JP2016166853A - Location estimation device and location estimation method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a location estimation device capable of estimating a precise position of a moving body without disposing an artificial marker, or the like.SOLUTION: A location estimation device 101 estimates a position of a moving body 100 on a road surface 102. The location estimation device 101 includes a lighting part 11 that is provided in the moving body 100 and lights the road surface 102, and an imaging part 12 that is provided in the moving body 100, includes an optical axis not in parallel with an optical axis of the lighting part 11, and captures an image of the road surface 102 lighted by the lighting part 11. The location estimation device 101 also includes a control part 14 for acquiring road information including a position and characteristics of the corresponding road surface 102. The control part 14 determines a collation area from a captured road surface image, extracts the characteristics of the road surface from the road surface image in the collation area, and estimates a position of the moving body 100 by collation processing for collating the extracted characteristics of the road surface 102 with the road surface information. The control part 14 also determines effectiveness of the collation area, and performs collation processing when determining that the collation area is effective.SELECTED DRAWING: Figure 1

Description

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

  Patent Document 1 discloses a moving body 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. Thereby, the position of the moving body can be detected from the image taken by the moving body.

JP 2010-102585 A

  However, in patent document 1, the position on the floor surface of a moving body is detected by arranging an artificial marker such as a dot pattern on the floor surface. For this reason, it is necessary to arrange an artificial marker on the floor in advance for position detection. In addition, in order to estimate the precise position of the moving body, it is necessary to arrange artificial markers over a wide range in units of smaller areas. For this reason, there exists a problem that arrangement | positioning of an artificial marker requires a huge effort.

  The present disclosure provides a position estimation device capable of estimating a precise position of a moving body without arranging an artificial marker or the like.

  A position estimation device according to the present disclosure is a position estimation device that estimates a position of a moving body on a road surface. The position estimation device is provided on the moving body and illuminates the road surface. And an imaging unit that images the road surface illuminated by the illumination unit. The position estimation device also includes a control unit that acquires road surface information including the position and the corresponding road surface characteristics. The control unit determines a collation region from the captured road surface image, extracts a road surface feature from the road surface image in the collation region, and determines a position of the moving body by a collation process for collating the extracted road surface feature with the road surface information. presume. The control unit further determines the validity of the collation area, and performs the collation process when it is determined that the collation area is valid.

  Further, the position estimation method in the present disclosure is a position estimation method for estimating the position of the moving object on the road surface, and illuminates the road surface using an illumination unit provided in the moving object, and is provided in the moving object. The road surface illuminated by the illumination unit is imaged using an imaging unit having an optical axis that is not parallel to the optical axis. Further, road surface information including the position and the corresponding road surface characteristics is acquired. In addition, the verification area is determined from the captured road surface image, the road surface features are extracted from the road surface image in the verification area, and the position of the moving body is estimated by the verification process that compares the extracted road surface characteristics with the road surface information. To do. Furthermore, in the estimation of the position of the moving object, the validity of the collation area is determined, and the collation process is performed when it is determined that the collation area is valid.

  The position estimation apparatus according to the present disclosure can estimate the precise position of the moving body without arranging an artificial marker or the like.

The figure which shows the structure of a moving body provided with the position estimation apparatus which concerns on Embodiment 1. FIG. Flowchart for explaining an example of position estimation operation of the position estimation apparatus according to Embodiment 1 The flowchart which shows an example of the feature extraction process of the position estimation apparatus which concerns on Embodiment 1. The figure which shows the example of the shape of the illumination area illuminated from the illumination part which concerns on Embodiment 1 The figure which shows the feature arrangement | sequence of the light / dark feature obtained when the imaged image which concerns on Embodiment 1 is binarized The figure which shows an example of the road surface information which concerns on Embodiment 1. The flowchart which shows an example of the acquisition process of the road surface information which concerns on 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 The figure for demonstrating an example of the method of extracting the unevenness | corrugation of the road surface which concerns on other embodiment The figure for demonstrating another example of the method of extracting the unevenness | corrugation of the road surface which concerns on other embodiment

  Hereinafter, embodiments will be described in detail with reference to the drawings as appropriate. However, more detailed description than necessary may be omitted. For example, detailed descriptions of already well-known matters and repeated descriptions for substantially the same configuration may be omitted. This is to avoid the following description from becoming 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 is not intended to limit the subject matter described in the claims. Absent.

(Embodiment 1)
Hereinafter, the first embodiment will be described with reference to FIGS.

[1-1. Constitution]
First, the configuration of the position estimation apparatus according to the present embodiment will be described with reference to 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 estimation device 101 is a device that estimates the position and orientation of the moving body 100 on the road surface 102. The position estimation apparatus 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 illumination 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 a light source such as an LED (Light Emitting Diode), an optical system that forms parallel light, and the like.

  Note that the parallel light is illumination in which light beams are parallel. The illumination unit 11 can illuminate a region having the same size by irradiating parallel light regardless of the irradiation distance (distance from the illumination unit 11 to the road surface 102). The illumination unit 11 may illuminate parallel light emitted from the telecentric optical system using, for example, a telecentric optical system. Moreover, you may illuminate using the parallel light irradiated with the some spot light which has a straight advance property arrange | positioned in parallel mutually. 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 set accurately and normal verification can be realized.

  The imaging unit 12 is provided in the moving body 100. The imaging unit 12 has an optical axis that is non-parallel to the optical axis of the illumination unit 11 and images the road surface 102 illuminated by the illumination unit 11. Specifically, the imaging unit 12 images the road surface 102 including an illumination area (described later) illuminated by the illumination unit 11. The imaging unit 12 is realized by a camera, for example.

  Here, the illumination part 11 and the imaging part 12 are provided in the state fixed to the bottom part of the vehicle body of the mobile body 100, for example. The optical axis of the imaging unit 12 is preferably perpendicular to the road surface. For this reason, when it is assumed that the moving body 100 is disposed on a flat road surface, the imaging unit 12 is fixed so that its optical axis is perpendicular to the road surface. Further, since the illumination unit 11 has an optical axis that is non-parallel to the optical axis of the imaging unit 12, the illumination unit 11 irradiates parallel light obliquely onto the planar road surface so that the road surface captured by the imaging unit 12 is imaged. A part of the area (hereinafter referred to as “imaging area”) is illuminated (hereinafter referred to as “illumination area”).

  The control unit 14 stores road surface information that is stored in a memory 13 to be described later and in which the position and direction are associated with the characteristics of the road surface 102 in advance. The control unit 14 extracts the feature of the road surface 102 from the picked-up road surface image, and estimates the position of the moving body 100 by collation processing for collating the acquired road surface information with the extracted feature 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 directed by the collation process. For example, the control unit 14 identifies the two-dimensional arrangement 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 identified two-dimensional arrangement. Further, the control unit 14 performs, for example, a process of collating a binarized image obtained by binarizing a road surface image obtained by imaging the shade of the road surface 102 with road surface information as a collation process. Here, the position of the moving body 100 is a position on the road surface 102 where the moving body 100 moves, and the direction is a direction in which the front surface of the moving body 100 faces on the road surface 102. The control unit 14 is realized by, for example, a processor and a memory storing a program.

  The memory 13 stores road surface information indicating the correspondence between the characteristics of the road surface 102 and the positions. Note that the road surface information may not be stored in the memory 13 and may be acquired 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 directions in absolute positions are associated in advance. In the present embodiment, the road surface information is information in which the position and direction are associated with the characteristics 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 the light and shade of the road surface 102. The road surface information is information in which a binarized image obtained by binarizing a road surface image in which the density of the road surface 102 is captured is associated as a road surface feature. In addition, it is preferable that the road surface 102 which becomes the basis of road surface information is the surface of the road comprised with the material which has features, such as shading, asperity, a color, etc. of surfaces, such as asphalt, concrete, and wood, which are not uniform.

  The GNSS 15 determines a rough position of the moving object. In other words, the GNSS 15 is a position estimation unit that performs position estimation with coarser accuracy than the position of the moving body estimated by the control unit 14. The GNSS 15 is realized 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 the driven gear of the moving body 100.

  The communication part 17 acquires the road surface information which should be memorize | stored in the memory 13 from the outside by communication as needed. In short, the road surface information stored in the memory 13 does not have 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 associated with road surface features and positions around the world, or may be information associated with road surface features and positions in a predetermined country. The road surface information may be information associated with road surface features and positions in a predetermined area, or may be information associated with road surface features and positions in a predetermined facility such as a factory. Good. As described above, the road surface information may be information in which road surface features are associated with positions and directions. The communication part 17 is implement | achieved by the communication module which can perform communication by a mobile telephone communication network etc., for example.

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

  FIG. 2 is a flowchart for explaining an example of the position estimation operation of the position estimation apparatus 101 according to the first embodiment.

  First, the illumination unit 11 illuminates the road surface (S101). Specifically, the illumination unit 11 illuminates the road surface by emitting parallel light from an oblique direction with respect to the illumination region of the imaging region imaged by the imaging unit 12.

  Next, the imaging unit 12 images the road surface (S102). Specifically, the imaging unit 12 images a road surface that includes 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 road surface information stored in the memory 13 and in which the position or direction and the characteristics 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). Details of the feature extraction processing (hereinafter referred to as “feature extraction processing”) in step S104 will be described later with reference to FIG.

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

  FIG. 3 is a flowchart illustrating an example of feature extraction processing of the position estimation apparatus 101 according to the first embodiment.

  In the feature extraction process, first, the control unit 14 performs the feature extraction process from the captured image based on the shape of the illumination region of the parallel light irradiated on the road surface 102 by the illumination unit 11 (hereinafter referred to as “illumination shape”). The collation area to be determined is determined (S201).

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

  FIG. 4 is a diagram illustrating an example of the shape (illumination shape) of the illumination region illuminated from the illumination unit 11 according to Embodiment 1. The illumination shapes shown in (a) to (d) of FIG. 4 are the shapes of illumination areas when viewed from road surface information. The illumination shape by the illumination part 11 shown by (a)-(d) of FIG. 4 may be determined for every position estimation apparatus 101, and may be changeable in one position estimation apparatus. For example, the illumination shape may be changed according to the road surface condition. In FIG. 4, hatched areas (a) to (d) indicate illumination areas, and dot area areas (c) and (d) indicate spot areas irradiated with spot light.

  (A) of FIG. 4 is a figure which shows the example whose illumination shape is square shape. In this case, the shape and affinity of a general image sensor are high, and the detection values of a plurality of elements constituting the image sensor can be employed at a high rate without waste. When the illumination shape is a quadrangle shape, the verification 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 region based on the rectangular position of the illumination shape is used as the collation shape, the collation shape is, for example, set to a region having the same center as the illumination shape and an area that is 10% smaller than the illumination shape. Also good.

  FIG. 4B is a diagram illustrating an example in which the illumination shape is circular. In this case, the collation shape can be made constant in collation with different directions.

  FIG. 4C is a diagram illustrating an example in which a plurality of spot areas (areas irradiated with spot light) that specify rectangular collation areas are combined in addition to a wider 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 collation shape has the same center as the rectangular area specified by connecting a plurality of spot areas, for example, and the rectangular area. The area may be set to be 10% smaller than the area.

  FIG. 4D is a diagram showing an example in which a plurality of spot areas that specify a circular collation area are combined in addition to the illumination area of FIG. In this case, the collation area is a circular area specified by connecting a plurality of spot areas. Thus, the effect similar to FIG.4 (b) can be acquired by making arrangement | positioning of several spot area | region circular.

  The collation areas shown in (c) and (d) of FIG. 4 may be determined for each position estimation apparatus 101, or may be changed by one position estimation apparatus. For example, the size and shape of the matching area may be changed according to the road surface condition.

  Note that the spot areas in FIGS. 4C and 4D may be irradiated with spot light colored in red or the like, or may be irradiated with spot light having a luminance different from that of the illumination area. It is good. Further, the light applied to the spot area may have a wavelength different from that of 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, the collation area is determined as described above from the road surface image in which the illumination areas shown in FIGS. 4A to 4D are captured.

  Next, the control unit 14 determines the effectiveness of the collation area determined in step S201 in consideration of the influence of the deformation of the shape of the illumination area (S202). When the moving body 100 is inclined with respect to the road surface 102, the shape of the illumination area illuminated by the illumination unit 11 (illumination shape) can be deformed. For this reason, step S202 is performed in order to consider the influence by such a deformation | transformation. Note that step S202 may be omitted if the validity of the verification area can be secured in advance. The inclination of the moving body 100 with respect to the road surface 102 can be determined by the illumination shape deviating from the prescribed shape. For example, the determination can be made by changing the rectangular aspect ratio of the illumination area in FIG. 4A or changing the circular shape in FIG. 4B to an ellipse. If a rotationally symmetric pattern such as a circle (or a shape close to a circle) in (b) of FIG. 4 or (d) of FIG. 4 is included, determination for detecting an inclination in an arbitrary direction can be facilitated.

  When it is determined that the collation area is valid (Yes in S202), the control unit 14 extracts a feature array (S203). In other words, 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 degree of deformation. . Therefore, the control part 14 may correct | amend the shape of a collation area | region and will transfer to the feature extraction process, if it is a predetermined fixed deformation | transformation. For example, in the case of illumination including a circular shape as shown in FIGS. 4B and 4D, the short axis of the ellipse is used as a reference, the center of the ellipse is the same, and the radius is the short axis. It is conceivable that a circular shape that becomes

  Even if the road surface 102 is imaged at the same position, if the size (scale) of the collation region changes, the extracted feature array becomes a completely different array. Variations in the distance between the illumination unit 11 and the road surface 102 can cause such a change in the size of the collation area. In the present disclosure, such a change can be detected by setting the size of the collation area using illumination light, and if the change has been changed, the collation area can be corrected to an appropriate size.

  On the other hand, when it determines with the collation area | region being invalid (No in S202), the control part 14 returns to step S101. That is, the control unit 14 is a case where the above-described deformation has occurred in the illumination area of the captured road surface image, and if the predetermined amount of deformation exceeds a predetermined degree, the feature of the captured image The extraction process is terminated, and the process proceeds to a position estimation operation using a new captured image (that is, the process returns to step S101).

  In the feature array extraction, the control unit 14 extracts a feature array indicating the lightness and darkness of the road surface 102 from the collation region of the captured road surface image. Here, the density is not an array of shades on a scale equivalent to the size of the moving body 100, but an array of shades on a micro scale that does not affect the traveling of the moving body 100 or the like. Capturing a feature array on such a scale is possible using a camera with sufficient resolution corresponding to the microscale. When the feature array to be extracted is shaded, the control unit 14 may extract, as a feature array, a value expressing the average luminance for each predetermined area as a feature array from the collation area of the road image, or a value expressed as a multivalue May be extracted as a feature array. As the characteristic arrangement, an arrangement of irregularities and colors (wavelength spectrum characteristics) may be adopted instead of the gray arrangement.

  FIG. 5 is a diagram showing a feature array of light and dark features obtained when the captured image according to Embodiment 1 is binarized. Specifically, in FIG. 5, when the collation region 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, and the average value in the block is calculated. It is a figure which shows the example of the light-dark feature arrangement | sequence obtained by binarizing by whether it exceeds the predetermined threshold value.

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

  Returning to FIG. 2, the control unit 14 estimates the position or orientation of the moving body 100 by the matching process for matching the acquired road surface information with the feature (S105). Here, the road surface information is information in which position information indicating a position is associated with a feature array as a road surface feature. 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. That is, the road surface information shown in FIG. 6 associates the characteristic arrangement of the road surface with the position coordinates x and y. The minimum unit of the black and white 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 performs the matching process with the feature array in FIG. 5 for each position and orientation, thereby calculating the similarity in the road surface information of the extracted feature array. The similarity can be calculated by applying an index used for general pattern matching, such as evaluating a difference by expressing the feature array as a vector. The control unit 14 estimates the position and orientation as a result of the matching process. In the collation process, the control unit 14 employs a position and orientation in which the collation degree (similarity) exceeds a predetermined reference and has the highest collation degree as the position and orientation of the moving body 100. In addition, when the collation degree does not exceed a predetermined reference, or when there are a plurality of positions with similar collation degrees, it is determined that the reliability of the collation processing result is low. When it is determined that the reliability of the collation processing result is low, position estimation may be performed again, or position and orientation determination values including reliability (for example, information indicating position coordinates together with information indicating low reliability) ) May be output.

  In addition, it is desirable to use robust matching (M-estimation, minimum median method, etc.) for the collation processing. When the position and orientation of the moving body 100 are determined using the characteristics of the road surface 102, there may be a case where the moving body 100 does not completely match due to the presence of foreign matter or damage on the road surface 102. As the size of the feature array used for the matching process is larger, the amount of information included in the feature array is larger, and accurate matching becomes possible. However, the processing cost required for collation increases. Therefore, instead of using a feature array having a size larger than a necessary and sufficient feature array, robust matching that performs accurate matching even if some of the obstacles are concealed is used for position estimation using the road surface 102 It is effective for.

  In addition, when the road surface information including a wide range of position information is a target of the matching process, the matching process becomes enormous. Therefore, in order to speed up the matching process, a hierarchical matching process in which detailed matching is performed after rough matching may be performed. For example, the control unit 14 may narrow down and acquire road surface information based on a result of rough position estimation by the GSNN 15. The road surface information acquisition process 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 process, 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 rough 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 a position with a high accuracy. Further, position information of a base station such as a public wireless network or a wireless LAN, or a position estimation result based on signal strength of wireless communication may be used.

  Next, the control part 14 acquires the road surface information of the area | region containing a rough position (S302). Specifically, the control unit 14 acquires road surface information including position information in the vicinity of the rough position through the communication unit 17 from the external database using the result of the rough position estimation.

  In this way, the capacity required for the memory 13 can be reduced by obtaining the road surface information of the area including the position after performing the rough position estimation. In addition, the data size of road surface information to be collated can be reduced. Therefore, it is possible to reduce the processing load for the collation process.

  The control unit 14 may perform collation processing 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 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 and capture an image as the measured moving speed is faster. When the measured moving speed is faster than a predetermined speed, the control unit 14 may increase the shutter speed of the imaging unit 12 and perform imaging. The control unit 14 may perform image processing for sharpening a captured image when the measured moving speed is faster than a predetermined speed. This is because when the speed of the moving body 100 is high, a verification error due to motion blur is likely to occur. As described above, by referring to the speed of the moving body 100, uncertain matching can be avoided.

[1-3. Effect]
As described above, in the present embodiment, the position estimation device 101 is a position estimation device that estimates the position or orientation of the moving body 100 on the road surface, and includes the illumination unit 11, the imaging unit 12, and the control unit 14. With. The illumination unit 11 is provided on the moving body 100 and irradiates 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 images the road surface 102 illuminated by the illumination unit 11. The control unit 14 acquires road surface information in which the position and direction are associated with the road surface characteristics in advance. Further, the control unit 14 determines a collation area from the captured road surface image, determines the validity of the collation area, extracts the characteristics of the road surface 102 from the road image of the collation area determined to be valid, and extracts the extracted road surface The position and orientation of the moving body 100 are estimated by a collation process that collates the characteristics 102 and the acquired road surface information.

  According to this, by using the characteristics of the road surface 102 that already include a random feature in a minute area and collating with the road surface information previously associated with the position or direction, the position or direction (the moving body 100 is directed). Direction). Therefore, it is possible to estimate a precise position (for example, a position with accuracy in mm) 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 shielded by obstacles or structures around the moving body, and the position estimation can be continued stably. is there.

  In addition, since the control unit 14 performs the matching process only on the matching area determined to be valid, it can prevent the matching process from being accurately executed due to deformation or inclination of the road surface. Position estimation is possible.

  The road surface information is information in which information indicating an absolute position as a position is associated with the characteristics of the road surface 102 in advance. For this reason, the absolute position on the road surface where the mobile body 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 parallel light, the size of the area of the road surface 102 that is illuminated is large even if the distance between the illumination unit 11 and the road surface changes. Change can be reduced. Thereby, in the road surface image imaged by the imaging unit 12, the size of the road surface 102 can be estimated more accurately only by specifying the collation area from the area (illumination area) of the road surface 102 illuminated by the illumination unit 11. . Therefore, the position of the mobile body 100 can be estimated more accurately.

  The road surface information is information in which information indicating the two-dimensional arrangement of the light and shade of the road surface 102 is associated with the position as a feature of the road surface 102. The control unit 14 specifies the two-dimensional arrangement of the light and shade of the road surface 102 from within 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 a two-dimensional arrangement of shades of the road surface 102, the image differs depending on the orientation of the captured image even at the same position. For this reason, while estimating the position of a moving body, it can carry out easily estimating the direction (direction which the moving body is facing) of a moving body.

  The road surface information is information in which a binarized image obtained by binarizing a road surface image in which the density 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 collating a binarized image obtained by binarizing a road surface image obtained by binarizing the road surface 102 with road surface information as a collation process.

  For this reason, the feature by the shading of the road surface 102 can be simplified. Thereby, the data size of road surface information can be made small and the processing load concerning collation processing can be reduced. In addition, 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 estimation apparatus 101 may further include a position estimation unit corresponding to the GNSS 15 that performs position estimation with coarser accuracy than the position of the moving body 100 estimated by the control unit 14. The control unit 14 may narrow down and acquire road surface information based on the result of position estimation by the position estimation unit. For this reason, the capacity required for the memory 13 can be reduced. In addition, the data size of road surface information to be collated can be reduced. Therefore, it is possible to reduce the processing load for the collation process.

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

(Other embodiments)
As described above, the first embodiment has been described as an example of the technique disclosed in the present application. However, the technology in the present disclosure is not limited to this, and can also be applied to an embodiment in which changes, replacements, additions, omissions, and the like are appropriately performed. Moreover, it is also possible to combine each component demonstrated in the said embodiment and it can also be set as a new embodiment.

  Thus, other embodiments will be exemplified below.

  For example, in the above-described embodiment, the density 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. Since the illumination unit 11 is tilted 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 expressed in multiple values may be adopted as a feature of the road surface 102. In this case, the road surface 102 is characterized in that, for example, the convex portion is bright and the concave portion is dark, so that by binarizing the brightness, the binarized image as shown in FIG. It may be obtained at.

  Thus, when extracting the unevenness | corrugation of the road surface 102, the light irradiated by the illumination part 11 is not the light irradiated with uniform brightness in an illumination area, but the pattern which is light which forms a predetermined pattern Light may be used. The pattern light is, for example, a known fringe pattern light (see FIG. 8A), a dot arrangement, a lattice pattern light (see FIG. 8B), or the like. In short, the illumination part 11 should just irradiate pattern light. When the illumination unit 11 irradiates such pattern light, the uneven feature of the road surface 102 as described later can be easily detected.

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

  When the stripe pattern light as shown in FIG. 8A is irradiated on the road surface from an oblique direction, the bright and dark edge portions of the stripe pattern light can be extracted as a wavy line L1 as shown in FIGS. . 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 straight lines L2 of the bright and dark edge portions obtained by imaging the road surface without the unevenness in the X direction. That is, as shown in FIG. 9, the wavy line L1 is divided into a plurality of regions (for example, the above-mentioned block unit regions) in the Y-axis direction. At this time, for each of the plurality of regions, “1” indicating that the wavy line L1 is convex in the X-axis direction plus side of the straight line L2 in the region is “1”, and exists on the X-axis direction minus side. If there are many pixels to perform, it may be “0” indicating that it is concave.

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

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

  In this case, the bright / dark edge portion may be a bright / dark edge from the top of the stripe pattern light, or may be a dark / light edge from above.

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

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

  When the uneven feature is used, it is possible to detect a feature that is not easily affected by a change in local luminance distribution on the road surface due to rain or dirt.

  In addition to shading and unevenness, color features may be used as road surface features, or road surface features may be obtained from images captured using invisible light (such as infrared light). By using colors, it is possible to increase the amount of information and improve the discrimination performance. In addition, by using invisible light, it is possible to make the light emitted from the illumination unit less visible to the human eye.

  Further, as the characteristics of the road surface 102, an array such as SIFT (Scale Invariant Feature Transform), FAST (Features From Accelerated Segment Test), or SURF (Speed-Up Robust Features) may be used.

  Further, as the feature amount, a spatial change amount (differential value) may be used instead of values such as shading, unevenness, and color as described above. For example, in the case of lateral differentiation, the effect of ambient light is expressed using a discrete differential value expression such as 1 if the differential value increases, 0 if the differential value is the same, and -1 if the differential value is small. It can be made difficult to receive.

  In the above embodiment, an example has been described in which a moving body moving on the road surface 102 images the road surface and estimates the position. However, for example, a wall surface may be imaged 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 of imaging the wall surface. Here, the road surface includes a wall surface.

  In the said embodiment, structures other than the illumination part 11, the imaging part 12, and the communication part 17 of the position estimation apparatus 101 may exist on a cloud. The road surface image imaged by the imaging unit 12 may be transmitted to the cloud through the communication unit 17, and the position estimation process 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 or the imaging unit 12. Thereby, the contrast of the shading feature of the road surface 102 can be improved, and the error of position estimation can be reduced.

  In the above-described embodiment, in order to perform high-speed collation, after performing rough position estimation, the road surface information is acquired by narrowing down to the area including the position of the result of rough position estimation, and the acquired road surface information is used. Although collation processing is speeded up by performing collation processing, it is not restricted to this. For example, an index or a hash table may be constructed in advance so that high-speed collation can be performed.

  In the above embodiment, the control unit 14 determines a matching area from the captured road surface image (S201 in FIG. 3), and determines its effectiveness from the shape of the matching area (S202). It is not limited to. Instead, the control unit 14 determines the effectiveness of the matching area based on the result of extracting the characteristics of the road surface 102 from the road surface image of the matching area (for example, the feature array obtained in S203 in FIG. 3). Also good. For example, the control unit 14 can determine the effectiveness on the basis that the feature amount of the unevenness that should be originally obtained cannot be obtained above a certain amount, or that the matching with the map is not sufficient. 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 accurately performed due to deformation or inclination of the road surface, and more accurately. Position estimation is possible.

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

  It should be noted that the control unit 14 among the components constituting the position estimation apparatus 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 including 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 this purpose, the accompanying drawings and detailed description are provided.

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

  Since the above-described embodiment is for illustrating the technique in the present disclosure, various modifications, replacements, additions, omissions, and the like can be made within the scope of the claims or an equivalent scope thereof.

  The present disclosure can be applied to a position estimation device that can estimate a precise position of a moving object without arranging an artificial marker or the like. Specifically, the present disclosure can be applied to a mobile robot, a vehicle, a wall surface inspection device, 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)
16 speedometer 17 communication unit 100 moving object 101 position estimation device 102 road surface

Claims (13)

A position estimation device for estimating the position of a moving object on a road surface,
An illumination unit that is provided on the moving body and illuminates the road surface;
An imaging unit that is provided on the moving body, has an optical axis that is non-parallel to the optical axis of the illumination unit, and that images the road surface illuminated by the illumination unit;
A controller that acquires road surface information including the position and the characteristics of the corresponding road surface,
The controller is
Determine the verification area from the captured road surface image,
Extracting road surface features from the road surface image in the verification region;
Estimating the position of the moving body by a collation process for collating the extracted road surface characteristics with the road surface information,
Determine the validity of the verification region;
Performing the matching process when it is determined that the matching region is valid,
Position estimation device. The illumination unit illuminates using parallel light,
The position estimation apparatus according to claim 1. The road surface information includes information indicating an absolute position as the position.
The position estimation apparatus according to claim 1. The road surface information further includes information indicating a direction at the position,
The control unit estimates the position and orientation of the moving body by the verification process.
The position estimation apparatus according to claim 1. The position estimation apparatus according to claim 1, wherein the illumination unit illuminates the road surface using pattern light, which 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 characteristics of the corresponding road surface of the road surface information include information indicating a two-dimensional arrangement of road surface shading or unevenness,
The control unit specifies a two-dimensional arrangement of shading or unevenness on the road surface from within the area illuminated by the illumination unit in the road image as a feature of the extracted road surface, and uses the specified two-dimensional arrangement. The position estimation apparatus according to claim 1, wherein the matching process is performed based on the position matching process. The feature of the corresponding road surface of the road surface information includes a binarized image obtained by binarizing a road surface image obtained by imaging the shading or unevenness of the road surface,
The control unit generates a binarized image obtained by binarizing the road surface image obtained by imaging the lightness or unevenness of the road surface as a feature of the extracted road surface, and the generated binary The position estimation apparatus according to claim 1, wherein a process of collating the digitized image with the road surface information is performed as the collation process. A position estimator that performs coarse position estimation with accuracy greater than the position of the moving body estimated by the controller;
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 apparatus according to claim 1, wherein the control unit performs the collation process according to a moving speed of the moving body. The control unit determines the effectiveness of the verification region based on the illumination shape formed on the road surface by the illumination unit.
The position estimation apparatus according to claim 1. The control unit determines the effectiveness of the matching region based on the extracted characteristics of the road surface;
The position estimation apparatus according to claim 1. A position estimation method for estimating the position of a moving object on a road surface,
Illuminating the road surface using an illumination unit provided on the moving body,
Using the imaging unit provided on the moving body and having an optical axis that is non-parallel to the optical axis of the illumination unit, the road surface illuminated by the illumination unit is imaged,
Obtain road surface information including the position and corresponding road surface features,
Determine the verification area from the captured road surface image,
Extracting road surface features from the road surface image in the verification region;
Estimating the position of the moving body by collation processing for collating the extracted road surface characteristics with the road surface information,
In the estimation of the position of the moving body, the effectiveness of the matching area is determined, and the matching process is performed when the matching area is determined to be valid.
Position estimation method.

Images