Method for estimating the dimensions of an object from points acquired by a lidar sensor

Abstract

Examples present a process (100) of at least one dimension belonging to a reflective surface of an object comprising: an acquisition (110) by a lidar of a set of points; a selection (120) of light beams from a group of points of the same object; an estimation (130), from the three-dimensional coordinates of the points, of the values ​​of a first and a second dimension of the object; an estimation (140), from the intensity values ​​of the points, of a reflectivity value of the object; then for at least one specific point of the group: an estimation (150) of a cross-section of a light beam; an estimation (160) of a theoretical intensity value received by the lidar sensor following the emission of the beam; a determination (170) of an intensity error associated with the specific point from a difference between the estimated theoretical intensity and the measured intensity;and a correction (180) of the estimation of the value of the first dimension from the intensity error. Figure of the abstract: [Fig. 3];

Description

Title of the invention: Method for estimating the dimensions of an object from points acquired by a lidar sensor. Technical field

[0001] This disclosure relates to the field of processing data acquired by a lidar sensor. Prior art

[0002] A lidar (Light Detection And Ranging) sensor is a sensor emitting light waves and determining, from the reflection of these light waves, a matrix of points representing a lidar environment.

[0003] A lidar sensor can be used to measure an object in its environment by acquiring a point cloud representing the sensor's surroundings, with the object then represented by a plurality of points in the point cloud. In particular, when the lidar sensor is mounted on a vehicle, it can, for example, be used to detect objects on the road. In the context of driver assistance functions, especially autonomous driving functions, detecting and determining the dimensions of an object on the road allows the vehicle to react accordingly. Specifically, when dealing with a small object, its precise dimensions can determine whether or not the vehicle can pass over it without risking a collision.

[0004] However, the determination of the dimensions of objects, and more particularly of small objects located several tens of meters from the vehicle, is limited by the resolution of the lidar sensor, so that the objects may sometimes be under or oversized.

[0005] The present disclosure improves this situation. Summary

[0006] In this regard, a method is proposed for estimating at least one dimension belonging to a reflective surface of an object, the method being implemented by a device comprising at least one lidar sensor, a computer, and a memory, the memory storing a model of the object comprising a first dimension and a second dimension of the reflective surface of the object and a reflectivity of the reflective surface of the object; the method comprising: - an acquisition by the lidar sensor of a set of points, each point being associated with three-dimensional coordinates in space and an intensity value; - a selection from the set of points of a group of presumed points ap to start with the same objective; - an estimation, from the three-dimensional coordinates of the points of the group of points, of the values ​​of a first and a second dimension of the object corresponding respectively to the first and second dimensions of the reflection surface of the model of the object; - an estimate, based on the intensity values ​​of the group of points presumed to belong to the object, of a reflectivity value for the object corresponding to the reflectivity of the reflection surface of the object model; then for at least one specific point of the group of points presumed to belong to the object: • an estimate of a section of a specific light beam whose reflection by the object's reflecting surface allowed the specific point to be obtained, from the estimated dimensions of the object's reflecting surface and a divergence of the light beam determined from a distance between the specific point and the lidar sensor; • an estimate of a theoretical intensity value received by the lidar sensor following the emission of the specific light beam, from the estimated cross-section of the specific light beam and the estimated reflectivity value of the reflecting surface; • a determination of an intensity error associated with the specific point based on the difference between the estimated theoretical intensity value and the measured intensity value associated with the specific point following the emission of the specific light beam; and - a correction of the estimation of the value of the first dimension from the intensity error associated with at least one specific point.

[0007] Optionally, a correction of the estimation of the value of the first dimension from the intensity error associated with at least one specific point includes minimizing an error of a cost function calculated from the intensity error associated with at least one specific point by iteratively varying the value of the first dimension.

[0008] Optionally, the method further includes a correction of the estimation of the value of the second dimension from the intensity error associated with at least one specific point, the correction including a minimization of an error of a cost function calculated from the intensity error associated with at least one specific point by iteratively varying the value of the second dimension.

[0009] Optionally, the method further includes a correction of the reflectivity value estimate based on the intensity error associated with at least one specific point, the correction comprising minimizing an error in a cost function calculated from the intensity error associated with at least one specific point. iteratively varying the reflectivity value.

[0010] Optionally, the cost function corresponds to the sum of the intensity errors of the specific points for which the intensity error has been determined.

[0011] Optionally, the first dimension of the object model corresponds to a height of the object's reflection surface and in which the second dimension of the object model corresponds to a width of the object's reflection surface.

[0012] Optionally, the theoretical intensity value received by the lidar sensor following the emission of the specific light beam is estimated from a function characterizing the intensity received by the lidar following the reflection of a light beam emitted by the lidar sensor on a surface as a function of: a. a distance between this surface and the lidar sensor, b. of a reflectivity of this surface, and c. of a section of the light beam reflected on this surface.

[0013] Optionally, the plurality of points is enriched by new acquisitions from the lidar sensor and the process is implemented iteratively from the enriched plurality of points.

[0014] Optionally, the method further includes determining a trajectory of a vehicle carrying the device from the corrected estimate of the value of the first dimension.

[0015] The application also relates to a device comprising at least one lidar sensor, a computer, and memory, the device being configured to implement any one of the methods described in this disclosure. In particular, the device may be mounted on a vehicle.

[0016] The application further relates to a computer program product comprising instructions for implementing any of the processes presented in this disclosure when that program is executed by a processor.

[0017] Finally, the application relates to a non-transient computer-readable recording medium on which is recorded a program for the implementation of any of the methods presented in this disclosure when this program is executed by a processor.

[0018] The method according to this disclosure thus makes it possible to estimate the length of at least one side of the reflective surface of an object captured by the lidar sensor accurately, and in particular more accurately than a conventional estimate based solely on the three-dimensional coordinates of the points presumed to belong to the object, these dimensions being potentially under- or overestimated due to the resolution of the lidar sensor. In certain options, the method estimates the length of both sides of the object's reflective surface (width L and height h). These estimates of the dimensions of the object's reflective surface can, in particular, be used to determine a vehicle trajectory adapted to the precise dimensions of the object, which can help avoid situations in which a vehicle trajectory determined on the basis of underestimated object dimensions could be detrimental to the safety of the vehicle and road users. Brief description of the drawings

[0019] Other features, details and advantages will become apparent upon reading the detailed description below, and upon analysis of the accompanying drawings, on which: [Fig.l] Fig.l schematically represents a device enabling the implementation of a method for estimating a dimension belonging to a reflection surface of an object. [Fig.2] Fig.2 represents an example of a lidar sensor mounted on a vehicle. [Fig.3] Fig.3 represents an example of a method for estimating at least one dimension belonging to a reflection surface of an object. [Fig.4] Fig.4 represents an example of a cross-section of light beams emitted by the lidar sensor and reflected by the reflective surface of an object. Description of the embodiments

[0020] An example of a device 1 enabling the realization of a method for estimating a dimension belonging to a reflection surface of an object is now described with reference to [Fig.1].

[0021] Device 1 can be adapted to be mounted on a vehicle 2.

[0022] Device 1 comprises a lidar sensor 10 (Light Detection And Ranging in English), a calculator 11 and a memory 12.

[0023] The memory 12 can store the code instructions executed by the computer 11, which control the acquisition of data by the lidar sensor 10 and its processing. The computer thus accesses the information stored in memory. The memory 12 can also be adapted to store the data acquired by the lidar sensor 10.

[0024] The memory 12 may, for example, include ROM (Read-Only Memory), RAM (Random Access Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), or any other suitable type of storage medium. The memory may, for example, include optical, electronic, or magnetic storage media.

[0025] The lidar sensor 10 is adapted to acquire three-dimensional point clouds (x, y, z) representing the environment of the vehicle 2 and in particular representing the environment in front of the vehicle. The lidar sensor 10 can thus be oriented to emit light beams representing the environment in front of the vehicle, i.e. along its direction of movement represented by the arrow d in [Fig. 2]. The lidar sensor 10 can, for example, be mounted on the bodywork at the front of vehicle 2.

[0026] With regard to three-dimensional coordinates, the x-coordinate of a point corresponds to an abscissa coordinate of the point relative to the sensor 10. The y-coordinate of a point corresponds to an ordinate coordinate of the point relative to the sensor 10. The z-coordinate of a point corresponds to an elevation coordinate of the point relative to the lidar sensor 10. In [Fig. 2], and assuming the contours of the vehicle 2 and the lidar sensor 10 are shown in top view, the abscissa X and ordinate Y axes of the lidar sensor 10 are shown. The elevation Z axis of the sensor 10 is perpendicular to the X and Y axes. In this [Fig. 2], the lidar sensor 10 is therefore mounted at the front of the vehicle and is oriented to emit light beams in the direction of travel of the vehicle.

[0027] Each point acquired by the lidar sensor 10 is also associated with an intensity value. This is the intensity received by the lidar sensor after reflection of the light beam on a surface.

[0028] With reference to [Fig. 3], an example of a method 100 is now presented that the device 1 according to this disclosure is configured to implement. In particular, the device 1 is configured to implement a method for estimating at least one dimension belonging to a reflective surface of an object. The reflective surface of the object corresponds to a surface of the object that has reflected the light beams emitted by the lidar sensor 10. A dimension of this surface here refers to a height or a width of this surface. The aim is therefore to estimate the length of at least one side of this surface accurately, and in particular more accurately than a conventional estimate based solely on the three-dimensional coordinates of the points presumed to belong to the object.Indeed, such an estimation is notably limited by the resolution of the lidar sensor 10, so that for objects located at a long distance from the lidar sensor, the error rate in the estimated dimensioning of the object relative to the size of the object can be significant.

[0029] To enable this estimation, memory 12 stores a model of the object comprising a first dimension, for example a height h, and a second dimension, for example a width L, of the object's reflective surface. The object model also includes a reflectivity of the object's reflective surface. The object model therefore comprises three parameters: the first and second dimensions of the object's reflective surface, and the reflectivity of the object's reflective surface. Reflectivity is a value that reflects the degree to which the object's reflective surface is reflective to light, and therefore the degree to which the object's reflective surface is reflective to the light beams emitted by the lidar sensor 10. One of the objectives of the process is to determine precise values ​​of the object's reflection surface by estimating the values ​​of the model parameters as developed later in the document.

[0030] In this case, the method 100 relies on the fact that a light beam emitted by a lidar sensor 10 diverges during its propagation. It is therefore not concentrated at a single point when it strikes the surface on which it is reflected (hereafter referred to as the reflection surface). Rather, it is a section of the light beam that strikes the reflection surface. This section generally has an elliptical shape when the entire beam is reflected by the reflection surface. Furthermore, since the entire beam is reflected by the reflection surface, this section is larger the further the reflection surface is from the lidar sensor 10, as the light beam diverges more and more as it moves away from the lidar sensor 10.Therefore, the intensity of the light beam reflected by the object's reflective surface and captured by the lidar sensor 10 depends on the cross-section of the light beam actually reflected by the object's reflective surface. Specifically, the reflected cross-section of a light beam emitted for a point acquired on an edge of the object's reflective surface at a given distance will not correspond to a complete theoretical cross-section of the light beam striking that surface. This means, in practical terms, that a complementary cross-section of the light beam will not be reflected by the reflective surface. This situation is illustrated in [Fig. 4], which shows a reflective surface Sr and five light beams Fl at F5, three of which are completely reflected by the reflective surface Sr (namely, light beams F2, F3, and F4), and two are partially reflected by this surface Sr (namely, light beams Fl and F5).Here, the intensity measured by the lidar sensor 10 will be lower for points Fl and F5 than for points F2 to F4, assuming that the entire reflecting surface Sr has the same reflectivity. As explained later, the method 100 cleverly uses the intensity information of the points associated with an object's reflecting surface Sr to correct an initial estimate of at least one dimension of this reflecting surface Sr, made from the three-dimensional coordinates of the points. In this way, the dimensions of the reflecting surface Sr of an object captured by a lidar sensor 10 are no longer estimated solely from the three-dimensional coordinates of the points belonging to that object, but also from the intensity associated with these points, using the correlation between this intensity and the reflection cross-section of the light beams associated with these points on the reflecting surface Sr.Therefore, process 100 makes it possible to reduce the number of collisions due to objects whose size estimation accuracy is limited due to the resolution of the lidar sensor 10. In particular, in the context of autonomous driving functions, the vehicle may have to modify its tra. This is particularly important when an object, such as a tire, is present on the road ahead. In such cases, accurately determining the object's width and height is crucial for determining the vehicle's trajectory, whether it involves going around the object or simply passing over it, between the vehicle's wheels if the object's height allows. This is especially advantageous when the vehicle is traveling at high speed and an extremely rapid decision is required. It is understandable that a decision such as emergency braking or a sudden swerve that is not made because the dimensions of the object detected by the lidar sensor have been underestimated raises safety concerns, as a more accurate estimate, such as that provided by the method described in this disclosure, would have enabled the appropriate decision to be made.

[0031] The method 100 thus comprises an operation 110, as illustrated by [Fig. 3], of acquiring a set of points by the lidar sensor 10. Each point acquired by the lidar sensor 10 is associated with three-dimensional coordinates (x, y, z) in space and with an intensity value.

[0032] The method 100 includes an operation 120, as illustrated by [Fig.3], of selecting, from the set of points acquired by the lidar sensor 10, a group of points presumed to belong to the same object.

[0033] In examples, the selection of the point group is carried out using the nearest neighbor criterion. In other words, the selection of the point group may include a selection of a set of points whose distance to their nearest neighbor is less than a predetermined distance threshold.

[0034] Of course, the method 100 can include the selection of several groups of points if several groups of points can be determined as presumed to each belong to the same object. The subsequent operations of the method 100 can then be carried out for each of the groups of points determined at the end of operation 120.

[0035] The method 100 comprises an operation 130, as illustrated in [Fig. 3], for estimating the values ​​of a first and a second dimension of the object. The first and second dimensions of the object correspond respectively to the first and second dimensions of the reflection surface of the object model. These values ​​are estimated from the three-dimensional coordinates of the points in the point group selected after operation 120.

[0036] In examples, the values ​​of the first and second dimensions can be estimated from the points in the point group having the most extreme coordinates on the x-axis and z-axis. In particular, in examples, the value of the first dimension, when it is the height h of the The reflective surface of object Sr can correspond to the difference between the highest elevation value associated with a point in the point group and the lowest elevation value associated with a point in the point group. In examples, the value of the first dimension, when referring to the width of the reflective surface of object Sr, can correspond to the difference between the highest x-coordinate value associated with a point in the point group and the lowest elevation value associated with a point in the point group. Thus, operation 130 provides a first estimate of the dimensions of the reflective surface of the object detected by the lidar sensor 10, based on the three-dimensional coordinates of the points in the point group presumed to belong to the object.

[0037] The method 100 includes an operation 140, as illustrated in [Fig. 3], for estimating a reflectivity value of the object. The reflectivity value of the object corresponds to the reflectivity of the reflective surface of the object model. This reflectivity value of the object is estimated from the intensities of the points in the group of points presumed to belong to the object. In some examples, the reflectivity of a point in the point set can be determined from the distance between the point and the lidar sensor, the intensity of the light beam used to obtain the point, and the intensity received by the lidar sensor following the reflection of this beam. Thus, in some examples, the reflectivity of the object can be determined as corresponding to an average of the reflectivities of the points in the group of points presumed to belong to the object.In other examples, the object's reflectivity can be determined as the average of the reflectivity values ​​of a selection of points belonging to the point group. In these other examples, a reduced number of points are selected to determine the object's reflectivity, for example, by removing points with clearly erroneous intensity values. It is therefore assumed in this operation that the object's reflective surface has a substantially homogeneous reflectivity.

[0038] Following operation 140, several operations will be performed for at least one specific point in the group of points. These operations are advantageously performed for each point in the group of points presumed to belong to the object.

[0039] The method 100 comprises an operation 150, as illustrated in [Fig. 3], of estimating a cross-section of a specific light beam whose reflection by the object's reflecting surface yielded the specific point. This operation therefore involves estimating the cross-section of the light beam that was actually reflected. As explained previously, it is understood that a cross-section associated with a point acquired by the lidar sensor 10 and corresponding to a point positioned in the middle of the object's reflecting surface Sr will correspond to the entire cross-section of the light beam emitted by the lidar sensor 10. Conversely, a The section associated with another point acquired by the lidar sensor 10 and corresponding to a point positioned on one end of the object's reflective surface Sr will correspond to a section truncated compared to the entire section of the light beam emitted by the lidar sensor 10. These are the situations explained previously with reference to [Fig. 4].

[0040] In examples, the cross-section of the specific light beam whose reflection by the object's reflecting surface yielded the specific point is estimated from the estimated dimensions of the object's reflecting surface and a divergence of the light beam determined from a distance between the specific point and the lidar sensor. Indeed, the divergence of the light beam emitted by the lidar sensor 10 increases with distance. Therefore, knowing the orientation of the lidar sensor and the distance at which the specific point is located relative to the lidar sensor 10 using its three-dimensional coordinates, it is possible to determine the theoretical cross-section of the light beam at that distance.It is therefore possible to determine the projection of this section onto the reflecting surface Sr of the object by knowing the orientation of the specific light beam and the dimensions of this reflecting surface Sr, which were estimated during operation 130. A distance between the specific point and the lidar sensor 10 corresponds to the Euclidean distance between the specific point and the lidar sensor 10.

[0041] The method 100 includes an operation 160, as illustrated in [Fig. 3], for estimating a theoretical intensity value received by the lidar sensor following the emission of the specific light beam, based on the estimated cross-section of the specific light beam and the estimated reflectivity value of the reflecting surface. This step involves estimating a theoretical intensity that the lidar sensor 10 should have received after reflection of the light beam on the reflecting surface Sr of the object, considering the cross-section of the light beam reflected from this surface estimated during operation 150 and the reflectivity of this surface estimated during operation 140.

[0042] Thus, when the estimated intensity for the specific point does not correspond to the intensity actually measured by the lidar sensor 10 for that specific point, the cross-section of the light beam estimated to have been reflected by the reflecting surface contains an error, since the theoretical intensity is calculated from this cross-section. However, the cross-section of the light beam is itself estimated from the dimensions of the reflecting surface, so it is these estimated dimensions of the reflecting surface that are erroneous. Therefore, by comparing the theoretical intensity value with the intensity value actually measured by the lidar sensor 10 for the specific point, the method provides information on the accuracy of the dimensions of the reflecting surface estimated during operation 130.

[0043] In examples, the theoretical intensity value received by the lidar sensor following the emission of the specific light beam is estimated from a function characterizing the intensity received by the lidar sensor following the reflection of a light beam emitted by the lidar sensor on a surface as a function of: a. a distance between this surface and the lidar sensor, b. of a surface reflectivity, and c. of a section of the light beam reflected by this surface.

[0044] This function can for example be predetermined empirically from acquisitions of one or more lidar sensors, advantageously from acquisition of lidar sensor 10, by varying the reflectivity of the reflection surfaces of the objects, their position relative to the lidar sensor and the cross-section of the light beams reflected by the reflection surface of these objects.

[0045] Thus, by initializing the different parameters of this function with the distance between the specific point and the lidar sensor, the reflectivity of the object's reflective surface estimated during operation 140 and the section of the light beam reflected by the object's reflective surface which enabled obtaining the specific point estimated during operation 150, the function makes it possible to calculate the theoretical intensity value of operation 160.

[0046] The method 100 includes an operation 170, as illustrated in [Fig. 3], for determining an intensity error associated with the specific point. The intensity error associated with the specific point is obtained from the difference between the theoretical intensity value estimated during operation 160 and the intensity value actually measured by the lidar sensor 10 and associated with the specific point following the emission of the specific light beam. As explained previously, this intensity error reflects an error in estimating the dimensions of the object's reflective surface Sr.

[0047] Operations 140, 150, 160, and 170 are therefore performed for at least one specific point and advantageously for each point in the group of points presumed to belong to the same object. Consequently, at the end of operation 170, at least one intensity error associated with a specific point has been calculated.

[0048] The method then includes an operation 180, as illustrated in [Fig.3], of correcting the estimation of the value of the first dimension from the intensity error associated with at least one specific point.

[0049] In examples, the operation 180 of correcting the estimate of the value of the first dimension includes a correction of the estimate of the value of the first dimension in order to reduce the at least one intensity error.

[0050] In early examples, operation 180 corrects the estimation of the value of the first dimension from the intensity error associated with at least one A specific point can involve minimizing the error of a cost function calculated from the intensity error associated with at least one specific point by iteratively varying the value of the first dimension. In these early examples, the cost function expresses the error to be minimized as a function of the value of the first dimension of the object model.

[0051] In second examples, complementary to the first examples, the method may include an operation 181 of correcting the estimation of the value of the second dimension by minimizing the error of the cost function calculated from the intensity error associated with at least one specific point by iteratively varying the value of the second dimension. In these second examples, the cost function expresses the error to be minimized as a function of the value of the first dimension and the value of the second dimension of the object model.

[0052] In third examples, complementary to the first examples and possibly the second examples, the method may include an operation 182 for correcting the estimate of the reflectivity value by minimizing the error in the cost function calculated from the intensity error associated with at least one specific point by iteratively varying the reflectivity value. In these third examples, the cost function expresses the error to be minimized as a function of the value of the first dimension and the reflectivity value of the object model. When these third examples are implemented in addition to the second examples, the cost function expresses the error to be minimized as a function of the value of the first dimension, the value of the second dimension, and the reflectivity value of the object model.

[0053] It should be understood by iterative variation of a parameter of the model of the object as choosing a value of this object at a given iteration and implementing again operations 140 to 170 taking into consideration this new value of the parameter of the model.

[0054] In the first, possibly second and / or third examples, the error to be minimized of the cost function may correspond to the sum of the intensity errors associated with the specific points for which the intensity error was determined during operation 170.

[0055] Thus, by iteratively varying the value of the first dimension, by optionally iteratively varying the value of the second dimension and / or the reflectivity value of the object model, and by repeating operations 140 to 170 at each iteration, it is possible, with an optimization algorithm, to minimize the error of the cost function, which is determined from at least one error of intensity determined at the end of operation 170. Minimizing the error of the cost function by an iterative variation of the first dimension, optionally values ​​of the second dimension and reflectivity, therefore means determining new values ​​minimizing the sum of the differences between the actual intensities of the specific points of the group of points and their theoretical intensity determined at the end of operation 170. This amounts to considering that the section of the light beams associated with the specific points of the group of points presumed to belong to the object and estimated during operation 150 is closer to the section of the light beams actually reflected by the reflecting surface of the object for the acquisition of these points.However, this section is determined from the dimensions of the object's reflective surface, so when the error in the cost function decreases, it means that the dimensions of the reflective surface are closer to the actual dimensions of the reflective surface than when they were estimated solely from the three-dimensional coordinates of the points in the group of points assumed to belong to the object. Method 100 therefore allows for a more accurate estimation of the value of the first dimension, possibly the values ​​of the second dimension and / or the reflectivity of the reflective surface, than values ​​obtained from the three-dimensional coordinates of points.

[0056] In the first, possibly second and / or third examples, the determination of the iterative variation of the values ​​of the model parameters of the object to be corrected in order to minimize the error of the cost function is performed by an optimization algorithm. In some examples, the optimization algorithm chosen to determine the iterative variation of the values ​​of the model parameters of the object to be corrected may, for example, correspond to a Levenberg-Marquadt algorithm.

[0057] In examples where several parameters of the object model are corrected by minimizing the cost function, the cost function expresses the error to be minimized as a function of the values ​​of the object model parameters to be corrected, and the minimization of the cost function includes calculating the Jacobian matrix of the error to be minimized as a function of the object model parameters expressed in the cost function. Calculating the Jacobian matrix of the cost function error as a function of the object model parameters to be corrected allows for optimizing the convergence of the optimization algorithm towards error minimization by determining the relative impact of variations in each object model parameter on minimizing the cost function error.

[0058] In examples, and as illustrated by [Fig. 3], the method 100 may further include an operation 190 for determining a vehicle trajectory from the corrected estimate of the value of the first dimension determined during operation 180. The vehicle trajectory may also be determined from the corrected estimate of the value of the second dimension determined during operation 181. The vehicle trajectory may, for example, be determined so as to avoid the object whose dimensions were recalculated during process 100.

[0059] In examples, the plurality of points acquired by the lidar sensor 10 and on which the method is implemented is enriched by new acquisitions from the lidar sensor 10, and the method is implemented iteratively from the enriched plurality of points. In this case, the more points there are in a group of points presumed to belong to the same object, the more precisely its dimensions can be determined using the method according to this disclosure.

[0060] Thus, the method 100 according to this disclosure makes it possible to estimate the length of at least one side of the reflective surface of an object captured by the lidar sensor 10 accurately and in particular more accurately than a conventional estimate based solely on the three-dimensional coordinates of the points presumed to belong to the object, these dimensions being able to be under or overestimated due to the resolution of the lidar sensor 10. In examples, the method 100 estimates the length of both sides of the reflective surface of the object (width L and height h).These estimates of the dimensions of the object's reflective surface can be used, in particular, to determine a vehicle trajectory adapted to the precise dimensions of the object, which can help avoid situations in which a vehicle trajectory determined on the basis of underestimated object dimensions could be detrimental to the safety of the vehicle and road users.

Claims

Claims

1. Method for estimating at least one dimension belonging to a reflection surface (Sr) of an object, the method being implemented by a device (1) comprising at least one lidar sensor (10), a computer (11) and a memory (12), the memory (12) storing a model of the object comprising a first dimension and a second dimension of the reflection surface of the object and a reflectivity of the reflection surface of the object; the method comprising: - an acquisition (110) by the lidar sensor (10) of a set of points, each point being associated with three-dimensional coordinates in space and with an intensity value; - a selection (120) from the set of points of a group of points presumed to belong to the same object; - an estimation (130), from the three-dimensional coordinates of the points of the group of points, of the values of a first and a second dimension of the object corresponding respectively to the first and second dimensions of the reflection surface (Sr) of the model of the object; - an estimate (140), from the intensity values of the group of points presumed to belong to the object, of a reflectivity value of the object corresponding to the reflectivity of the reflection surface of the model of the object; then for at least one specific point of the group of points presumed to belong to the object: • an estimation (150) of a section of a specific light beam whose reflection by the reflection surface (Sr) of the object made it possible to obtain the specific point, from the estimated dimensions of the reflection surface of the object and a divergence of the light beam determined from a distance between the specific point and the lidar sensor; • an estimate (160) of a theoretical intensity value received by the lidar sensor (10) following the emission of the specific light beam, from the estimated section of the specific light beam and the estimated reflectivity value of the reflection surface; a determination (170) of an intensity error associated with the specific point from a difference between the estimated theoretical intensity value and the measured intensity value associated with the specific point following the emission of the specific light beam; and a correction (180) of the estimation of the value of the first dimension from the intensity error associated with at least one specific point.

2.

3.

4. Method according to the preceding claim, in which a correction of estimating the value of the first dimension from the intensity error associated with at least one specific point comprises minimizing an error of a cost function calculated from the intensity error associated with at least one specific point by iteratively varying the value of the first dimension. A method according to any preceding claim, further comprising: a correction (181) of the estimation of the value of the second dimension from the intensity error associated with at least one specific point, the correction comprising a minimization of an error of a cost function calculated from the intensity error associated with at least one specific point by iteratively varying the value of the second dimension. A method according to any preceding claim, further comprising: a correction (182) of the estimate of the reflectivity value from the intensity error associated with at least one specific point, the correction comprising a minimization of an error of a cost function calculated from the intensity error associated with at least one specific point by iteratively varying the reflectivity value.

5. A method according to any one of claims 2 to 4, wherein the error of the cost function corresponds to the sum of the intensity errors of the specific points for which the intensity error was determined.

6. A method according to any preceding claim, wherein the first dimension of the object model corresponds to a height (h) of the reflection surface (Sr) of the object and wherein the second dimension of the object model corresponds to a width (L) of the reflection surface of the object.

7. Method according to any one of the preceding claims, in which the theoretical intensity value received by the lidar sensor following the emission of the specific light beam is estimated from a function characterizing the intensity received by the lidar following the reflection of a light beam emitted by the lidar sensor on a surface as a function of: a. a distance between this surface and the lidar sensor, b. a reflectivity of this surface, and c. a section of the light beam reflected on this surface.

8. Method according to any one of the preceding claims, in which the plurality of points is enriched by new acquisitions of the lidar sensor (10) and the method is implemented iteratively from the enriched plurality of points.

9. Method according to any one of the preceding claims, further comprising a determination (190) of a trajectory of a vehicle (2) carrying the device from the corrected estimate of the value of the first dimension.

10. Device (1) comprising at least one lidar sensor (10), a computer (11) and a memory (12), the device (1) being configured to implement any one of the methods according to claims 1 to 9.

11. Vehicle (2) carrying a device (1) according to the preceding claim.

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