WO2025191163A1 - Method for calibrating onboard cameras and enriching a calibration database shared by multiple vehicles - Google Patents

Abstract

The invention relates to a method for calibrating cameras (2), the method comprising: - prior to calibrating a camera (2) of a vehicle (1), selecting at least one viable candidate zone for calibration on the basis of: - the location of the vehicle (1) and/or a route thereof entered into the navigation system (7); and - information relating to the possibility of self-calibration of the camera (2) on the basis of the environment of the vehicle (1) as observed by the camera (2); - acquiring images required for the self-calibration of the camera when the vehicle (1) is present in the candidate zone for calibration (2); - enriching and/or updating the database at least when the result of the self-calibration is either success or failure, including at least information relating to the result of the self-calibration in the candidate zone.

Description

Description

Title of the invention: Method for calibrating on-board cameras and enriching a calibration database, shared by several vehicles

Technical field

[0001] The present invention relates to autonomous vehicles or vehicles offering the possibility of assisted driving, equipped with cameras which are arranged to observe the road and the environment of the vehicle and provide the necessary data to the on-board computer which manages the driving and/or assists the driver.

[0002] The invention relates more particularly to a method of calibrating the cameras on board the vehicle.

[0003] By “vehicle” we mean land vehicles, aircraft and boats.

[0004] The term “camera” encompasses digital image acquisition devices operating in the visible or non-visible spectrum, of any resolution and technology.

Prior art

[0005] Cameras are sensors widely used in vehicles for advanced driver assistance systems (ADAS), parking, infotainment and multimedia applications in general. In order to provide a correct image, each camera requires calibration of different intrinsic and extrinsic parameters.

[0006] The intrinsic parameters are internal to the camera and include in particular the focal length, the image magnification factors or “aspect ratio”, the distortion parameter (“skew factor”) and the coordinates of the projection of the optical center of the camera on the image plane (“principal point”).

[0007] The extrinsic parameters define the positioning of a frame of reference linked to the camera in relation to another given frame of reference, and include in particular the rotation and translation components for moving from one frame of reference to another.

[0008] A study on camera self-calibration is published by Olivier Faugeras, QT Luong and Stephen J. Maybank in European Conference on Computer Vision, 19 May 1992

[0009] A conventional initial calibration process is generally implemented in the factory at the end of production. This calibration can conventionally be carried out using targets as described in publications US 9,978,146 B2 and CN 107,133,988 B.

[0010] It is also known to carry out the calibration while driving with the vehicle equipped with the camera to be calibrated along a route provided for this purpose, as described in US 11,265,514 B2.

[0011] It is often necessary to carry out a calibration during the life of the vehicle. automatic or self-calibration of cameras. This method consists of determining different camera parameters directly from several uncalibrated images and unstructured scenes, notably without targets or other special objects, unlike the classic initial calibration process defined above.

[0012] Publications US 9,201,425 B1, JP 6458439 B2, WO 2019/233330, and US 110 554951 B2 describe various methods for self-calibration of motor vehicle cameras.

[0013] US patent 10970 878 B2 discloses a method for automatically calibrating the cameras of a motor vehicle based on the use of a reference map. A calibration model is generated from the comparison between certain location characteristics (or “features”) identified on the one hand on images taken by the vehicle’s cameras and on the other hand entered on the reference map, knowing where the vehicle is located.

[0014] This solution is not entirely satisfactory since it requires the use of a relatively large high-definition map (or “HDmap”). By “high-definition map” is meant a map including relatively more precise information than a traditional map, such as for example the number of lanes on roads, road markings, or the types and positions of traffic signs. In addition, when the latter is contained on a remote server, this method may require the transfer of a relatively large volume of data, which is restrictive with regard to the bandwidth of the associated communication system.

Summary of the invention

[0015] The invention aims to facilitate the self-calibration of the cameras of a vehicle and it achieves this, according to one of its aspects, by means of a method for calibrating on-board cameras and enriching a calibration database, shared by several vehicles each equipped with at least one on-board camera to be calibrated, a navigation system allowing the location of the vehicle and a map in memory and/or access to this map, the latter listing areas that can be used by the vehicle and associated location characteristics likely to be useful for calibrating the on-board camera, method comprising:

- prior to calibrating a vehicle camera, the selection of at least one practicable area suitable for calibration from:

- the location of the vehicle and/or a route of the vehicle entered into the navigation system and

- information relating to the possibility of self-calibration of the camera from the environment of the vehicle as observed by the camera, this information being provided by the calibration database and/or from the map,

[0016] - the acquisition of images necessary for self-calibration of the camera when the vehicle is present in the area candidate for calibration,

[0017] - enriching and/or updating the database at least when the result of F autocalibration is one of success and failure, at least with information relating to the result of F autocalibration in said candidate zone.

[0018] By “practicable area”, it is meant that the area is accessible by the vehicle, and can in particular be reached or crossed by it. In particular, all the practicable areas of a given terrestrial region form a convex sub-region.

[0019] By “suitable for calibration” area is meant an area with specific location characteristics observable by the camera and allowing the camera to successfully self-calibrate. An area is thus all the more qualified as suitable for calibration as the probability associated with successful calibration is high.

[0020] Thanks to the invention, the data flows required for carrying out F self-calibration can remain relatively small.

[0021] The use of the shared database according to the invention leads to a high-performance and relatively inexpensive self-calibration method. The number of images to be processed in order to calibrate the camera can be reduced due to the quality of the specific location characteristics in the area, as well as the associated algorithmic complexity.

[0022] The method according to the invention can also make it possible to plan the execution of the various associated tasks in time as best as possible, in order to reduce the computational load relating to self-calibration and any additional costs linked to the quantity of computer memory required.

[0023] Enriching the database makes it possible to obtain over time data useful for self-calibration that is increasingly precise, reliable and exhaustive, and therefore to further improve calibration performance.

[0024] The invention makes it possible to efficiently track and list information representative of the capacity of a given area to allow correct calibration of the camera.

[0025] The invention can in particular make it possible to have calibration performances comparable to those which would be obtained in the factory.

[0026] Furthermore, replacing factory calibration with self-calibration reduces factory vehicle production time and costs and reduces the need for maintenance.

[0027] Furthermore, the invention does not require the use of high-definition mapping, and a gain can thus be achieved in terms of the overall cost of the system.

[0028] The need for camera calibration can be previously identified from the detection of a defect or distortion in at least one image provided by it, and/or from a command from a user of the vehicle or an operator responsible for its maintenance.

[0029] The aforementioned candidate zone may already be listed in the database, the latter then comprising a corresponding label and/or a score representative of the self-calibration success rate associated with the zone.

[0030] In this way, the continuous updating of the score of an area by the different vehicles that go there makes it possible to obtain a precise and reliable dynamic indicator of the capacity of said area to allow successful self-calibration. It is thus possible to update the score according to the results of the self-calibration attempts of the different vehicles, in particular by incrementing or decrementing it, in particular by a predefined value, in particular constant, at each successful or unsuccessful attempt, or by a predefined value given by a law making the increment value depend on the number of evaluations already carried out by the different vehicles.

[0031] The score update can in particular be carried out as a function of a number of consecutively positive or negative evaluations of the candidate zone, for example when several consecutive evaluations are either positive or negative. In this way, it is possible to dynamically amplify the trend of the evaluations of said zone and to converge more quickly towards a score representative of the actual performance of the latter.

[0032] It is possible to update the score in real time or in delayed time. The vehicle can, for example, store information relating to the success or failure of the self-calibration attempt, and subsequently enrich the database based on the result, for example when the conditions for a low-cost data transfer are obtained. Alternatively, the score is updated substantially at the same time as the success or failure of the self-calibration attempt is noted.

[0033] The selected candidate area may be the one with the highest score.

[0034] The value of the score in the database can be initialized, before feedback from the vehicles, from a probability of calibration success relative to said practicable area, calculated from the map.

[0035] The candidate area may not be listed in the database, the selection of the candidate area being carried out from a probability of success of F autocalibration obtained from the map or calculated from it.

[0036] The selected candidate area may be the one with the highest probability of success.

[0037] The probability of calibration success can thus be calculated from location characteristics suitable for calibration identified on the map, in particular the presence of linear road segments, multi-lane road segments, segments of roads near an urban area or a city center, traffic signs, road markings, peripheral infrastructure elements such as bus stops, telephone boxes or fire hydrants, and/or a value of the slope of a road segment.

[0038] Alternatively, the probability of calibration success is already provided in the map and extracted from it without calculation.

[0039] The probability of success of the calibration can be refined from dynamic information, in particular available parking spaces with ground markings at a given time, for example outside store opening hours it is likely that the parking lot will be little occupied and vice versa, and/or from real-time geolocated traffic information, dense traffic being likely to reduce the visibility of the markings. This makes it possible to improve the accuracy of the estimation of the probability of success of the calibration at a given time.

[0040] The map and/or the probability of success of the calibration can also be updated, if necessary, according to new location characteristics suitable for calibration identified on the images provided by at least one camera of a vehicle moving in said area.

[0041] When the calibration is successful, the vehicle can communicate to a database update server a success notification for the area where the calibration was carried out.

[0042] When the calibration fails, the vehicle can communicate to a database update server a failure notification for this area.

[0043] The base can list at least one label identifying the areas recognized as suitable for calibration, or vice versa.

[0044] When the calibration fails and the score associated with said zone becomes lower than a predefined threshold, a failure label can be recorded and/or updated in the database.

[0045] The presence of a label in the database makes it possible to effectively distinguish areas suitable for calibration from areas not suitable for calibration. It is then possible, for example, to filter the data relating to the areas, during the search phase for an area, so as to consider only those suitable for calibration.

[0046] In the presence of reliable means of communication between the vehicle and a remote server through which access to the database is made, it is not necessary to provide for the duplication of all or part of the database in a memory of the vehicle. However, in order to facilitate consultation of the database and shorten access times, the database may, if necessary, be duplicated at least partially in the memory of the vehicle, and the enrichment of said database may be carried out deferred, for example by a merging operation.

[0047] The map can be low or medium resolution. [0048] The invention also relates to a vehicle equipped with at least one on-board camera, a navigation system enabling the vehicle to be located and a map in memory and/or access to this map, the latter listing areas that can be used by the vehicle and associated location characteristics that may be useful for calibrating the on-board camera, the vehicle having access to a calibration database shared by several vehicles, access to the database being via a means of communication, the vehicle further comprising a computer program product comprising code instructions which, when executed on a computer system of the vehicle, enable the implementation of the method according to the invention as defined above.

[0049] Such a computer program product can make it possible to implement the method with all or part of the method characteristics defined above.

Brief description of the drawings

[0050] The invention may be better understood by reading the detailed description which follows, non-limiting examples of its embodiment, and by examining the attached drawing, in which:

[0051] [Fig.l] [Fig.l] illustrates, schematically and partially, an example of equipment of a vehicle adapted to implement the method according to the invention.

[0052] [Fig.2] [Fig.2] illustrates, schematically and partially, an example of the architecture of a system suitable for implementing the method according to the invention.

[0053] [Fig.3] [Fig.3] is a schematic representation of an example of data relating to practicable areas as it could be listed in the database.

[0054] [Fig.4] [Fig.4] is a schematic representation of an example of data relating to passable areas as it might be listed on the map.

[0055] [Fig.5] [Fig.5] is a block diagram illustrating steps of an example of a calibration method according to the invention.

[0056] [Fig.6] [Fig.6] illustrates steps of an example of a method for calibrating and enriching the shared database based on an identification of the area from the map.

[0057] [Fig.7] [Fig.7] illustrates steps of an example of a self-calibration process and enrichment of the calibration database from an identification of listed zones.

[0058] [Fig.8] [Fig.8] is a schematic representation of an example display on a user interface.

Detailed Vehicle Description

[0059] [Fig.l] illustrates an example of a motor vehicle in top view. having equipment suitable for implementing the method according to the invention.

[0060] The vehicle 1 may be thermal, for example of the gasoline, diesel, gas, hydrogen, or hybrid type, or even electric.

[0061] The vehicle 1 may comprise different sensors, in particular at least one camera 2.

[0062] The camera 2 can be mounted on the roof, on one side, at the front, at the rear, outside or inside the vehicle 1, in particular behind the windshield.

[0063] The vehicle 1 may optionally comprise, as illustrated, at least one radar 3 making it possible to precisely measure the distances of objects present in the vicinity of the vehicle 1.

[0064] In the example considered, the vehicle 1 has left and right side radars 3, a front radar 4.

[0065] The vehicle 1 is also equipped with a computer 5, which receives data from the camera 2 and any radars 3,4.

[0066] The computer 5 comprises one or more processors executing one or more programs allowing the implementation of the method according to the invention.

[0067] In particular, the computer 5 can control the various tasks necessary for carrying out the self-calibration of the camera 2 at least.

[0068] The computer 5 may be composed of all or part of an on-board computer system comprising, for example, in addition to the aforementioned processor(s), at least one RAM memory, at least one ROM memory used to save the applications of the vehicle 1, one or more possible digital-to-analog converters as well as one or more input/output interfaces used to communicate with the various sensors.

[0069] The computer 5 can access an on-board memory 6 storing a digital map enabling it to be provided with information relating to the environment of the vehicle 1. The map can in particular enable the identification of the different zones 33 that can be used by the vehicle.

[0070] The map can be of variable resolution, of the road, topographic, orthophotographic, satellite, nautical, aeronautical type, or even of the hybrid type by combining several types of views.

[0071] The map may include two-dimensional or even three-dimensional data, and in particular the locations (longitude and latitude) of the paths that can be used in a given space.

[0072] The map may in particular include additional information relating to certain specific areas or locations, such as for example the presence and/or form of infrastructures, of characteristic places in the environment (for example historical monuments or rivers). [0073] The vehicle 1 further comprises a navigation and location system 7, preferably with a GNSS receiver, for example of the GPS type, enabling in particular the driver to enter a route for the vehicle 1 and/or the latter to know its position in real time.

Autocalibration system

[0074] [Fig.2] shows a self-calibration system 10 for implementing the invention, comprising a set of means on board the vehicle as well as a set of remote means.

[0075] By “remote means”, it is meant that these means are not physically present on board the vehicle 1, and that their access is via a communication means 13 of any type, for example of the 4G or 5G network type or other.

[0076] The computer 5 can be connected to any type of interface 31 making it possible to present information to the user of the vehicle 1.

[0077] The latter can use the camera 2 to capture a certain number of images or videos of the environment and can use the location system 7 to collect location data associated with each captured image or video.

[0078] As illustrated in [Fig.2], the set of remote means may comprise a remote server, in particular “cloud” 16.

[0079] The shared calibration database may be stored on this server 16.

[0080] The server 16 is accessible by a set of several vehicles 1 according to the invention, the connection of each of the vehicles to the server 16 being able to be carried out via a secure protocol.

[0081] The database comprises at least one organized collection of structured data relating to different practicable areas.

[0082] The database can be hierarchical, network, object-oriented, relational, non-relational or NoSQL.

[0083] The database can be controlled by any database management system. [0084] The data contained in the database can be encrypted if necessary.

[0085] Different types of access to the database can be defined, in particular as administrator or restricted access, such as for example only with the possibility of consulting and reading data, or only the possibility of modifying or adding data.

Database

[0086] [Fig.3] illustrates an example of a data sample 20 relating to several practicable areas listed in the database.

[0087] Each data line of the base relates to a practicable area 33, and comprises for example, as shown, its location 21, a score 22 and the number of ratings 23.

[0088] The location 21 of an area can be defined by a first latitude coordinate and a second longitude coordinate.

[0089] Alternatively, the location 21 of an area may be defined by the convex hull of a plurality of points, each point being defined by a first latitude coordinate and a second longitude coordinate.

[0090] Alternatively, the location 21 of an area can be defined by a circle of radius R and the coordinates of the center of this circle.

[0091] The score 22 of a zone is a quantity representative of the actual success rate of self-calibration in the zone. This score 22 is a positive number between 0 and a strictly positive number V, for example between 0 and 1. The higher the score 22, and therefore here close to 1, the more the zone considered has in practice given rise to successful self-calibrations.

[0092] A score of 22 not entered in the database means that the data is not available, for example that it has not yet been initialized following one or more self-calibration attempts.

[0093] The score 22 is modified according to the result of the self-calibration attempts, in particular increased following a successful self-calibration, and decreased otherwise.

[0094] The updating of the score 22 can for example be carried out by a constant or variable increment, and/or can take as a parameter the number of previous evaluations 23, and/or the results of the most recent self-calibration attempts.

[0095] The number of evaluations 23 corresponds to the number of updates of the associated score 22 by the different vehicles 1. Thus, after a vehicle 1 makes a self-calibration attempt in a given zone, it sends to the server 16 corresponding information in the form of a notification of failure or success, the associated score 22 is updated, and the number of evaluations 23 is incremented.

[0096] In the example of [Fig.3], the first line of data describes an area particularly suited to calibration, its score 22 being close to 1, and the number of evaluations 23 is relatively large.

[0097] The third line also describes an area particularly well suited to calibration, the score 22 being close to 1, but less certain given the relatively low number of evaluations 23.

[0098] Each data line may also include a label providing information on the suitability of the zone for calibration, for example OK or NOK, respectively indicating whether the associated zone is suitable for calibration or not suitable.

[0099] Labels can be useful for providing synthetic information about the capacity of a given area to allow successful self-calibration in practice.

[0100] The initialization of the label of a zone can be carried out following a first self-calibration attempt by a vehicle which has entered this zone.

[0101] For example, after a first successful attempt, the label could be initialized to OK.

[0102] Similarly, after a first failed attempt, the label may be initialized to NOK.

Map data

[0103] [Fig.4] illustrates an example of a data sample 24 relating to several practicable areas 33 from the map.

[0104] Each line of data from the map relates, for example, to a usable area 33, and includes, for example, as illustrated, its location 21, a probability of success 25, as well as a list of characteristics 26 of the place or “features”.

[0105] The probability of success 25 is for example a positive number between 0 and a strictly positive number V, for example between 0 and 1

[0106] The higher the probability of success 25, and therefore closer to 1, the more the zone considered theoretically gives rise to successful self-calibrations.

[0107] The probability of success 25 of an area can be calculated a priori, even before a vehicle goes into the area and makes a self-calibration attempt, from the location information 26 provided by the map.

[0108] Alternatively, the probability of success 25 is calculated a posteriori, after the self-calibration attempt has taken place.

[0109] The probability of success 25 is established from the number and nature of the different location characteristics suitable for calibration 26 present in the area. Certain characteristics 26 may be more suitable than others for leading to a successful calibration.

[0110] The characteristics 26 of an area can be directly extracted from the map by the calculator 5.

[0111] In [Fig.4], the fourth and fifth lines correspond to two zones where no attempt at self-calibration has yet been made by a vehicle 1. The fourth zone, however, seems particularly suited to calibration in view of the probability of success 25 established from the location characteristics 26 present, unlike the fifth zone.

Calibration process

[0112] An example of a method for calibrating a camera 2 will now be described with regard to [Fig.5].

[0113] It is considered that the vehicle 1 has at least one camera 2 to be recalibrated and launches in step 100 a mode of searching for an area suitable for calibration.

[0114] The location of the vehicle 1 is recorded by the computer 5 using the location system 7.

[0115] The computer 5 accesses the shared database via the communication means 13 in order to extract the coordinates 21 of at least one area close to the current position of the vehicle 1 or its route, suitable for calibration. An area can be considered close if it is at a distance less than a predefined value, for example 5km.

[0116] To do this, it can send a request to the server 16 to select the zone having an OK success label closest to and on the route of the vehicle 1.

Card identification process

[0117] If no zone suitable for calibration is returned after consulting the shared database in step 50, a card identification process is initiated in step 101.

[0118] This identification process by map 101 illustrated in [Fig.6] allows the identification of an area suitable for calibration based on knowledge of a probability of success 25 associated with this area, obtained using the map.

[0119] In step 103 the calculator 5 accesses the map and calculates at least one probability of success 25 relating to an area of which it knows the coordinates 21.

[0120] This calculation can be carried out by an algorithm which defines for a given zone the probability of success 25 from its location characteristics 26 identified on the map.

[0121] For example, an area with a large number of location features 26 ensuring the presence of many vertical and horizontal lines, and/or parallel lines and/or regular spacing, will lead to a higher probability of success 25 than an area with location features including, for example, only a few such lines or other geometric patterns of interest for calibration.

[0122] The law of calculation of probability can be determined empirically by carrying out tests and/or from a mathematical model.

[0123] In this way, the probabilities of a predefined number of zones, located on the route or within a given radius around vehicle 1 in the search phase, can be calculated.

[0124] The selection of the zone can then be carried out in step 104 by the computer 5.

This selection can consist, for example, of taking the one with the best probability of success 25, or with the best ratio of probability of success 25 to distance to be covered to get there.

[0125] A filter may be applied where appropriate when selecting the area to take into account the route of the vehicle 1 entered in the navigation system 7, and for example exclude areas where access by the vehicle 1 would result in a detour of a distance greater than a predefined value. [0126] When the vehicle 1 goes to the selected area, the computer 5 executes in step 105 a calibration algorithm which generates in a manner known per se a calibration model from the images taken by the camera 2. The images taken by the latter advantageously include at least part of the location characteristics 26 of the area, identified a priori on the map.

[0127] If the self-calibration attempt is successful, a success notification is sent by the vehicle 1 to the server 16 via the communication means 13 for updating and/or enriching the database.

[0128] If the autocalibration attempt fails, a failure notification is communicated and search mode 100 is initiated again.

[0129] Finally, the card identification process ends with a step 106 of sharing the result and the associated data.

[0130] Step 106 may include recording the coordinates 21 of the area in the shared database.

[0131] The score 22 can be initialized if necessary by taking as value the probability of success 25 calculated by the vehicle 1 during this process.

Identification process by listed areas

[0132] Returning to [Fig.5], we see that if at least one zone close to the vehicle 1 and suitable for calibration is listed in the base and received by the vehicle 1 in return for the query from the server 16 in step 50, an identification process by listed zones 102 as illustrated in [Fig.7] is initiated.

[0133] Such a process allows the identification of an area suitable for calibration from the labels and/or scores 22, and/or associated evaluation numbers 23.

[0134] When the identification by listed zones 102 is initiated, a zone is selected as a target by the computer 5 in step 107. The selection of a zone can be carried out by a selection algorithm which determines an optimal zone from a set of given zones, in particular locations 21, scores 22 and associated numbers of evaluations 23. For example, the selection algorithm is configured to select from a set of zones the one with the best score 22, or with the best performance ratio, the performance ratio of a given zone being defined for example by a function taking as parameters its score 22, its number of evaluations 23 and its relative distance to the vehicle 1. The set of zones considered by the selection algorithm can be filtered by taking into account the route of the vehicle 1 entered in the navigation system 7, in particular by excluding the zones whose access by the vehicle 1 would cause a detour of a distance greater than a predefined value.

[0135] When the vehicle 1 then goes to the area selected in step 105, the computer 5 executes a calibration algorithm which generates a calibration model at from the images taken by camera 2, as described above.

[0136] The result of the self-calibration attempt is evaluated in step 60.

[0137] If the self-calibration attempt is successful, a success notification is communicated by the vehicle 1 to the server 16 via the communication means 13 in step 110.

[0138] The score 22 associated with the zone can then be increased by the server 16.

[0139] Similarly, if the self-calibration attempt fails, a failure notification is communicated by the vehicle 1 to the server 16 in step 111.

[0140] The score 22 associated with the zone can be reduced by the server 16.

[0141] If the score 22 reaches a predefined minimum threshold at the end of the update, a label

NOK is also registered for the area.

[0142] In the event of a failed self-calibration attempt, the candidate zone search process can be initiated again, and the process resumes at step 100, searching for a new candidate zone.

Displaying areas suitable for calibration

[0143] The self-calibration process can take place without warning the user of the vehicle 1.

[0144] Alternatively, the user can be warned, and can give his consent to go to the selected area, or even can drive the vehicle 1 in this area.

[0145] In all cases, the method may include the display on a plan of the location 21 of the zone(s) suitable for calibration.

[0146] [Fig.8] illustrates an example of display 30 on a screen 31 of the location of different zones suitable for calibration 35, listed in the database and/or identified from the map.

[0147] A marker 32 materializes the position of the vehicle 1 within the practicable 33 and non-practicable 34 zones.

[0148] Where appropriate, the adapted zones 35 are displayed with specific colors depending, for example, on the chances of successful calibration.

Variants

[0149] Of course, the invention is not limited to the examples which have just been described.

[0150] In a variant, the database is duplicated at least partially in the memory 6 of the vehicle 1, and the enrichment of said database is carried out deferred, for example by a merging operation. The duplication of the database can be carried out periodically from the version recorded on the server 16.

[0151] In a variant, the card is stored on the server 16; access to the card is then via the communication means 13.

[0152] In a variant, at least one probability of success 25 is already provided on the map, and this is then extracted without calculation.

[0153] It is possible, if desired, to re-evaluate the probability of success 25 of the calibration, to take into account a difference between the location characteristics 26 actually observed in the area and those listed on the map.

[0154] The success probabilities 25 can be calculated in the background, outside of an activation of the zone identification process by card as described above.

[0155] At least part of the calculations relating to the generation of the calibration model can be carried out on the server 16. The results can then be transmitted to the computer 5 via the communication means 13.

[0156] The types of location features 26 identified on the map may be predefined. Conversely, their number may increase as new relevant types of location features 26 become identifiable.

[0157] Although the invention is preferably applied to the calibration of cameras 2 of motor vehicles, it extends to other types of vehicles such as ships and aircraft. In the latter case, the self-calibration is carried out in areas sufficiently close to the ground, or even on the ground, for example when driving or parking.

Claims

Claims [Claim 1] Method for calibrating on-board cameras (2) and enriching a calibration database, shared by several vehicles (1) each equipped with at least one on-board camera (2) to be calibrated, a navigation system (7) allowing the location of the vehicle (1) and a memory map (6) and/or access to this map, the latter listing areas (33) accessible by the vehicle (1) and associated location characteristics (26) likely to be useful for calibrating the on-board camera (2), method comprising: - prior to calibrating a camera (2) of a vehicle (1), the selection of at least one practicable area (33) candidate for calibration from: - the location (21) of the vehicle (1) and/or a route thereof entered into the navigation system (7) and - information relating to the possibility of self-calibration of the camera (2) from the environment of the vehicle (1) as observed by the camera (2), this information being provided by the calibration database and/or from the map, - the acquisition of images necessary for the self-calibration of the camera when the vehicle (1) is present in the area candidate for calibration (2), - the enrichment and/or updating of the database at least when the result of F autocalibration is one of success and failure, at least with information relating to the result of F self-calibration in said candidate area. [Claim 2] Method according to claim 1, in which the need for calibration of the camera (2) is previously identified from the detection of a defect or distortion in at least one image provided by the latter, and/or from a command from a user of the vehicle (1) or from an operator responsible for its maintenance. [Claim 3] Method according to one of claims 1 and 2, in which the candidate zone is already listed in the database, the latter then comprising a corresponding label and/or a score (22) representative of the self-calibration success rate associated with the zone. [Claim 4] Method according to claim 3 in which the score (22) is updated according to the results of the self-calibration attempts of the different vehicles (1), in particular by incrementing or decrementing it, in particular of a predefined value, in particular constant, at each successful or unsuccessful attempt, or of a predefined value given by a law making the increment value dependent on the number of evaluations (23) already carried out by the different vehicles (1). [Claim 5] Method according to one of claims 3 and 4, in which the updating of the score (22) is carried out as a function of a number of consecutively positive or negative evaluations (23) of the candidate zone. [Claim 6] Method according to one of the three preceding claims, in which the selected candidate area is the one having the highest score (22). [Claim 7] Method according to one of claims 1 and 2, in which the candidate zone is not listed in the database, the selection of the candidate zone being carried out from a probability of success (25) of F self-calibration (25) obtained from the map or calculated from it. [Claim 8] A method according to claim 7, wherein the selected candidate area is the one having the highest probability (25) of success. [Claim 9] Method according to one of claims 7 and 8, in which the probability of success (25) is calculated from location characteristics suitable for calibration (26) identified on the map, in particular the presence of linear road segments, multi-lane road segments, road segments near an urban area or a city center, traffic signs, road markings, peripheral infrastructure elements such as bus stops, telephone boxes or fire hydrants, and/or a value of the slope of a road segment. [Claim 10] Method according to claims 7 and 8, wherein the probability of success (25) of calibration is already provided in the map and extracted therefrom without calculation. [Claim 11] Method according to one of claims 7 to 10, in which the probability of success (25) is refined from dynamic information, in particular available parking spaces with road markings at a given time and/or real-time geolocated traffic information. [Claim 12] Method according to one of the preceding claims, in which when the calibration is successful, the vehicle communicates to a database update server (16) a success notification for the area where the calibration was carried out. [Claim 13] Vehicle (1) equipped with at least one on-board camera (2), a system navigation system (7) allowing the location of the vehicle (1) and a map in memory (6) and/or access to this map, the latter listing areas that can be used by the vehicle (1) and associated location characteristics (26) that may be useful for calibrating the on-board camera (2), the vehicle (1) having access to a calibration database shared by several vehicles (1), access to the database being via a communication means (13), the vehicle further comprising a computer program product configured to: - prior to calibrating a camera (2) of a vehicle (1), select at least one practicable area (33) candidate for calibration from at least: • the location (21) of the vehicle (1) and/or a route of the vehicle (1) entered into the navigation system (7) and • at least one piece of information relating to the possibility of self-calibration of the camera (2) from the environment of the vehicle (1) as observed by the camera (2), this information being provided by the calibration database and/or from the map, - when the vehicle (1) is present in the area candidate for calibration, proceed with the acquisition of the images necessary for F camera self-calibration (2), - enrich or update the database at least when the result of F autocalibration is one of success and failure, at least with information relating to the result of F autocalibration in said candidate zone.