JP2013187862A - Image data processing device, image data processing method, and program for image data processing - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a technology by which an influence of a low accuracy image data can be suppressed, in a technology for dealing by integrating feature points of objects to be photographed and external orientation elements of each camera obtained from a plurality of cameras.SOLUTION: An image data processing device includes: a coordinate conversion unit 206 for performing coordinate conversion of three-dimentional coordinates at the feature points calculated for each of a plurality of cameras; a weight coefficient applying unit 207 for setting wight reflecting respective accuracy of the respective cameras to the respective cameras; and a data integration unit 208 for performing data integration of the feature points obtained for each of the cameras by calculating an average value of the feature points corresponding to the respective cameras subjected to the coordinate conversion in a state where the weight is reflected.

Description

  The present invention relates to a technique for handling image data obtained from a plurality of cameras in an integrated manner.

  A technique for detecting the position of a camera using the principle of photogrammetry is known (for example, see Patent Document 1). Japanese Patent Application Laid-Open No. 2004-228561 describes a technique for performing image capturing with a plurality of cameras from a moving vehicle and handling image data obtained from a plurality of different cameras in an integrated manner.

JP 2007-171048 A JP 2007-142517 A

  For example, when multiple cameras are mounted on a vehicle and shooting is performed from these multiple cameras, due to factors such as camera installation location, camera vibration, device performance and standard differences, disturbances (such as electronic noise), There may be a difference in the accuracy of the image data obtained from each camera. In this case, if the calculated values based on the image data are simply integrated, the accuracy of the integrated data decreases due to the influence of the low-precision image data.

  In such a background, the present invention is a technology that can suppress the influence of low-accuracy image data in a technology that handles the feature points of shooting targets obtained from a plurality of cameras and the external orientation elements of each camera. For the purpose of provision.

  According to the first aspect of the present invention, the three-dimensional coordinates of the feature points of the imaging target are calculated based on an image data receiving unit that receives image data from a plurality of cameras and a stereo pair image obtained from each of the plurality of cameras. The feature point calculation unit, the position and orientation calculation unit that calculates the position and orientation of the plurality of cameras, and the three-dimensional coordinates of the feature points calculated for each of the plurality of cameras and the position calculation unit A coordinate conversion unit that converts the position and orientation of each of the plurality of cameras onto specific integrated coordinates; a weight setting unit that sets a weight reflecting the accuracy of each of the plurality of cameras; With the weights reflected, the feature point data corresponding to each of the plurality of coordinate-converted cameras and the position of each of the plurality of cameras. By calculating at least one average value of the data and data of the plurality of cameras each posture is image data processing apparatus characterized by comprising a data integration unit that performs data integration.

  According to the first aspect of the present invention, the three-dimensional coordinates of the feature points of the object to be photographed and the external orientation elements of the camera are calculated from images taken by a plurality of cameras, and the values are integrated on the integrated coordinates. To be treated. At this time, a weight is given to the data derived from each camera according to the accuracy of each camera, and an average value is calculated in consideration of this weight. Thus, the integrated value is calculated in a state where the contribution of the high-precision image data is large and the contribution of the low-precision image data is small. The stereo pair image according to the first aspect of the present invention may be obtained from a plurality of frame images constituting a moving image, or may be obtained using a dedicated camera capable of capturing a stereo image or a plurality of cameras. It may be.

  According to a second aspect of the present invention, in the first aspect of the invention, the data integration unit is configured to determine a position and a posture of a base portion on which the plurality of cameras obtained from a measuring device other than the camera are arranged. At least one of the data is also integrated. According to the second aspect of the present invention, data obtained from a device capable of acquiring a part of data of an external orientation element such as an accelerometer, an inclinometer, or a GPS device can be included in the integrated data.

  According to a third aspect of the present invention, in the first or second aspect of the present invention, the plurality of cameras are mounted on a vehicle.

  According to a fourth aspect of the invention, in the third aspect of the invention, at least one of the position of the vehicle and the speed of the vehicle is calculated based on the image data received by the image data receiving unit. Features.

  The invention according to claim 5 is the invention according to any one of claims 1 to 4, wherein linearized collinears with respect to the feature points on a frame image constituting a moving image photographed by the plurality of cameras are provided. A step of establishing a conditional expression; a step of assigning the weight set to each camera to the linearized collinear conditional expression; and a step of calculating the average value from the collinear conditional expression to which the weight is attached And is performed.

  The invention according to claim 6 is the invention according to any one of claims 1 to 5, further comprising a weight estimation unit for estimating a weight of a specific camera among the plurality of cameras, and the weight estimation. And a step of giving provisional accuracy to the specific camera, and a distribution of calculated values of the position of the specific camera reflecting the provisional accuracy based on an image captured by the specific camera. A step of obtaining as a distribution, a step of obtaining, as a second distribution, a distribution of calculated values of the position of the specific camera that reflects the known measurement accuracy based on a position measurement unit whose measurement accuracy is known; A step of estimating a weight of the specific camera based on the provisional accuracy when a difference between the first distribution and the second distribution satisfies a specific condition.

  According to the sixth aspect of the present invention, the degree of certainty of position information measured by a device whose accuracy is known is evaluated as a distribution of calculated position values, and this is based on a camera with provisional accuracy. An appropriate accuracy given to the camera is estimated by comparison with the distribution of the calculated position values. In this process, if the provisional accuracy given to the camera is not appropriate (that is, the difference from the substance is large), the first distribution and the second distribution tend to deviate from specific conditions. When the provisional accuracy given to the camera is appropriate (that is, close to the substance), the fact that the first distribution and the second distribution tend to satisfy a specific condition is utilized.

  The invention according to claim 7 calculates the three-dimensional coordinates of the feature points of the imaging target based on the image data receiving step for receiving image data from a plurality of cameras and a stereo pair image obtained from each of the plurality of cameras. The feature point calculating step, the position and orientation calculating step for calculating the position and orientation of the plurality of cameras, the three-dimensional coordinates of the feature point calculated for each of the plurality of cameras, and the position calculating unit A coordinate conversion step for converting the position and orientation of each of the plurality of cameras onto specific integrated coordinates, a weight setting step for setting a weight reflecting the accuracy of each of the plurality of cameras, and a state in which the weight is reflected , The feature point data corresponding to each of the plurality of cameras subjected to the coordinate transformation, and each of the plurality of cameras. By calculating at least one average value of the data Data and the attitude of the plurality of cameras of location, an image data processing method characterized by comprising a data integration step for integrating data.

  The invention according to claim 8 is a program for causing a computer to read and execute the image data receiving unit that receives image data from a plurality of cameras and a stereo pair image obtained from each of the plurality of cameras. A feature point calculation unit that calculates three-dimensional coordinates of feature points to be imaged, a position and orientation calculation unit that calculates positions and orientations of the plurality of cameras, and the feature points calculated for each of the plurality of cameras A coordinate conversion unit that converts the position and orientation of each of the plurality of cameras calculated by the position calculation unit onto specific integrated coordinates, and a weight that reflects the accuracy of each of the plurality of cameras. A weight setting unit for reflecting the weight, and the feature corresponding to each of the plurality of coordinate-converted cameras in a state in which the weight is reflected. By calculating an average value of at least one of the point data, the position data of each of the plurality of cameras, and the attitude data of the plurality of cameras, the data is operated as a data integration unit that integrates the data. This is a program for image data processing.

  ADVANTAGE OF THE INVENTION According to this invention, the technique which can suppress the influence of low-precision image data in the technique which integrates and handles the feature point of the imaging | photography object obtained from the several camera and the external orientation element of each camera is provided.

It is a block diagram of an embodiment. It is a block diagram of an image data processing device of an embodiment. It is a flowchart which shows an example of operation | movement of embodiment. It is explanatory drawing explaining the principle of template matching. It is an image figure which shows a mode that two frame images are obtained from a different position with a time difference. It is explanatory drawing explaining the forward meeting method. It is explanatory drawing explaining a back intersection method. It is explanatory drawing explaining a relative orientation. It is a flowchart which shows an example of operation | movement of embodiment.

1. First embodiment (overall configuration)
FIG. 1 shows a vehicle 100 that is an example of a moving object. The vehicle 100 is, for example, a passenger car. The moving body is not limited to a vehicle, and may be a construction vehicle, a heavy machine, an aircraft, a ship, or the like as long as it moves. In FIG. 1, the upper direction in the figure is the front of the vehicle 100. The vehicle 100 includes an image data processing device 200. The vehicle 100 includes a forward monitoring camera 101, a speed detection camera 102, a landscape shooting camera 1 (103), a landscape shooting camera 2 (104), a landscape shooting camera 3 (105), an accelerometer 106, and an inclinometer 107. GPS device 108 and laser scanner 109 are provided. These devices are connected to the image data processing apparatus 200.

  The front monitoring camera 101 is a camera for monitoring the front of the vehicle 100 and shoots a moving image of the front of the vehicle 100. The moving image captured by the front monitoring camera 101 is used for detecting an obstacle or detecting an object that may collide with the vehicle 100. The moving image data of the moving image captured by the front monitoring camera 101 is sent to a function unit (not shown) that performs the above-described obstacle detection process and the like, and is also sent to the image data processing apparatus 200. For example, the speed detection camera 102 captures a moving image of the road surface in front of the vehicle 100. The moving image data of the moving image is subjected to image processing in a speed calculation unit (not shown), and the speed of the vehicle 100 is calculated. In this example, moving image data of a moving image taken by the speed detection camera 102 is sent to the image data processing device 200 at the same time as being sent to the speed calculation unit.

  The landscape shooting camera 1 (103), the landscape shooting camera 2 (104), and the landscape shooting camera 3 (105) take a moving image of the landscape around the vehicle 100 and send the image data to the image data processing device 200. . Note that the content of the landscape to be photographed is not particularly limited as long as it can be seen from the vehicle 100. In addition, the shooting ranges (fields of view) of the cameras may or may not overlap.

  The accelerometer 106 measures the acceleration applied to the vehicle 100 and sends the measurement data to the image data processing device 200. The inclinometer 107 measures the inclination of the vehicle 100 and sends the measurement data to the image data processing device 200. The GPS device 108 is a device that calculates a position based on signals sent from a plurality of artificial satellites, and is a GPS receiver that uses a general Global Positioning System. As means for obtaining position information, in addition to GPS, means for obtaining position information from road signs, optical beacons or radio beacons provided on road surfaces or roads can be cited.

  The laser scanner 109 irradiates the measurement target with laser light and receives the reflected light, thereby acquiring information on the distance, azimuth, and elevation (or depression) from the laser scanner installation position (viewpoint) to the measurement point. And based on these information, it has a function which calculates the information which concerns on the three-dimensional position coordinate of a measurement point. Further, the laser scanner acquires information on the intensity, color, hue, saturation, etc. of the reflected light from the measurement point. Based on such information, the laser scanner calculates three-dimensional point cloud position data including the three-dimensional coordinate value of the measurement object. The three-dimensional point cloud position data of the measurement object acquired by the laser scanner 109 is sent to the image data processing apparatus 200.

(Image data processing device)
Hereinafter, the image data processing apparatus 200 will be described. FIG. 2 shows a block diagram of the image data processing device 200. The image data processing apparatus 200 includes a CPU, a storage device such as a memory and a hard disk device, and a computer having various interfaces, and has the following functional units. Note that the functional units shown below are not limited to those configured as software, and at least a part thereof may be configured with dedicated hardware. It is also possible to configure the image data processing device 200 using a normal computer.

  The image data processing apparatus 200 includes a moving image data acquisition unit 201, a position / posture data acquisition unit 202, a three-dimensional point cloud position data acquisition unit 203, a feature point coordinate calculation unit 204, a position / posture calculation unit 205, a coordinate conversion unit 206, A weighting factor assigning unit 207, a data integrating unit 208, and a weighting factor calculating unit 209 are provided. The moving image data acquisition unit 201 is a moving image captured by the forward monitoring camera 101, the speed detection camera 102, the landscape shooting camera 1 (103), the landscape shooting camera 2 (104), and the landscape shooting camera 3 (105). Accept image data of an image. The position / attitude data acquisition unit 202 receives outputs from the accelerometer 106, the inclinometer 107, and the GPS device 108. The three-dimensional point cloud position data acquisition unit 203 receives the three-dimensional point cloud position data of the measurement object output from the laser scanner 109.

  The feature point coordinate calculation unit 204 calculates the three-dimensional coordinates of the feature points of the photographed object (for example, landscape) based on the moving image data received by the moving image data acquisition unit 201. The calculation of the three-dimensional coordinates is performed for each camera. For example, based on the moving image data of the moving image captured by the front monitoring camera 101, based on the calculation of the three-dimensional coordinates of the feature points of the object to be imaged and the moving image data of the moving image captured by the landscape shooting camera 1 (103). The calculation of the three-dimensional coordinates of the feature points of the object to be imaged is performed separately. The position / orientation calculation unit 205 calculates the external orientation elements (position and orientation) of the camera that captured the corresponding video based on the three-dimensional coordinates of the feature points of the measurement target calculated by the feature point coordinate calculation unit 204, and the position / The position and orientation of the integrated center (vehicle 100) are calculated from the data relating to the position and orientation of the vehicle 100 received by the orientation data acquisition unit 202.

  The coordinate conversion unit 206 is obtained from the forward monitoring camera 101, the speed detection camera 102, the landscape shooting camera 1 (103), the landscape shooting camera 2 (104), the landscape shooting camera 3 (105), and the laser scanner 109. A coordinate conversion process is performed so that the obtained data can be handled in an integrated manner on one coordinate system. For example, the coordinate data on the integrated coordinates so that the calculation result of the three-dimensional coordinates of the feature points of the object to be photographed based on the moving image photographed by the landscape photographing camera 1 (103) can be handled on the preset integrated coordinates. The coordinates are converted into integrated coordinates of the external orientation elements obtained based on the moving image. Here, in the coordinate transformation of the external orientation element, the external orientation element at the integration center obtained from a specific camera is calculated. The integration center external orientation element obtained from this specific camera is based on the external orientation element of the camera in consideration of the offset position and offset angle of the corresponding camera (i.e., (Calculation of vehicle position and attitude). The origin of the integrated coordinates is set in advance at an appropriate position on the vehicle 100. The direction of the coordinate axis is not particularly limited as long as it is a direction convenient for calculation.

  The weighting factor assigning unit 207 is obtained by converting the coordinate values of the three-dimensional coordinates of the feature points of the measurement object obtained for each camera onto the integrated coordinates, the external orientation element at the integrated center obtained for each camera, and the inclination A weighting factor reflecting the accuracy of each camera is assigned to each of the values of the inclination and position of the vehicle 100 obtained from the total 107 and the GPS device 108. The weighting factor is set in advance in consideration of the accuracy and priority of each device. It is also possible to prepare a plurality of combinations of weighting factors and switch them as appropriate, or to dynamically change the setting of the weighting factors.

  The data integration unit 208 integrates the data for each device in a form that reflects the weight. For example, the measurement calculated based on the three-dimensional coordinate value of the feature point of the measurement object calculated based on the image data from the landscape shooting camera 1 (103) and the image data from the landscape shooting camera 2 (104). The three-dimensional coordinate values of the feature points of the object are integrated on the integrated coordinate system. At this time, the calculation average in a form reflecting the weight set for each device is calculated, and the coordinate value of the feature point is obtained. The weight coefficient estimation unit 209 estimates the weight coefficient by a method described later.

(Example of processing)
Hereinafter, an example of the operation of the image data processing apparatus 200 will be described. FIG. 3 is a flowchart illustrating an example of a processing procedure performed in the image data processing apparatus 200. The program for executing the flowchart of FIG. 3 is stored in an appropriate memory area included in the image data processing apparatus 200, and is read and executed by the CPU included in the image data processing apparatus 200. Note that a form in which a program for executing the flowchart of FIG. 3 is stored in an appropriate external storage medium and provided to the image data processing apparatus 200 from there is also possible.

  The processing shown in FIG. 3 is a moving image captured by the forward monitoring camera 101, the speed detection camera 102, the landscape shooting camera 1 (103), the landscape shooting camera 2 (104), and the landscape shooting camera 3 (105). Is performed on each of the data (moving image data). Since the processing for each camera is the same, hereinafter, it will be simply referred to as a camera and described.

  When the processing shown in FIG. 3 is started, first, the moving image data acquisition unit 201 acquires moving image data of a moving image captured by each camera. Here, the moving image is composed of a plurality of continuous images (each of these images is referred to as a frame image) taken in the process of relatively moving the camera and the object little by little. The moving image data acquisition unit 201 sequentially acquires a plurality of frame images obtained in time series in real time.

  In parallel with the acquisition of the moving image data from each camera, the processes in and after step S102 are performed. The processing from step S102 to step S106 is performed by the feature point coordinate calculation unit 204. In step S102, initial values (initial values of external orientation elements) of the camera positions and orientations in the first and second frames are acquired. In this process, first, a reference point is set in the real space to be measured, and the three-dimensional coordinates of the reference point in the real space are measured using a total station or GPS. Alternatively, a reference point whose three-dimensional coordinates in the real space are known is prepared. Next, the reference point is photographed by the camera, and the initial values of the internal orientation element and external orientation element of the camera are calculated by the backward intersection method based on the three-dimensional coordinates of the reference point and the image coordinates in the frame image.

  As another method, three-dimensional coordinate values of a plurality of shooting positions may be given using a total station or GPS, and an initial value of an external orientation element may be obtained from stereo images shot at a plurality of shooting positions. As another method, a plurality of images are taken by moving a reference plate on which a plurality of reference points with known distances are drawn without using a total station or GPS, and a plurality of reference points are taken. A method may be used in which the image coordinates of the reference point are calculated from a single image and the initial values of the camera position and orientation are obtained using relative orientation (in this case, the obtained coordinate system is a local coordinate system). Becomes). The details of the specific operation of the backward intersection method and relative orientation will be described later.

  After step S102, feature points are extracted from the captured object from the frame image of the acquired moving image (step S103). Here, a frame image of at least three frames of the first frame, the second frame, and the third frame. Feature points are extracted from each of the above. Filters such as Moravec, Laplacian, and Sobel are used for feature point detection. After extracting the feature points, the corresponding feature points are specified between different frame images and tracked (step S104). Here, the correspondence between the feature points in the first frame, the second frame, and the third frame is specified. Information for specifying the correspondence between feature points in different frame images is embedded in each frame image.

  In this example, template matching is used for tracking feature points. Examples of template matching include a residual sequential detection method (SSDA), a cross-correlation coefficient method, and the like. Hereinafter, an example of template matching will be described.

Template matching is a method in which the coordinate data of images in two coordinate systems are compared with each other, and the correspondence between the two images is obtained by the correlation between the two. In template matching, a correspondence relationship between feature points of an image viewed from two viewpoints is obtained. FIG. 4 is an explanatory diagram for explaining the principle of template matching. In this method, as shown in the drawing, a template image of N ₁ × N ₁ pixel is moved on a search range (M ₁ −N ₁ +1) ² in an input image of M ₁ × M ₁ pixel larger than that, The upper left position of the template image is calculated so that the cross-correlation function C (a, b) expressed by the following equation 1 is maximized (that is, the degree of correlation is maximized).

  The above processing is performed while changing the magnification of one image and rotating it. And the area | region where both images correspond is calculated | required on the conditions from which the correlation became the maximum, Furthermore, the corresponding point is detected by extracting the feature point in this area | region.

  By using template matching, a matching portion between two images to be compared can be specified, and the correspondence between the two images can be known. In this method, the relative positional relationship between the two images is determined so as to maximize the correlation between the two images. The correlation between the two images is determined by the feature points of both images.

  After tracking the feature points in step S104, the three-dimensional coordinates of the corresponding feature points are calculated in a plurality of frame images, in this case, the first frame image and the second frame image. (Step S105).

  In this example, two frame images (a frame image of the first frame and a frame image of the second frame) acquired from different positions with a time difference are used as stereo pair images, and two frame images are obtained by the principle of triangulation. The three-dimensional coordinates of the feature points for which the correspondence relationship is specified are calculated. FIG. 5 is an image diagram showing this state. As shown in FIG. 5, the camera moves in the right direction in the figure when shooting the second frame, as compared to when shooting the first frame. Therefore, although not simultaneously, two frame images obtained by photographing a plurality of feature points of the photographing object indicated by the x mark from two different positions and directions are obtained with a slight time difference. Here, since the position and orientation of the camera in the first frame and the position and orientation of the camera in the second frame are acquired and known in step S102, the three-dimensional coordinates of the feature points are obtained from the principle of stereoscopic photogrammetry. Can do.

Hereinafter, an example of a detailed procedure of this process will be described. Here, the three-dimensional coordinates of the feature points for which the correspondence is specified in the frame image of the first frame and the frame image of the second frame are calculated using the forward intersection method. FIG. 6 is an explanatory diagram for explaining the forward intersection method. The forward intersection method is a method of observing a direction from _two known points (O ₁ , O ₂ ) toward the unknown point P and obtaining the position of the unknown point P as an intersection of these direction lines. In FIG. 6, the coordinate system of the target space is O-XYZ. Here, the coordinates (X ₀₁ , Y ₀₁ , Z ₀₁ ) of the camera projection center O ₁ of the _first frame image and the tilts (ω ₁ , φ ₁ , κ ₁ ) of the camera coordinate axes and the second frame The coordinates (X ₀₂ , Y ₀₂ , Z ₀₂ ) of the projection center O ₂ of the camera of the image and the inclinations (postures) (ω ₂ , φ ₂ , κ ₂ ) of the camera coordinate axes are already acquired and known in step S102. Also, the internal orientation elements (focal length, principal point position, lens distortion coefficient) are known.

Here, the feature point p ₁ (x ₁ , y ₁ ) on the first frame image and the corresponding feature point p ₂ (x ₂ , y ₂ ) on the second frame image are respectively Obtained from the frame image. Therefore, the three-dimensional coordinates (X, Y, Z) of the feature point P, which is an unknown point in the target space, are determined as the intersection of the light beam O ₁ p ₁ and the light beam O ₂ p ₂ . However, in reality, there is an error, and the two light beams do not accurately intersect, so the intersection position is obtained by the least square method. Specifically, two collinear conditional expressions (Equation 2) are established. A known external orientation element, internal orientation element, and image coordinates of corresponding points are substituted for this.

  Further, the approximate value (X ′, Y ′, Z ′) of the unknown point P plus the correction amount (X ′ + ΔX, Y ′ + ΔY, Z ′ + ΔZ) is substituted into this collinear conditional expression. Then, Taylor expansion is performed around the approximate value to linearize, and the correction amount is obtained by the least square method. The approximate value is corrected by the obtained correction amount, and the same operation is repeated to obtain a converged solution. By this operation, the three-dimensional coordinates P (X, Y, Z) of the feature points are obtained. This calculation is performed for all feature points, and the three-dimensional coordinates of a plurality of feature points whose correspondence is specified in the frame image of the first frame and the frame image of the second frame are calculated. The above is a detailed example of the contents of the process performed in step S105.

  After step S105, an external orientation element of the camera in the third frame is calculated based on the calculated three-dimensional coordinates of the feature point of the measurement object (step S106). With this process, the position and orientation of the camera in the third frame are obtained. Here, the external orientation element of the camera is calculated using the backward intersection method.

Hereinafter, a detailed example of the calculation performed in step S106 will be described. FIG. 7 is an explanatory diagram for explaining the backward intersection method. The backward intersection method is a method of observing directions from the unknown point O toward three or more known points P ₁ , P ₂ , P ₃ and obtaining the position of the unknown point O as an intersection of these direction lines. Here, the three-dimensional coordinates of the three characteristic points of the measurement object obtained in step S105 are set as the reference points P ₁ , P ₂ , and P ₃ in FIG. The camera's external orientation elements (X ₀ , Y ₀ , Z ₀ , ω, φ, κ) are calculated. There are single photograph orientation, DLT method, and relative orientation as the backward intersection method.

First, the case of calculating the external orientation elements (X ₀ , Y ₀ , Z ₀ , ω, φ, κ) of the camera in the third frame using single photo orientation will be described with reference to FIG. Single photo orientation uses the collinear condition that holds the reference point in a single photo, and uses the camera position O (X ₀ , Y ₀ , Z ₀ ) and camera posture (ω , Φ, κ). The collinear condition means that the projection center O, the photographic image (p ₁ , p ₂ , p ₃ ) and the measurement point (in this case, the feature point) (Op ₁ P ₁ , Op ₂ P ₂ , Op ₃ P ₃ ) It is a condition that it is on a straight line. Here, the camera position O (X ₀ , Y ₀ , Z ₀ ) and the camera posture (ω, φ, κ) are external orientation elements.

  First, the camera coordinate system is assumed to be x, y, z, the photographic coordinate system x, y, and the reference coordinate system X, Y, Z which is the coordinate system of the measurement object. It is assumed that photographing is performed in a direction in which the camera is sequentially rotated counterclockwise by ω, φ, and κ counterclockwise with respect to the positive direction of each coordinate axis. Then, the four image coordinates (only three points are shown in FIG. 7) and the corresponding three-dimensional coordinates of the reference point are substituted into the quadratic projective transformation equation shown in the following equation 3, and an observation equation is established to set the parameter b1. -B8 is obtained.

Then, the external orientation elements (X ₀ , Y ₀ , Z ₀ , ω, φ, κ) are obtained from the following Expression 4 using the parameters b1 to b8 of Expression 3. Here, (X ₀ , Y ₀ , Z ₀ ) are coordinates indicating the position of the camera in the third frame, and (ω, φ, κ) is the posture (orientation) of the camera at that position.

  As described above, on the basis of the three-dimensional coordinates of the feature points of the measurement object, the external orientation element of the camera in the third frame is calculated using the single photograph orientation.

  Next, a procedure for calculating the position and orientation of the camera in the third frame based on the three-dimensional coordinates of the feature points of the measurement object by the DLT method will be described. The DLT method approximates the relationship between the photographic coordinates and the three-dimensional coordinates of the target space with a cubic projective transformation formula. The basic formula of the DLT method is as follows. Details of the DLT method are described in “Shunji Murai: Analytical Photogrammetry, p46-51, p149-155” and the like.

  If the denominator of Equation 5 is deleted, the linear form of Equation 6 can be derived.

Further, when Expression 6 is transformed, the following Expression 5 is obtained.

Substituting the three-dimensional coordinates of six or more reference points into Equation 7 and solving using the least square method, eleven unknown variables L _{1 to} L ₁₁ that determine the relationship between the photo coordinates and the target point coordinates are obtained. You can get it. Note that L _{1 to} L ₁₁ include external orientation elements.

  Next, calculation of external orientation elements by the relative orientation method will be described. The relative orientation is a method in which a relative external orientation element is obtained even if there is no known point. If there is a known point, absolute coordinates can be obtained by performing absolute orientation.

FIG. 8 is an explanatory diagram for explaining relative orientation. In the relative orientation, an external orientation element is obtained from six or more corresponding points in the left and right images. In the relative orientation, a coplanar condition that two rays connecting the projection centers O ₁ and O ₂ and the reference point P must be in the same plane is used. Equation 8 below shows the coplanar conditional expression. Here, for example, the left image corresponds to the frame image of the second frame, and the right image corresponds to the frame image of the third frame.

As shown in FIG. 8, the origin of the model coordinate system is taken as the left projection center O ₁ , and the line connecting the right projection center O ₂ is taken as the X axis. For the scale, the base length is the unit length. At this time, the parameters to be obtained are the rotation angle κ ₁ of the left camera, the rotation angle φ _{1 of} the Y axis, the rotation angle κ _{2 of the} right camera, the rotation angle φ ₂ of the Y axis, and the rotation angle φ _{2 of} the X axis. There are five rotation angles ω ₂ . In this case, since the rotation angle ω ₁ of the X axis of the left camera is 0, there is no need to consider it. Under such conditions, the coplanar conditional expression of Equation 8 becomes as shown in Equation 9, and each parameter can be obtained by solving this equation.

  Here, between the model coordinate system XYZ and the camera coordinate system xyc, a coordinate transformation relational expression as shown in the following equation 10 holds.

Using these equations, an unknown parameter (external orientation element) is obtained by the following procedure.
(1) The initial approximate values of unknown parameters (κ ₁ , φ ₁ , κ ₂ , φ ₂ , ω ₂ ) are normally 0.
(2) The coplanar conditional expression of Equation 8 is Taylor-expanded around the approximate value, and the value of the differential coefficient when linearized is obtained by Equation 9, and an observation equation is established.
(3) A least square method is applied to obtain a correction amount for the approximate value.
(4) The approximate value is corrected.
(5) Using the corrected approximate value, the operations (1) to (4) are repeated until convergence.

When the relative orientation converges, connection orientation is further performed. The connection orientation is a process of unifying the inclination and scale between models to make the same coordinate system. When this processing is performed, a connection range represented by the following formula 11 is calculated. As a result of the calculation, if ΔZ _j and ΔD _j are equal to or less than a predetermined value (for example, 0.0005 (1/2000)), it is determined that the connection orientation has been normally performed.

  By the relative orientation, the position and orientation of the camera in the third frame are obtained. As described above, the moving images captured by the forward monitoring camera 101, the speed detection camera 102, the landscape shooting camera 1 (103), the landscape shooting camera 2 (104), and the landscape shooting camera 3 (105) are recorded. Based on each of the data (moving image data), the calculation of the three-dimensional coordinates of the feature point to be imaged (step S105) and the external orientation element (its position and orientation) of the camera in the third frame are performed.

  Next, the coordinate conversion unit 206 performs a process of converting the three-dimensional coordinates of the feature points to be photographed obtained by each camera and the external orientation elements into integrated coordinates (step S107). That is, the position and orientation of each camera are different, and the three-dimensional coordinates of the feature points of the imaging target obtained by each camera at this time and the external orientation elements are unique to each camera with the position of each camera as the origin. It is a value in the coordinate system. In other words, different cameras have different coordinate systems, and data cannot be handled in an integrated manner. Thus, in step S107, the coordinate data of the feature points related to each camera and the data of the external orientation elements are coordinate-converted into a specific coordinate system (referred to as integrated coordinates).

Here, the coordinate system of the integrated coordinates is (X ₁ , Y ₁ , Z ₁ ), and the coordinate system of a specific camera (for example, the landscape photographing camera 1 (103)) is (X ₂ , Y ₂ , Z ₂ ). And the matrix indicating the rotation between the coordinate systems is a _ij, and the offset (translation distance) between the coordinate systems is (X _x , Y _y , Z _z ), There is a relationship.

Since the relative positional relationship and posture relationship of each camera in the vehicle 100 are known in advance, a _ij and (X _x , Y _y , Z _z ) are known. Therefore, in each camera, it is possible to perform coordinate conversion to the integrated coordinates of the three-dimensional coordinate data of the feature point of the photographing object based on the photographed moving image and the external orientation element calculated in step S107. The same applies to other cameras. Here, the conversion of the external orientation element takes the position and orientation of the integrated center (that is, the position and orientation of the vehicle) based on the external orientation element of the camera in consideration of the offset position and offset angle of the corresponding camera. Perform the desired calculation. That is, a calculation is performed to convert the external orientation element (camera position and orientation) of the corresponding camera into the position and orientation of the integrated center (vehicle).

  Here, if there are no various errors associated with each camera, the three-dimensional coordinate data of the feature points in the same part of the object to be photographed obtained from each camera and the external orientation element calculated in step S107 are integrated. The value converted into the value of is consistent among the cameras. However, the accuracy and resolution of the camera itself, errors during data transmission, the influence of vibration, obstacles (trees, figures, etc.) between the camera and the object to be photographed, and differences in posture with respect to the object to be photographed make it There are different errors. Therefore, in step S108, after adjusting the data in consideration of the influence of the error, the data obtained from each camera is set in a state that can be handled in an integrated manner.

  Details of step S108 will be described below. In step S108, bundle adjustment is performed on the value obtained by converting the three-dimensional coordinate data of the feature point of the photographing target obtained from each camera and the external orientation element calculated in step S107 into that at the integration center. Bundle adjustment is an optimization process that is performed in order to increase calculation accuracy when calculating the position and orientation of each camera using a plurality of images taken at different shooting positions.

  In bundle adjustment, based on the collinear condition that the feature point of the measurement object, the point on the frame image (photo image), and the light beam (bundle) connecting the three points of the projection center must be on the same straight line, An observation equation is set for each light beam, and simultaneous adjustment is performed by the least square method to optimize the three-dimensional coordinates of the external orientation elements of each camera and the feature points of the measurement object. Here, when performing bundle adjustment, a weighting factor reflecting the accuracy of data from each camera is introduced, and bundle adjustment is performed in a state where weighting is performed for each camera. By introducing the concept of weighting to bundle adjustment, it is possible to integrate data obtained from each camera in a state where weighting is given and obtain integrated data reflecting the accuracy of each camera.

  Hereinafter, the principle of a method of setting weights for a plurality of cameras and handling them in an integrated manner will be described. First, in order to simplify the description, an example will be described in which the external orientation elements of each of n cameras are weighted and handled in an integrated manner.

Here, it is assumed that there are n cameras whose relative positional relationships are known. For example, assume n cameras fixed to a vehicle. Here, the external orientation elements of the integration center O _c are assumed to be (X _c , Y _c , Z _c , ω _c , φ _c , κ _c ). The integration center is the position of the origin of the integrated coordinates. The integration center external orientation element obtained from the camera 1 is O _c1 (X _c1 , Y _c1 , Z _c1 , ω _c1 , φ _c1 , κ _c1 ), and the integration center external orientation element obtained from the camera n. Is O _cn (X _cn , Y _cn , Z _cn , ω _cn , φ _cn , κ _cn ). The integration center external orientation element obtained from the camera 1 is determined from the external orientation element of the camera 1 in consideration of the offset position and offset angle of the camera 1 (that is, the position and orientation of the vehicle). ) Is a calculated value.

  For example, when the reliability of the data from each camera is the same, the average external orientation element (integrated external orientation element) in the integration center that integrally captures n cameras is the external orientation element of each camera. It is obtained as an average value evenly distributed. However, if the accuracy of the camera 1 is relatively high, but the accuracy of the camera 2 is relatively low, simply taking an average greatly affects the low accuracy of the camera 2 and an error in the integrated value. Will increase. In this case, by introducing a mechanism in which the contribution of the relatively high-precision camera 1 is large and the contribution of the relatively low-precision camera 2 is small, information on the camera 2 is ignored while suppressing a decrease in precision. It can be taken in without.

Therefore, the above-described external orientation elements at the integrated center of each camera are weighted so that the contribution to the integrated external orientation elements at the integrated center reflects the accuracy of each camera. That is, first, a weighting coefficient given to each camera is set to W (= 1 / σ ² ). The weighting factor W is a parameter indicating accuracy, and the maximum value is W = 1. For example, the weighting factor when there is no error and the accuracy is 100% reliable is W = 1. Also, for example, the weighting factor for a variable that has some error and whose accuracy is 80% reliable is W = 0.8.

Then, a weight coefficient W (W _Xn , W _Yn , W _Zn , W _ωn , W _φn , W _κn ) is _added to the external orientation element for each camera. Here, a value X _c obtained by integrating the external orientation elements of n cameras at the integration center reflecting the weight of each camera is expressed by Equation 13. Here, a case where a preset value is used as the weighting factor W will be described, but it is also possible to estimate by a method described later.

A specific example of _Xc will be described. First, consider two cameras 1 and 2 with different accuracy. Then, it is assumed that the integration center external orientation element X ₁ obtained from the camera 1 is considered, and that the weight coefficient is W _X1 . Further, the integration center external orientation element X ₂ obtained from the camera 1 is considered, and its weight coefficient is assumed to be W _X2 . In this case, the number 13, the external orientation parameters X _C in the integrated center in consideration of weighting is a number 14.

For example, it is assumed that the error of the camera 1 does not need to be considered, and the error of the camera 2 is not negligible, and W _X1 = 1 and W _X2 = 0.5 are set. That is, since there is an error in the camera 2, it is assumed that the weight coefficient is set to a half value compared to the case of the camera 1 in consideration of the error. In this case, Equation 14 becomes Equation 15.

This is based on the external orientation parameters of the camera 1 and camera 2, in the calculation of the exterior orientation parameters X _C in the integrated center in consideration of weighting, external orientation parameters of the camera 1 than the external orientation parameters of the camera 2 and more preferentially It is reflected. Thus, large contribution of may camera 1 without considering the errors, with the contribution of the camera 2 there is an error is small, performs the operation for obtaining the average value, the calculation of the integrated external orientation parameters X _C in the integrated center Is done.

  The above is the basic idea of a method for integrating data obtained from a plurality of cameras using weighting. In the above case, only the external orientation elements are targeted for the sake of simplicity of explanation, but in fact, in addition to the external orientation elements, the values related to the three-dimensional coordinates of the feature points of the shooting target acquired by each camera are also included. The same calculation is performed in consideration of the weight for each camera. Hereinafter, an example of this case will be described. First, the basic equation of the collinear condition in the single photograph orientation relating to each camera coordinate-converted into the coordinate system of the integration center is expressed by the following equation (16).

Here, the photograph corresponds to the frame image, and the photograph coordinates correspond to the image coordinates in the frame image. Since Equation 16 is not linear with respect to external orientation elements and ground coordinate values, a method is adopted in which the expansion is performed around the approximate value using Taylor's expansion, linearized ignoring higher-order terms, and the solution obtained by repeated calculation. Is done. At this time, the approximate value for the unknown variable is shown with the subscript “a” in the upper right, and the correction amount for the approximate value is expressed as shown in the following Expression 17, with v, δ, and Δ added. Here, _{v x,} v _y is the observed photographic coordinates ^x a, means the residuals of ^{y a.}

  Expression 18 is obtained by Taylor expansion and linearization of Expression 17.

  If Expression 18 is expressed using a matrix in order to simplify Expression 18, Expression 19 can be written.

  Equation 19 can be further written as Equation 20.

  Equations (19) and (20) are linearized collinear conditional expressions for a plurality of feature points on a frame image constituting a moving image taken by a plurality of cameras, and are linear with respect to the observation values of the feature points on each frame image. It is a generalized observation equation. Note that the notation such as (2, 1) and (2, 5) in Equation 20 indicates the size of the rows and columns of the matrix.

In Equations 19 and 20, the weight given to each camera is not considered. Here, assuming that there are n target cameras, the weighting factor W of the camera is multiplied by each term of Equation 20 to obtain Equation 21. The weighting process is performed in the weighting coefficient assigning unit 207. W _i and W _ij are weighting factors set for each camera.

  Equation 21 is obtained by adding the weight set to each camera to the linearized collinear conditional expression expressed by Equation 20. Expression 21 can also be conceptually expressed as Expression 22 below.

In the case where there are n cameras, bundle adjustment is performed by simultaneously n-numbering the number 21 described above. At this time, a successive approximation method is used. That is, the correction amount is obtained by the least square method, the approximate value is corrected by the obtained correction amount, and the same operation is repeated to obtain the convergence solution. At this time, an operation for obtaining an average value reflecting the weight is performed by the method described in connection with Equation (13). Thus, the integrated values of the external orientation elements (X ₀ , Y ₀ , Z ₀ , ω, φ, κ) of the photograph i captured by the coordinates of the integrated center taking into account the information of the weighted n cameras, and the correspondence The integrated value of the ground coordinate values (X, Y, Z) of the feature points is calculated.

  The integrated value obtained here is the average (weighted average) of the data obtained from multiple cameras, reflecting the weight given to each camera. The contribution rate is reflected. According to this calculation, the contribution rate corresponding to the accuracy (error factor) of each camera is set by weighting, so that when the measurement values obtained from each camera are handled in an integrated manner, a relatively high-accuracy camera It is possible to make the contribution ratio of the measurement value based on the camera relatively high and make the contribution ratio of the measurement value based on the camera with relatively low accuracy relatively low. For this reason, it is possible to capture data based on an image taken by a camera with a low accuracy into the integrated data while suppressing the occurrence of an error that occurs in the measurement value when handled in an integrated manner due to the influence of the camera with a low accuracy. .

  When the integrated data is obtained, it is stored in an appropriate storage area (step S109), and the processing of the next cycle after step S110 is started. In step S110 and subsequent steps, the processing of step 101 to step S109 is basically repeated.

  In step S110, camera external orientation elements (camera position and orientation) in the second and third frames are acquired. Here, the external orientation element in the second frame is given as an initial value in step S102, and the external orientation element in the third frame is obtained by calculation in step S107.

  For example, in the process of step S110 of the second processing cycle, the external orientation elements of the camera in the (n−1) = 3rd frame and (n−2) = 2 frame are known at that time. So it gets. In the third processing cycle, in the processing corresponding to step S110, the external orientation elements of (n-1) = 4th frame and (n-2) = 3th frame known at that time are acquired. (The external orientation element in the fourth frame is calculated by the process corresponding to step S106 in the second order). Thus, in the process corresponding to step S110 in the cycle of calculating the nth frame external orientation element, the (n-1) th frame and (n-2) th external orientation elements are acquired.

  Further, in a process (not shown) corresponding to step S103 performed after step S110, a new feature point is extracted from the frame image to be processed. Further, in the processing corresponding to step S103 and step S104 performed after step S112, extraction and tracking of feature points newly appearing in the frame images of the third frame and the previous second frame are performed. For example, feature points that do not appear in the first frame but newly appear in the second frame and the third frame are extracted, and further, a process for specifying the corresponding relationship is performed. And the process corresponding to the process below step S105 is repeated.

Hereinafter, an example of a procedure for calculating the three-dimensional coordinates of feature points that newly appear in the second and third frames and have been extracted and tracked will be described. In this case, the left coordinate O ₁ in FIG. 6 is the camera position of the second frame, and the right coordinate O ₂ is the camera position of the third frame. Here, the coordinates (X ₀₁ , Y ₀₁ , Z ₀₁ ) of the camera projection center O ₁ and the tilts (ω ₁ , φ ₁ , κ ₁ ) of the camera projection center O ₁ of the second frame image have already been acquired in step S102. Is known. In addition, the coordinates (X ₀₂ , Y ₀₂ , Z ₀₂ ) of the camera projection center O ₂ and the tilt (posture) (ω ₂ , φ ₂ , κ ₂ ) of the camera coordinate axes of the frame image of the third frame are determined in step S106. Already calculated and known. Also, the internal orientation elements (focal length, principal point position, lens distortion coefficient) are known. Further, the image coordinate p _{1 in the} second frame image and the image coordinate p ₂ in the third frame image can be obtained from each frame image. Therefore, the feature points newly appearing in the second frame and the third frame, extracted and tracked, and the three-dimensional coordinates P of the re-extracted feature points can be obtained. This is the same in the third and subsequent processing cycles.

That is, 4 The procedure of calculating three-dimensional coordinates of the feature points in the frame and subsequent coordinates o ₁ on the left side in FIG. 6 is a (n-2) th frame camera position, right coordinates o ₂ is (n-1 ) The camera position of the frame. Here, the coordinates (X ₀₁ , Y ₀₁ , Z ₀₁ ) of the camera projection center O ₁ and the inclinations (ω ₁ , φ ₁ , κ ₁ ) of the camera projection center O ₁ of the frame image of the (n-2) th frame, and ( n-1) The coordinates (X ₀₂ , Y ₀₂ , Z ₀₂ ) of the camera projection center O ₂ of the frame image of the frame and the tilt (posture) (ω ₂ , φ ₂ , κ ₂ ) of the camera coordinate axes are known. is there. Also, the internal orientation elements (focal length, principal point position, lens distortion coefficient) are known. Also, (n-2) and the image coordinates p ₁ in the frame of the frame image (n-1) image coordinates p ₂ in the frame of the frame image can be obtained from each frame image. Therefore, the three-dimensional coordinates P of the feature points in the (n-2) th frame and the (n-1) th frame can be obtained.

  In the process in the second cycle corresponding to step S106 performed after step S110, the external orientation elements (position and orientation) of the camera in the fourth frame are calculated by the backward intersection method. There are single photograph orientation, DLT method, and relative orientation as the backward intersection method, any of which may be used. Hereinafter, an example in which the external orientation elements (position and orientation) of the camera in the fourth frame are calculated using the relative orientation will be described.

  In this case, the external orientation element of the camera in the fourth frame is calculated by the mutual orientation shown in Equations 8 to 11 and FIG. The relative orientation is a method for obtaining an external orientation element by using 6 or more corresponding points in the left and right images. At this time, for example, the left image corresponds to the frame image of the third frame, and the right image corresponds to the frame image of the fourth frame. Here, the external orientation element of the third frame is known, and the three-dimensional coordinates of a plurality of feature points are also known. Further, the image coordinates of the feature points in the two frame images can be obtained from the respective frame images. Therefore, it is possible to calculate the external orientation element of the fourth frame that is an unknown number. This is the same in the third and subsequent processing cycles (for example, in the case of calculating the external orientation element of the camera in the fifth frame and the sixth frame).

  For example, when the external orientation element of the camera in the fourth and subsequent frames is calculated by the relative orientation shown in FIG. 8, for example, the left image corresponds to the (n-1) th frame image, and the right image corresponds to the nth frame image. . Here, the external orientation element of the (n−1) th frame is known, and the three-dimensional coordinates of a plurality of feature points are also known. Further, the image coordinates of the feature points in the two frame images can be obtained from the respective frame images. Therefore, the external orientation element (camera position and orientation) of the nth frame, which is an unknown number, can be calculated.

  In this way, by repeating the series of processing, the processing for the (n + 1) th frame, the (n + 2) th frame, the (n + 3) th frame (n: natural number) is performed, and the (n + 1) th frame and ( The calculation of the three-dimensional coordinates of the feature points in the image captured at the (n + 2) th frame and the calculation of the external orientation elements of each camera at the (n + 3) th frame are sequentially performed. Further, in the processing corresponding to step S108 in each processing cycle, bundle adjustment is performed in consideration of the weight set for each of the plurality of cameras, and the three-dimensional coordinates in the integrated coordinates of the feature points of the imaging target obtained from each camera are obtained. The coordinate data and the external orientation element data at the integration center obtained from each camera are integrated.

  The position of the vehicle 100 is calculated momentarily by calculating the external orientation element at the integration center that is repeatedly performed as described above. That is, the position information of the moving vehicle 100 can be calculated using the process of FIG. Further, the speed of the vehicle 100 can be calculated from the calculated change in position.

(Superiority)
According to the method described above, the accuracy of each photographing means is reflected on the value of the three-dimensional coordinates of the feature point of the photographing object calculated using a plurality of photographing means having different precisions and the external orientation element of each photographing means. Can be integrated and handled in the form. When this technology is used, it becomes possible to make a three-dimensional data of an image of a surrounding landscape seen from a vehicle using a plurality of cameras without using an all-around camera. In addition, it is possible to obtain three-dimensional data of a landscape around the vehicle that is difficult to obtain with an all-around camera.

2. Second Embodiment In step S108, measurement values derived from data other than camera image data can be added to data to be further integrated. Hereinafter, this example will be described. In the configuration shown in FIG. 1, the acceleration of the vehicle 100 is measured by the accelerometer 106, the inclination is measured by the inclinometer 107, and the position is measured by the GPS device 108. For example, the measurement value of the inclinometer 107 is data relating to the orientation of the external orientation element. Therefore, the inclination data of the vehicle 100 measured by the inclinometer 107 can be taken into Equation 21. At this time, weights in consideration of the accuracy of the inclinometer 107 and the GPS device 108 are set in the inclinometer 107 and the GPS device 108, and the measured values are taken into the corresponding portions of Equation 21. By doing so, the inclination data of the vehicle 100 measured by the inclinometer 107 can be taken into the integrated data.

  Further, the position of the vehicle 100 is obtained from the GPS device 108 or the measurement data of the GPS device 108 and the accelerometer 106, and this is taken into the data portion of the position of Formula 21, so that the accelerometer 106 and the GPS device are integrated into the integrated data. 108 measured values can be reflected.

3. Third Embodiment In step S108, the three-dimensional point cloud position data of the measurement object measured by the laser scanner 109 can also be captured. In this case, the three-dimensional point cloud position data and the attitude data of the measurement object measured by the laser scanner 109 are taken into Equation 21. At this time, the data relating to the laser scanner 109 is weighted, and the data is taken into Equation 21 in consideration of the accuracy.

4). Fourth Embodiment In this example, as a means for obtaining a stereo pair image, a stereo camera that captures two moving images from two different viewpoints is used. Note that only a part of the cameras having a plurality of stereo cameras capable of moving image shooting may be used, or all of the plurality of cameras may be used. In addition, a means for obtaining a stereo pair image may be configured by combining two cameras. In the case of this example, the configuration shown in FIG. 2 performs processing for a stereo pair image in addition to the functions described in relation to FIG. It is assumed that the relationship between the relative positions and postures of the left and right cameras constituting the stereo camera is known as a set value.

  In this example, first, stereo pair moving image data is acquired from a camera. This stereo pair moving image is composed of a moving image from a right image acquisition camera and a moving image from a left image acquisition camera. Here, the left and right moving images are composed of a plurality of frame images taken continuously in time series. Next, feature points are extracted from the left and right stereo pair images of the second frame in the acquired stereo pair moving image, and the correspondence relationship between the feature points extracted from the left and right stereo images in the second frame is specified. .

  Here, since the positional relationship and the posture relationship between the left and right cameras constituting the stereo camera are known, the three-dimensional coordinates of the identified feature points are calculated using the principle shown in FIG. Other processes are the same as those in the first embodiment.

5. Fifth Embodiment It is also possible to estimate the weighting coefficient, that is, the accuracy of observation values of a specific camera or measurement device, from the observation values of other devices by calculation. An example will be described below. Here, an example of processing for estimating the weight of a camera using a measurement value of the GPS device, considering a GPS device and one camera, will be described. FIG. 9 is a flowchart for explaining an example of processing for estimating the accuracy of the observation value of a specific camera or measurement apparatus from the observation value of another device. The processing shown in FIG. 9 is executed in the weighting factor estimation unit 209 in FIG. The program for executing the flowchart of FIG. 9 is stored in an appropriate memory area included in the image data processing apparatus 200, and is read and executed by the CPU included in the image data processing apparatus 200. Note that a form in which a program for executing the flowchart of FIG. 9 is stored in an appropriate external storage medium and provided to the image data processing apparatus 200 from the storage medium is also possible.

  Here, an example of a process for estimating the weighting factor of the landscape observation camera 1 (103) using the measurement value of the GPS device 108 according to the GPS device 108 and the landscape observation camera 1 (103) of FIG. explain. In addition, in the position information to be acquired, it is assumed that the relationship between the accuracy (for example, information such as an error of 10 cm) and the weighting factor given to the device is known in advance.

  When the processing in FIG. 9 is started, the position and accuracy of the GPS device 108 in the fn frame are acquired from the GPS device 108 (step S301). Here, the accuracy is the accuracy of measurement data obtained from the GPS device. The GPS device has a function of outputting a signal related to the accuracy of position information output from the GPS device based on the number of supplemented GPS satellites, the signal decoding state, the type of signal received from the satellite, and the like. In step S301, using this function, information related to the accuracy of the position information of the fn frame acquired from the GPS device 108 is acquired together with the position information. Note that n is, for example, about n = 3 to 10.

  Next, images from the (f−n) th frame to the fth frame shot by the landscape shooting camera 1 (103) are acquired, feature points of the shooting target are extracted from these images, and further, in each frame A corresponding point (corresponding point) of the feature point is acquired (step S302). This processing is the same as the processing performed in steps S103 and 104 in FIG. Next, provisional accuracy of the landscape photographing camera 1 (103) is set (temporary setting) (step S303). For example, provisional setting is performed with the same accuracy as the GPS device 108 whose accuracy is known.

Next, calculation of the position Xf ₁ of the landscape photographing camera 1 (103) in the f-th frame based on the corresponding points acquired in step S302 is performed using a Kalman filter (step S304). In this process, a process for obtaining an external orientation element of the landscape photographing camera 1 (103) from the three-dimensional coordinates of the feature points of the photographing object captured in the plurality of frame images photographed by the landscape photographing camera 1 (103), That is, the position Xf ₁ of the landscape photographing camera 1 (103) is calculated by the method used in steps S105 and S106 in FIG. At this time, by using a Kalman filter to determine the probability distribution of the location of the landscape photographing camera 1 that reflects the accuracy temporarily set (103) at step S303 as Xf _1. In addition, the initial value regarding the attitude | position of the landscape camera 1 (103) shall be obtained from the information of another apparatus.

  When the Kalman filter is used, the calculation result is a Gaussian distribution. On the other hand, when a particle filter described later is used, a result that there are a plurality of value candidates is obtained. Of course, either the Kalman filter or the particle filter may be used, or a combination of both. However, the Kalman filter can be used for 3D coordinate values that can be linearly calculated (by the collinear conditional expression), and the camera that performs nonlinear calculation can be used. It is appropriate to use a particle filter for the position.

After step S304, the position in the f-th frame (the position of the GPS device 108) and the accuracy of this position information are acquired from the measurement value of the GPS device 108 (step S305). Then, based on the GPS information acquired in step S305, the position Xf ₂ landscape photographing camera 1 (103) in the f th frame is calculated using the particle filter (step S306). At this time, since the positional relationship between the GPS device 108 and landscape photographing camera 1 (103) is known, the calculation of Xf ₂ is performed in consideration of the positional relationship.

Then, the difference between Xf ₁ and Xf ₂ is evaluated, and it is determined whether or not the difference is minimum. In practice, step S303~S306 processing performed a plurality of cycles, by varying Xf ₁ and landscape photographing camera 1 provisionally set in step S303 as the difference between Xf ₂ is reduced temporary setting accuracy (103) gradually, Xf ₁ and whether the difference between Xf ₂ is a minimum value, but is determined in step S307. Here, the difference between Xf ₁ and Xf _2, the distribution of Xf ₁ and Xf ₂ is calculated as the difference between the position indicating the peak.

When the minimum value of the difference between Xf ₁ and Xf ₂ is obtained, it is acquired as the appropriate accuracy (estimated accuracy) by which the temporary setting accuracy of the landscape photographing camera 1 (103) at that time is estimated (step S308). . Then, based on the estimation accuracy of the landscape shooting camera 1 (103) acquired in step S308 and the accuracy of the GPS information acquired in step 305, the estimated value of the weight coefficient of the landscape shooting camera 1 (103) is calculated. (Step S309).

  In other words, if the accuracy is obtained as a quantitative value, the relationship between the accuracy and the weighting factor given to the device is known in advance, and therefore the weighting factor corresponding to the estimation accuracy calculated in step S308 can be calculated. .

  Then, it is determined whether or not there is still a frame to be processed (step S310). If there is a frame to be processed, the processing after step S301 is executed again, and processing for the new f-th frame is performed. Done. If there is no frame to be processed and it is the last frame, the process is terminated.

Determination in step S307 is whether or not the minimum value, instead of determining whether or not below the threshold the difference between Xf ₁ and Xf ₂ is predetermined, may be a content determining.

  Here, an example in which the weight of the camera is estimated using GPS has been described. However, any device other than GPS can be used as long as the device can obtain position information and the accuracy thereof. Examples of such devices include an odometer that tracks position information from rotation of a tire, a position detection device that detects a marker that gives position information to the vehicle when the vehicle passes.

  In addition, the first camera whose accuracy is known and the second camera whose accuracy is unknown (or the accuracy varies depending on the nature of the subject to be photographed) are based on a moving image photographed by the first camera. A method of estimating the accuracy of the second camera on the basis of vehicle position information (corresponding to the GPS position information of the present embodiment) is also possible.

(Other)
As a means for specifying the position of the camera (the position of the vehicle 100) based on information given from the outside, the position is determined from information on optical beacons and radio wave beacons provided along roads and on road surfaces other than GPS (or in addition to GPS) Means for obtaining information can also be used.

  In the embodiment, a vehicle running on a road is exemplified as a moving body, but the moving place is not limited to a road. For example, the present invention can be applied to a construction vehicle or a vehicle that travels on a place other than a road. The moving body is not limited to a vehicle, and may be a ship, an aircraft, a movable robot, or the like.

  The present invention can be used in a technique for integrally processing image data obtained from a plurality of cameras.

Claims (8)

An image data receiving unit for receiving image data from a plurality of cameras;
Based on stereo pair images obtained from each of the plurality of cameras, a feature point calculation unit that calculates three-dimensional coordinates of feature points to be imaged, a position and orientation calculation unit that calculates the positions and orientations of the plurality of cameras,
A coordinate conversion unit that converts the three-dimensional coordinates of the feature points calculated for each of the plurality of cameras and the positions and orientations of the plurality of cameras calculated by the position calculation unit onto specific integrated coordinates;
A weight setting unit that sets a weight reflecting the accuracy of each of the plurality of cameras to each of the plurality of cameras;
At least one of the feature point data corresponding to each of the plurality of coordinate-converted cameras, the position data of each of the plurality of cameras, and the attitude data of each of the plurality of cameras in a state of reflecting the weight. An image data processing apparatus comprising: a data integration unit that integrates data by calculating one average value.   The data integration unit also performs integration of at least one of position and orientation data of a base portion on which the plurality of cameras obtained from a measurement device other than the camera are arranged. Image data processing apparatus.   The image data processing apparatus according to claim 1, wherein the plurality of cameras are mounted on a vehicle.   The image data processing apparatus according to claim 3, wherein at least one of the position of the vehicle and the speed of the vehicle is calculated based on the image data received by the image data receiving unit. In the integration of the data,
Establishing a linearized collinear conditional expression for the feature points on a frame image constituting a moving image captured by the plurality of cameras;
Attaching the weight set to each camera to the linearized collinear conditional expression;
The image data processing apparatus according to claim 1, wherein the step of calculating the average value from the weighted collinear conditional expression is performed. A weight estimation unit for estimating a weight of a specific camera among the plurality of cameras;
The weight estimation unit includes:
Providing provisional accuracy to the particular camera;
Obtaining a distribution of calculated values of the position of the specific camera reflecting the provisional accuracy as a first distribution based on an image captured by the specific camera;
Obtaining a distribution of calculated values of the position of the specific camera reflecting the known measurement accuracy as a second distribution based on the position measurement means whose measurement accuracy is known;
The step of estimating the weight of the specific camera based on the provisional accuracy when a difference between the first distribution and the second distribution satisfies a specific condition is performed. The image data processing device according to any one of? An image data receiving step for receiving image data from a plurality of cameras; a feature point calculating step for calculating three-dimensional coordinates of feature points to be imaged based on stereo pair images obtained from each of the plurality of cameras; A position and orientation calculation step for calculating the position and orientation of the camera;
A coordinate conversion step of coordinate-converting the three-dimensional coordinates of the feature points calculated for each of the plurality of cameras and the positions and orientations of the plurality of cameras calculated by the position calculation unit onto specific integrated coordinates;
A weight setting step for setting a weight reflecting the accuracy of each of the plurality of cameras;
With the weights reflected, at least one of the feature point data corresponding to each of the plurality of coordinate-converted cameras, the position data of each of the plurality of cameras, and the attitude data of the plurality of cameras. An image data processing method comprising: a data integration step of integrating data by calculating an average value. A program that is read and executed by a computer,
An image data receiving unit for receiving image data from a plurality of cameras;
A feature point calculation unit that calculates three-dimensional coordinates of a feature point to be imaged based on a stereo pair image obtained from each of the plurality of cameras;
A position and orientation calculation unit for calculating the positions and orientations of the plurality of cameras;
A coordinate conversion unit that converts the three-dimensional coordinates of the feature points calculated for each of the plurality of cameras and the positions and orientations of the plurality of cameras calculated by the position calculation unit onto specific integrated coordinates;
A weight setting unit that sets weights reflecting the accuracy of each of the plurality of cameras;
With the weights reflected, at least one of the feature point data corresponding to each of the plurality of coordinate-converted cameras, the position data of each of the plurality of cameras, and the attitude data of the plurality of cameras. A program for processing image data, wherein the program is operated as a data integration unit that integrates data by calculating an average value.

Images