Method and device for managing the movements of a vehicle circulating in an industrial complex

Abstract

The invention relates to a method for managing the movements of a vehicle (1) in an industrial complex, said vehicle comprising an obstacle detection apparatus and a location apparatus which interacts with a satellite positioning system. The invention also relates to a device (100) implementing such a method as well as a vehicle comprising such a device. Figure for the abstract: 1

Description

Title of the invention: Method and device for managing the movements of a vehicle circulating in an industrial complex Technical field of the invention

[0001] The present invention relates to the field of systems that enable the autonomous movement of vehicles. The invention relates in particular to a method for managing, by a computer device on board a vehicle, the movements of the vehicle in an industrial complex, said vehicle carrying an obstacle detection device and a location device interacting with a satellite positioning system. The invention also relates to a device implementing such a method. The invention applies to land motor vehicles, in particular, robots or other transport shuttles. State of the prior art

[0002] It is known that obstacle detection is an essential element of autonomous traffic. Indeed, the implementation of autonomous traffic requires a precise understanding of the traffic environment in order to be able to determine a traffic zone in which the vehicle can circulate safely without colliding with an obstacle. It is also known that the obstacle detection systems on board most modern robots that are intended to carry out transport tasks within industrial or logistics environments still remain simple and limited, because these types of vehicles tend to circulate indoors and in highly limited and controlled spaces, where the obstacles they are likely to encounter are limited both in type and frequency. These vehicles therefore do not need a high-level obstacle detection solution to be able to circulate safely in their environment.Thus, the most common practice in obstacle detection for such vehicles generally consists of using a proximity sensor that sends a stop signal when an obstacle is detected near the vehicle. However, the simplicity of these obstacle detection systems does not allow for more advanced behaviors other than stopping in the event of an immediate collision. In fact, the applications of these vehicles therefore remain limited, because in more complex situations, particularly for outdoor operations, the safety of their circulation is no longer assured. In addition, any obstacle will immobilize the vehicle until it is removed, which is not feasible for use in an industrial environment without risking harming the safety and efficiency of operations.

[0003] Furthermore, if we refer to autonomous circulation in the field of vehicles In road traffic, we see that modern obstacle detection solutions are dominated by artificial intelligence, particularly artificial neural networks, which, through a deep supervised learning phase, are able to detect and classify obstacles based on information provided by one or more sensors, such as cameras, lidars or radars. However, these AI-based obstacle detection solutions are conditioned by the training phase. In other words, if the artificial intelligence has been trained to detect and classify only cars and pedestrians, it will not be able to handle a situation where the obstacle in the vehicle's path is a bicycle.This is not usually a problem for road traffic, where traffic is structured and the obstacles that can be found in the traffic environment are of a limited nature; on a highway, one should only encounter vehicles such as cars, motorcycles, buses or trucks, so artificial intelligence only needs to know how to handle other types of obstacles. On the other hand, this is not the case in the context of industrial environments, in which obstacles can be of very diverse nature.

[0004] Furthermore, since the quantity and quality of data needed to properly train an AI obstacle detector is immense, this data is primarily obtained from previously established traffic data sets, which implies that the training data is de facto limited to the obstacles and situations contained in these data sets, which, although diverse, do not cover all possible situations.

[0005] Consequently, even though artificial intelligence has demonstrated its qualities for managing autonomous traffic in the context of road vehicles, we encounter blocking implementation difficulties for vehicles which circulate in less standardized and less predictable environments, such as industrial or logistics complexes, due to the diverse nature of the obstacles and the scarcity of available training data.

[0006] Furthermore, artificial neural networks, due to their complexity, require a huge amount of resources to operate in real time, as they are composed of myriads of neurons and layers that must be calculated every time an input arrives. Parallelization is therefore a key element of artificial intelligence, where thousands of tasks must be executed simultaneously so that detections can be generated at a sufficient frequency to navigate safely. However, to enable this parallelization, graphics processing units (GPUs) are required, but these are too complex to integrate and too expensive to be used on board more basic vehicles such as robots or other unmanned vehicles intended to take over transport tasks within logistics environments or industrial complexes. Summary of the invention

[0007] The invention aims to solve these problems. In particular, it aims to provide a solution to enable robust obstacle detection at low cost that can allow the safe autonomous circulation of vehicles intended to carry out transport tasks within industrial complexes.

[0008] To achieve this objective, the invention relates, according to a first aspect, to a method for managing, by a computer device on board a vehicle, the movements of the vehicle in an industrial complex, said vehicle carrying an obstacle detection device and a location device which interacts with a satellite positioning system, the method comprising the steps of: i. determining, using said obstacle detection apparatus, data characterizing an occupancy grid of said industrial complex in which locations occupied by static obstacles are identified; ii. determining, using said obstacle detection apparatus, data characterizing a first occupancy grid of a space located near the vehicle in which locations occupied by static or mobile obstacles are identified; iii. associating said first occupancy grid of a space located near the vehicle and said occupancy grid of said industrial complex in order to obtain a second occupancy grid of said space in which locations occupied by mobile obstacles are identified; iv. processing said second occupancy grid of said space so as to obtain data characterizing a third occupancy grid of said space in which locations occupied by mobile obstacles are identified; v. tracking the mobile obstacles identified in said third occupancy grid of said space in order to obtain data characterizing a fourth occupancy grid of said space in which locations occupied by mobile obstacles are identified; and vi. manage said vehicle movements according to said fourth grid of occupation of said space.

[0009] According to a variant, step i) may comprise the steps of: • determine data characterizing a filtered point cloud based on data characterizing an original point cloud generated by the obstacle detection device; • determine data characterizing several flat portions of said filtered point cloud on the basis of several pre-established height values; and • aggregate said portions so as to obtain data characterizing a part of said occupancy grid of said industrial complex.

[0010] According to another variant, said filtered point cloud can be determined by neglecting the points of the original point cloud which correspond to the ground of said industrial complex.

[0011] According to yet another variant, said filtered point cloud can be determined by neglecting at least one inconsistent point of the original point cloud.

[0012] According to yet another variant, step i) may comprise a step consisting of interacting with the location device in order to determine data characterizing a current location of the vehicle.

[0013] According to yet another variant, step i) may comprise a step consisting of obtaining data characterizing an entry relating to the occupation of at least one location of said industrial complex.

[0014] According to yet another variant, step ii) may comprise the steps of: • obtaining data characterizing a cloud of points corresponding to said space generated by said obstacle detection device; and • cancel the height component of each point of said point cloud corresponding to said space.

[0015] According to yet another variant, step iii) may comprise a step consisting of determining a fraction of said occupancy grid of said industrial complex which corresponds to said space.

[0016] According to yet another variant, step iv) may comprise the steps of: • apply an averaging filter; and • perform piecewise denoising; and • perform contour detection.

[0017] According to yet another variant, step v) can be carried out using an unscented Kalman filter and data association using the Ma-halanobis distance.

[0018] According to a second aspect, the invention relates to a device for managing the movements of a vehicle in an industrial complex, the device comprising at least one information processing unit, comprising at least one processor, and a data storage medium, which are configured to implement a method as described above.

[0019] According to a third aspect, the invention relates to a computer program comprising program code instructions for executing the steps of a method as described above when said program is executed by at least one processor.

[0020] According to a fourth aspect, the invention relates to a support usable in an or- diner on which a program as described above is recorded.

[0021] According to a fifth aspect, the invention relates to a vehicle comprising an obstacle detection apparatus and a location apparatus interacting with a satellite positioning system, said vehicle carrying a device as described above.

[0022] According to a variant, said obstacle detection device may be a lidar. Brief description of the drawings

[0023] Other characteristics and advantages of the invention will appear on examining the detailed description below, and the appended drawings, in which:

[0024] [Fig-1] is a schematic illustration of a vehicle according to the invention;

[0025] [Fig.2] is a functional diagram of a device according to the invention; and

[0026] [Fig.3] is a flowchart of the steps of a method according to the invention. Detailed description of the invention

[0027] [Fig.l] schematically illustrates a vehicle 1 according to the invention. This comprises a single obstacle detection apparatus 2, which consists of a lidar arranged horizontally so as to allow 360° perception all around the vehicle, a location apparatus 3, which interacts with a satellite positioning system, and a guidance and propulsion apparatus 4, which conventionally allows the vehicle 1 to be moved and steered. Advantageously, the vehicle 1 according to the invention further comprises a device 100 for managing the movements of a vehicle in an industrial complex within the meaning of the present invention, as described below, which implements a method for managing the movements of a vehicle in an industrial complex within the meaning of the present invention, as described below.

[0028] When implementing the method, the device 100 according to the invention initially determines, using its single obstacle detection device 2, an occupancy grid for the entire industrial complex which identifies only free locations and locations occupied by static obstacles. Then, during subsequent movements of the vehicle, the device 100 according to the invention determines in real time a grid for allocating the space near the vehicle, the one which is in the detection field of the obstacle detection device 2 (eg 80 meters all around the vehicle), in which free locations and locations occupied by static or mobile obstacles are identified.It then combines this local occupancy grid determined in real time and a chosen fragment of the occupancy grid which covers the entire industrial complex determined previously to establish a new occupancy grid of the space near the vehicle in which only the free locations and the occupied locations. by moving obstacles are identified. This new occupancy grid is then processed using various filters and other denoising modules in order to refine the detection of moving obstacles, then tracking is implemented to eliminate any erroneous detections. The product of these steps, which consists of a definitive occupancy grid, is finally used to control the movements of the vehicle 1 by means of its guidance and propulsion apparatus 4. As will be seen below, it is in this way that the invention makes it possible, at lower cost, due to the use of a single obstacle detection device and without resorting to artificial intelligence, to autonomously manage the movements of a vehicle such as a robot intended to take charge of transport tasks within an industrial complex.

[0029] The device 100 for managing the movements of a vehicle in an industrial complex according to the invention is illustrated in [Fig. 2]. It is fundamentally a computer device which comprises at least one information processing unit 101, comprising one or more processors, a data storage medium 102, on which is recorded in particular a program which comprises program code instructions for the execution of the steps of the method according to the invention described later, and an input and output interface 103 allowing the reception and transmission of data.

[0030] Preferably, the device 100 according to the invention is hosted on an independent computer and it interacts via its input and output interface 103 and by means of a wired communication network of the vehicle (e.g. CAN, Ethernet) - shown in [Fig.l] by the two-way arrows - with the obstacle detection device 2, with the location device 3 and with the guidance and propulsion equipment 4. As a result, the device 100 can, in particular, determine an occupancy grid based on data generated by the obstacle detection device 2, obtain location data generated by the location device 3 and manage the guidance of the vehicle 1.

[0031] According to the invention, all the elements described above contribute to enabling the implementation, on board a motor vehicle, of a method for managing the operation of the movements of the vehicle in an industrial complex, as described below in connection with [Fig.3].

[0032] [Fig. 3] illustrates by means of a flowchart the steps of the method according to the invention. According to a first step 301 of the method according to the invention, the device 100 according to the invention determines, using said obstacle detection apparatus 2, data characterizing an occupancy grid of said industrial complex in which locations occupied by static obstacles are identified. Preferably, this first step 201 of the method is implemented only once at during a phase preliminary to the operational use of the vehicle 1. During this phase, the vehicle 1 travels throughout the industrial complex in order to determine only the locations which are occupied by static obstacles. From this occupancy grid of the industrial complex, called global, the device 100 according to the invention will thus be able to establish a traffic zone in which the vehicle 1 can move without colliding with the static obstacles which are installed within the industrial complex. And to determine this global occupancy grid, the vehicle 1 will carry out a succession of movements during which it will carry out the succession of steps which is described below.

[0033] Firstly, the device 100 according to the invention will, during each of these movements, acquire data generated by the obstacle detection apparatus 2. More precisely, it will firstly acquire data characterizing a so-called “original” point cloud which are generated by the obstacle detection apparatus 2. Indeed, a lidar conventionally generates point clouds where each point corresponds to an impact of the laser on an obstacle. Then, the device 100 according to the invention will filter the original point cloud to remove all the points which correspond to the ground of the industrial complex, thus obtaining a so-called “filtered” point cloud. Then, taking into account the fact that the filtered point cloud thus obtained is in three dimensions, the processing of which would prove to be far too heavy, the device 100 according to the invention will endeavor to transform this three-dimensional data into an occupancy grid, by definition two-dimensional.To do this, the device 100 according to the invention determines data characterizing several flat portions of said filtered point cloud on the basis of several pre-established height values. In other words, it divides the filtered point cloud into several horizontal portions located at several heights between the ground and the height at which the obstacle detection device 2 is arranged which, superimposed on each other, form the point cloud. Each of these portions is therefore of infinitesimal height, thus forming an occupancy grid, de facto in two dimensions, each portion forming such an occupancy grid for a particular detection height. Then, the device 100 according to the invention proceeds by aggregating, by stacking in a way, all the portions on top of each other to obtain an occupancy grid of the area of ​​the industrial complex in which the vehicle 1 is located at that moment.This is how the device 100 according to the invention finally obtains data characterizing a part of the occupation grid of the industrial complex, in which free locations and locations occupied by static obstacles, whatever their height, are identified.

[0034] Then, as the vehicle 1 moves, these parts of the industrial complex occupation grid thus established must be assembled in order to establish the grid of global occupancy. To do this, the device 100 according to the invention does not proceed by accumulation, because the addition of each point received would cause the appearance of dynamic elements, such as passing vehicles or isolated errors, in the grid. This is why it implements a simultaneous localization and digitization (SLAM) methodology, which compares the consistency of subsequent readings from the obstacle detection device 2 to add only the elements that are consistent over time, i.e. that have not moved. Thus, one or more points in the filtered point cloud deemed inconsistent (which correspond to impacts that are not static) can at this stage be neglected (i.e. removed).Then, to know where to add the parts of the occupation grid of the industrial complex, the device 100 according to the invention interacts with the location apparatus 3 to determine data characterizing a current location of the vehicle. Thus, by proceeding in sequence, the device 100 according to the invention will progressively establish the occupation grid of the industrial complex in which free locations and locations occupied by static obstacles are identified. And it is from this occupation grid of the industrial complex that it can establish a traffic zone in which the vehicle 1 can move without colliding with a static obstacle.

[0035] Then, according to an advantageous variant, the device 100 according to the invention obtains during this first step 201 of the method data characterizing an entry relating to the occupation of at least one location of the industrial complex, which it uses to possibly modify the occupation grid of the industrial complex determined in the manner explained above. Indeed, it is possible to find in an industrial complex locations which are only temporarily free, for example in an automobile assembly plant having a parking lot for storing vehicles before their transport. Consequently, at the time of generation of the occupation grid, the free parking locations are considered as free spaces, but they may well be occupied later, for example by a new vehicle produced.However, since this vehicle was not present at the time of the passage of the vehicle 1 at this location, the location corresponding to the parking place of the vehicle produced was not considered as a static obstacle. To avoid this problem, an operator can manually enter on a remote system the occupation of certain locations of the industrial complex, and it is these entries which are obtained at this stage by the device 100 according to the invention to further refine the occupation grid of the industrial complex in order to be able to take into account such situations of sporadic occupation of certain locations of the complex.

[0036] According to a second step 202 of the method according to the invention, the device 100 according to the invention determines, using the obstacle detection apparatus 2, data characterizing a first occupation grid of a space located near the vehicle in which locations occupied by static or mobile obstacles are identified. And, unlike the previous step, this second step 202 of the method is carried out in real time during each operational movement of the vehicle 1. And to implement this second step 202, the device 100 according to the invention begins as previously by first obtaining data characterizing a three-dimensional point cloud corresponding to said space which are generated by the obstacle detection apparatus 2. However, unlike the previous step, no division of this point cloud into flat portions is carried out. On the contrary, the point cloud is here directly flattened in order to obtain a two-dimensional point cloud, in other words an occupation grid.To do this, the device 100 according to the invention simply proceeds by canceling the height component of each point of the point cloud corresponding to said space. This is how it obtains the first occupancy grid corresponding to the space near the vehicle, the one which is within the range of the detection field of the obstacle detection device 3 (approximately 80 meters). Thus, at the end of this second step 202, the device 100 has the global occupancy grid in which locations occupied by static obstacles are identified and a grid of occupation of the space near the vehicle 1, called "local", in which locations occupied by static or mobile obstacles are identified.

[0037] According to a third step 303 of the method according to the invention, the device 100 according to the invention associates the first occupation grid of a space located near the vehicle, that obtained during the previous step 202, and the occupation grid of the industrial complex, that determined during the first step 201 of the method, in order to obtain a second occupation grid of the space located near the vehicle in which locations occupied by mobile obstacles are identified. In other words, the device 100 according to the invention determines at this stage the occupation of the space near the vehicle 1 by mobile obstacles. To do this, the device 100 according to the invention proceeds in the manner described below.

[0038] The first occupancy grid of the space located near the vehicle 1, that determined during the previous step 202, identifies locations which are at the current time occupied by obstacles, whether static or mobile. Consequently, if the occupancy grid of the industrial complex, that generated during the first step 201 of the method, is subtracted from this first occupancy grid, only the mobile obstacles remain, those which are not identified by the occupancy grid of the industrial complex which was previously determined.

[0039] To enable such subtraction, the device 100 according to the invention must first determine a fragment of the occupation grid of the industrial complex. which corresponds to the space considered. This fragment of the overall occupancy grid must be determined in such a way that it corresponds to the current range, position and angle of the first occupancy grid of the space located near the vehicle 1, in order to then be able to compare the two grids, cell by cell. To enable this, the device 100 according to the invention needs to know the position and orientation of the vehicle with respect to the occupancy grid of the industrial complex. For this purpose, it uses the location device 3 to determine the current location of the vehicle.Then, once it has determined the relevant global occupancy grid fragment, which has the same size as the first occupancy grid of the space located near the vehicle, it can then proceed to the subtraction, and thus obtain the second occupancy grid of the space located near the vehicle in which only the locations occupied by moving obstacles are identified.

[0040] Then, according to a fourth step 204 of the method according to the invention, the device 100 according to the invention processes said second grid of occupation of the space located near the vehicle so as to obtain data characterizing a third grid of occupation of the space located near the vehicle in which locations occupied by moving obstacles are identified. Indeed, the second grid of occupation of the space located near the vehicle, that determined during the previous step 203 is not sufficient to directly manage the movements of the vehicle 1 in a safe manner, because it only contains information on the locations surrounding the vehicle which are occupied (or free).Furthermore, the implementation of the previous step 203, in particular when determining the fragment of the occupancy grid corresponding to the space located near the vehicle 1, is highly dependent on the accuracy of the location, it is subject to noise and point errors when the fragment of the overall occupancy grid is not perfectly aligned with the first occupancy grid of the space located near the vehicle, that determined during the second step 202 of the method. Furthermore, each individual cell of the second occupancy grid only has information relating to its own occupancy, and not to its environment or its relationship with adjacent cells. Consequently, obstacles are not segmented from each other, and it is therefore not possible to identify whether adjacent cells form a single obstacle, such as a car.It is for these reasons that the device 100 according to the invention carries out this step of processing the second grid of occupation of the space located near the vehicle which was obtained at the end of the previous step 203 of the method, and this, so that groupings of adjacent locations can possibly be merged and the noise can be attenuated.

[0041] To do this, the device 100 according to the invention relies at this stage on the fact that an occupancy grid is in reality a matrix, in the same way as a binary image. Thus, the same techniques as those used in image processing can be applied to perform groupings, not of pixels, but of cells. The device 100 according to the invention therefore proceeds at this stage by starting with a first step consisting of applying an averaging filter, preferably determining 10x10 squares around a cell considered. Then, it performs a step consisting of performing piecewise denoising so that the isolated noise is erased from the grid and the obstacles occupying several adjacent cells of the grid are easier to identify. Finally, it proceeds by performing a step consisting of performing contour detection in order to find all the clusters of occupied cells and segment them; the resulting clusters are the detected moving obstacles.Contour extraction further allows to obtain not only the size and center of each cluster (in cells), but also its orientation relative to vehicle 1. Finally, all clusters found are transformed back from cells to meters, by multiplying them by the fixed size of a cell of an occupancy grid.

[0042] Thus, at the end of this fourth step 204, the device 100 according to the invention knows all the moving obstacles located near the vehicle 1 as well as their estimated positions relative to the vehicle 1 in meters. It therefore has all the information necessary to be able to manage the movements of the vehicle 1 in complete safety.

[0043] Nevertheless, according to a fifth step 205 of the method, the device 100 according to the invention advantageously proceeds by tracking the mobile obstacles identified in said third grid of occupation of the space located near the vehicle in order to obtain data characterizing a fourth grid of occupation of the space located near the vehicle in which locations occupied by mobile obstacles are identified. Thanks to this fifth step 205, the device 100 according to the invention improves the robustness and consistency of the detections. It improves the robustness by considerably reducing the number of false positives by only taking into account the obstacles that have been detected in several consecutive detections. In addition, it also reduces the number of false negatives by tracking the obstacles that are temporarily hidden or that have not been correctly detected at a given time.And it improves consistency because it prevents sporadic sudden jumps and inaccurate detections by associating new detections with previous ones, which smooths the output of the obstacle detector. Furthermore, thanks to this tracking step, the device 100 according to the invention is able to estimate the speed of each obstacle by comparing the detections and the following images, so that this information is also taken into account to ensure the guidance of the vehicle 1. And to implement this tracking, the device 100 according to the invention preferentially uses an unscented Kalman filter and a data association using the Ma- distance. halanobis.

[0044] Finally, according to a sixth and final step 206 of the method, the device 100 according to the invention manages the movements of the vehicle using the fourth occupancy grid, that determined during the previous step 205 of the method. Indeed, on the basis of this and the circulation zone determined during the first step 201 of the method from the occupancy grid of the industrial complex, the device 100 according to the invention can at this stage instruct the guidance and propulsion apparatus 4 in order to manage the movements of the vehicle 1 without the risk of it colliding with any obstacle, static or mobile.

[0045] Thus, thanks to the method and the device according to the invention described above, a solution is provided to enable at low cost robust obstacle detection which can allow the safe autonomous circulation of vehicles intended to take charge of transport tasks within industrial complexes.

Claims

Claims

1. Method for managing, by a computer device (100) on board a vehicle (1), the movements of the vehicle in an industrial complex, said vehicle carrying an obstacle detection device and a location device which interacts with a satellite positioning system, characterized in that the method comprises the steps of: i. determining, using said obstacle detection apparatus, data characterizing an occupancy grid of said industrial complex in which locations occupied by static obstacles are identified; ii. determining, using said obstacle detection apparatus, data characterizing a first occupancy grid of a space located near the vehicle in which locations occupied by static or mobile obstacles are identified; iii. associating said first occupancy grid of a space located near the vehicle and said occupancy grid of said industrial complex in order to obtain a second occupancy grid of said space in which locations occupied by mobile obstacles are identified; iv. processing said second occupancy grid of said space so as to obtain data characterizing a third occupancy grid of said space in which locations occupied by mobile obstacles are identified; v. tracking the mobile obstacles identified in said third occupancy grid of said space in order to obtain data characterizing a fourth occupancy grid of said space in which locations occupied by mobile obstacles are identified; and vi. manage said vehicle movements according to said fourth grid of occupation of said space.

2. Method according to claim 1, characterized in that step i) comprises the steps of: • determine data characterizing a filtered point cloud based on data characterizing an original point cloud generated by the obstacle detection device; • determining data characterizing several flat portions of said filtered point cloud on the basis of several pre-established height values; and • aggregating said portions so as to obtain data characterizing a part of said occupancy grid of said industrial complex.

3. Method according to claim 2, characterized in that said filtered point cloud is determined by neglecting the points of the original point cloud which correspond to the ground of said industrial complex.

4. Method according to one of claims 2-3, characterized in that said filtered point cloud is determined by neglecting at least one inconsistent point of the original point cloud.

5. Method according to one of claims 2-4, characterized in that step i) comprises a step consisting of interacting with the location device in order to determine data characterizing a current location of the vehicle.

6. Method according to one of claims 2-5, characterized in that step i) comprises a step consisting of obtaining data characterizing an entry relating to the occupation of at least one location of said industrial complex.

7. Method according to one of the preceding claims, characterized in that step ii) comprises the steps of: • obtaining data characterizing a cloud of points corresponding to said space generated by said obstacle detection device; and • canceling the height component of each point of said cloud of points corresponding to said space.

8. Method according to one of the preceding claims, characterized in that step iii) comprises a step consisting of determining a fraction of said occupancy grid of said industrial complex which corresponds to said space.

9. Method according to one of the preceding claims, characterized in that that step iv) includes the steps of: • applying an averaging filter; • performing piecewise denoising; and • performing edge detection.

10. Method according to one of the preceding claims, characterized in that step v) is carried out using an unscented Kalman filter and a data association using the Mahalanobis distance.

11. Device (100) for managing the movements of a vehicle in an industrial complex, characterized in that the device comprises at least one information processing unit (101), comprising at least one processor, and a data storage medium (102), which are configured to implement a method according to any one of the preceding claims.

12. A computer program comprising program code instructions for executing the steps of a method according to any one of claims 1 to 11 when said program is executed by at least one processor.

13. Support usable in a computer, characterized in that a program according to claim 12 is recorded therein.

14. Vehicle comprising an obstacle detection device and a location device interacting with a satellite positioning system, characterized in that said vehicle carries a device according to claim 11.

15. Vehicle according to claim 14, characterized in that said obstacle detection apparatus is a lidar.

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