If the result of determination is that the threshold value has not been exceeded, the storage step (# 41) is skipped and the number of repetitions is incremented (# 42).

  Next, it is determined whether or not the number of repetitions has reached a predetermined number (may be exceeded or not) (leave determination step, # 43). If the predetermined number of times has not been reached, the process returns to the sample extraction process (# 1), and the following determination process (# 4) is performed to evaluate a new shape model L. If the predetermined number of times has been reached, the shape model L having the highest degree of coincidence is selected from the stored shape models L, and this is used as the contour shape as the recognition result (certification process, # 51). ). Here, in the determination step (# 4), when there is no thing exceeding the coincidence threshold, it is determined that there is no corresponding in the certification step (# 51).

  As described above, in both the first method shown in FIG. 7 and the second method shown in FIG. 8, the shape model L determined based on the subset is recognized as the contour shape. In general, it is considered that the shape model L determined based on a small number of samples does not reproduce an accurate contour shape. However, in the present invention, the degree of coincidence between the shape model L and all samples in the sample group S is evaluated. Therefore, it can be considered that the shape model L can reproduce (recognize) the contour shape almost accurately. Thus, the ability of the shape model L determined from the small number of samples constituting the subset to reproduce the contour shape greatly contributes to a reduction in the amount of calculation.

  As described above, acknowledging the shape model L as a contour shape as a recognition result as it is greatly contributes to a reduction in the amount of calculation. However, the present invention is not limited to this. In the case where the computing means has a sufficient capacity such as the microcomputer 2A, the contour shape may be recalculated.

  For example, if the shape model L whose coincidence exceeds a threshold value is used as a reference, the samples s constituting the sample group S can be defined as inliers and outliers. In the certification process, this inlier and outlier are certified. Then, the shape is recalculated using the least square method or the like for all samples s certified as inliers (recalculation step). As described above, the least square method may not be able to reproduce the shape correctly under the influence of the noisy sample s. However, in this recalculation process, since the noisy sample s is removed as an outlier, an accurate contour shape can be reproduced.

[Third embodiment]
FIG. 9 is a schematic block diagram of an object recognition apparatus according to the third embodiment of the present invention. As shown in FIG. 9, the microcomputer 2A includes a relative arrangement calculation unit 3 (relative arrangement calculation means). As described above, the shape recognition unit 2 recognizes the contour shape of the parked vehicle 20 as viewed from the vehicle 10, that is, the bumper shape. In this recognition, since the surface shape information of the parked vehicle 20 is acquired using the distance sensor 1, distance information between the vehicle 10 and the parked vehicle 20 can also be obtained at the same time. The relative arrangement calculation unit 3 calculates the relative arrangement of the vehicle 10 and the parked vehicle 20 using the distance information and the contour shape.

  Here, the relative arrangement is a relative position between each part of the vehicle 10 and each part of the parked vehicle 20. The external shape of the vehicle 10 is known because it is its own shape. The contour shape of the parked vehicle 20 viewed from the vehicle 10 can be recognized as described above. As a result, the relative arrangement calculation unit 3 calculates the relative arrangement of the vehicle 10 and the parked vehicle 20 as shown in FIG. In FIG. 10, for easy understanding, the entire parked vehicle 20 is indicated by a dotted line. However, in actuality, the relative arrangement between the recognized contour shape E and the vehicle 10 is calculated. Of course, when the contour shape E is recognized including other places, all relative arrangements can be calculated.

  Moreover, you may display this relative arrangement | positioning on alerting means, such as a display. When a navigation system or the like is mounted on the vehicle 10, the monitor may also be used. In displaying (notifying), the outer shape of the vehicle 10 and the recognized contour shape E are displayed. Alternatively, the entire parked vehicle 20 may be represented as an illustration based on the contour shape E, and the relative arrangement relationship between the vehicle 10 and the parked vehicle 20 may be displayed.

  Further, the notification is not limited to visual display as described above, but may be notified by sound (including sound) using a buzzer, a chime, or the like. Some navigation systems have a voice guide function, and this voice guide function may also be used in the same manner as the use of a monitor.

  FIG. 9 shows a mode in which a moving state detecting means 4 for detecting the moving state of the vehicle 10 such as a wheel speed sensor 4a and a steering angle sensor 4b is further provided. If the input information from the movement state detection means 4 is taken into account, the near future layout can be calculated. That is, it is possible to estimate (predict) the future relative arrangement relationship as well as know the current relative arrangement in which the contour shape E is recognized.

  The wheel speed sensor 4a is a sensor provided in each wheel portion of the vehicle 10 (front right FR, front left FL, rear right RR, rear left RL). This is, for example, a rotation sensor using a Hall IC. The steering angle sensor 4b is a sensor that detects the rotation angle of the steering of the vehicle 10 and the rotation angle of the tire. Alternatively, it may be an arithmetic device that calculates the steering angle based on the measurement results (the difference between the rotation speeds and rotation speeds of the left and right wheels) at each wheel portion of the wheel speed sensor 4a described above.

  Taking into account the movement state detected by these sensors, the relative positional relationship between the vehicle 10 and the contour E of the parked vehicle 20 is calculated. The traveling direction is estimated by the steering angle sensor 4b, and the traveling speed is estimated by the wheel speed sensor 4a. Then, the relative trajectory of the predicted trajectory of the vehicle 10 and the contour shape E of the parked vehicle 20 and the vehicle 10 after several seconds is calculated. FIG. 11 shows an example of the relative arrangement relationship between the vehicle 10 and the contour shape E of the parked vehicle 20 calculated in this way. Reference numeral 10A denotes a near future position of the vehicle 10, that is, an estimated (predicted) position.

[Other Embodiments]
In the above description, the distance sensor 1 that detects the surface shape information of the parked vehicle 20 as the vehicle 10 moves as shown in FIG. However, the object detection means according to the present invention is not limited to this. The distance sensor 1 outputs surface shape information regardless of the movement of the vehicle 10, and can be selected for each movement distance and every elapsed time in the subsequent information processing. Further, a scanning unit that scans a wide-angle area with respect to the parked vehicle 20 regardless of the movement of the vehicle 10 may be provided, and surface shape information may be detected based on the obtained scanning information. That is, not only a point sensor such as the distance sensor 1, but also a sensor that can obtain a signal (surface shape information) reflecting the shape of an object, such as a one-dimensional sensor, a two-dimensional sensor, or a three-dimensional sensor.

  FIG. 12 shows an example in which a one-dimensional sensor is used as the object detection means according to the present invention. Here, a scanning laser sensor is used as an example of a one-dimensional sensor. As shown in FIG. 12, the object (parked vehicle 20) is scanned radially from the sensor position (position of the scanning means 1a). The distance distribution can be measured by the reflection of the laser wave from each position of the object. If the azimuth angle θ when the laser wave is emitted is detected by an encoder or the like, surface shape information can be obtained in the same manner as shown in FIG. And it can map to XY orthogonal coordinate.

  As another example of the one-dimensional sensor, an ultrasonic radar, an optical radar, a radio radar, a triangulation distance meter, or the like may be used.

As the two-dimensional sensor, there is a scanning radar that can scan in the horizontal and vertical directions. By using this scan type radar, it is possible to obtain information related to the shape in the horizontal direction and the shape in the vertical direction of the target object.
As well-known two-dimensional sensors, there are image input means such as a CCD (Charge Coupled Device) and a camera using a CIS (CMOS Image Sensor). Various feature quantities such as contour information and intersection information may be extracted from image data obtained from this camera to obtain information on the surface shape.

  The same applies to the three-dimensional sensor, and for example, information regarding the shape may be obtained using image data taken in stereo.

[Other forms of use]
As described above, the embodiment of the present invention has been described with respect to the method and apparatus for recognizing the contour shape using the parked vehicle 20 as an object, and these additional features. The “object” is not limited to an obstacle such as a parked vehicle or a building, but includes various objects such as a road lane, a stop line, and a parking frame. That is, the recognition target is not limited to the contour shape of a three-dimensional object, and can be applied to shape recognition of a planar pattern.

  Further, it is used not only when the vehicle 10 is moving forward as shown in FIGS. 10 and 11, but also when the vehicle is moved backward and parked between the vehicles 20a and 20b as shown in FIG. it can. Of course, the present invention can also be applied to a so-called switchback in which the vehicle moves forward as shown in FIG. 1 and then moves backward as shown in FIG. In this case, after the contour shape E of the parked vehicles 20a and 20b is reliably recognized, the vehicle can proceed between the two vehicles.

Explanatory drawing which shows the example in case the vehicle carrying the object recognition apparatus which concerns on this invention recognizes another vehicle 1 is a schematic block diagram of an object recognition apparatus according to a first embodiment of the present invention. The figure which shows the result of having measured the surface shape information of the parked vehicle of FIG. Scatter chart in which the measurement results shown in FIG. 3 are mapped onto two-dimensional orthogonal coordinates FIG. 4 is an explanatory diagram for calculating the degree of coincidence between a first shape model and a sample group determined from samples arbitrarily extracted from the sample group shown in the scatter diagram of FIG. Explanatory drawing which calculates the coincidence of the second shape model and the sample group determined from the sample arbitrarily extracted from the sample group shown in the scatter diagram of FIG. The flowchart explaining the 1st method (1st embodiment) which recognizes an outline shape from the sample group shown in the scatter diagram of FIG. The flowchart explaining the 2nd method (2nd embodiment) which recognizes an outline shape from the sample group shown in the scatter diagram of FIG. Schematic block diagram of an object recognition device according to a third embodiment of the present invention The figure which shows an example (3rd embodiment) of the relative arrangement | positioning relationship between the vehicle carrying the object recognition apparatus computed by the relative arrangement | positioning calculating part of FIG. 9, and the outline shape of another vehicle. The figure which shows the other example (3rd embodiment) of the relative arrangement | positioning relationship between the vehicle carrying the object recognition apparatus computed by the relative arrangement | positioning calculating part of FIG. 9, and the outline shape of another vehicle. The figure which shows the example (other embodiment) in the case of using a one-dimensional sensor as an object detection means based on this invention. The figure which shows the other example (other usage pattern) of the relative arrangement | positioning relationship between the vehicle carrying the object recognition apparatus computed by the relative arrangement calculating part of FIG. 9, and the outline shape of another vehicle.

Explanation of symbols

1 Distance sensor (object detection means)
2 Shape recognition unit (shape recognition means)
2A microcomputer S specimen group s specimen

Claims (8)

An object recognition device for recognizing an object existing around a moving body,
Object detection means for detecting surface shape information of the object, and shape recognition means for recognizing the contour shape of the object,
The shape recognition means is an object recognition device for recognizing the contour shape by calculating a degree of coincidence of the sample group with a shape model determined based on a sample arbitrarily extracted from a sample group constituting the surface shape information.   The object recognition apparatus according to claim 1, wherein the object detection unit detects the surface shape information based on a distance between a surface of the object and the moving body.   The object recognition apparatus according to claim 1, wherein the surface shape information represents an outer contour of the object.   4. The apparatus according to claim 1, further comprising: a relative arrangement calculation unit that calculates a relative arrangement relationship between the moving body and the object based on detection and recognition results of the object detection unit and the shape recognition unit. The object recognition apparatus described in 1.   The object recognition apparatus according to claim 4, further comprising a movement state detection unit that detects a movement state of the moving body, wherein the relative arrangement calculation unit calculates the arrangement relation based on the movement state.   The object recognition device according to claim 1, wherein the object detection unit detects the surface shape information of the object as the moving body moves.   The said object detection means is provided with the scanning means which scans the wide angle area with respect to the said object irrespective of the movement of the said mobile body, The said surface shape information of the said object is detected based on the obtained scanning information. The object recognition apparatus as described in any one of. An object recognition method for recognizing an object existing around a moving object,
An object detection step for detecting surface shape information of the object, and a shape recognition step for recognizing the contour shape of the object, the shape recognition step,
A sample extraction step of extracting an arbitrary sample from a sample group constituting the surface shape information;
A shape model setting step for determining a shape model based on the extracted sample;
A degree of coincidence calculation step of calculating the degree of coincidence of the sample group with respect to the shape model;
An object recognition method for recognizing the contour shape by performing

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