Method and device for detecting a calibration fault in a stereoscopic vision system.

Abstract

A method for detecting a calibration defect in a stereoscopic vision system installed in a vehicle (10), the stereoscopic vision system comprising an array of at least two cameras (11, 12) arranged so as to each acquire an image of a scene from a different viewpoint, the optical axes representing an orientation of the field of view of each camera being oriented non-parallel, the method being characterized in that it comprises a step of determining a value representative of a calibration defect of the stereoscopic vision system based on a comparison result of a displacement matrix between the cameras (11, 12) and a matrix of extrinsic parameters of the stereoscopic vision system. Figure for the abstract: Figure 1

Description

Title of the invention: Method and device for detecting a calibration fault in a stereoscopic vision system. technical field

[0001] The present invention relates to methods and devices for detecting a calibration fault of a stereoscopic vision system embedded in a vehicle, for example in a motor vehicle. Technological background

[0002] Many modern vehicles are equipped with Advanced Driver-Assistance Systems (ADAS). Such ADAS systems are passive and active safety systems designed to eliminate human error in driving all types of vehicles. ADAS systems use advanced technologies to assist the driver while driving and thus improve performance. ADAS systems use a combination of sensor technologies to perceive the environment around a vehicle and then provide information to the driver or act on certain vehicle systems.

[0003] There are several levels of ADAS, such as reversing cameras and blind spot sensors, lane departure warning systems, adaptive cruise control or automatic parking systems.

[0004] Vehicle-mounted ADAS systems are powered by data obtained from one or more on-board sensors such as, for example, cameras. These cameras make it possible, in particular, to detect and locate other road users or any obstacles present around a vehicle in order, for example: - to adapt the vehicle's lighting according to the presence of other road users; - to automatically regulate the vehicle's speed; - to act on the braking system in case of risk of impact with an object.

[0005] The reliability of the data from these cameras and the proper functioning of the driver assistance devices using this data therefore depend on the correct calibration of the cameras. Summary of the present invention

[0006] One object of the present invention is to solve at least one of the problems of the technological background described above.

[0007] Another object of the present invention is to improve the reliability of the data from these cameras.

[0008] Another object of the present invention is to improve road safety, in particular by improving the reliability of AD AS systems powered by data obtained from at least one camera.

[0009] According to a first aspect, the present invention relates to a method for detecting a calibration fault in a stereoscopic vision system embedded in a vehicle, the stereoscopic vision system comprising a set of cameras of at least two cameras arranged so as to each acquire an image of a three-dimensional scene from a different point of view, the optical axes representing an orientation of the field of vision of each camera being oriented in a non-parallel manner, the method being characterized in that it comprises the following steps: - reception of first and second data respectively representative of a first and second image acquired by respectively a first and second camera of the set of cameras at the same first temporal instant of acquisition; - detection of at least one object in the scene by analyzing the first image, a set of first pixels of the first image being associated with at least one object; - determination of a set of second pixels in the second image corresponding to the set of first pixels; - determination of a displacement matrix between the first and second cameras as a function of the sets of first and second pixels; - comparison of the displacement matrix and a matrix of extrinsic parameters of the stereoscopic vision system, called the extrinsic matrix, the extrinsic parameters including a parameter representing a rotation and a parameter representing a translation in three dimensions between the first camera and the second camera, a result of the comparison being a so-called comparison matrix; - determination of a representative value of the calibration error as a function of the comparison matrix.

[0010] Such a method makes it possible to detect a calibration defect of the stereoscopic vision system by determining a value representative of a difference between a relative positioning between the first and second cameras determined for example during a previous calibration, represented here by the matrix of extrinsic parameters, and a current relative positioning, represented here by the displacement matrix.

[0011] According to an embodiment of the method, a calibration fault is detected when the representative value of the calibration fault is greater than a threshold, the representative value of the calibration fault being a standard of the comparison matrix.

[0012] According to another variant of the method, a calibration fault is detected when the representative value of the calibration fault is greater than a threshold, said value being an absolute value of an element of the comparison matrix.

[0013] Comparison to a threshold makes it possible to define a value from which a defect Calibration is, for example, something to consider; the stereoscopic vision system then requires a new calibration.

[0014] According to yet another variant of the process, the comparison matrix is ​​obtained by the following function: X -T - T* With: • X; the comparison matrix, • T; the displacement matrix, and • T*: the extrinsic matrix of the stereoscopic vision system.

[0015] According to an additional method variant, the determination of the representative value of the calibration error is obtained by the following formula: Il JT II =Lf|ï,| With: • || X || the representative value of a calibration fault, • X; the comparison matrix, and • xi: an element of the comparison matrix.

[0016] According to another variant of the method, the determination of the representative value of the calibration error is obtained by the following formula: II XII = (WC) 1 • || X || the representative value of a calibration fault, • X; the comparison matrix, and • xi: an element of the comparison matrix.

[0017] According to a further variant, the method further includes a step of rectifying the first and second images according to the extrinsic parameters of the stereoscopic vision system, so as to render horizontal epipolar lines in the first and second rectified images.

[0018] Rectifying the images then facilitates their analysis, thus simplifying the steps of determining a displacement matrix and comparison.

[0019] According to a variant of the method, the displacement matrix is ​​determined by a convolutional neural network associated with a monoscopic vision system comprising the first camera.

[0020] Such a convolutional neural network is commonly used to exploit parts of an image seen by one of the two cameras; it is then integrated, for example, into a prediction algorithm, by a monoscopic vision system, of the depth of an object located in the three-dimensional scene.

[0021] According to a second aspect, the present invention relates to a device for detecting a calibration fault in a stereoscopic vision system embedded in a vehicle, the device comprising a memory associated with a processor configured for implementing the steps of the method according to the first aspect of the present invention.

[0022] According to a third aspect, the present invention relates to a vehicle, for example of the automobile type, comprising a device as described above according to the second aspect of the present invention.

[0023] According to a fourth aspect, the present invention relates to a computer program which includes instructions adapted for carrying out the steps of the process according to the first aspect of the present invention, in particular when the computer program is executed by at least one processor.

[0024] Such a computer program may use any programming language and be in the form of source code, object code, or an intermediate form between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0025] According to a fifth aspect, the present invention relates to a computer-readable recording medium on which is recorded a computer program comprising instructions for carrying out the steps of the process according to the first aspect of the present invention.

[0026] On the one hand, the recording medium can be any entity or device capable of storing the program. For example, the medium can include a storage means, such as a ROM, a CD-ROM or a microelectronic circuit-type ROM, or a magnetic recording means or a hard disk drive.

[0027] On the other hand, this recording medium can also be a transmissible medium such as an electrical or optical signal, such a signal being able to be transmitted via an electrical or optical cable, by conventional or radio frequency, by self-directing laser beam, or by other means. The computer program according to the present invention can, in particular, be downloaded from an Internet-type network.

[0028] Alternatively, the recording medium may be an integrated circuit in which the computer program is incorporated, the integrated circuit being adapted to execute or to be used in the execution of the process in question. Brief description of the figures

[0029] Other features and advantages of the present invention will become apparent from the description of the particular and non-limiting embodiments of the present invention below, with reference to the attached Figures 1 to 4, in which:

[0030] [Fig-1] schematically illustrates a non-parallel stereoscopic vision system equipping a vehicle, according to a particular and non-limiting example of the present invention;

[0031] [Fig.2] illustrates a flowchart of the different operations of a process of detection of a calibration fault of a non-parallel stereoscopic vision system mounted in the vehicle of [Fig.1], according to a particular and non-limiting embodiment of the present invention;

[0032] [Fig.3] illustrates a flowchart of the different steps of a method for detecting a calibration fault of a non-parallel stereoscopic vision system mounted in the vehicle of [Fig.1], according to a particular and non-limiting embodiment of the present invention.

[0033] [Fig.4] schematically illustrates a device configured for the detection of a calibration fault of a non-parallel stereoscopic vision system mounted in the vehicle of [Fig.1], according to a particular and non-limiting embodiment of the present invention. Description of examples of achievements

[0034] A method and device for detecting a calibration fault of a stereoscopic vision system, mounted in a vehicle, will now be described in what follows with joint reference to Figures 1 to 4. The same elements are identified with the same reference signs throughout the description that follows.

[0035] The terms "first," "second" (or "firsts," "seconds"), etc., are used in this document by arbitrary convention to allow for the identification and distinction of different elements (such as operations, means, etc.) implemented in the embodiments described below. Such elements may be distinct or correspond to a single element, depending on the embodiment.

[0036] According to a particular and non-limiting example of an embodiment of the present invention, a method for detecting a calibration fault of a stereoscopic vision system embedded in a vehicle is for example implemented by a computer of the vehicle's embedded system controlling this stereoscopic vision system.

[0037] The stereoscopic vision system comprises a set of cameras, at least two of which are arranged so that each acquires an image of a scene from a different viewpoint, the optical axes representing an orientation of the field of view of each camera being oriented non-parallel. The stereoscopic vision system is thus said to be non-parallel.

[0038] To this end, the method comprises steps for receiving first and second data points, respectively representative of a first and second image acquired by a first and second camera from the camera array at the same first acquisition time. The detection of at least one object in the scene is performed by analyzing the first image, a set of first pixels of the first image being associated with at least one object. The determination of a set of second pixels in the second image corresponding to the set of first pixels is then performed. pixels.

[0039] A displacement matrix between the first and second cameras is determined as a function of the sets of first and second pixels and this displacement matrix is ​​compared to a matrix of extrinsic parameters of the stereoscopic vision system, a result of the comparison being a so-called comparison matrix.

[0040] A representative value of the calibration error is then determined as a function of the comparison matrix.

[0041] Fig. 1 schematically illustrates a non-parallel stereoscopic vision system equipping a vehicle 10 moving in an environment 1, according to a particular and non-limiting embodiment of the present invention;

[0042] Such an environment 1 corresponds, for example, to a road environment consisting of a network of roads accessible to the vehicle 10.

[0043] In this example, vehicle 10 corresponds to a vehicle with an internal combustion engine, an electric motor(s), or a hybrid vehicle with an internal combustion engine and one or more electric motors. Vehicle 10 thus corresponds, for example, to a land vehicle such as a car, a truck, a bus, or a motorcycle. Finally, vehicle 10 corresponds to an autonomous or non-autonomous vehicle, that is to say, a vehicle operating according to a predetermined level of autonomy or under the total supervision of the driver.

[0044] The vehicle 10 advantageously comprises several on-board cameras 11, 12, each configured to acquire images of a scene in the environment of the vehicle 10. This set of cameras 11, 12 forms the stereoscopic vision system. Two cameras 11 and 12 are illustrated in [Fig. 1]. The invention is not limited, however, to a stereoscopic vision system comprising two cameras but extends to any stereoscopic vision system comprising two or more cameras, for example, two, three, four, or five cameras.

[0045] The two cameras 11, 12 have known intrinsic parameters. These parameters include, in particular: - the focal length fl of the first camera 11; - the focal length f2 of the second camera 12; - distortions which are due to imperfections in the optical system of each camera; - the direction Cl of the optical axis of the first camera 11; - the C2 direction of the optical axis of the second camera 12; and - the respective resolutions of cameras 11, 12.

[0046] The intrinsic parameters characterize the transformation that associates, for an image point, the camera coordinates to the pixel coordinates, in each camera. These parameters do not change if the camera is moved.

[0047] Distortions, which are due to imperfections in the optical system such as Defects in the shape and positioning of camera lenses will deflect light beams and thus induce a positioning error for the projected point compared to an ideal model. It is then possible to refine the camera model by introducing the three distortions that generate the most significant effects: radial, decentering, and prismatic distortions, induced by defects in lens curvature, parallelism, and coaxiality of the optical axes. In this example, the cameras are assumed to be perfect, meaning that distortions are either not taken into account or their correction is addressed during image acquisition.

[0048] These two cameras 11, 12 are arranged so that each acquires an image of a scene from a different viewpoint. The first viewpoint is, for example, located on or in the left-hand rearview mirror of vehicle 10 or at the top of the windshield of vehicle 10. The second viewpoint is, for example, located on or in the right-hand rearview mirror of vehicle 10 or at the top of the windshield of vehicle 10. If both cameras are located at the top of the windshield of the vehicle, they are then positioned at a certain distance. In this example, the first camera 11 is located at the top of the windshield of vehicle 10, and the second camera 12 is located in the right-hand rearview mirror of vehicle 10.

[0049] A first marker is associated with the first camera 11: - the direction of the y-axis is defined by the position of the second camera 12, so as to place the second camera 12 on the y-axis of the first camera 11. The distance B separating the two cameras 11,12 is called the reference base (in English "baseline") and the direction separating the two cameras 11,12 is that of the x-axis; - the direction of the y-axis is defined as orthogonal to that of the x-axis and orthogonal to that of the optical axis Cl of the first camera 11; - The direction of the z-axis is defined as orthogonal to the directions of the x and y axes. The three axes x, y and z thus form an orthonormal coordinate system.

[0050] Extrinsic parameters related to the position of cameras 11, 12 are the following parameters: - 3 translations in the x, y, and z directions: Tx, Ty, and Tz forming the translation vector T; and - 3 rotations around the x, y and z axes: Rx, Ry and Rz, constituting the rotation matrix R.

[0051] Determining the extrinsic parameters constitutes the problem of calibrating a stereoscopic vision system.

[0052] A key constraint of stereoscopic vision systems used in automobiles is, for example, the large distance between the two cameras. Indeed, to cover a measurement range of 200 meters, the reference base must be 60 cm for cameras commonly used in this field.

[0053] The two cameras 11, 12 acquire images of a scene located in front of the vehicle 10, the first camera alone covering a first acquisition field 13, the second camera alone covering a second acquisition field 14 and the two cameras 11, 12 both covering a third acquisition field 15. The first and third acquisition fields 13, 15 thus allow a monoscopic view of the scene by the first camera 11, the second and third acquisition fields 14, 15 allow a monoscopic view of the scene by the second camera 12 and the third acquisition field 15 allows a stereoscopic view of the scene by the stereoscopic vision system composed of the two cameras 11, 12.

[0054] The directions Cl, C2 of the optical axes representing an orientation of the field of vision of each camera are oriented non-parallel so as to obtain the third acquisition field 15 of the environment 1 as wide as possible.

[0055] It is evident that it is possible to use such a stereoscopic vision system to take images of scenes located on the sides or behind the vehicle 10 by equipping it with cameras placed and oriented differently.

[0056] The images acquired by cameras 11, 12 at a given time t1 are in the form of data representing pixels characterized by: - coordinates in each image; and - data relating to the colors and brightness of objects in the observed scene, for example in the form of RGB (Red Green Blue) or HSL (Hint, Saturation, Luminosity) colorimetric coordinates.

[0057] The images acquired by cameras 11 and 12 represent views of the same scene taken from different viewpoints, the camera positions being distinct. For example, this scene includes: - buildings; - road infrastructure; - other stationary users, for example a parked vehicle; and / or - other mobile users, for example another vehicle, a cyclist or a moving pedestrian.

[0058] These images are sent to a computer of a device equipping the vehicle 10 or stored in a memory of a device accessible to a computer of a device equipping the vehicle 10.

[0059] Figure [Fig.2] illustrates a flowchart of the different operations of a process for detecting a calibration fault of a non-parallel stereoscopic vision system embedded in the vehicle of [Fig.1], according to a particular and non-limiting embodiment of the present invention.

[0060] Process 2 is, for example, implemented by one or more processors of one or more computers embedded in the vehicle 10, for example by a computer controlling the stereoscopic vision system.

[0061] In a first operation 21, the computer receives first data representative of a first image acquired by a first camera 11 of the set of cameras at a first time instant tl of acquisition.

[0062] In a second operation 22, the computer receives second data representing a second image acquired by a second camera 12 of the set of cameras at the same first time instant tl of acquisition.

[0063] The two images received correspond to two views of the same scene taking place around the vehicle 10 at the same first given time instant tl.

[0064] The following operation 23 consists of detecting at least one object in the scene by analyzing the first image, a set of first pixels of the first image being associated with at least one object. This operation amounts to extracting the feature elements from the first image.

[0065] A method for extracting features is known to a person skilled in the art, such as the SIFT method (from the English "Scale Invariant Feature Transform", in French "Transformation de caractère invariante à l'échelle") or the "Corner Detection" method (in French "Détection d'Angle").

[0066] According to a particular embodiment, in order to facilitate the analysis of the two received images, the first and second images are rectified in an operation 28, following a method known to those skilled in the art. Such a method is described, for example, in "Projective Rectification of Uncalibrated Infrared Stereo Images with Global Consideration of Distortion Minimization" by Benoit Ducarouge, Thierry Sentenac, Florian Bugarin and Michel Devy, dated July 16, 2009.

[0067] The rectification method consists of reorienting the epipolar lines so that they are parallel with the horizontal axis of the image. This method is described by a transformation that projects the epipoles to infinity and whose corresponding points are necessarily on the same ordinate.

[0068] A rectification algorithm consists, for example, of 4 steps: - Rotate (virtually) the first camera 11 so that the epipole goes to infinity along the horizontal axis of the frame associated with it; - Apply the same rotation to the second camera to return to the initial geometric configuration; - Rotate the second camera by the rotation associated with the rotation matrix 'R', corresponding to the extrinsic parameter of the starting stereoscopic vision system; - Adjust the scale in both camera reference points.

[0069] It should be noted that this operation 28 is optional; indeed, the rectification simplifies the matching of pixels in stereo images. The corresponding pixel in the The second image, one pixel away from the first image (and vice versa), is positioned on the same line. Based on knowledge of the epipolar geometry and therefore a fundamental matrix of the stereoscopic vision system, the objective is then to determine a pair of projective transformations, called homographies, which reorient the epipolar projections parallel to the lines of the images, and therefore to the horizontal axis of the rectified cameras.

[0070] The next operation consists of determining 24 a set of second pixels in the second image corresponding to the set of first pixels, this operation is called "stereo matching" (in English "feature matching").

[0071] Stereo matching is the process of finding pixels in stereoscopic views that correspond to the same 3D point in the three-dimensional scene. Rectified epipolar geometry simplifies this process of finding matches on the same epipolar line. It is not necessary to calculate the coordinates of the 3D point to find the corresponding pixel on the same line in the other image.

[0072] The stereo matching operation 24 is performed, for example, by implementing an optical flow calculation method based on the set of first pixels. Such a method is described in particular in "UnOS: Unified Unsupervised Optical-flow and Stereo-depth Estimation by Watching Videos" by Yang Wang, Peng Wang, Zhenheng Yang, Chenxu Luo, Yi Yang and Wei Xu, June 2019.

[0073] The optical flow calculation method is performed, for example, by a convolutional neural network (CNN). This type of tool is commonly used in image processing.

[0074] In an operation 25, a displacement matrix between the first 11 and second 12 cameras is determined as a function of the sets of first and second pixels.

[0075] This displacement matrix allows us to evaluate the displacement between the shooting position of the first image and the shooting position of the second image.

[0076] A computer for the stereoscopic vision system or a computer for a monoscopic vision system including the first camera 11 determines this matrix, for example by means of a convolutional neural network. It is common to have this type of convolutional neural network in order to exploit the parts of the first image that include objects of the three-dimensional scene located outside the field of view of the second camera 12, i.e. in the first acquisition field 13.

[0077] The determination of this displacement matrix is ​​known to the person skilled in the art and is presented, for example, in the document Unsupervised Learning of Depth and Ego-Motion from Video by Tinghui Zhou, Matthew Brown, Noah Snavely and David G. Lowe published on August 1, 2017.

[0078] In an operation 26, the displacement matrix is ​​compared to a matrix of extrinsic parameters of the stereoscopic vision system, called the extrinsic matrix.

[0079] The extrinsic parameters include a parameter representing a rotation and a parameter representing a translation in three dimensions between the first camera 11 and the second camera 12.

[0080] The result of the comparison is a so-called comparison matrix.

[0081] According to a particular embodiment, the comparison matrix is ​​obtained by the following function:

[0082] [Math.l] X = T - T*

[0083] With: • X; the comparison matrix, • 7: the displacement matrix, and • T: the extrinsic matrix of the stereoscopic vision system.

[0084] The extrinsic matrix of the stereoscopic vision system is defined in particular according to the values ​​of the following extrinsic parameters: • Rx, Ry, and Rz for rotation, and • Tx, Ty and Tz for translation.

[0085] If the first and second images have been previously rectified during an operation 28, then the extrinsic matrix of the stereoscopic vision system after rectification is particular and has the following form:

[0086] [Math.2] 0 0 0 B 0 0 0 0 .0 0 0 0.

[0087] With B the reference basis.

[0088] In an operation 37, a representative value of the calibration fault is determined as a function of the comparison matrix.

[0089] According to a first particular embodiment, the determination of the representative value of the calibration error is obtained by the following formula:

[0090] [Math.3] |X|| =E,k,|

[0091] With: • || X || the representative value of a calibration fault, • X; the comparison matrix, and • xi: an element of the comparison matrix.

[0092] According to a second particular embodiment, the determination of the value is obtained representative of a calibration defect by the following formula:

[0093] [Math.4] It X II = (¾^ 2 ) 1

[0094] With: • || X || the representative value of a calibration fault, • X; the comparison matrix, and • xi: an element of the comparison matrix.

[0095] According to the first and second specific embodiments, the representative value of the calibration error is a norm of the comparison matrix. Such a value makes it possible to determine an overall calibration error.

[0096] According to a third particular embodiment, the determination of the representative value of the calibration error is an absolute value of an element of the comparison matrix. The representative value of the calibration error is then obtained by the following formula:

[0097] [Math.5] E - | xJ

[0098] With: • E the representative value of a calibration defect, • xi: an element of the comparison matrix.

[0099] According to this third example, if the first and second images have been rectified and if Xi corresponds to the element located at the position of the representative element of the reference basis in the extrinsic matrix, then the representative value of a calibration error is characteristic of a reference basis error. Such an error also generates a scale error when determining depths using the stereoscopic vision system.

[0100] For all the embodiments above, the representative value of the calibration error is, for example, compared to a threshold. A calibration error is then detected when the representative value of the calibration error is greater than this threshold.

[0101] The threshold value is, for example, a value obtained as a percentage of the norm of the extrinsic matrix for the first and second particular embodiments, or a percentage of the absolute value of the reference basis for the third particular embodiment. The percentage is, for example, 103%, 105%, or 110% and is defined, for example, according to the permissible error in determining depths by the stereoscopic vision system.

[0102] In conclusion, this invention proposes a process for detecting a calibration defect in the stereoscopic vision system. The calculation operation of the value A representative value of such a defect allows for the detection of a calibration error related to extrinsic parameters of the stereoscopic vision system, particularly the reference frame. A new calibration of the stereoscopic vision system is recommended, for example, when the representative value of the calibration error exceeds a defined threshold.

[0103] Figure [Fig. 3] illustrates a flowchart of the different steps of a method for detecting a calibration fault of a non-parallel stereoscopic vision system mounted in the vehicle of Figure [1], according to a particular and non-limiting embodiment of the present invention.

[0104] The method 3 is for example implemented by a device on board the first vehicle 10 or by the device 4 of the [Fig.2].

[0105] In a first step 31, first representative data of a first image acquired by a first camera 11 of said camera set at a first time instant tl of acquisition are received.

[0106] In a second step 32, second data representing a second image acquired by a second camera 12 of said camera set at the same first time instant tl of acquisition are received.

[0107] In a third step 33, at least one object is detected in the scene by analyzing the first image, a set of first pixels of the first image being associated with at least one object.

[0108] In a fourth step 34, a set of second pixels corresponding to the set of first pixels is determined in the second image.

[0109] In a step 35, a displacement matrix between the first 11 and second 12 cameras is determined as a function of the sets of first and second pixels.

[0110] In a step 36, the displacement matrix and an extrinsic parameter matrix of the stereoscopic vision system, called the extrinsic matrix, are compared, one result of the comparison being a so-called comparison matrix.

[0111] The extrinsic parameters include a parameter representing a rotation and a parameter representing a translation in three dimensions between the first camera 11 and the second camera 12.

[0112] In a step 37, a representative value of the calibration error is determined as a function of the comparison matrix.

[0113] According to one variant, the variants and examples of the operations described in relation to Figures 1 and 2 apply to the steps of the process in [Fig.3].

[0114] Figure 4 schematically illustrates a device 4 configured for detecting a calibration fault in a stereoscopic vision system mounted in a vehicle 10, according to a particular and non-limiting embodiment of the present invention. The device 4 corresponds, for example, to a device mounted in the first vehicle 10, for example a computer.

[0115] Device 4 is, for example, configured to carry out the operations and / or steps described opposite Figures 1, 3, and 4. Examples of such a device 4 include, but are not limited to, embedded electronic equipment such as a vehicle's on-board computer, an electronic control unit such as an ECU (Electronic Control Unit), a smartphone, a tablet, or a laptop computer. The elements of device 4, individually or in combination, may be integrated into a single integrated circuit, into several integrated circuits, and / or into discrete components. Device 4 may be implemented in the form of electronic circuits or software (or computer) modules, or a combination of electronic circuits and software modules.

[0116] The device 4 comprises one (or more) processor(s) 40 configured to execute instructions for carrying out the steps of the process and / or for executing instructions from the software embedded in the device 4. The processor 40 may include integrated memory, an input / output interface, and various circuits known to those skilled in the art. The device 4 further comprises at least one memory 41, for example, volatile and / or non-volatile memory, and / or includes a memory storage device that may include volatile and / or non-volatile memory, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic disk, or optical disk.

[0117] The computer code of the embedded software(s), including the instructions to be loaded and executed by the processor, is for example stored in memory 4L

[0118] According to various particular and non-limiting embodiments, the device 4 is coupled in communication with other similar devices or systems (for example other computers) and / or with communication devices, for example a TCU (Telematic Control Unit), for example via a communication bus or through dedicated input / output ports.

[0119] According to a particular and non-limiting embodiment, the device 4 includes a block 42 of interface elements for communicating with external devices. The interface elements of the block 42 include one or more of the following interfaces: - Radio frequency (RF) interface, for example Wi-Fi® (according to IEEE 802.11), for example in the 2.4 or 5 GHz frequency bands, or Bluetooth® (according to IEEE 802.15.1), in the 2.4 GHz frequency band, or Sigfox using UBN (Ultra Narrow Band) radio technology, or LoRa in the 868 MHz frequency band, LTE (Long-Term Evolution), LTE-Advanced (or in French LTE-advanced); - USB interface (from the English "Universal Serial Bus" or "Universal Serial Bus" in French); HD MI interface (from the English "High Definition Multimedia Interface", or "High Definition Multimedia Interface" in French); - LIN interface (from the English "Local Interconnect Network", or in French "Réseau interconnecté local").

[0120] According to another particular and non-limiting embodiment, the device 4 includes a communication interface 43 which enables communication with other devices (such as other computers in the embedded system) via a communication channel 430. The communication interface 43 corresponds, for example, to a transmitter configured to transmit and receive information and / or data via the communication channel 430. The communication interface 43 corresponds, for example, to a wired network of the CAN (Controller Area Network), CAN FD (Controller Area Network Flexible Data-Rate), FlexRay (standardized by ISO 17458) or Ethernet (standardized by ISO / IEC 802-3) type.

[0121] According to a particular and non-limiting embodiment, the device 4 can provide output signals to one or more external devices, such as a display screen 440, touch or not, one or more speakers 450 and / or other peripherals 460 (projection system) via the output interfaces 44, 45, 46 respectively. According to a variant, one or more of the external devices is integrated into the device 4.

[0122] Of course, the present invention is not limited to the embodiments described above but extends to a method for detecting a calibration fault or to a method for calibrating a stereoscopic vision system installed in a vehicle, which would include secondary steps without departing from the scope of the present invention. The same would apply to a device configured for implementing such a method.

[0123] The present invention also relates to a vehicle, for example an automobile or more generally an autonomous land-powered vehicle, comprising the device 4 of [Fig.4].

Claims

Claims

1. Method for detecting a calibration fault in a stereoscopic vision system on board a vehicle (10), the stereoscopic vision system comprising a set of cameras of at least two cameras (11, 12) arranged so as to each acquire an image of a three-dimensional scene from a different point of view, the optical axes representative of an orientation of the field of vision of each camera being oriented in a non-parallel manner, said method being characterized in that it comprises the following steps: - reception (31, 32) of first and second data respectively representative of a first and second image acquired by respectively a first and second camera (11, 12) of said set of cameras at the same first acquisition time instant;- detecting (33) at least one object in said scene by analyzing said first image, a set of first pixels of the first image being associated with said at least one object; - determining (34) a set of second pixels in said second image corresponding to said set of first pixels; - determining (35) a displacement matrix between said first and second cameras (11, 12) as a function of said sets of first and second pixels; - comparing (36) said displacement matrix and a matrix of extrinsic parameters of said stereoscopic vision system, called extrinsic matrix, said extrinsic parameters comprising a parameter representative of a rotation and a parameter representative of a translation in three dimensions between the first camera (11) and the second camera (12), a result of said comparison being a so-called comparison matrix;- determination (37) of a value representative of said calibration defect as a function of said comparison matrix;

2. The method of claim 1, wherein a calibration fault is detected when said value is greater than a threshold, said value being a standard of said comparison matrix.

3. The method of claim 1, wherein a calibration fault is detected when said value is greater than a threshold, said value being an absolute value of an element of said comparison matrix.

4. Method according to one of claims 1 to 3, for which the comparison matrix is obtained by the following function: X = T -T* With: • X; the comparison matrix, • T; the displacement matrix, and • T*: the extrinsic matrix of the stereoscopic vision system.

5. Method according to claim 4, for which said determination (37) of said value is obtained by the following formula: Il X II =E,.kl With: • || X || the value representative of a calibration defect, • X ; the comparison matrix, and • xi: an element of the comparison matrix.

6. Method according to claim 4, for which said determination (37) of said value is obtained by the following formula: II XII • || X || the value representative of a calibration defect, • X ; the comparison matrix, and • x>: an element of the comparison matrix.

7. Method according to one of claims 1 to 6, further comprising a step of rectifying (28) the first and second images as a function of said extrinsic parameters of the stereoscopic vision system, so as to render horizontal epipolar lines in the first and second rectified images.

8. Method according to one of claims 1 to 7, for which the displacement matrix is determined by a convolutional neural network associated with a monoscopic vision system comprising the first camera (11).

9. Device (4) for detecting a calibration fault in a stereoscopic vision system on board a vehicle (10), said device (4) comprising a memory (41) associated with at least one processor (40) configured for implementing the steps of the method according to any one of claims 1 to 8.

10. Vehicle (10) comprising the device (4) according to claim 9.

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