Method for calibrating an image sensor, associated device and motor vehicle.

Abstract

Method for calibrating a first image sensor included in a motor vehicle traveling in an environment, comprising the following steps: Receiving, from the first image sensor, a first image (I1) of the environment,Receiving, from a second image sensor, a second image I2) of the environment, the second image sensor being included in the environment and fixed,Association of first geometric elements (I1_SEG300, I1_SEG400, I1_LIGN1) of the first image with second geometric elements (I2_SEG300, I2_SEG400, I2_LIGN1) of the second image,Determining first 3-dimensional coordinates of the first geometric elements (I1_SEG300, I1_SEG400, I1_LIGN1) from second 3-dimensional coordinates of the associated second geometric elements,Determining an orientation of the first image sensor of the motor vehicle,from the first 3-dimensional coordinates of the first geometric elements (I1_SEG300, I1_SEG400, I1_LIGN1).Figure for the abstract: figure 2,

Description

Title of the invention: Method for calibrating an image sensor, associated device and motor vehicle.

[0001] The invention relates to motor vehicles.

[0002] The increasing automation of driving motor vehicles requires precise perception of the environment of the motor vehicle using image sensors of these motor vehicles. However, any error in the knowledge of the calibration, in particular in the knowledge of the orientation of the image sensors, can impact the accuracy of this perception, or even render it inoperative. This is the case, in particular, when the perception of the environment of the vehicle is obtained from the fusion of images from several image sensors of the motor vehicle.

[0003] To overcome this drawback, the invention relates to a method for calibrating a first image sensor included in a motor vehicle, implemented while the motor vehicle is traveling in an environment surrounding the motor vehicle, the method being characterized in that it comprises the following first steps: • Reception, from the first image sensor, of a first image of a part of the environment, acquired by the first image sensor, then • Obtaining (in other words: detecting or determining) the first geometric elements from (in other words: in) the first image, • And, reception, from a second image sensor, of a second image of the part of the environment, acquired by the second image sensor, the second image sensor being included in the environment and fixed relative to the Earth's surface (in other words: fixed relative to the ground), then • Obtaining (in other words: Detecting or determining) second geometric elements from (in other words: in) the second image, obtaining the second geometric elements comprising determining second 3-dimensional coordinates of each second element of the second geometric elements, • Following the step of obtaining the first geometric elements and the step of obtaining the second geometric elements, association of each first geometric element of the first geometric elements with a (different) element of the second geometric elements, • Determination of first 3-dimensional coordinates of each first element of the first geometric elements from the second 3-dimensional coordinates of the element (in other words: of an element) of the second geometric elements associated with said each first element during the association step, • Then, determination of an orientation of the first image sensor of the motor vehicle, from the first 3-dimensional coordinates of each first element of the first geometric elements.

[0004] Thus, thanks to the invention, the first image sensor can be calibrated while the motor vehicle is traveling on a public road, relying on the second sensor, without requiring the motor vehicle to be driven to a place dedicated to such calibration.

[0005] For example, the second 3-dimensional coordinates of each second element of the second geometric elements are obtained from initial data comprising for example: • A location and / or orientation of the second image sensor (known prior to the first steps), and / or • Characteristics (for example, geographical location, slope) of an element of the environment, for example the roadway or a ground marking (known prior to the first stages) for example when all or part of the second geometric elements touch the element of the environment.

[0006] Alternatively: • The second geometric elements are fixed and their coordinates are known and fixed (this may be the case of second elements obtained from fixed objects), • The second 3-dimensional coordinates are obtained directly by a radar or a laser remote sensor.

[0007] It is understood that the second image sensor is fixed relative to the earth's surface (in other words: relative to the ground) by the fact that, for example, the second sensor is fixed (for example welded, screwed, glued or sealed), directly to the earth's surface, or indirectly to the earth's surface (in other words: to the ground:), for example by means of at least one of: • A building, • A road sign (fixed in the ground) • Street furniture, • Vegetation whose roots are in the ground (for example a tree), • A post.

[0008] The vehicle is preferably in motion. Alternatively, it is stationary, for example at a stop sign or a traffic light.

[0009] It will be apparent to those skilled in the art that at some point during the process, coordinates or data in a second reference frame fixed relative to the ground (in other words: relative to the second image sensor) are converted into coordinates or data in a first reference frame fixed relative to the motor vehicle (when it is moving).

[0010] For example, all of the first steps can be implemented in the second frame, and a conversion of the orientation in the first frame can then be carried out.

[0011] Conversely, the initial data can be converted into the first reference frame. Thus, all of the first steps can be implemented in the first reference frame.

[0012] This conversion may be performed, alternatively, at intermediate steps of the method. Thus, the step of determining first 3-dimensional coordinates may comprise such a conversion. Alternatively, the determination of first 3-dimensional coordinates may be implemented directly. That is, the first 3-dimensional coordinates of each first element of the first geometric elements may be a copy of the second coordinates of the second element associated with said each first element during the association step.

[0013] In a known manner: • This conversion includes a step of determining a relative position of the first reference point with respect to the second reference point, and • Such a relative position can be determined from a geographical location and an orientation of the motor vehicle.

[0014] The method can thus comprise a step of receiving a geographical location and an orientation of the motor vehicle (for example from a satellite geolocation module of the motor vehicle, for example of the so-called “GPS” type, or alternatively from a detector of the road infrastructure).

[0015] Alternatively, a wheel (or several wheels) of the motor vehicle is positioned (temporarily) against a movable wedge so that the geographical location and orientation of the motor vehicle are constant. According to this alternative, the second 3-dimensional coordinates can be directly expressed in the first reference frame, without conversion.

[0016] The method according to the invention may comprise: • A step of analyzing the environment (i.e.; detection and / or recognition and / or estimation of the distance of objects in the environment) from images received from the first image sensor, taking into account the orientation of the first image sensor, A step of controlling a component of the motor vehicle (for example: a propulsion motor, a brake, a screen or a projector) from a result of this analysis step.

[0017] During the step of determining the orientation of the first image sensor of the motor vehicle, a location of the first image sensor, for example in the first reference frame, can be considered as predetermined, the precision of this position having a minor influence on the analysis of the environment. Alternatively, the step of determining the orientation of the first image sensor of the motor vehicle comprises a determination of a location of the first image sensor, for example in the first reference frame.

[0018] The orientation may include, for example, a roll angle, a pitch angle, a yaw angle of the image sensor.

[0019] A person skilled in the art knows how to implement the step of determining the orientation of the first image sensor of the motor vehicle, from the first 3-dimensional coordinates of each first element of the first geometric elements. This step corresponds to a well-known problem in computer vision. As examples, the following methods can be used: By solving a system of equations by singular value decomposition (SVD) and nonlinear optimization, as for example described in the article: “LiDAR-camera calibration using line correspondences. Sensors”, Bai, Z., Jiang, G., & Xu, A. (2020), Sensors, 20(21), 6319. By supervised learning and training of a convolutional network, as for example described in the article: “CalibRCNN: Calibrating camera and LiDAR by recurrent convolutional neural network and geometry constraints”, Shi, J., Zhu, Z., Zhang, J., Liu, R., Wang, Z., Chen, S., & Liu, H. IEEE / RSJ International conference on Intelligent Robots and Systems, 2020, pp. 10197-10202, IEEE.

[0020] According to one embodiment: The first image sensor and the second image sensor are (video) cameras, and The first image and the second image are 2-dimensional images.

[0021] According to a first variant, in one embodiment, the first image sensor is a laser remote sensor, for example also called “LiDAR”. and for example the second image sensor is also a laser remote sensor, for example also called “LiDAR”.

[0022] In this case, the invention can be implemented, among others, for example, by the method described in the following publication: • “Pairwise LIDAR calibration using multi-type 3D geometry features in natural scene”. M He, H Zhao, F Davoine, J Cui, H Zha. 2013 IEEE / RSJ International Conference on Intelligent Robots and and Systems (IROS), 2013.

[0023] According to one embodiment: • The step of obtaining the second geometric elements comprises a step of determining second bounding boxes, (images) of first mobile objects (i.e. each of the second bounding boxes encompassing one of the first mobile objects (in other words: a second image of one of the first mobile objects)) in (in other words: from) the second image, at least part of the second geometric elements being obtained from the second bounding boxes, and • The step of obtaining the first geometric elements comprises a step of determining first bounding boxes (of images) of the first mobile objects (i.e. each of the first bounding boxes encompassing one of the first mobile objects (in other words: a first image of one of the first mobile objects)) in (in other words: from) the first image, at least part of the first geometric elements being obtained from the first bounding boxes.

[0024] Alternatively, other approaches are possible based, for example, on the pixels of the first and second images.

[0025] The step of determining the second bounding boxes can be implemented by an artificial intelligence model, for example by an electronic neural network. Those skilled in the art know various methods for implementing such a step. By way of example, the methods described in the following articles can be cited: • By supervised learning of a convolution network, described for example in the article: “3D Bounding box detection from monocular images”, Catà Villà, M., Master's thesis, Universitat Politècnica de Catalunya. 2019, • By learning including geometric constraints, described for example in the article: “Deep monocular 3d object detection with closed-form geometry constraints” Naiden, A., Paunescu, V., Kim, G., Jeon, B., & Leordeanu, 2019 IEEE international conference on image processing (ICIP) (pp. 61-65).

[0026] The step of determining the first bounding boxes is completely conventional in the automotive field, and can be implemented by an artificial intelligence model, for example by an artificial neural network.

[0027] According to one embodiment, the first mobile objects are pedestrians. Alternatively, for example, they are motor vehicles or cyclists.

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[0035] According to one embodiment: • The second bounding boxes are: • Rectangular parallelepipeds (or paving stones), or • Cylinders, (or alternatively, for example, right prisms). • The first bounding boxes are rectangles. For example in this embodiment: • The second bounding boxes each include two horizontal bases, • The first bounding boxes are rectangles comprising two horizontal segments. Alternatively, for example, the second bounding boxes are prisms, cones, or spheres, and the first bounding boxes are trapezoids, triangles, or circles, respectively. According to one embodiment, the second geometric elements and the first geometric elements comprise (or are) segments. In this case, for example, of course, the first 3-dimensional coordinates of each first element of the first geometric elements and / or the second 3-dimensional coordinates of each second element of the second geometric elements may be the coordinates of the ends of the segments. For example : • Each of the two horizontal bases includes a center, • Each of the two horizontal segments of the first bounding boxes includes a center. According to one embodiment: • The second geometric elements comprise a second vertical segment (i.e.: parallel to the direction of gravity) connecting the centers of the two horizontal bases (i.e.: connecting the center of one of the two horizontal bases to the center of the other base of the two horizontal bases) of one of the second bounding boxes, • The first geometric elements comprise a first vertical segment connecting the centers of the two horizontal segments (i.e.: parallel to the Earth's horizon) (i.e.: connecting the center of one of the two horizontal segments to the center of the other segment of the two horizontal segments) of one of the first bounding boxes. Alternatively, the first and second bounding boxes comprising edges, the segments are these edges (vertical and / or horizontal). Alternatively, the first geometric elements comprise vertices of the first bounding boxes and the second geometric elements comprise vertices of the second bounding boxes. The association step then returns to a well-known problem called “Perspective-N-Point” for which the skilled person knows several approaches.

[0036] In the case of the first geometric elements, the person skilled in the art can easily implement processing to distinguish the vertical segments from the horizontal segments.

[0037] For example, an approximate direction of the horizontal and the vertical may be known (an orientation, (admittedly approximate, that the method according to the invention specifies,) of the first image sensor being known prior to the implementation of the first steps). A segment whose direction is closer to an approximate direction of the horizontal than to an approximate direction of the vertical is determined as horizontal. A segment whose direction is closer to an approximate direction of the vertical than to the direction of the horizontal is determined as vertical.

[0038] Alternatively, the first vertical segment is determined by the artificial intelligence model, for example by the artificial neural network which determines the first bounding boxes.

[0039] The horizontal and vertical segments can also be determined by recognition of an object (a roadway, a pedestrian) or a dimension ratio between the vertical segments and the horizontal segments.

[0040] According to one embodiment: • The step of detecting the second geometric elements comprises a step of detecting (images) of a second ground marking line in the second image, the second geometric elements comprising segments extending along the entire length of the second of the ground marking lines, and • The step of detecting the first geometric elements comprises a step of detecting (images) a first line of ground marking in the first image, the first geometric elements comprising segments extending all along the first line of ground marking.

[0041] A person skilled in the art knows various methods for implementing the step of detecting the first line of marking on the ground. Mention may be made, by way of example, of the methods described in the following article: • . “Recent progress in road and lane detection: a survey. », Hillel, AB, Lerner, R., Levi, D., & Raz, G. Machine vision and applications, 25(3), 727-745.

[0042] Concerning the step of detecting the second line of marking on the ground, the person skilled in the art knows various methods for implementing the step of detecting the second lines of road markings. Examples include the following methods: • By supervised learning using a convolution network, for example described in the following article: • “3d-lanenet: end-to-end 3d multiple lane detection Garnett”, N., Cohen, R., Pe'er, T., Lahav, R., & Levi, D. Proceedings of the IEEE / CVF International Conference on Computer Vision pp. 2921-2930, 2019. • By semi-supervised learning using Transformers, for example described in the following article: • “Performer: 3d lane detection via perspective transformer and the openlane benchmark”. Chen, L., Sima, C., Li, Y., Zheng, Z., Xu, J., Geng, X. & Yan, J.. European Conference on Computer Vision, 2022 October, pp. 550-567. Cham: Springer Nature Switzerland.

[0043] The person skilled in the art knows several solutions for implementing the association step, for example: • By a geometric search for the closest point between two lines to be matched described for example in the following article: • “An invariant, closed-form solution for matching sets of 3D lines”, Kamgar-Parsi, B., & Kamgar-Parsi, B. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 11-431. 2004 IEEE Computer Society, • By random selection and probabilistic estimation (family of Monte-Carlo type methods), for example using the so-called “RANSAC” algorithm, for example described in the following article: • “Camera pose estimation based on PnL with a known vertical direction”, Lecrosnier, L., Boutteau, R., Vasseur, P., Savatier, X., & Fraundorfer, F. IEEE Robotics and Automation Letters, 4(4), 3852-3859. 2019.

[0044] In fact, the association step associates each element of the first geometric elements obtained from a first detection of an object, in the first image, with an element of the second geometric elements obtained from a second detection of this object, in the second image.

[0045] According to one embodiment, the method comprises second steps, the second steps being implemented by the motor vehicle, the second steps comprising: • Before the association step, a reception step (and a sending step) of the second image (from (in other words: originating from) the second image sensor) and second geometric elements, via radiofrequency communication (for example of the so-called “V2X” type), • And, before the step of determining the first 3-dimensional coordinates, a step of receiving (and a step of sending) the second 3-dimensional coordinates of each second element of the second geometric elements (up to the motor vehicle or in other words, to the motor vehicle). • The following steps: • The step of receiving, from the first image sensor, the first image, • The stage of detecting the first geometric elements, • The association stage, • The step of determining the first 3-dimensional coordinates of each first element of the first geometric elements, • The step of determining the orientation of the first image sensor.

[0046] Other distributions of the stages are of course possible.

[0047] For example, the step of detecting the second geometric elements can be implemented in the motor vehicle (the initial data must, in this case, be received by the motor vehicle).

[0048] Alternatively, all of the first steps can be implemented by the road infrastructure which can then communicate the orientation to the motor vehicle.

[0049] The invention also relates to a computer program comprising instructions, executable by a microprocessor or a microcontroller or a computer, for implementing the second steps, when it is executed by the microprocessor or the microcontroller or the computer.

[0050] The second steps can be implemented by an electronic device (or a motor vehicle). The invention therefore also relates to an electronic device (or a motor vehicle) configured to implement the second steps, as well as a motor vehicle comprising the electronic device.

[0051] The characteristics and advantages of the computer program, the electronic device, and the vehicle are identical to those of the method according to the invention (without it being necessary to repeat them here).

[0052] When the electronic device, the motor vehicle, (or another element) is “configured to” (or “capable of”) carrying out or implementing a step or an operation, this implies, for example, that the element comprises means for carry out the step or operation. The means preferably include electronic means, for example a computer program, data in memory, specialized electronic circuits, wired or wireless connections, a microprocessor and / or a microcontroller.

[0053] Other characteristics and advantages of the present invention will appear more clearly on reading the detailed description which follows, comprising embodiments of the invention given as non-limiting examples and illustrated by the appended drawings, in which: • [Fig.l] represents an electronic device and a motor vehicle, according to an embodiment of the invention, in top view, • [Fig.2] represent images obtained during the implementation of the process during steps of the process of [Fig.3]. • [Fig.3] represents an implementation of the method according to the invention.

[0054] In [Fig.l], certain elements are, of course, seen through transparency.

[0055] Detailed description of an exemplary embodiment of the invention, with reference to Figures 1 to 3.

[0056] [Fig.l] represents a vehicle 100 which is a motor vehicle. The vehicle 100 comprises a microprocessor 110 connected (by connections represented by a solid line) to the following components of the vehicle 100: • A screen 140, • A 120 camera, and • A satellite geolocation module 130 (for example of the so-called “GPS” type).

[0057] With reference to [Fig. 3], during steps S00bis to S40bis and S00 to S70, the vehicle 100 travels on a public road 600, in an environment env100 which comprises a tree 500, and a camera 200 comprising a microprocessor 210.

[0058] The camera 200 is fixed relative to the ground 700. It is for example bolted to a post (not shown) itself sealed to the ground 700.

[0059] In step S00, the microprocessor 110 receives, from the camera 120, a first image II, [Fig.2], of a part pl00 of the environment env100, acquired by the camera 120.

[0060] The part plOO of the environment envlOO includes the pedestrian 300, the pedestrian 400 and a ground marking MARQ1.

[0061] In step SOObis, the microprocessor 210 receives, from the camera 200, an image 12, represented [Fig.2], of the part plOO of the environment envlOO, acquired by the camera 200.

[0062] For example, steps S00 and SOObis are implemented simultaneously. Alternatively, steps S00 and SOObis are separated by a time taken into account during the association stage.

[0063] In step S10, the microprocessor 110 determines the bounding boxes I1_BOX300 and I1_BOX400 of pedestrians 300 and 400 respectively in the image II.

[0064] In step SlObis, the microprocessor 210 determines the bounding boxes I2_BOX300 and I2_BOX400 of pedestrians 300 and 400 respectively in the image 12.

[0065] For example, the bounding boxes I1_BOX300, 11_BOX400, 12_BOX300 and I2_BOX400 are determined according to methods cited above.

[0066] In step S20, the microprocessor 110 determines the segments Il_SEG300 and Il_SEG400 connecting the centers of two horizontal segments of the bounding boxes I1_BOX300, I1_BOX400.

[0067] In step S20bis, the microprocessor 210 determines the segments I2_SEG300 and I2_SEG400 connecting the centers of two horizontal bases of the bounding boxes I2_BOX300 and I2_BOX400.

[0068] In step S30, the microprocessor 110 detects the ground marking line I1_MARQ1, and determines the segment I1_LIGN1 extending along the entire length of the ground marking line I1_MARQ1, according to for example one of the methods mentioned above.

[0069] In step S30bis, the microprocessor 210 detects the ground marking line I2_MARQ1, and determines the segment I2_LIGN1 extending along the entire length of the ground marking line I2_MARQ1.

[0070] In step S40bis, the microprocessor 210 determines the 3-dimensional coordinates of the segments I2_SEG300,12_SEG400,12_LIGN1, for example the vertices of the segments I2_SEG300,12_SEG400,12_LIGN1, in the frame rep2.

[0071] These 3-dimensional coordinates are obtained from a location and an orientation of the camera 200 in the frame rep2, for example in the memory of the microprocessor 210.

[0072] The characteristics of the roadway 600 and the ground 700 which surrounds it, for example in memory of the microprocessor 210 (for example, its altitude, its geographical coordinates, its slope, in the reference frame 2) are also used to determine, not only the segment I2_LIGN1, but also the segments I2_SEG300 and I2_SEG400, because the pedestrians are located directly on the ground 700 (i.e.: the bottom of the segments I2_SEG300 and I2_SEG400 touches the ground).

[0073] In step S40, the segments I2_SEG300,12_SEG400,12_LIGN1, the 3-dimensional coordinates of the segments I2_SEG300,12_SEG400,12_LIGN1, as well as the image 12, are transmitted, by radiofrequency communication, for example of the so-called “V2X” type, by the microprocessor 210 to the microprocessor 110 which receives them.

[0074] In step S50, the microprocessor 110 associates: • Segment Il_SEG300 to segment I2_SEG300, • Segment Il_SEG400 to segment I2_SEG400, • The segment I1_LIGN1 to the segment I2_LIGN1.

[0075] This association step can be implemented by one of the methods cited above, known to those skilled in the art.

[0076] In step S50, the microprocessor 110 converted into a fixed repi reference frame relative to to vehicle 100, the 3-dimensional coordinates of the segments:

[0077] Il_SEG300 equal to the coordinates of the segment I2_SEG300,

[0078] Il_SEG400 equal to the coordinates of the segment I2_SEG400,

[0079] I1_LIGN1 equal to the coordinates of the segment I2_LIGN1.

[0080] Step S50 comprises, for this, a step of determining a relative position of the reference point repi with respect to the reference point rep2, from a geographical location and an orientation of the vehicle 100 coming from the satellite geolocation module 130 (and, of course, from the reference point rep2).

[0081] In step S60, the microprocessor 110 determines an orientation of the camera 120, in the repi frame, from the 3-dimensional coordinates of the segments Il_SEG300, Il_SEG400 and I1_LIGN1, using one of the methods mentioned below.

[0082] In step S70, the microprocessor 110 analyzes the environment from images received from the camera 120 and controls the screen 140 from a result of this analysis.

Claims

Claims

1. Method for calibrating a first image sensor (120) included in a motor vehicle (100), implemented while the motor vehicle (100) is traveling in an environment (envi00) surrounding the motor vehicle (100), the method being characterized in that it comprises the following first steps: • Reception (S00), from the first image sensor (120), of a first image (II) of a part (plOO) of the environment (envlOO), acquired by the first image sensor (120), • Obtaining (S20, S30) first geometric elements (Il_SEG300, Il_SEG400,11_LIGN1) from the first image (II), • Reception (SOObis), from a second image sensor (200), of a second image (12) of the part (plOO) of the environment (envlOO), acquired by the second image sensor (200), the second image sensor being included in the environment (envlOO) and fixed relative to the ground, • Obtaining (S20bis, S30bis) second geometric elements (I2_SEG300,12_SEG400,12_LIGN1) from the second image (12), obtaining the second geometric elements (I2_SEG300,12_SEG400,12_LIGN1) comprising a determination (S40bis) of second 3-dimensional coordinates of each second element of the second geometric elements (I2_SEG300,12_SEG400,12_LIGN1), • Association (S50) of each first geometric element of the first geometric elements (Il_SEG300, Il_SEG400, I1_LIGN1) with an element of the second geometric elements (I2_SEG300, I2_SEG400, I2_LIGN1), • Determination (S60) of first 3-dimensional coordinates of each first element of the first geometric elements (Il_SEG300, Il_SEG400,11_LIGN1) from the second 3-dimensional coordinates of the element of the second geometric elements associated with said each first element during the association step, • Then, determination (S70) of an orientation of the first image sensor (120) of the motor vehicle (100), from the first 3-dimensional coordinates of each first element of the first geometric elements (Il_SEG300, Il_SEG400, I1_LIGN1).

2. Calibration method according to the preceding claim in which: • The first image sensor (120) and the second image sensor (200) are cameras, and • The first image (II) and the second image (12) are 2-dimensional images.

3. Calibration method according to the preceding claim in which: • The step of obtaining the second geometric elements (I2_SEG300, I2_SEG400, I2_LIGN1) comprises a step of determining (S10bis) second bounding boxes (I2_BOX400, I2_BOX300) of first mobile objects (300, 400) in the second image (12), at least a portion of the second geometric elements (I2_SEG300, I2_SEG400, I2_LIGN1) being obtained from the second bounding boxes, and • The step of obtaining the first geometric elements (I1_SEG300, I1_SEG400, I1_LIGN1), comprises a step of determining (S10) first bounding boxes (I1_BOX300, I1_BOX400) of the first mobile objects (I2_SEG300,12_SEG400,12_LIGN1) in the first image (II), at least part of the first geometric elements (Il_SEG300, Il_SEG400,11_LIGN1) being obtained from the first bounding boxes (I1_BOX300,11_BOX400), and

4. Calibration method according to the preceding claim in which: • The second bounding boxes (I2_BOX400, I2_BOX300) are: • Rectangular parallelepipeds, or • Cylinders, and And, the first bounding boxes (I1_BOX300, I1_BOX400) are rectangles.

5. A calibration method according to any preceding claim wherein the second geometric elements and the first geometric elements comprise segments.

6. Calibration method according to any one of claims 3 to 5 in which the first moving objects are pedestrians.

7. A calibration method according to any one of the preceding claims comprising second steps, the second steps being implemented by the motor vehicle (100), the second steps comprising: • Before the association step, a step of receiving the second image (12), and the second geometric elements (I2_SEG300, I2_SEG400, I2_LIGN1), via radiofrequency communication, • And, before the step of determining the first 3-dimensional coordinates of each first element of the first geometric elements (I1_SEG300, I1_SEG400, I1_LIGN1), a step of receiving the second 3-dimensional coordinates of each second element of the second geometric elements (I2_SEG300, I1_SEG400, I1_LIGN1), • The following steps: • The step of receiving, from the first image sensor (120), the first image (II),• The step of detecting the first geometric elements (Il_SEG300, Il_SEG400,11_LIGN1), • The association step, • The step of determining the first 3-dimensional coordinates of each first element of the first geometric elements (Il_SEG300, Il_SEG400, Il-LIGNl), • The step of determining the orientation of the first image sensor (120).,

8. Computer program comprising instructions, executable by a microprocessor or a microcontroller, for implementing the

9.

10. second steps according to claim 7, when executed by the microprocessor or the microcontroller. Electronic device (110) configured to implement second steps according to claim 7. Motor vehicle (100) comprising the electronic device (110) according to the preceding claim.

Images