JP6667065B2 - Position estimation device and position estimation method - Google Patents

Description

  The present disclosure relates to a position estimating device and a position estimating method for estimating a position of a moving object on a road surface.

  Patent Literature 1 discloses a moving object position detection system (position estimation device) that captures a dot pattern drawn on a floor surface and associates the captured dot pattern with position information. Thus, the position of the moving body can be detected from the image taken by the moving body.

JP 2010-102585 A

  However, in Patent Literature 1, an artificial marker such as a dot pattern is arranged on the floor to detect the position of the moving body on the floor. For this reason, it is necessary to arrange an artificial marker on the floor surface in advance for position detection. Further, in order to estimate the precise position of the moving object, it is necessary to arrange artificial markers over a wide range in smaller units. For this reason, there is a problem that it takes enormous effort to arrange artificial markers.

  The present disclosure provides a position estimating device that can estimate a precise position of a moving object without placing an artificial marker or the like.

Position estimation apparatus according to the present disclosure provides a position estimation device for estimating a position of the moving body in the road, provided in a mobile body, an illumination unit to form an illumination area illuminates the road surface, is provided to the mobile And an imaging unit for imaging a road surface including the illumination area . A control unit is provided for acquiring road surface information including road positions and road surface characteristics corresponding to the road positions. The control unit extracts road surface features from the captured image captured by the imaging unit , determines the validity of the captured image based on a change in the shape of the illumination region of the captured image, and determines that the captured image is valid. There line collation processing between the extracted feature and the road surface information, to evaluate the position of the moving body.

The position estimation method according to the present disclosure is a position estimation method for estimating a position of a moving object on a road ,
Using the illumination unit provided on the moving body to illuminate the road surface to form an illumination region, with is that the imaging unit provided on the moving body, to image a road surface comprising an illumination area. Further, road surface information including the road position and the corresponding road surface characteristics is acquired. Also, extracting a feature of the road surface from the captured photographed image by the imaging unit, to determine the effectiveness of the captured image based on a change in shape of the illumination area of the captured image, is extracted when it is determined that effective captured images There line verification processing with the characteristics and the road surface information, to evaluate the position of the moving body.

  The position estimation device according to the present disclosure can estimate a precise position of a moving object without placing an artificial marker or the like.

FIG. 2 is a diagram illustrating a configuration of a moving object including the position estimation device according to the first embodiment. Flowchart for describing an example of a position estimating operation of the position estimating device according to Embodiment 1. 9 is a flowchart illustrating an example of a feature extraction process of the position estimation device according to the first embodiment. FIG. 4 is a diagram showing an example of a shape of an illumination area illuminated from the illumination unit according to the first embodiment. FIG. 6 is a diagram showing a feature array of light and shade features obtained when binarizing a captured image according to the first embodiment; Diagram showing an example of road surface information according to Embodiment 1. Flowchart showing an example of road surface information acquisition processing according to Embodiment 1. The figure which shows the example of the illumination pattern illuminated from the illumination part which concerns on other embodiment. The figure which shows the example of the illumination pattern illuminated from the illumination part which concerns on other embodiment. Diagram for explaining an example of a method for extracting road surface unevenness according to another embodiment The figure for explaining another example of the method of extracting the unevenness of the road surface according to another embodiment.

  Hereinafter, embodiments will be described in detail with reference to the drawings as appropriate. However, an unnecessary detailed description may be omitted. For example, a detailed description of a well-known item or a redundant description of substantially the same configuration may be omitted. This is to prevent the following description from being unnecessarily redundant and to facilitate understanding by those skilled in the art.

  The inventor provides the accompanying drawings and the following description in order for those skilled in the art to fully understand the present disclosure, and it is not intended that the present invention limit the subject matter described in the claims. Absent.

(Embodiment 1)
The first embodiment will be described below with reference to FIGS.

[1-1. Constitution]
First, the configuration of the position estimation device according to the present embodiment will be described using FIG.

  FIG. 1 is a diagram illustrating a configuration of a moving object 100 including a position estimation device 101 according to Embodiment 1.

  The position estimating device 101 is a device that estimates the position and the direction of the moving body 100 on the road surface 102. The position estimation device 101 includes an illumination unit 11, an imaging unit 12, a memory 13, a control unit 14, a global navigation satellite system (GNSS) 15, a speedometer 16, and a communication unit 17.

  The lighting unit 11 is provided on the moving body 100 and illuminates a part of the road surface 102. The illumination unit 11 illuminates using parallel light. The illumination unit 11 is realized by, for example, a light source such as an LED (Light Emitting Diode), an optical system that forms parallel light, and the like.

  In addition, parallel light is illumination in which light beams are parallel. By irradiating the parallel light, the illumination unit 11 can illuminate an area of the same size regardless of the irradiation distance (the distance from the illumination unit 11 to the road surface 102). The illumination unit 11 may illuminate, using, for example, a telecentric optical system, parallel light emitted by the telecentric optical system. Alternatively, the illumination may be performed using parallel light emitted by a plurality of spot lights having straightness arranged in parallel with each other. When parallel light is used, the size of the region can be made constant regardless of the distance from the illumination unit 11 to the road surface 102, and the region necessary for position estimation can be accurately set, and normal collation can be realized.

  The imaging unit 12 is provided on the moving body 100. The imaging unit 12 has an optical axis that is not parallel to the optical axis of the illumination unit 11 and captures an image of the road surface 102 illuminated by the illumination unit 11. Specifically, the imaging unit 12 captures an image of the road surface 102 including an illumination area (see below) illuminated by the illumination unit 11. The imaging unit 12 is realized by, for example, a camera.

  Here, the illumination unit 11 and the imaging unit 12 are provided, for example, in a state fixed to the bottom of the vehicle body of the moving body 100. The optical axis of the imaging unit 12 is preferably perpendicular to the road surface. Therefore, assuming that the moving body 100 is disposed on a planar road surface, the imaging unit 12 is fixed such that its optical axis is perpendicular to the road surface. Further, since the illumination unit 11 has an optical axis that is not parallel to the optical axis of the imaging unit 12, the illumination unit 11 irradiates the plane road surface with parallel light obliquely, so that the imaging unit 12 captures the road surface. A part of the area (hereinafter, referred to as an “imaging area”) (hereinafter, referred to as an “illumination area”) is illuminated.

  The control unit 14 acquires road surface information stored in the memory 13 described later and in which the position and the direction are associated with the features of the road surface 102 in advance. The control unit 14 extracts the features of the road surface 102 from the captured road surface image, and estimates the position of the moving body 100 by a matching process of matching the acquired road surface information with the extracted features of the road surface 102. The control unit 14 may estimate the direction of the moving body, which is the direction in which the moving body 100 is facing, by the collation processing. The control unit 14 specifies, for example, a two-dimensional arrangement of shades of the road surface 102 from the area illuminated by the illumination unit 11 in the road surface image, and performs a matching process based on the specified two-dimensional arrangement. In addition, the control unit 14 performs, for example, a process of comparing a binary image obtained by binarizing a road surface image in which the shading of the road surface 102 is captured with the road surface information as a matching process. Here, the position of the moving body 100 is a position on the road surface 102 on which the moving body 100 moves, and the direction is a direction in which the front of the moving body 100 faces on the road surface 102. The control unit 14 is realized by, for example, a processor, a memory storing a program, and the like.

  The memory 13 stores road surface information indicating a correspondence between a feature and a position of the road surface 102. Note that the road surface information may not be stored in the memory 13 and may be obtained from the outside by communication during the matching process. The memory 13 is realized by, for example, a nonvolatile memory.

  The position associated with the road surface information is information indicating an absolute position. Further, the road surface information may be information in which a direction at an absolute position is associated in advance. In the present embodiment, the road surface information is information in which a position and a direction are associated with features of the road surface 102 in advance.

  The feature of the road surface 102 included in the road surface information is information indicating a two-dimensional arrangement of shades of the road surface 102. The road surface information is information in which a binarized image obtained by binarizing a road surface image obtained by capturing the shading of the road surface 102 is associated as a feature of the road surface. In addition, it is preferable that the road surface 102 on which the road surface information is based is a road surface made of a material such as asphalt, concrete, wood, or the like, whose characteristics such as shading, unevenness, and color are not uniform.

  The GNSS 15 determines a rough position of the moving object. That is, the GNSS 15 is a position estimating unit that performs position estimation with a higher accuracy than the position of the moving body estimated by the control unit 14. The GNSS 15 is implemented by, for example, a GPS module that estimates a position by receiving a signal from a GPS (Global Positioning System) satellite.

  The speedometer 16 measures the moving speed of the moving body 100. The speedometer 16 is realized by, for example, a speedometer that measures the speed of the moving body 100 from a rotation signal obtained from a driven gear of the moving body 100.

  The communication unit 17 acquires road surface information to be stored in the memory 13 from the outside as necessary. In short, the road surface information stored in the memory 13 does not need to be all of the road surface information, and may be a part of the road surface information. That is, the road surface information may be information in which features and positions of road surfaces around the world are associated, or information in which features and positions of road surfaces in a predetermined country are associated. Further, the road surface information may be information in which road surface features and positions in a predetermined area are associated, or information in which road surface characteristics and positions in a predetermined facility such as a factory are associated. Good. As described above, the road surface information may be information in which road surface features are associated with positions and directions. The communication unit 17 is realized by, for example, a communication module capable of performing communication by a mobile phone communication network or the like.

[1-2. motion]
The operation of the position estimation device 101 configured as described above will be described below.

  FIG. 2 is a flowchart for describing an example of the position estimation operation of position estimation device 101 according to Embodiment 1.

  First, the lighting unit 11 illuminates a road surface (S101). Specifically, the illumination unit 11 illuminates a road surface by emitting parallel light from an oblique direction to an illumination region in an imaging region captured by the imaging unit 12.

  Next, the imaging unit 12 captures an image of the road surface (S102). Specifically, the imaging unit 12 captures an image of a road surface including all the illumination areas illuminated by the illumination unit 11. That is, the entire illumination area is included in the imaging area.

  Next, the control unit 14 acquires the road surface information stored in the memory 13 and in which the position or the direction and the feature of the road surface 102 are associated in advance (S103).

  Next, the control unit 14 extracts features from the road surface image captured by the imaging unit 12 (S104). The details of the process of extracting features in step S104 (hereinafter, referred to as “feature extraction process”) will be described later with reference to FIG.

  Next, a feature extraction process (S104) for extracting a feature from a road surface image will be described with reference to FIG.

  FIG. 3 is a flowchart illustrating an example of a feature extraction process of the position estimation device 101 according to Embodiment 1.

  In the feature extraction processing, first, the control unit 14 performs the feature extraction processing from the captured image based on the shape of the illumination area of the parallel light illuminated on the road surface 102 by the illumination unit 11 (hereinafter, referred to as “illumination shape”). Is determined (S201).

  Here, a specific example of the illumination shape is shown in FIG.

  FIG. 4 is a diagram illustrating an example of a shape (illumination shape) of an illumination area illuminated from the illumination unit 11 according to the first embodiment. The illumination shapes shown in (a) to (d) of FIG. 4 are the shapes of the illumination area as viewed from the road surface information. The illumination shape by the illumination unit 11 shown in FIGS. 4A to 4D may be determined for each position estimation device 101 or may be changeable by one position estimation device. For example, the illumination shape may be changed according to the condition of the road surface or the like. In FIG. 4, the hatched areas (a) to (d) indicate the illumination area, and the dot areas of (c) and (d) indicate the spot areas irradiated with the spot light.

  FIG. 4A is a diagram illustrating an example in which the illumination shape is a square shape. In this case, the affinity with the shape of a general imaging element is high, and the detection values of a plurality of elements constituting the imaging element can be adopted at a high ratio without waste. When the illumination shape is a square shape, the collation region may be the entire rectangle of the illumination shape, or may be a region inside the illumination shape based on the illumination shape. When the inner area based on the rectangular position of the illumination shape is set as the collation shape, the collation shape is set to, for example, an area having the same center as the illumination shape and having an area 10% smaller than the illumination shape. Is also good.

  FIG. 4B is a diagram illustrating an example in which the illumination shape is circular. In this case, it is possible to make the matching shape constant in matching in different directions.

  FIG. 4C is a diagram illustrating an example in which a plurality of spot areas (areas irradiated with spot light) that specify a rectangular collation area are combined in addition to a wide illumination area. In this case, the collation area is a rectangular area specified by connecting a plurality of spot areas. Also in this case, as in the example of FIG. 4A, the matching shape is, for example, the same as the center of the rectangular area specified by connecting a plurality of spot areas, and the rectangular area It may be set to a region whose area is 10% smaller than the area.

  FIG. 4D is a diagram illustrating an example in which a plurality of spot regions that specify a circular collation region are combined in addition to the illumination region in FIG. 4C. In this case, the collation area is a circular area specified by connecting a plurality of spot areas. In this way, by arranging the plurality of spot regions in a circular shape, the same effect as in FIG. 4B can be obtained.

  The collation regions shown in FIGS. 4C and 4D may be determined for each position estimation device 101 or may be changeable by one position estimation device. For example, the size and shape of the collation area may be changed according to the road surface conditions and the like.

  The spot areas in FIGS. 4C and 4D may be areas to which a spot light colored in red or the like is irradiated, or areas to which a spot light having luminance different from the light in the illumination area is irradiated. It may be. Further, the light applied to the spot area may have a different wavelength from the light applied to the illumination area. That is, the spot area is an area irradiated with spot light that is light that can be distinguished from the illumination area.

  In step S201, a collation area is determined from a road surface image of the illumination area shown in FIGS. 4A to 4D as described above.

  Next, the control unit 14 determines the validity of the collation region determined in step S201 by considering the influence of the deformation of the shape of the illumination region and the like (S202). When the moving body 100 is inclined with respect to the road surface 102, the shape (illumination shape) of the illumination area illuminated by the illumination unit 11 may be deformed. Therefore, step S202 is performed in order to consider the influence of such deformation and the like. If the validity of the matching area can be secured in advance, step S202 may be omitted. The inclination of the moving body 100 with respect to the road surface 102 can be determined by the deviation of the illumination shape from the prescribed shape. For example, the determination can be made by changing the aspect ratio of the rectangular shape of the illumination area in FIG. 4A or changing the circular shape in FIG. 4B into an ellipse. By including a rotationally symmetric pattern such as the circle (or the shape close to the circle) in FIG. 4B or FIG. 4D, it is possible to easily determine the inclination in any direction.

  When the control unit 14 determines that the matching area is valid (Yes in S202), the control unit 14 extracts a feature sequence (S203). That is, the control unit 14 continues the feature extraction process when the above-described deformation occurs in the illumination area of the captured road surface image and the deformation is less than a predetermined fixed degree. . Therefore, the control unit 14 may correct the shape of the collation area and shift to the feature extraction process if the deformation is a predetermined fixed degree. For example, in the case of illumination including a circular shape as in (b) of FIG. 4 or (d) of FIG. 4, the center is the same as the ellipse and the radius is the short axis with respect to the minor axis of the ellipse. It is conceivable that a circular shape which becomes a collation area is obtained.

  Note that even if the road surface 102 is imaged at the same position, if the size (scale) of the matching area changes, the extracted characteristic arrays will be completely different. A change in the distance between the illumination unit 11 and the road surface 102 may cause such a change in the size of the verification area. In the present disclosure, such a change can be detected by setting the size of the collation region using the illumination light, and if it has changed, it can be corrected to a collation region of an appropriate size.

  On the other hand, when the control unit 14 determines that the collation area is invalid (No in S202), the process returns to step S101. In other words, the control unit 14 determines whether the illumination area of the captured road surface image is deformed as described above, and if the deformation exceeds a predetermined fixed degree, the control unit 14 determines the characteristics of the captured image. The extraction process ends, and the process proceeds to a position estimation operation using a new captured image (that is, the process returns to step S101).

  In the extraction of the feature array, the control unit 14 extracts a feature array indicating the shading of the road surface 102 from the matching area of the captured road surface image. Here, the shading is not an array of shading on a scale equivalent to the size of the mobile unit 100, but an array of shading on a micro scale that does not affect the traveling of the mobile unit 100 or the like. It is possible to image a feature array on such a scale by using a camera having a sufficient resolution corresponding to a micro scale. When the feature array to be extracted is shaded, the control unit 14 may extract, as a feature array, a value representing the average luminance of each fixed area in binary from the matching area of the road surface image, or a value represented in multiple values. May also be extracted as a feature array. Note that, as the characteristic array, an array of unevenness and color (wavelength spectrum characteristics) may be adopted instead of the array of light and shade.

  FIG. 5 is a diagram showing a feature array of light and shade features obtained when the captured image according to the first embodiment is binarized. Specifically, FIG. 5 shows that, when the collation area is divided into a plurality of blocks including a plurality of pixels, an average value of pixel values in the block is calculated for each of the plurality of blocks. FIG. 7 is a diagram illustrating an example of a light and shade feature array obtained by binarizing a signal based on whether or not a predetermined threshold is exceeded;

  When the control unit 14 extracts a feature array as shown in FIG. 5 as a feature of the road surface, the control unit 14 ends the feature extraction process in step S104, and proceeds to the next step S105.

  Returning to FIG. 2, the control unit 14 estimates the position or the direction of the moving body 100 by a matching process of matching the acquired road surface information with the features (S105). Here, the road surface information is information in which position information indicating a position is associated with a feature array as a characteristic of the road surface. In step S105, specifically, the control unit 14 performs the matching process by evaluating the similarity between the feature array extracted in step S203 and the feature array associated with the position information in the road surface information. Do.

  FIG. 6 is a diagram illustrating an example of road surface information according to the first embodiment. The horizontal axis and the vertical axis in FIG. 6 indicate the position in the x-axis direction and the position in the y-axis direction, respectively. In other words, the road surface information shown in FIG. 6 associates the characteristic array of the shading of the road surface with the position coordinates x and y. The minimum unit of the monochrome pattern indicates that the position coordinates x and y are one block of the feature array in the range of 0 to 100 in each of the x-axis direction and the y-axis direction. In this case, the control unit 14 calculates the similarity in the road surface information of the extracted feature array by performing the matching process with the feature array in FIG. 5 for each of the position and the orientation. The calculation of the similarity can be performed by applying an index used for general pattern matching, such as evaluating the difference by expressing the feature array as a vector. The control unit 14 estimates a position and an orientation as a result of the matching process. The control unit 14 adopts, as the position and the orientation of the mobile unit 100, the position and the orientation in which the collation degree (similarity) exceeds the predetermined reference and the collation degree is the highest in the collation processing. If the degree of collation does not exceed the predetermined reference or if there are a plurality of positions having similar degrees of collation, it is determined that the reliability of the collation processing result is low. If it is determined that the reliability of the collation processing result is low, the position may be estimated again, or the position and orientation determination values including the reliability (for example, the information indicating the position coordinates together with the information indicating that the reliability is low) ) May be output.

  It is desirable to use robust matching (M-estimation, minimum median method, etc.) for the matching process. When the position and orientation of the moving body 100 are determined using the characteristics of the road surface 102, a perfect match may not occur due to the presence of foreign matter, damage, or the like on the road surface 102. The larger the size of the feature sequence used for the matching process, the larger the amount of information included in the feature sequence, and the more accurate the matching. However, the processing cost required for matching increases. Therefore, instead of using a feature array having a size larger than the necessary and sufficient feature array, robust matching that performs accurate matching even when concealment of an obstacle or the like is partially included is performed by position estimation using the road surface 102. It is effective for

  When road surface information including a wide range of position information is to be subjected to the matching process, the matching process is enormous. Therefore, in order to speed up the matching process, a hierarchical matching process in which a detailed matching is performed after a rough matching may be performed. For example, the control unit 14 may narrow down and acquire the road surface information based on the result of the coarse position estimation by the GSNN 15. The process of acquiring road surface information in step S103 in this case will be described with reference to FIG.

  FIG. 7 is a flowchart illustrating an example of a road surface information acquisition process according to the first embodiment.

  In the road surface information acquisition processing, first, the GSNN 15 performs rough position estimation (S301). In this way, by limiting the position information to be collated in advance, it is possible to reduce the time required for the collation processing and the processing amount (processing load) related to the collation processing. Note that the coarse position estimation is not limited to using the position information acquired by the GSNN 15, and a position near the position information determined in the past may be used as the coarse position. Alternatively, position information of a base station such as a public wireless network or a wireless LAN, or a result of position estimation based on signal strength of wireless communication may be used.

  Next, the control unit 14 acquires road surface information of an area including a position with a low accuracy (S302). Specifically, the control unit 14 obtains, from the external database, road surface information including the position information near the coarse position through the communication unit 17 using the result of the rough position estimation.

  As described above, by acquiring the road surface information of the area including the position after performing the rough position estimation, the capacity required for the memory 13 can be reduced. Further, the data size of the road surface information to be compared can be reduced. Therefore, it is possible to reduce the processing load required for the matching process.

  The control unit 14 may perform the matching process according to the moving speed of the moving body 100 measured by the speedometer 16. For example, the control unit 14 may be limited to perform the operation when the measured moving speed is less than a predetermined speed. The control unit 14 may increase the shutter speed of the imaging unit 12 to perform imaging as the measured moving speed increases. When the measured moving speed is higher than a predetermined speed, the control unit 14 may increase the shutter speed of the imaging unit 12 to perform imaging. When the measured moving speed is higher than a predetermined speed, the control unit 14 may perform image processing for sharpening a captured image. This is because when the speed of the moving body 100 is high, a matching error due to motion blur is likely to occur. By referring to the speed of the mobile unit 100 as described above, uncertain matching can be avoided.

[1-3. Effects etc.]
As described above, in the present embodiment, the position estimating device 101 is a position estimating device that estimates the position or the direction of the moving body 100 on the road surface, and includes the illumination unit 11, the imaging unit 12, and the control unit 14. And The lighting unit 11 is provided on the moving body 100 and illuminates the road surface 102. The imaging unit 12 is provided on the moving body 100, has an optical axis that is not parallel to the optical axis of the illumination unit 11, and captures an image of the road surface 102 illuminated by the illumination unit 11. The control unit 14 acquires road surface information in which the position and the direction are associated with road surface characteristics in advance. Further, the control unit 14 determines a collation region from the captured road surface image, determines the validity of the collation region, extracts features of the road surface 102 from the road surface image of the collation region determined to be valid, and extracts the extracted road surface. The position and the orientation of the moving object 100 are estimated by a matching process of matching the feature of 102 with the acquired road surface information.

  According to this, by using the features of the road surface 102 that already includes random features in a minute area, the position or the direction (the moving body 100 is oriented) is collated with the road surface information previously associated with the position or the direction. Direction). Therefore, it is possible to estimate a precise position (for example, a position with a precision of mm unit) of the moving body 100 without arranging an artificial marker or the like. Further, since the position is estimated by imaging the road surface 102, the field of view of the imaging unit 12 is not obstructed by obstacles, structures, and the like around the moving body, and the position estimation can be stably continued. is there.

  Further, since the control unit 14 performs the matching process only on the matching area determined to be valid, it is possible to prevent the matching process from being unable to be executed accurately due to the deformation or inclination of the road surface, and to achieve a more accurate matching process. Position estimation becomes possible.

  The road surface information is information in which information indicating an absolute position as a position and a feature of the road surface 102 are associated in advance. For this reason, the absolute position on the road surface where the mobile unit 100 is located can be easily estimated.

  The illumination unit 11 illuminates using parallel light. According to this, since the illumination unit 11 illuminates the road surface 102 by irradiating the parallel light, even if the distance between the illumination unit 11 and the road surface changes, the size of the area of the illuminated road surface 102 is reduced. Changes can be reduced. Accordingly, in the road surface image captured by the imaging unit 12, the size of the road surface 102 can be more accurately estimated only by specifying the collation region from the region (illumination region) of the road surface 102 illuminated by the illumination unit 11. . Therefore, the position of mobile unit 100 can be more accurately estimated.

  The road surface information is information in which information indicating a two-dimensional arrangement of shading of the road surface 102 as a feature of the road surface 102 is associated with a position. The control unit 14 specifies a two-dimensional arrangement of shades of the road surface 102 from the area illuminated by the illumination unit 11 in the road surface image, and performs a matching process based on the specified two-dimensional arrangement.

  According to this, since the feature of the road surface 102 is the two-dimensional arrangement of the shading of the road surface 102, the image differs depending on the direction of the captured image even at the same position. Therefore, it is possible to easily estimate the position of the moving object and the direction of the moving object (the direction in which the moving object is facing).

  In addition, the road surface information is information in which a binarized image obtained by binarizing a road surface image in which the shading of the road surface 102 is captured is associated with a position as a feature of the road surface 102. The control unit 14 performs a process of comparing a binarized image obtained by binarizing a road surface image in which the shading of the road surface 102 is captured with road surface information as a collation process.

  For this reason, it is possible to simplify the characteristics of the road surface 102 by shading. As a result, the data size of the road surface information can be reduced, and the processing load for the matching process can be reduced. Further, since the data size of the road surface information stored in the memory 13 can be reduced, the storage capacity of the memory 13 can be reduced.

  Further, the position estimating device 101 may further include a position estimating unit corresponding to the GNSS 15 that performs position estimation with a higher accuracy than the position of the mobile unit 100 estimated by the controller 14. The control unit 14 may narrow down and acquire the road surface information based on the result of the position estimation by the position estimation unit. Therefore, the required capacity of the memory 13 can be reduced. Further, the data size of the road surface information to be compared can be reduced. Therefore, it is possible to reduce the processing load required for the matching process.

  The control unit 14 may perform the matching process according to the moving speed of the moving body 100. For this reason, uncertain matching can be avoided.

(Other embodiments)
As described above, Embodiment 1 has been described as an example of the technology disclosed in the present application. However, the technology in the present disclosure is not limited to this, and can be applied to embodiments in which changes, replacements, additions, omissions, and the like are made as appropriate. Further, it is also possible to form a new embodiment by combining the components described in the above embodiment.

  Therefore, other embodiments will be described below.

  For example, in the above embodiment, the shading of the road surface 102 is extracted as a feature of the road surface 102. However, the present invention is not limited to this, and the unevenness of the road surface 102 may be extracted. When the illumination unit 11 is inclined with respect to the optical axis of the imaging unit 12, a shadow corresponding to the unevenness of the road surface 102 is generated. Therefore, an image in which the generated shadow is represented by a multi-value may be adopted as a feature of the road surface 102. In this case, the features of the road surface 102 are, for example, that the convex portions are bright and the concave portions are dark. Therefore, by binarizing the luminance, the binarized image as shown in FIG. May be obtained.

  When the unevenness of the road surface 102 is extracted as described above, the light emitted by the illumination unit 11 is not a light emitted with uniform brightness in the illumination area, but a light that forms a predetermined pattern. Light may be used. The pattern light is, for example, a known stripe pattern light (see FIG. 8A), a dot array, or a lattice pattern light (see FIG. 8B). In short, the illumination unit 11 only needs to be able to emit the pattern light. By irradiating such pattern light with the illumination unit 11, it is possible to easily detect unevenness characteristics of the road surface 102 as described later.

  FIG. 9 is a diagram illustrating an example of a method for extracting road surface unevenness according to another embodiment. FIG. 10 is a diagram for explaining another example of a method for extracting road surface unevenness according to another embodiment. Specifically, FIG. 9 and FIG. 10 are diagrams for explaining a method of extracting road surface unevenness features using stripe pattern light.

  When a stripe pattern light as shown in FIG. 8A is applied to the road surface from an oblique direction, as shown in FIGS. 9 and 10, the bright and dark edges of the stripe pattern light can be extracted as a wavy line L1. . That is, FIGS. 9 and 10 are examples in which one of a plurality of bright and dark edge portions of the stripe pattern light is extracted.

  FIG. 9 shows an example in which the unevenness is determined based on which of the X direction the light L2 is shifted from the straight line L2 of the bright and dark edge portion obtained by imaging the road surface without the unevenness. That is, as shown in FIG. 9, the wavy line L1 is divided into a plurality of regions (for example, the above-described block unit region) in the Y-axis direction. At this time, for each of the plurality of regions, the wavy line L1 is set to “1” indicating that the pixel is convex if there are more pixels on the plus side in the X-axis direction than the straight line L2 in the corresponding region, and is present on the minus side in the X-axis direction. If there are many pixels to be processed, it may be set to “0” indicating that the pixel is concave.

  Also, as shown in FIG. 10, the wavy line L1 is divided into a plurality of regions (for example, the above-described block unit region) in the Y-axis direction. At this time, for each of the plurality of regions, if the number of pixels having an upwardly convex shape in the region is larger than the number of pixels having a downwardly convex shape, the region is set to “1” indicating that the region is convex. If the number of pixels having a convex shape is larger than the number of pixels having an upward convex shape, it may be set to “0” indicating that the pixel is concave.

  If the above processing is performed for each of a plurality of bright and dark edge portions of the stripe pattern light, the value of the unevenness in the X-axis direction can be calculated, and a two-dimensional arrangement of unevenness characteristics can be obtained.

  In this case, the bright and dark edge portions may be bright and dark edges from the top of the stripe pattern light, or dark and bright edges from the top.

  Further, a stereo camera or a laser range finder may be used for detecting the unevenness.

  When such a feature of the unevenness is adopted as the feature of the road surface 102, the degree of unevenness of the road surface 102 may be expressed numerically.

  The use of the concavo-convex feature makes it possible to detect a feature that is not easily affected by a local change in the luminance distribution on the road surface due to rainfall or dirt.

  In addition, a feature of a color other than shading and unevenness may be used as a feature of the road surface, or a feature of the road surface may be obtained from an image captured using invisible light (such as infrared light). By using colors, the amount of information increases, and the discrimination performance can be improved. In addition, by using invisible light, light emitted from the illumination unit can be made less likely to be seen by human eyes.

  The features of the road surface 102 may be an array of SIFT (Scale Invariant Feature Transform), FAST (Features from Accelerated Segment Test), SURF (Speed-Up Robust Features), and the like.

  Further, as the feature amount, a spatial change amount (differential value) may be used instead of the value itself such as shading, unevenness, and color as described above. For example, in the case of differential in the horizontal direction, the influence of ambient light is expressed by using a discrete differential value expression such as 1 when the differential value increases, 0 when the differential value is the same, and -1 when the differential value decreases. Can also be made less susceptible.

  In the above-described embodiment, an example has been described in which the moving body moving on the road surface 102 images the road surface and estimates the position. However, for example, an image of a wall surface may be taken while moving along a wall surface of a building, a tunnel, a dam, or the like, and the position may be estimated using a result obtained by imaging the wall surface. Here, the road surface includes a wall surface.

  In the above embodiment, components other than the illumination unit 11, the imaging unit 12, and the communication unit 17 of the position estimation device 101 may be on a cloud. The road surface image captured by the imaging unit 12 may be transmitted to the cloud via the communication unit 17, and the position estimation processing may be performed on the cloud.

  In the above embodiment, the specular reflection component of the road surface 102 may be reduced by attaching a polarizing filter to at least one of the illumination unit 11 and the imaging unit 12. As a result, the contrast of the shading feature of the road surface 102 can be improved, and errors in position estimation can be reduced.

  In the above embodiment, in order to perform high-speed matching, after performing rough position estimation, narrow down to an area including the position of the result of the coarse position estimation to acquire road surface information, and using the acquired road surface information. The speed of the matching process is increased by performing the matching process, but is not limited thereto. For example, an index or a hash table may be constructed in advance so that high-speed collation can be performed.

  Further, in the above-described embodiment, the control unit 14 determines the matching area from the captured road surface image (S201 in FIG. 3) and determines the validity from the shape of the matching area (S202). It is not limited to. Instead, the control unit 14 determines the validity of the matching area based on the result of extracting the features of the road surface 102 from the road surface image of the matching area (for example, the feature array obtained in S203 of FIG. 3). Is also good. For example, the control unit 14 can determine the validity based on a criterion such that a feature amount of unevenness which should be originally obtained cannot be obtained in a certain amount or more, or a sufficient match is not found in comparison with a map. Thereby, since the control unit 14 performs the matching process only on the matching area determined to be valid, it is possible to prevent the matching process from being unable to be executed accurately due to the deformation or inclination of the road surface, and the like. Position estimation becomes possible.

  The present disclosure can also be realized as a position estimation method.

  It should be noted that the control unit 14 of the constituent elements of the position estimation device 101 according to the present disclosure includes a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory) communication interface, and an I / O. It may be realized by software such as a program executed on a computer having a port, a hard disk, a display, or the like, or may be realized by hardware such as an electronic circuit.

  As described above, the embodiments have been described as examples of the technology in the present disclosure. For that purpose, the accompanying drawings and the detailed description have been provided.

  Accordingly, among the components described in the accompanying drawings and the detailed description, not only those components that are essential for solving the problem, but also those that are not essential for solving the problem in order to exemplify the technology. May also be included. Therefore, it should not be immediately recognized that these non-essential components are essential based on the fact that the non-essential components are described in the accompanying drawings and the detailed description.

  The embodiments described above are intended to exemplify the technology in the present disclosure, and various changes, replacements, additions, omissions, and the like can be made in the scope of the claims or the equivalents thereof.

  INDUSTRIAL APPLICABILITY The present disclosure is applicable to, for example, a position estimating device that can estimate a precise position of a moving object without placing an artificial marker or the like. Specifically, the present disclosure is applicable to mobile robots, vehicles, wall surface inspection devices, and the like.

DESCRIPTION OF SYMBOLS 11 Illumination part 12 Image pick-up part 13 Memory 14 Control part 15 GNSS (Global Navigation Satellite System)
Reference Signs List 16 speedometer 17 communication unit 100 moving object 101 position estimation device 102 road surface

Claims (19)

A position estimating device for estimating a position of a moving object on a road,
An illumination unit provided on the moving body and illuminating a road surface of the road to form an illumination area,
An imaging unit provided on the moving body and imaging a road surface including the illumination area,
A control unit that acquires road surface information including the road position and the corresponding road surface characteristics,
The control unit includes:
Extracting road surface features from the image captured by the image capturing unit,
Determine the validity of the captured image based on a change in the shape of the illumination area of the captured image,
Performing a matching process with the extracted features and the road surface information when it is determined that the captured image is valid, to evaluate the position of the moving body ,
Position estimation device. The illumination unit illuminates using parallel light,
The position estimation device according to claim 1. The road surface information includes information indicating an absolute position as the position,
The position estimation device according to claim 1. The road surface information further includes information indicating a direction at the position,
The control unit estimates a position and an orientation of the moving body by the matching processing.
The position estimation device according to claim 1. The position estimating device according to claim 1, wherein the lighting unit illuminates the road surface using pattern light that is light that forms a predetermined pattern. The position estimation device according to claim 5, wherein the pattern light is a stripe pattern light or a lattice pattern light. The corresponding road surface feature of the road surface information includes information indicating a two-dimensional arrangement of shading or unevenness of the road surface,
The control unit specifies a two-dimensional arrangement of shading or unevenness of the road surface from within the area illuminated by the illumination unit in the two-dimensional arrangement , as the feature of the extracted road surface, and specifies the specified two-dimensional arrangement. The position estimating device according to claim 1, wherein the matching process is performed based on: The feature of the corresponding road surface of the road surface information includes a binarized image obtained by binarizing a road surface image in which shading or unevenness of the road surface is captured,
The control unit generates a binarized image obtained by binarizing the road surface image in which lightness or unevenness of the road surface is captured as a feature of the extracted road surface, and generates the generated binary image. The position estimating apparatus according to claim 1, wherein a process of comparing the converted image with the road surface information is performed as the matching process. The control unit further includes a position estimating unit for performing coarser position estimation than the position of the moving body to be estimated,
The position estimation device according to claim 1, wherein the control unit narrows down and acquires the road surface information based on a result of the position estimation by the position estimation unit. The position estimation device according to claim 1, wherein the control unit performs the matching processing according to a moving speed of the moving body. The control unit determines the validity of the area for performing the matching process based on an illumination shape formed on the road surface by the illumination unit,
The position estimation device according to claim 1. The position estimating device according to claim 1, wherein the control unit determines the validity of a region in which the matching process is performed based on the extracted characteristics of the road surface. The imaging unit has an optical axis non-parallel to the optical axis of the illumination unit,
The position estimation device according to claim 1. The optical axis of the imaging unit is perpendicular to the road surface,
The position estimating device according to claim 13 . The shape of the illumination area is a rotationally symmetric pattern,
The position estimation device according to claim 1. The control unit determines the inclination of the moving body based on a change in the shape of the illumination area,
The position estimation device according to claim 1. The illumination unit irradiates the road surface using a telecentric optical system,
The position estimation device according to claim 1. A speedometer for measuring a moving speed of the moving body,
The control unit performs the matching process only when the moving speed of the moving object measured by the speedometer is equal to or less than a predetermined speed,
The position estimating device according to claim 10. A position estimation method for estimating a position of a moving object on a road,
Forming an illumination area by illuminating the road surface of the road using an illumination unit provided in the moving body,
Using an imaging unit provided in the moving body, image a road surface including the illumination area,
Obtain road surface information including the road position and the corresponding road surface characteristics,
Extracting road surface features from the image captured by the image capturing unit,
Determine the validity of the captured image based on a change in the shape of the illumination area of the captured image,
Performing a matching process with the extracted features and the road surface information when it is determined that the captured image is valid, to evaluate the position of the moving body ,
Position estimation method.

Images