A method for implementing a LIDAR device using a descriptor that utilizes distance evaluation

Abstract

The present invention relates to a method of implementing a Light Detection and Ranging (LIDAR) device in a motor vehicle, comprising the steps of determining a first descriptor, determining a second descriptor, and identifying corresponding environmental signatures in the first and second descriptors, wherein each of said indicators of the environmental signature is configured to assume a value representative of one of the following four states: a state in which the separation distance is outside a first predetermined distance range, a state in which the separation distance is in a lower segment of the first predetermined distance range, a state in which the separation distance is in an upper segment of the first predetermined distance range, and a state in which the separation distances are substantially equal, within a predetermined factor.

Description

[Technical field]

[0001] The present invention relates to the automotive field, and more specifically to LIDAR devices used in automobiles.

[0002] A LIDAR (Light Detection and Ranging) device is a device that makes it possible to detect objects and other elements in the automobile's surroundings and to measure the distance between the vehicle and the detected objects.

[0003] LIDAR devices used in automobiles generally include a light emitter designed to emit an incident light beam, i.e., towards the vehicle's surroundings. The LIDAR device also includes a photodetector designed to receive the light beam reflected back by objects located around the vehicle. By measuring the time elapsed between the emission and reception of the light beam, and by taking into account the propagation speed of light, the LIDAR device allows the detection of objects around the vehicle and the determination of the distance of these objects from the vehicle.

[0004] LIDAR devices are generally used in automobiles, for example, to assist the driver in certain maneuvers or to implement cruise control systems. LIDAR devices are also used in autonomous vehicles, i.e. vehicles that can drive autonomously without a human driver. In particular, LIDAR devices are essential for the driving system for autonomous vehicles, due to their accuracy, allowing the autonomous vehicle to perceive its surroundings, which is a very important operation that allows the vehicle to adapt its trajectory to the environment.

[0005] When LIDAR devices are used in automobiles, the reliability of the LIDAR devices is to ensure safety. Moreover, when LIDAR devices are used in autonomous vehicles, this reliability is extremely important since the safety of the occupants and the surroundings of the vehicle depends on it inter alia.

[0006] When the LIDAR device is used in a vehicle, it allows the detection of successive sequences of the external environment and the matching of the same objects present in these various sequences. Thus, the LIDAR device allows the vehicle to perceive the movements of objects around the vehicle, which movements are associated with the relative movement of the vehicle and / or with the specific movements of these objects. Thus, by matching objects between the various sequences detected by the LIDAR device, it is possible to detect and quantify the movement of objects from one sequence to another. This recognition of movement is the basis for the use of LIDAR devices in automobiles, and more particularly in autonomous vehicles, in particular to ensure the safety of the vehicle and its surroundings, for example by detecting any movement of objects, in particular movements that may interfere with the vehicle. [Background technology]

[0007] LIDAR devices for use in automobiles are known that are equipped with means for matching objects.

[0008] Prior art LIDAR devices generally determine a point cloud for each detected sequence of the surroundings, which represents the surroundings of the vehicle at a given moment. For each of these point clouds, complex algorithms are generally used to detect singular points, called "interest points", in the point cloud. After the different interest points are detected in the different sequences, these algorithms perform identification operations that allow the presence of the same interest point in the different sequences to be recognized.

[0009] Prior art LIDAR devices require significant computational resources to execute these complex algorithms, and also require the use of high resolution photodetectors to enable accurate and efficient detection of interest points.

[0010] In prior art LIDAR devices, increasing performance and detection security necessarily involves increasing the resolution of the photodetectors as well as increasing computing power. Summary of the Invention [Problem to be solved by the invention]

[0011] The object of the present invention is to improve upon the prior art methods. [Means for solving the problem]

[0012] To this end, the present invention relates to a method for using a Light Detection and Ranging (LIDAR) device in a vehicle, comprising the steps of: emitting an incident light beam from the vehicle towards its external environment; receiving the reflected light beam as returned by a photodetector in the vehicle; associating with each cell of the photodetector which receives the reflected light a value representative of a quantity related to the reflected light; Includes.

[0013] The method includes the following steps: determining a first descriptor including a first set of environmental signatures for a selected area of ​​a cell of the photodetector, the first descriptor corresponding to a first sequence for receiving reflected light rays; determining a second descriptor including a second set of environmental signatures for a selected area of ​​the photodetector cells, the second descriptor corresponding to a second sequence for receiving the reflected light beam; identifying corresponding environmental signatures in the first and second descriptors; Further comprising: The environmental signature of a particular cell of the photodetector is defined as a set of distance indices, each associated with one cell of a predetermined pattern of environmental cells of the particular cell, each of the distance indices being in one of the following four states: the cell is associated with a separation distance that is outside a first predetermined distance range for separation distances associated with the particular cell; the cell is associated with a separation distance that is in the lower segment of the first predetermined distance range; the cell is associated with a separation distance that is in the upper segment of the first predetermined distance range; The cell is associated with a separation distance that is substantially equal, within a predetermined factor, to the distance associated with the particular cell. The eigenvalue is adapted to take on a value representing one of

[0014] The term "descriptor" in this case encompasses a unique descriptor or a list of descriptors.

[0015] Such a method using a LIDAR device is based on simple operations that require limited computational resources, and can be carried out using a LIDAR device with low-resolution photodetectors, ensuring maximum detection security of objects in the vehicle's surroundings and matching the objects to identify their movements.

[0016] The present invention counters the trend towards increasing the resolution of photodetectors and increasing computing resources, as encountered in the prior art, and makes it possible to relax the requirements on the resolution of the photodetector and its computing power while improving detection in terms of performance and safety.

[0017] Indeed, the invention is not based on complex operations for identifying "interesting points" in the sequences detected by the photodetector, but rather on a general characterization of these various sequences by a set of environmental signatures, the simple and unique character of which forms the set of signatures, significantly reducing the computational resources required for the matching step.

[0018] Thus, the present invention allows the use of simple and robust LIDAR devices with low resolution photodetectors, which are therefore compliant with automotive standards for low cost production and high reliability levels, which was not the case for prior art LIDAR devices, whose footprint, cost and reliability levels do not meet automotive production standards.

[0019] The method according to the invention works particularly well in detecting objects from one sequence to another, if these objects have a uniform surface and / or if these objects are moving towards or away from the detection device. The method therefore detects the contours of objects and the bodies of these similar objects, even when the objects are moving. In other words, the method according to the invention can be used to detect more objects compared to the prior art, with fewer resources in terms of optical equipment and computing power.

[0020] The method according to the invention may comprise the following additional features, either alone or in combination: During the step of determining the first descriptor, the selection range of cells includes only cells associated with the separation distance, and during the step of determining the second descriptor, the selection range of cells includes only cells associated with the separation distance; The first and second descriptors are each determined by the following operations, which are performed sequentially for each particular cell of a selection range of cells: a first operation of determining an environmental signature of the particular cell; an operation of adding the environmental signature of the particular cell to the set of environmental signatures if the set of environmental signatures does not contain any environmental signature identical to the environmental signature of the particular cell; an operation of adding the environmental signature of the particular cell to the set of environmental signatures, where the environmental signature is associated with a characteristic element if the set of environmental signatures already contains an environmental signature identical to the environmental signature of the particular cell; the characteristic element being a luminous intensity value associated with a particular cell; the predetermined pattern of environmental cells used to determine the environmental signature of a particular cell is comprised of a predetermined number of cells surrounding the particular cell according to a predetermined pattern of relative placement of the environmental cells with respect to the particular cell; a set of indices used to determine the environmental cells of a particular cell are formed by a set of binary numbers each assigned to a cell of a predetermined pattern of environmental cells; each of said binary numbers includes two bits encoding four values ​​representing said index; Binary numbers are expressed in the following format: assigning a first binary number to the environment cell if the environment cell is associated with a separation distance that is outside the first predetermined range; assigning a second binary number to the environment cell if the environment cell is associated with a separation distance that is in a lower segment of the first predetermined range; assigning a third binary number to the environment cell if the environment cell is associated with a separation distance that is in an upper segment of the first predetermined range; assigning a fourth binary number to an environment cell if the environment cell is associated with a spacing substantially equal to, within a predetermined factor, the spacing associated with the particular cell; A method for assigning to each cell of a predetermined pattern of environmental cells of a particular cell, said predetermined value being weighted for each environmental cell according to the distance separating said environmental cell from the particular cell; the predetermined distance range and the predetermined coefficient have values ​​for each environmental cell that depend on the location of the particular cell on the sensor; the environmental signatures, each associated with a cell of a predetermined pattern of environmental cells, each arranged as a word made up of binary digits arranged in a predetermined order relative to the predetermined pattern of environmental cells; During the step of associating with each cell of the photodetector receiving the reflected light a value representative of a quantity related to the reflected light, the representation value is a separation distance, the separation distance being defined as a value representative of the distance between a cell and an object returning said reflected light, The method includes a matching step in which each environmental signature of the first descriptor is compared with each environmental signature of the second descriptor; In said matching step, a dissimilarity value is determined for each pair of environmental signatures, and the pair of environmental signatures considered to match is the pair of environmental signatures having the lowest dissimilarity value; The dissimilarity value is a maximum dissimilarity value for a pair of environmental signatures, the pair including, on the one hand, an environmental signature for a separation distance that is outside the first predetermined range, and, on the other hand, an environmental signature for a separation distance that is within the first predetermined range; a median dissimilarity value for a pair of environmental signatures, the pair including, on the one hand, an environmental signature for a separation distance that is in the lower segment of the first predetermined range, and, on the other hand, an environmental signature for a separation distance that is in the upper segment of the first predetermined range; A minimum dissimilarity value for a pair of environmental signatures, the pair including, on the one hand, an environmental signature for a separation distance that is in the lower or upper segment of a first predetermined range, and, on the other hand, an environmental signature for a distance that is substantially equal, within a predetermined factor, to the separation distance associated with the particular cell. is determined as:

[0021] Other characteristics and advantages of the invention will become apparent from the following non-limiting description, with reference to the accompanying drawings, in which: [Brief description of the drawings]

[0022] [Figure 1] 2 illustrates steps of the method according to the invention. [Diagram 2] 2 shows steps for generating a descriptor in the method of FIG. 1; [Diagram 3] 2 shows a schematic representation of a part of a photodetector of a LIDAR device used by the method according to the invention; [Figure 4] 4 shows a first predetermined distance range used by the method according to the invention. [Diagram 5] 3 illustrates the generation of an environmental signature according to the present invention. [Figure 6] 1 illustrates a schematic diagram of an environmental signature according to the present invention; [Figure 7] FIG. 4 is a view similar to FIG. 3 for a first variant. [Figure 8] FIG. 4 is a view similar to FIG. 3 for a second variant. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] The method according to the invention allows the use of a LIDAR device in an automobile to perceive the surroundings of the vehicle by detecting an optical flow that identifies the movement of objects around the vehicle. The method can be carried out using a LIDAR device with a low resolution equipped with basic computing means. This low resolution is, for example, 128x32 cells in the photodetector of the LIDAR device. The photodetector generally comprises a photosensitive plate formed by an array of elementary sensors, for example made up of photodiodes. Each cell of the photodetector, also called a "pixel", forms an elementary detection element.

[0024] In addition to this low resolution of 128 × 32 cells, the photodetector may also include a wide-angle lens, so that each cell of the photodetector will focus light rays corresponding to a large surface in the image of the vehicle's surroundings (e.g., 1 to 3 m from the vehicle per photodetector cell). 2 , 20m away).

[0025] The construction of a LIDAR device is known per se and will not be described in further detail here. It should be briefly noted that a LIDAR device comprises a light source designed to emit light pulses towards the surroundings of the vehicle and a photodetector formed by an array of elementary cells designed to receive and detect the light rays reflected by the objects in the surroundings of the vehicle, for example to determine a point cloud related to the separation distances from each object in the environment.

[0026] Thus, the method first includes the conventional steps of operating a LIDAR device; emitting an incident light beam from the vehicle towards its external environment; receiving the reflected light beam as returned by a photodetector in the vehicle; associating with each cell of the photodetector which receives the reflected light a value representative of a quantity related to the reflected light; are used together.

[0027] In the examples described herein, the value representing the quantity related to the reflected light is the separation distance, which is defined as the value representing the distance between the cell and the object returning said reflected light. This value representing the quantity related to the reflected light can be supplemented, for example, by the reflection intensity, the reflectivity of the surface of the object, or any other quantity that can be detected by the LIDAR device.

[0028] The separation distance can be calculated by the LIDAR device based on the travel time of each ray, which starts as an incident ray and returns as a reflected ray after reflecting off an object, and corresponds to the distance between this object and the photodetector cell that receives the reflected ray.

[0029] Thus, the LIDAR device has successive sequences in which light pulses are emitted and then collected by the photodetector. These successive sequences correspond to a picture of the surroundings. These successive sequences form an optical flow. This method makes it possible to identify the movements in the successive sequences, so that the movements of objects between the various sequences can be analyzed and quantified in order to enable an autonomous vehicle to perceive its external environment and adapt its driving to it.

[0030] To this end, the method considers each of the optical flow sequences separately and performs pairwise comparisons of these sequences to assess the translation between two successive sequences.

[0031] In this example, the implementation of the method is simply described for two consecutive sequences, it being understood that this basic method can be implemented successively for all consecutive sequences forming an optical flow.

[0032] FIG. 1 illustrates diagrammatically the execution of the method on two successive sequences, i.e. on two surrounding images each generating a point cloud relating to objects external to the vehicle.

[0033] In Fig. 1, a LIDAR device 1 is shown diagrammatically including various steps of the method. The rectangle S1 corresponds to a first sequence, during which the LIDAR device 1 acquires a point cloud corresponding to a first scene around the vehicle. The rectangle S2 corresponds to the acquisition of a second sequence immediately after the acquisition of the first sequence S1.

[0034] Thus, following the acquisition of the second sequence S2, the LIDAR device 1 has two point clouds corresponding to the images of sequences S1, S2 respectively.

[0035] The objective of the method is to detect the changes made between the sequence S1 and the sequence S2, which make it possible to determine the movement seen by the vehicle.

[0036] According to Fig. 1, the data originating from a first sequence S1 first undergoes a step F1 of determining a first descriptor D1 and then filtering this descriptor D1. The data originating from a second sequence S2 undergoes the same processing, including determining a second descriptor D2 and filtering this descriptor F2.

[0037] The method then performs a step FM of making a match M based on these two filtered descriptors and then filtering this match C. These filtered matches C can then be used by the LIDAR device, for example, to improve the detection of objects, or by other elements for controlling the vehicle, to be able to analyze the movement of the objects themselves or of objects identified in another way.

[0038] FIG. 2 shows in more detail the steps of determining the descriptors D1, D2 for each of the sequences S1, S2.

[0039] During the step of determining the descriptors, based on the point clouds corresponding to the sequences S1, S2, the method first identifies usable cells of the photodetector (step E1). In this case, usable cells of the photodetector are defined as cells that have actually received a light ray reflected by the presence of an object in the external environment. Photodetector cells that do not receive a reflected light ray do not detect the presence of an object and in this case are excluded from the cells considered usable. This is the case when the incident light ray does not find an object on its path and does not return to the photodetector since it is not reflected. Similarly, an external method may have marked certain cells as unusable for various reasons, such as identifying defects in the cells. Thus, determining the descriptors D1, D2 applies only to photodetector cells that have received a reflected light ray and / or to photodetector cells that have not been marked as unusable by any external method. These usable cells form the selection range of the photodetector cells.

[0040] In the following step E2, the method determines an environmental signature for one of the cells of the selection range. The method returns to this step E2 so that this step E2 and the next step are applied in turn to each of the cells of the selection range. Furthermore, the selection of cells can be limited to available cells that are not located at the edge of the photodetector plate.

[0041] Preferably, the available cells forming the selection range and therefore each of which undergoes step E2 and the next step can be processed in order, for example starting with the cell at the top left corner of the photodetector plate and then continuing with adjacent cells for each repetition of step E2 and the next step.

[0042] Step E2 is first performed for a first cell of the selection range. In this case, determining an environmental signature for this first cell comprises assigning a binary number to each of the cells surrounding said cell according to a predefined pattern. To simplify the terminology of the present application, throughout the present application, the cell for which an environmental signature is determined is referred to as a "certain cell" and the cells surrounding the particular cell according to a predefined pattern are referred to as "environmental cells". Determining environmental signatures is explained in more detail below with reference to Figures 3 to 8.

[0043] The environmental signature consists of a series of binary numbers related to the environmental cells of a particular cell.

[0044] The method then proceeds to step E3, which corresponds to the determination of this environmental signature for the particular cell. Step E4 then consists in adding this environmental signature to a list forming a set of environmental signatures. For the first iteration of step E2, i.e. for the first particular cell in question, no environmental signature has been recorded previously, and therefore the environmental signature of this iteration is added to all environmental signatures (step E4). If, in a subsequent iteration of steps E2 and the following step, which concerns the next cell of the selection, a new environmental signature is identified as being identical to one already present in the set of environmental signatures, then in step E5 the new signature in question is also added to the set of environmental signatures, but this time a characteristic element is also stored. In this example, this characteristic element is for example the value of the luminosity of the particular cell, or the average value of the luminosity of the particular cell and its environmental cell. This characteristic element is used to separate two identical environmental signatures of a set of environmental signatures.

[0045] The method then loops back to step E2 to begin a new iteration with the next cell in the selected range, which is then treated as a new particular cell.

[0046] After step E4 of adding the new signature to the set of signatures, the method proceeds to step E6, which determines whether the last cell of the selection range has been reached. Step E6 thus determines whether all cells of the selection range have certainly undergone the repetition of steps E2 and the following steps. During step E6, if the cell in question is not the last cell of the selection range, the method loops back to step E2 and then step E is performed for the next cell in the selection range, which is treated as a new identified cell.

[0047] If, during step E6, the cell in question is in fact the last cell of the selection, which means that a repetition of steps E2 and the following steps has been performed for the entire selection of cells, then the method proceeds to step E7, where a list of environmental signatures is generated.

[0048] Determining the environmental signature of a particular cell (step E2) will now be explained in more detail with reference to FIGS.

[0049] Figure 3 shows a part of a matrix plate forming a photodetector. This matrix is ​​made up of elementary light-sensitive cells (also called "pixels"). In the part shown in Figure 3, a central cell C0 is shown surrounded by other cells C1 (shown in grey) and C2 (shown in white). In this example, cell C0 is the particular cell for which the environmental signature is being determined.

[0050] The grey cells C1 are the surrounding cells of the cell C0, i.e. the cells arranged in a certain pattern (visible in grey) around the particular cell C0.

[0051] Determining the environmental signature of a particular cell C0 involves assigning a binary number to each of the cells C1 of the pattern. Other cells, such as cell C2, shown in white in Figure 3, and all other cells of the photodetector (not shown in the photodetector portion visible in Figure 3), are not considered for determining the environmental signature of a particular cell C0.

[0052] To determine which binary number is assigned to the environment cell C1, the principle is, in this example, assigning a first binary number if the separation distance associated with this environment cell C1 is significantly different from the separation distance associated with the particular cell C0; assigning a second binary number if the separation distance associated with this environment cell C1 is relatively close to, but smaller than, the separation distance associated with the particular cell C0; assigning a third binary number if the separation distance associated with this environment cell C1 is relatively close to but larger than the separation distance associated with the particular cell C0; assigning a fourth binary number if the separation distance associated with this environment cell C1 is substantially equal to the separation distance associated with the particular cell C0; Includes.

[0053] In this example, these four possibilities for the binary number of the environmental cell C1 are determined by a comparison of the separation distance associated with the particular cell C0 with the separation distance associated with said environmental cell C1. Figure 4 shows the location of these separation distances in a schematic manner, with the arrow 18 indicating a scale of distances that can be placed at any distance measured by the device with the reflected light beam that reaches the cell of the photodetector. On this scale, the separation distance associated with the particular cell C0 is indicated by a marker 10. The marker 10 therefore corresponds to the distance between the object that reflected the light beam on the particular cell C0 and the photodetector. Around this marker 10 is defined an equivalence range 15 in which the separation distance is considered to be substantially equal to the separation distance associated with the particular cell C0, within a given factor 19. The factor 19 is selected based on the physical properties of the material from which the photodetector is made, in particular the measurement resolution. In this example, the factor 19 is equal to 0.5 times the resolution of the photodetector.

[0054] A lower threshold 13 and an upper threshold 14 are also defined on the scale of the arrow 18, thus a lower segment 16 corresponding to a value of the separation distance smaller (within the limits of the threshold 13) than the separation distance associated with the particular cell C0; an upper segment 17 corresponding to a value of the separation distance greater than the separation distance associated with the particular cell C0 (within the limits of the threshold 14); A first predetermined distance range is formed.

[0055] Additionally, in this example, and referring again to FIG. 3, the binary number of each environment cell C1 is coded into two bits according to the following table:

[0056] [Table 1]

[0057] In the illustrative example of FIG. 3, the binary numbers associated with each of the environmental cells C1 are shown diagrammatically within each associated cell C1.

[0058] Fig. 5 shows diagrammatically the criteria for determining the binary number to be assigned to each of the environmental cells C1 when determining the signature for a particular C0. Fig. 5 shows diagrammatically the plate 2 of the photodetector (shown diagrammatically in a side view), the optical lens 3 of the photodetector and the surroundings of a vehicle according to a simple example in which two objects 4, 5 are present in the surroundings.

[0059] 5 shows diagrammatically, on a photodetector plate 2, a particular cell C0 and an environmental cell C1 for which a binary number is being determined. In this example, cell C0 is associated with a separation distance D1 corresponding to the distance between cell C0 and object 4 (schematically indicated in FIG. 4 by marker 10) and environmental cell C1 is associated with a separation distance D2 corresponding to the distance between cell C1 and object 5.

[0060] Next, cell C1 is If the separation distance D2 is greater than the threshold 14 or less than the threshold 13, the binary number 00 is assigned to the cell C1; If the separation distance D2 is greater than the threshold 13 or less than the distance D1 reduced by a factor of 19, the cell C1 is assigned the binary number 01; If the separation distance D2 is less than the threshold 14 or greater than the distance D1 increased by a factor of 19, the cell C1 is assigned the binary number 10; If the separation distance D2 is within the range 15, i.e., if the separation distance D2 is substantially equal to the distance D1 (equal to the distance D1 increased or decreased by the factor 19), then cell C1 has a binary number assigned to it such that cell C1 is assigned the binary number 11.

[0061] It is clear that the binary numbers shown here are only an example of an embodiment and that the method starts from the moment that four different binary numbers identify the four possibilities indicating for the state of cell C1 and can be used with any other binary numbers.

[0062] Optionally, thresholds 13 and 14 and coefficient 19 can be adjusted depending on the position of cell C1 on board 2. In this case, difference D2-D1 is evaluated by weighting it with distance D3 between cells C0 and C1 on board 2.

[0063] According to an embodiment, the method is carried out with a LIDAR device adapted to identify several layers of reflected light rays. These known LIDAR devices, called multi-layer LIDAR devices, allow several reflected light rays at the same cell of the photodetector to be obtained for the same sequence, which can take into account reaction phenomena. For example, the LIDAR device emits an incident light ray towards a semi-reflector, fog or any other element that causes partial reflection of the light ray, and the photodetector of the LIDAR device receives a first light ray reflected by the semi-reflecting element and then possibly other light rays reflected by objects located behind the semi-reflecting element that also reflect the incident light ray. In these multi-layer LIDAR devices, each cell of the photodetector is associated with several (generally up to four) separation distances. In this case, when assigning a binary number to the environmental cell C1, all separation distances associated with this cell C1 are taken into account.

[0064] If a binary number is assigned to each of the environmental cells C1 (visible in grey in FIG. 3) of a predefined pattern, the method determines a binary word that contains all the binary numbers of all the environmental cells C1 that correspond to a particular cell C0.

[0065] An example of this binary word 6 is shown in Fig. 6, whose numerical values ​​correspond to the example binary numbers assigned to each of the cells C1 in Fig. 3, read from left to right and top to bottom. The binary word 6 shown in Fig. 6 is a 32-bit binary word (having a predetermined pattern provided as an example in Fig. 3, including 16 environmental cells C1 evenly distributed around a particular cell C0). This 32-bit word 6 forms the environmental signature of the particular cell C0. This 32-bit format is sufficient to generate an environmental signature that gives good results in matching various sequences and corresponds to a common architecture using inexpensive processors.

[0066] In this example, the method also includes filtering operations (operations F1, F2 in FIG. 1) in which certain environmental signatures 6 are ignored based on consistency criteria. These consistency criteria are preferably simple in order to ensure a high execution speed of the method and low required computational resources, while avoiding false positives. These consistency criteria include, for example, ignoring all signatures 6 that contain an abnormally large number of the same binary digits. In the example of a 32-bit binary word 6 in FIG. 6, for example, signatures that contain the same binary digit 0 or the same binary digit 1 more than 24 times are ignored and are not included in the set of environmental signatures. The set of environmental signatures therefore includes signatures 6 that also have certain characteristics provided by the consistency criteria.

[0067] 1, during a matching step M, a set of environmental signatures corresponding to a first sequence S1 is compared with a set of signatures corresponding to a second sequence S2. Each signature 6 of a sequence that is identical to a signature 6 of the other sequence is identified as a transition from the sequence S1 to the other sequence S2.

[0068] In a matching step M, the environmental signature of each particular cell C0 of the second sequence S2 is compared with all the environmental signatures of the first sequence S1, ie the environmental signatures of each cell of the photodetector for the first sequence S1.

[0069] For this matching step M, the method uses the concept of "dissimilarity" between the two environmental signatures to be compared. This concept of dissimilarity does not relate to physical distance, but rather to the concept of separation distance in terms of the probability that an environmental signature corresponds or does not correspond to the same object as another environmental signature. Thus, a strong dissimilarity between two environmental signatures leads to the assumption that these two environmental signatures do not correspond to the same object from one sequence to another, and conversely, a weak dissimilarity between two environmental signatures (up to a certain threshold) leads to the assumption that the two environmental signatures identify the same object in both sequences considered.

[0070] In this example, the total dissimilarity between two environmental signatures is equal to the sum of the dissimilarities that separate each binary digit of the sequence considered with the binary digits of the other sequences. Therefore, each binary digit of an environmental signature is compared one by one with all binary digits of the other environmental signatures and these dissimilarities between the binary digits are added to obtain the dissimilarity value between the two environmental signatures.

[0071] According to a preferred embodiment, this concept of dissimilarity between two binary numbers is applied by assigning a dissimilarity value to each possible pair (a value based on the probability of a match between the binary numbers). The following table shows an example of the assignment of these values ​​for all possibilities of two binary numbers a and b.

[0072] [Table 2]

[0073] In this table, the binary number a can take on four values: 00, 01, 10, and 11. Similarly for the binary number b.

[0074] The pairs of binary numbers 00-00, 01-01, 10-10, and 11-11 are assigned a dissimilarity value of 0. This dissimilarity value of zero corresponds to a high probability of match, since the compared binary numbers are identical.

[0075] Other possible pairs of binary digits are assigned dissimilarity values ​​of A, B, or C (where A is the highest dissimilarity value and C is the lowest dissimilarity value). A move from a separation distance outside the predetermined distance range (between thresholds 13 and 14) to a separation distance within this range is deemed unlikely. Thus, the pairs of binary numbers 00-01, 00-10, and 00-11 are assigned a maximum dissimilarity value A. A move from a separation distance in the upper segment 17 of a given distance range (between thresholds 13 and 14) to a lower segment 16 of this range, or vice versa, is deemed reasonably likely. Thus, the pair of binary numbers 10-01 is assigned a median dissimilarity value B. A move from a separation distance in one of the segments (lower 16 or upper 17) of a given distance range (between thresholds 13 and 14) to the central segment 15 of this range, or vice versa, is considered highly likely. Therefore, the pairs of binary numbers 01-11, 10-11 are assigned the minimum dissimilarity value C. The values ​​of A, B, and C can be calibrated to a particular application. In this example, the values ​​of A, B, and C are 5, 2, and 1, respectively. During matching M, for each particular cell C0, the environmental signature of this cell C0 in the second sequence; and A dissimilarity value is calculated for each pair formed by each environmental signature of each cell in the first sequence.

[0076] The pair of environmental signatures exhibiting the lowest dissimilarity value is deemed to be a match, i.e., the object seen in cell C0 in the second sequence is deemed to be the same object as the object seen in another cell in the first sequence, since the pair of environmental signatures has the lowest dissimilarity.

[0077] Therefore, the same object can be identified between two sequences using very few computational resources and with a high level of operational safety. Unlike other methods with low resource usage, the dissimilarity computation technique also allows matching of environmental signatures even when the environmental signatures are not bit-wise identical.

[0078] Optionally, if two pairs of environmental signatures have equal dissimilarity values, the values ​​are separated based on another criterion, such as luminosity: the cell whose luminosity is closest to that of the particular cell of interest is selected.

[0079] In this example, the method also comprises a filtering step FM (see FIG. 1 ) which comprises filtering the signatures 6 identified as being present in both sequences S1 and S2 according to consistency criteria. As mentioned above, these consistency criteria are preferably simple. They start from the principle that objects around the vehicle can only move at speeds below a certain threshold and can relate to the concept of optical flow. For example, two identical signatures, one in sequence S1 and the other in sequence S2, are identified as relating to a movement between sequences S1 and S2 indicating a high speed, for example at a speed of 250 km / h, and this identified match is ignored.

[0080] Thus, in a final step C (FIG. 1), the method provides a filtered list of particular cells C0 that have matching environmental signatures 6 from one sequence S1 to the other sequence S2. The LIDAR device is thus provided with values ​​representative of the movement around it.

[0081] Variations of the method may be used, for example, Figures 7 and 8 provide two other illustrative examples of predefined patterns that can be applied around a particular cell C0 to determine an environmental signature.

[0082] Figure 7 shows a predefined pattern that returns a 32-bit binary word, but from different arrangements of the environment cell C2. With respect to Figure 8, the use of a predefined pattern of eight cells that returns a 16-bit environment signature is shown.

Claims

1. 1. A method for using a light detection and ranging (LIDAR) device in a motor vehicle, comprising the steps of: emitting an incident light beam from the vehicle towards its external environment; receiving the reflected light beam as returned by a photodetector (2, 3) of said vehicle; associating with each cell of the photodetector that receives the reflected light a value representing a quantity related to the reflected light; Including, The method comprises the steps of: a step (D1) of determining a first descriptor comprising a first set of environmental signatures (6) for a selected area of ​​cells of the photodetector, the first descriptor corresponding to a first sequence (S1) for receiving reflected light rays; a step (D2) of determining a second descriptor comprising a second set of environmental signatures (6) for a selected area of ​​cells of the photodetector, the second descriptor corresponding to a second sequence (S2) for receiving reflected light rays; (C) identifying matching environmental signatures (6) in the first and second descriptors; Further comprising: A specific cell (C ０ ) environmental signatures (6) of the particular cell (C ０ ) one cell (C １ ), each of the distance indices being associated with one of the following four states: The cell (C １ ) is the specific cell (C ０ a state associated with a separation distance that is outside a first predetermined distance range for the separation distance associated with The cell (C １ ) is associated with a separation distance that is in the lower segment of the first predetermined distance range; The cell (C １ ) is associated with a separation distance that is in the upper segment of the first predetermined distance range; The cell (C １ ) is the specific cell (C ０ ) is associated with a separation distance that is substantially equal, within a predetermined factor, to the separation distance associated with .times. ...

2. 2. The method of claim 1, wherein during the step of determining the first descriptor, the selected range of cells includes only cells associated with a separation distance, and during the step of determining the second descriptor, the selected range of cells includes only cells associated with a separation distance.

3. The first and second descriptors each correspond to a particular cell (C ０ ) the following operations are performed sequentially: The specific cell (C ０ a first operation of determining said environmental signature (6) of The set of environmental signatures corresponds to the particular cell (C ０ ) does not contain any environmental signature (6) identical to this environmental signature (6) of the particular cell (C ０ ) adding this environmental signature to said set of environmental signatures; The specific cell (C ０ ) to the set of environmental signatures, which environmental signature is added when the set of environmental signatures is in the range of the particular cell (C ０ ) already contains an environmental signature (6) identical to this environmental signature of The method of claim 1 , wherein the distance is determined by:

4. The characteristic element is the specific cell (C ０ 4. The method of claim 3, wherein the luminous intensity value is a value for the light intensity of the light source.

5. A specific cell (C ０ ) used to determine the environmental signature (6) of the １ ) is determined by the predetermined pattern of this particular cell (C ０ ) to the environmental cell (C １ ) according to a predetermined pattern of relative placement of the particular cell (C ０ ) by a predetermined number of cells (C １ 2. The method of claim 1, comprising:

6. A specific cell (C ０ ) used to determine the environmental signature (6) of the predetermined pattern of environmental cells (C １ 2. The method of claim 1, wherein the first and second binary numbers are formed by a set of binary numbers assigned to the first and second binary numbers.

7. 7. The method of claim 6, wherein each of said binary numbers includes two bits that encode four values ​​representing said index.

8. The binary number is The environmental cell (C １ ) is associated with a separation distance (D2) outside the first predetermined range, １ ) a first binary number; The environmental cell (C １ ) is associated with a separation distance (D2) in the lower segment of the first predetermined range, １ ) a second binary number; The environmental cell (C １ ) is associated with a separation distance (D2) that is in the upper segment of the first predetermined range, １ ) a third binary digit, The environmental cell (C １ ) is the specific cell (C ０ ) is associated with a separation distance (D2) that is substantially equal to the separation distance (D1) associated with the environmental cell (C １ ) to assign the fourth binary digit Thus, the specific cell (C ０ ) each cell (C １ 8. The method according to claim 6 or 7, characterized in that the data is assigned to

9. The predetermined value is set to each environmental cell (C １ ) for this environmental cell (D1) and the specific cell (C ０ 9. The method of claim 8, wherein the weighting is performed by the distance (D3) separating the two.

10. The predetermined distance range and the predetermined coefficient are set to each environmental cell (C １ ) for the particular cell (C ０ 9. The method of claim 8, wherein the value of the parameter .DELTA..times ...

11. Each of the cells of the predetermined pattern of environmental cells (C １ 8. The method according to claim 6 or 7, characterized in that the environmental signatures, each associated with a predetermined pattern of environmental cells, are arranged as a word (6) made up of the binary digits arranged in a predetermined order relative to the predetermined pattern of environmental cells.

12. 2. The method of claim 1, wherein during the step of associating a value representing a quantity related to the reflected light beam with each cell of the photodetector that receives the reflected light beam, the represented value is a separation distance, the separation distance being defined as a value representing the distance between the cell and an object that returns the reflected light beam.

13. 2. The method of claim 1, further comprising a matching step in which each environmental signature of the first descriptor is compared with each environmental signature of the second descriptor.

14. 14. The method of claim 13, wherein in the matching step, a dissimilarity value is determined for each pair of environmental signatures, and the pair of environmental signatures considered to match is the pair of environmental signatures with the lowest dissimilarity value.

15. The dissimilarity value is a maximum dissimilarity value for the pair of environmental signatures, the pair including, on the one hand, an environmental signature for a separation distance that is outside the first predetermined range, and, on the other hand, an environmental signature for a separation distance that is within the first predetermined range; a median dissimilarity value for the pair of environmental signatures, the pair including, on the one hand, an environmental signature for a separation distance in the lower segment of the first predetermined range, and, on the other hand, an environmental signature for a separation distance in the upper segment of the first predetermined range; On the one hand, it comprises an environmental signature relating to the separation distances in the lower or upper segments of the first predetermined range, and on the other hand, ０ a minimum dissimilarity value for the pair of environmental signatures that includes environmental signatures for distances that are substantially equal, within the predetermined factor, to the separation distance associated with the pair of environmental signatures; 15. The method of claim 14, wherein the value is determined as:

Images