System and method for constructing a map of an environment
Patent Information
- Application Number
- EP2023798191
- Authority / Receiving Office
- EP · EP
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2022-10-26
- Filing Date
- 2023-10-25
- Publication Date
- 2025-09-03
AI Technical Summary
Existing automatic mapping systems using lidar struggle to accurately identify areas of interest in an environment, leading to blurry maps due to subjective and imprecise thickness criteria for determining flat areas, which affects the quality of the trajectory estimation and map construction.
A system and method that preselects sets of initial points to apply local corrections to potential areas of interest, determining whether they are indeed areas of interest by transforming them through local trajectory or speed corrections, and constructs the map with corrected points, allowing for precise identification and correction of zones of interest.
This approach enables more precise and certain identification of areas of interest, improving the quality of the map construction by iteratively refining the trajectory corrections, allowing for real-time SLAM operations and accurate representation of environmental features.
Smart Images
Figure 1.1
Abstract
Description
[0001] DESCRIPTION
[0002] Title: System and method for constructing a map of an environment
[0003] FIELD OF THE INVENTION
[0004]
[0001] The present invention relates to a system and a method for constructing a map of an environment. More specifically, the invention relates to the field of automatic mapping of an environment from a three-dimensional point cloud acquired by a mapping device comprising a lidar (from the English acronym Light Detection And Ranging).
[0005] TECHNOLOGICAL BACKGROUND
[0006]
[0002] In the field of automatic mapping, an environment can be mapped by means of a measuring device, mobile in the environment, comprising at least one lidar. The lidar performs scans of the environment in which the device moves and thus acquires point clouds representative of the environment in which it evolves. Such an environment can be a building, a road, a construction site, etc. The map of the environment is constructed as points are acquired by the lidar, i.e. as the measuring device comprising the lidar moves.
[0007]
[0003] However, in some cases, the environment map thus constructed may not be representative of the environment, at least in some places. In this case, the environment map will appear blurred. This can happen because the lidar takes relative measurements, so it is necessary to know its position and orientation to be able to locate the points it returns at each acquisition in a global orthogonal coordinate system locating the mapped environment.
[0008]
[0004] When lidar measurements are used to jointly calculate the position and orientation of the device as well as the map, the technique implemented is called the SLAM technique (for the English acronym Simultaneous Localization and Mapping). It consists of the construction of the map of the environment by a measuring device comprising a lidar, combined with the identification of the trajectory and orientation of the measuring device.
[0009]
[0005] In particular, the trajectory of the measuring device is not known at every instant. Thus, each hypothetical trajectory of the measuring device may be associated with a different map of the environment. The quality of the map of the environment is in particular conditioned by knowledge of the trajectory of the measuring device, that is to say that it is necessary to find the correct trajectory of the measuring device in the environment to obtain a good quality map of the environment. Consequently, the trajectory of the device can be identified by searching for the one which leads to the best quality of the map of the environment.
[0010]
[0006] The search for the trajectory and the best quality map is carried out simultaneously.
[0007] To do this, it is possible to define areas of interest in the environment. Such areas may in particular comprise a flat area of the environment, for example a wall, a facade, a road, etc. Such areas of interest have the advantage of being easily mappable, so that they are of interest in estimating the trajectory of the measuring device in the environment. It is then a matter of focusing on such areas to improve the estimation of the trajectory and obtain a good quality map.
[0011]
[0008] One of the challenges of SLAM is that the mapping tends to be done automatically, without an external person defining which areas are of interest for estimating the trajectory of the measuring device.
[0012]
[0009] With this in mind, the prior art proposes to identify approximately flat areas, considered as areas of interest, on the point cloud and to select some of these areas as flat areas according to a quality criterion corresponding to the thickness of these areas.
[0013]
[0010] More precisely, the known method consists of carrying out a first approximate reconstruction of a point cloud from a trajectory of the device a priori. The approximately flat areas on the point cloud are identified. Their thickness is evaluated in order to determine whether it corresponds to a predefined thickness. It is therefore a question of comparing the thickness of the point cloud along an area with the predefined thickness. If the thickness of the point cloud along an area corresponds to the predefined thickness, these areas are selected as flat areas, then the overall trajectory of the device is modified to reduce the thickness of the approximately flat areas found, that is to say to make them actually flat.
[0014]
[0011] However, this solution is not satisfactory. Indeed, the thickness criterion is subjective and imprecise. For example, if too large a predefined thickness is tolerated, there is a risk of selecting areas which, in reality, are not flat.
[0015]
[0012] On the other hand, if a predefined thickness that is too low is tolerated, there is a risk of finding very few flat areas. This risk arises from the fact that, for a predefined thickness that is too low, the trajectory of the measuring device must be known with a correspondingly higher precision. Indeed, approximately flat areas only really appear flat once the trajectory of the measuring device is known. However, at the time of comparing the approximately flat area with the predefined thickness, the only map of the environment available is an approximate reconstruction of the environment from an approximate trajectory of the measuring device. Thus, if the predefined thickness is too thin, areas that are actually flat may not meet this criterion on the approximate map. Moreover, when such areas meet the thickness criterion, this means that they are already very thin.They cannot therefore be used to make any subsequent correction to the trajectory of the measuring device.
[0016]
[0013] The invention aims to at least partially solve the technical problems set out.
[0017] SUMMARY OF THE INVENTION
[0018]
[0014] Thus, the invention relates to a system for constructing a map of an environment, the system comprising:
[0019] - a measuring device comprising at least one lidar configured to acquire a point cloud representative of said environment in which the measuring device moves, a processor configured to
[0020] 'performing a preselection of a plurality of sets of initial points in the point cloud, each initial set of points of the plurality of sets of initial points being representative of a potential area of interest of the environment, 'for each initial set of points, searching for a local correction to be made to a movement of the measuring device along each potential area of interest represented by the initial set of points considered in order to determine whether each initial set of points actually represents an area of interest,
[0021] 'jointly correct the sets of initial points effectively representing an area of interest, by applying a global correction to a movement of the measuring device in the environment,
[0022] 'build the environment map with the corrected initial point sets actually representing an area of interest and the initial point sets not representing an area of interest.
[0023]
[0015] The invention also relates to a method of constructing a map of an environment, comprising:
[0024] - The acquisition, by a measuring device comprising at least one lidar, of a point cloud representative of said environment in which the measuring device moves,
[0025] The implementation, by a processor: 'Of a preselection of a plurality of sets of initial points in the point cloud, each initial set of points of the plurality of sets of initial points being representative of a potential area of interest of the environment, 'for each initial set of points, a search for a local correction to be made to a movement of the measuring device along each potential area of interest represented by the initial set of points considered in order to determine whether each initial set of points actually represents an area of interest,
[0026] 'a joint correction of the sets of initial points effectively representing an area of interest, by applying a global correction to a movement of the measuring device in the environment,
[0027] 'a construction of the environment map with the corrected initial point sets actually representing an area of interest and the initial point sets not representing an area of interest.
[0028]
[0016] Thus, contrary to what is known from the prior art, the areas of interest are not identified by looking at the approximately constructed map to identify areas that would resemble a plan, but by performing local trajectory corrections on sets of points, these corrections aiming to transform certain areas into areas of interest, and thus determine whether a set of points actually represents an area of interest or not. Advantageously, all the points of the point cloud belong to an initial set of points. Thus, the system makes it possible to efficiently determine which potential area of interest is actually an area of interest: indeed, if a local correction of the trajectory along a potential area of interest actually reveals an area of interest, this means that the potential area of interest is actually an area of interest.The overall correction of the trajectory can therefore be based on the area of interest actually determined.
[0029]
[0017] In particular, when the processor seeks to locally correct the trajectory, the local correction may reveal an area of interest or the local correction applied may reveal that the set of points considered is not an area of interest. For example, if the area of interest sought is a flat area, the local correction of the trajectory along the set of points considered will reveal the flat area. On the other hand, if the set of points considered is not a flat area, no local correction will reveal a flat area.
[0030]
[0018] According to different aspects, it is possible to provide one and / or the other of the characteristics below taken alone or in combination.
[0031]
[0019] According to one embodiment, the processor is configured to iterate the preselection, local correction and joint correction steps until a predefined criterion is met.
[0032]
[0020] This arrangement makes it possible to obtain a more precise correction of the local trajectory, and / or to determine more certainly whether an area of interest is actually an area of interest or not.
[0033]
[0021] According to one embodiment, the searches for local correction to be made to each initial set of points are carried out in parallel.
[0034]
[0022] Thus, the areas of interest are located more quickly than if the processor searched for local corrections in series. This makes it possible to have a system responding to the SLAM problem which operates in real time and instantly or almost instantly.
[0035]
[0023] According to one embodiment, the local correction to be made to a movement of the device comprises the correction of a local trajectory of the device along each potential area of interest and in which the joint correction comprises the correction of a global trajectory of the measuring device in the environment.
[0036]
[0024] According to one embodiment, the local correction to be made to a movement of the device comprises the correction of a local speed of the device along each potential zone of interest and in which the joint correction comprises the correction of a global trajectory of the measuring device in the environment.
[0037]
[0025] The correction can therefore be made on the trajectory or on the speed. The two corrections can be made in parallel or can be interchangeable.
[0038]
[0026] According to one embodiment, the overall correction to be made to a movement of the measuring device in the environment is determined by solving an optimization problem.
[0039]
[0027] According to one embodiment, the areas of interest comprise a flat area such as a road, a traffic sign or a wall, the processor being configured, for each potential area of interest corresponding to a potential flat area, to calculate a minimum thickness C' of said potential flat area obtained after the local correction of the movement of the measuring device, said minimum thickness being obtained by direct resolution of:
[0040] N is the number of points x t in the initial set of points considered, x = is the average of the points x t of the initial set of points, is the average of the instants associated with said points of the initial set of points, e st a calculation intermediary, )(%; - x is the covariance matrix, and the function A(-) returns the smallest eigenvalue of the matrix given as argument, and where the processor is configured to determine that the potential flat area is indeed an area of interest if C' is less than a predefined threshold.
[0041]
[0028] Thus, the calculation of the criterion for determining that an area is indeed an area of interest can be done directly. It does not require going through an intermediate optimization step. This advantage is specific to the case where the correction is applied to the speed and when the area of interest is a flat area.
[0042]
[0029] According to one embodiment, the areas of interest comprise a cylindrical area such as a post, the processor being configured, for each potential area of interest corresponding to a potential cylindrical area, to determine a correction of a local movement (Amouv) of the measuring device for which the distance is minimal between each of the N points x t of the initial set of points considered and a cylinder with center c, axis u and radius r according to a double minimization: min min-
[0043] Amouv u,c,r N where Amouv is a variation of the movement of the measuring device 10 at each point x, and where Xi=i, 2, (Amouv) is the position of the point i after applying a variation of movement Amouv, the processor being further configured to determine that a potential cylindrical area of interest is indeed a cylindrical area of interest if a result of the double minimization is less than a predefined threshold.
[0044]
[0030] Thus, the processor is configured to locate the areas of interest corresponding to cylindrical areas.
[0045]
[0031] According to one embodiment, for each initial set of points actually representing an area of interest, the processor is configured to delete the points present in the initial set of points but not belonging to said area of interest.
[0046]
[0032] This makes it possible to avoid distorting the overall correction of the trajectory by considering points which are not part of an area of interest.
[0047] BRIEF DESCRIPTION OF THE DRAWINGS
[0048]
[0033] Embodiments of the invention will be described below with reference to the drawings, briefly described below:
[0049]
[0034] [Fig. 1] represents a mapping system according to one embodiment.
[0035] [Fig. 2] illustrates steps of a method of constructing a map of an environment using the system according to one embodiment.
[0050]
[0036] [Fig. 3] illustrates steps of a method of constructing a map of an environment using the system according to another embodiment.
[0051]
[0037] In the drawings, like references designate like or similar objects.
[0052] DETAILED DESCRIPTION
[0053]
[0038] The invention relates to a mapping system shown in Figure 1. The system 1 comprises a measuring device 10 comprising at least one lidar 101. The measuring device may also comprise an inertial measurement unit (IMU) 102 and a camera 103. The IMU may comprise at least one accelerometer and at least one gyroscope.
[0054]
[0039] The measuring device may be mounted on an object that may be movable (not shown). The object may be a drone, a backpack carried by a moving person, a car, a robot, or any other movable object. The movable object comprising the measuring device 10 moves in an environment to be mapped. Such an environment may be indoors, for example inside a building, or outdoors, for example outside a building, on a street, on a road, in a construction site, etc.
[0055]
[0040] As the object moves, the lidar is configured to perform measurements. These measurements include the acquisition of point clouds representative of the environment. From the point cloud at least, a map of the environment can be constructed.
[0056]
[0041] The system 1 further comprises a processor 11. The processor 11 may be connected to the measuring device, so as to receive the data measured by the lidar, the camera and / or the IMU. In one configuration, the communication between the processor 11 and the measuring device 10 may be wired. In another configuration, the communication between the processor 11 and the measuring device 10 may be remote, for example by radio waves, Bluetooth or wifi.
[0057]
[0042] In one configuration, the measuring device 10 and the processor 11 exchange data via a respective communication interface 104 and 110.
[0058]
[0043] In one configuration, the processor 11 may be embedded in the measuring device 10. In another configuration, the processor 11 may be hosted on a remote server. According to this configuration, the communication is therefore remote, for example by radio waves, passing for example via the 4G and / or 5G network, Bluetooth or wifi.
[0059]
[0044] The system 1 may also include graphics cards as well as one or more high-capacity hard drives, of the order of a terabyte.
[0060]
[0045] Figure 2 illustrates steps of a method of constructing a map of an environment using system 1.
[0061]
[0046] In step S1, the measuring device 10 acquires data.
[0062]
[0047] More particularly, the lidar 101 acquires at least one point cloud representative of the environment. In a preferred configuration, the measuring device 10 moves at each instant and the lidar acquires a point cloud at each instant. Thus, a plurality of point clouds are acquired as the measuring device 10 moves through the environment.
[0048] The IMU may also acquire data, including data regarding the orientation and acceleration of the measuring device 10 as it moves through the environment.
[0063]
[0049] The data acquired by the lidar and / or the IMU are used to construct the map of the environment. More particularly, the map of the environment can be constructed after a first and only passage of the measuring device in the environment. The point clouds acquired by the lidar and the orientation and acceleration data of the measuring device 10 acquired by the IMU on a passage of the measuring device 10 in the environment are used to construct the map of the environment. More precisely, the acceleration measurement of the IMU, which is called the specific force, is defined as the difference between the acceleration and the gravity. This measurement provides indirect information on the acceleration of the device.
[0064]
[0050] In another embodiment, the map of the environment can be constructed after several passes of the measuring device in the environment. The point clouds acquired by the lidar and the orientation and acceleration data of the measuring device 10 acquired by the IMU on the passes of the measuring device 10 in the environment are used to construct the map of the environment.
[0065]
[0051] However, in order to construct a map of satisfactory quality, that is to say a clear and non-blurry map, it is necessary to know the trajectory of the measuring device 10 when it passes through the environment. Indeed, when the trajectory of the measuring device is not known precisely, the map of the environment appears blurred.
[0066]
[0052] The search for the trajectory of the measuring device and the construction of the map from the data acquired by the lidar 102 and the IMU 103 are carried out at the same time, and advantageously at the same time and in real time.
[0067]
[0053] In order to search for the trajectory of the measuring device 10, it may be advantageous to define areas of interest along which the search for the trajectory can be facilitated. These areas of interest may be, for example, a flat area such as a wall, a roadside, a sidewalk, etc., or even objects such as road signs and their support, or even wall corners.
[0068]
[0054] More particularly, each point of the point clouds acquired by the lidar 101 are described by three coordinates describing a position in the orthogonal reference frame attached to the measuring device, as well as a measurement time t. The points are also described by an intensity representing the fraction of the light energy of the laser emitted by the lidar 101 and reflected by the material touched. The areas of interest can therefore be determined on a criterion of distribution of the points in space (for flat areas, for example) or on an intensity criterion.
[0069]
[0055] In order to locate the areas of interest and to construct a map correctly representing these areas of interest, the processor preselects a plurality of sets of initial points in the point cloud in step S2, each initial set of points of the plurality of sets of initial points being representative of a potential area of interest of the environment.
[0056] The areas of interest are not known before the final construction of the map. As a result, it is a question of determining which areas of the environment are actually areas of interest. In order to miss the fewest areas of interest which can help in the construction of an accurate map of the environment, the sets of initial points preselected by the processor can cover all the points of the point cloud. More precisely, each point of the point cloud is integrated into at least one initial set of points.In other words, all areas of the environment can be preselected by the processor in step S2, in order to study them and determine whether they indeed represent an area of interest, as described in step S3.
[0070]
[0057] Then, in step S3, for each initial set of points, the processor 11 searches for a local correction to be made to a movement of the measuring device 10 along each potential area of interest represented by the initial set of points considered in order to determine whether each initial set of points actually represents an area of interest.
[0071]
[0058] For example, the areas of interest representing a flat area of the environment only appear flat, i.e. having a small thickness in the map of the environment, once the true trajectory of the measuring device 10 along this flat area has been determined.
[0072]
[0059] For areas of interest representing a traffic sign, or a wall corner, for example, they only appear clear once the true trajectory of the measuring device 10 along this area of interest has been determined.
[0073]
[0060] In the case of areas of interest representing a flat area, in order to determine whether potential areas of interest are actually flat areas, the processor 11 will locally correct a movement of the measuring device corresponding to a speed of the measuring device 10 along each potential area of interest. The local correction of the speed is independent for each potential area of interest, so that the processor 11 can search for each local correction in parallel. This greatly reduces the calculation time, so that the search for the trajectory of the measuring device 10 can be done in real time.
[0074]
[0061] Mathematically, the search for the local correction to be made to the movement of the measuring device 10 along each potential area of interest comprises the definition of a criterion C'CX. T) applied to each initial set of points X = x1,x2, ... ) representing the potential area of interest and to a series of associated instants T = (t lt t2, ... ) at the points of the initial set of points, and where C' corresponds to the smallest quadratic thickness, i.e. the minimum squared thickness, obtained following the local correction of the movement of the measuring device 10. This minimum squared thickness must be less than a predefined threshold to determine that a potential area of interest is indeed an area of interest, i.e. for example a flat area.
[0075]
[0062] Advantageously, the predefined threshold represents an expected squared thickness, which takes into account measurement noise but not reconstruction errors. In particular, LiDAR manufacturers indicate a distance uncertainty r ., in azimuth and in elevation 5e in the form of standard deviation or maximum error value. From this, we can deduce an overall squared error of measurement equal to ( - Sr - >2 avec r | a measured distance and e the elevation.
[0076]
[0063] The overall squared measurement error provides a possible value of the threshold if the uncertainties r ., , Se data given by the manufacturer represent maximum errors. If the values of the uncertainties r ., Se data given by the manufacturer are standard deviations, it is possible to multiply them by three to obtain a threshold representative of the expected squared thickness.
[0077]
[0064] According to another embodiment, in which the thickness, and not the thickness squared, is compared to a predefined threshold, the square root of the criterion C' is to be compared with the square root of the overall quadratic measurement error. When the values of the uncertainties r ., data given by the manufacturer are standard deviations, the uncertainty values must also be multiplied by three to obtain a threshold representative of the expected thickness.
[0078]
[0065] In the description which follows, and in a non-limiting manner, the criterion C' corresponds to or is representative of a squared thickness and the predefined threshold is representative of the overall quadratic measurement error.
[0079]
[0066] Thus, in order to determine whether the potential area of interest is actually an area of interest, an optimization problem can be solved. It takes the form:
[0080]
[0067] C' = min C(x1(Amouv), x2(Amouv), ...) (1) âmouv
[0081]
[0068] Where Amouv is a variation of the movement of the measuring device 10 at each point x, and where Xi=i, 2, (Amouv) is the position of the point i after applying a variation of movement Amouv. The initial position of the point x(0), for Amouv=0, is simply noted x. In other words, the processor 11 searches for a correction of all the positions equivalent to a variation of movement Amouv of the measuring device 10. The minimum value of C x1(Amouv), 2 (Amouv), ... ) is noted C'.
[0082]
[0069] Solving the optimization problem makes it possible to obtain the value of the criterion C' and to determine whether the potential area of interest is actually an area of interest by comparing C' to the predefined threshold.
[0083]
[0070] According to a second embodiment in which only the local speed is corrected, the optimization problem takes the form:
[0084]
[0071] C' = min C x1(Av),2(Av),...)
[0085] Av
[0086]
[0072] Where Av is a variation in the speed of the measuring device 10 at each point x, and where x(Av) can be written xt v, with i=1, 2, .... In other words, the processor 11 searches for a correction of all the positions equivalent to a variation in speed Av of the measuring device 10. The minimum value of C(x1(Av),2(Av), ... ) is noted C'(x1,x2, ... ).
[0087]
[0073] Furthermore, in an embodiment where only the local velocity is corrected and in which the criterion C corresponds to the smallest eigenvalue of the covariance matrix - x)(x f - X ) T , the result of the optimization problem (1) can then be obtained directly:
[0088]
[0075] With the three-coordinate points of the initial point set representing a potential area of interest, N the number of points in the initial point set considered, x is the average position of said points defined by x = t is the average position of said instants defined by t = tj, the sum Z ' i x i - x)(x f - ) r is the covariance matrix of the points, s = e t the function A(-) returns the smallest eigenvalue of the given matrix Lixti— t) as argument.
[0089]
[0076] According to this embodiment, once the value C' has been determined, it is then possible to determine whether the potential area of interest is actually an area of interest. In this example, the area of interest considered is a flat area. Indeed, the covariance matrix characterizes the distribution of the points in the local orthogonal reference system locating the points of the initial set of points representing the potential area of interest. Thus, if C' is less than the predefined threshold, it is determined that the area is flat.
[0090]
[0077] If the potential area of interest is not in reality one, that is to say if the potential area of interest is not flat, C' will take a value greater than the predefined threshold and this value will increase with the actual thickness of the cloud of points formed by the set of points associated with the potential area of interest considered.
[0091]
[0078] Thus, according to the second embodiment, the calculation of the value of the criterion C' is direct and does not require going through an optimization process. This advantage is specific to the case where a correction of the speed is sought. This embodiment also makes it possible to greatly reduce the calculation times as well as the memory allocated to the calculations.
[0092]
[0079] Furthermore, since the predefined threshold represents an expected quadratic thickness affected by measurement noise but not by reconstruction errors, it is accurately determined whether a potential area of interest is indeed an area of interest.
[0093]
[0080] The search for areas of interest representing flat areas in the environment was described above. As stated above, these areas may include a wall, a wall corner, a roadside edge, a sidewalk, but also a flat road sign.
[0094]
[0081] Generally, such traffic signs are carried by posts, of cylindrical shape. It may then be useful to define the panel and post assembly or simply the post as an area of interest.
[0095]
[0082] Regarding flat areas, one criterion for indicating that an area is flat is to determine that all the points in that area belong to the same plane, as described above. It is possible to define such a criterion for more complex shapes such as cylinders.
[0083] The question then is whether a cylinder can pass through all the points in the initial set of points corresponding to the cylindrical-shaped element. Such an element can be a post, but also a tree trunk, a stake, etc.
[0096]
[0084] In other words, a cylinder is defined by its axis u, its center c and its radius r. It can be determined that a cylinder passes through all the points of an initial set of points, or passes at least close to a majority of the points if the distance between each of these points, or the vast majority of the points, and this cylinder is small, or even equal to zero.
[0097]
[0085] The distance from a point x to such a cylinder is written - r . It is It is possible to determine that a cylinder passes through or near all or most points if the mean squared distance is less than the predefined threshold for the root mean square error of the Lidar distance measurements, usually provided by the Lidar manufacturer. This error is usually in the order of 1 cm but can vary depending on the Lidar used.
[0098]
[0086] According to this embodiment, to determine whether the parameters of the cylinder u, r, c correspond to the points xi, ... x n , the mean square distance of points xi, ... x n to the cylinder, representative of a thickness, is calculated according to:
[0099]
[0088] However, only the x points of the potential area of interest are known. The parameters u, c and r of the cylinder that the x points L could form are not known. The way to determine whether a cylinder passes through all points x L , or at least the majority of points, is to say that there exists at least one cylinder for which C will be minimal, or even equal to zero, where C is written:
[0100]
[0090] Finally, the minimum value of C by applying the Amouv motion correction, noted C', is the criterion for determining whether the potential area of interest is actually an area of interest. C' is obtained according to:
[0101] 2
[0102]
[0091] C'(%i, ■■■ , x N ) = min min- f | |1(Amouv) — c| | 2 — | |u mouv u,c,r N ' T (1(Amouv) — c)| | A 2
[0103]
[0092] Where Amouv is a correction of the movement of the measuring device along the potential area of interest, i.e. its trajectory and / or its speed.
[0104]
[0093] If the result of the double minimization is less than a predefined threshold such as the mean square error of the Lidar distance measurements, then the potential area of interest is determined to be indeed an area of interest, here a cylindrical shape.
[0105]
[0094] If the result of the double minimization is greater than a predefined threshold such as a multiple of the mean square error of the Lidar distance measurements, it is then determined that the potential area of interest represented by the initial set of points considered is not an area of interest.
[0095] It should be noted that, in cases where a correction of the local speed of the measuring device has been described, a correction of the local trajectory of the measuring device can be made as an alternative and / or in addition. In cases where a correction of the local trajectory of the measuring device has been described, a correction of the local speed of the measuring device can be made as an alternative and / or in addition.
[0106]
[0096] Finally, according to a last embodiment, the criterion for determining whether a potential area of interest is actually an area of interest is the distribution of the intensity of the points of an initial set of points. Here, the intensity represents the fraction of the light energy of the laser emitted by the lidar 101 and reflected by the material touched.
[0107]
[0097] Such an area of interest may be an inscription, for example an inscription on a wall, a vehicle, etc., or an image represented on a panel, for example an advertising panel or a road sign.
[0108]
[0098] In this embodiment, it is a local trajectory of the measuring device 10 along the potential area of interest which is corrected for each initial set of points representing a potential area of interest. It is then a matter of determining whether, for a potential area of interest, the variations in the arrangement of the points carrying an intensity are correlated with the variations in the position of the measuring device. In other words, it is a matter of determining whether a local variation in the trajectory of the measuring device along the potential area of interest comprising points carrying different intensities results in a different distribution of the points making it possible to define that a potential area of interest is actually an area of interest. In particular, such a difference in the distribution of the points carrying the intensity can make it possible to make an inscription readable which would not be without local correction of the measuring device along this area.
[0109]
[0099] Such a criterion E can be written:
[0110]
[0101] Where / j is the intensity measured at a point x t of the initial set of points, / = ^2; / ; is the average intensity in the area and ||-|| is the classical Euclidean norm of a vector, here of size 3.
[0111]
[0102] It is determined that a potential area of interest is indeed an area of interest when the value of criterion E reaches, for example, 0.5.
[0112]
[0103] More precisely, for all the embodiments described with reference to step S3, the calculation of the value of the criterion induces corrections to be made to the local movement of the measuring device 10, here to the speed or the trajectory of the measuring device 10. The calculation of the criterion makes it possible to determine with certainty whether a zone of interest can actually be obtained by locally modifying the movement of the measuring device 10 along the potential zone of interest.
[0113]
[0104] At the end of step S3, all areas of interest have been identified.
[0114]
[0105] Then, an optional step S4 can be implemented, in which, following the determination that a potential area of interest is indeed an area of interest, it is determined whether all the points of the initial set of points representing the area of interest actually belong to the area of interest. Indeed, in certain cases, points far from the area of interest may have been integrated into the area of interest by mistake. It is then a matter of finding these points and deleting them.
[0115]
[0106] To do this, it is possible to calculate the proximity of each point in the initial set of points to the area of interest, then to delete the points that are too far from this area of interest.
[0116]
[0107] More specifically, considering areas of interest such as wall corners, signs and / or posts and flat areas.
[0117]
[0108] In particular, for traffic signs, flat areas and wall corners, it is a matter of determining whether all the points of the initial set of points are indeed part of the same plane determined in step S3. The points not forming part of the plane formed by the area of interest are deleted.
[0118]
[0109] Similarly, for more complex areas of interest such as sign posts, it is necessary to determine whether all the points of the initial set of points are indeed part of the cylinder determined in step S3. The points not forming part of the cylinder formed by the area of interest are deleted.
[0119]
[0110] In step S5, a joint correction is applied to the sets of initial points effectively representing an area of interest by applying a global correction to a movement of the measuring device 10 in the environment. More precisely, the global correction is applied to a trajectory of the measuring device 10 in the environment.
[0120]
[0111] This global correction of the trajectory of the measuring device 10, and no longer local, is due to the fact that it is a question of finding a single trajectory making it possible to obtain a map of the environment on which all the identified zones of interest are represented, and not of finding different trajectories for each zone of interest.
[0121]
[0112] This is solved by means of an optimization problem. The trajectory is obtained by defining an optimization problem comprising two components: the structure criterion of the areas of interest (flatness for example) and the correspondence between the chosen trajectory and the additional measurements available, for example from an inertial unit. The resolution of such an optimization problem is well understood and off-the-shelf solvers offer it in a fairly generic framework, for example the GTSAM solver mentioned in the following publication:
[0122]
[0113] Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, JJ, & Dellaert, F. (2012). iSAM2: Incremental smoothing and mapping using the Bayes tree. The International Journal of Robotics Research, 31(2), 216-235.
[0123]
[0114] Then, once the overall trajectory of the measuring device has been corrected, the map of the environment is constructed with the corrected initial point sets which actually represent an area of interest and with the initial point sets which do not represent an area of interest.
[0124]
[0115] In one embodiment, the environment map and the correction of the overall trajectory of the measuring device 10 are performed simultaneously.
[0116] Figure 3 illustrates another embodiment according to the invention. Steps S1 to S5 described in relation to Figure 2 are identical. According to this embodiment, steps S2 to S4 are iterated until a predefined criterion is met in step S6.
[0125]
[0117] This predefined criterion can be achieved when between two iterations, the correction of the movement of the measuring device no longer changes.
[0126]
[0118] This arrangement makes it possible to obtain a more precise correction of the local trajectory, and / or to determine more certainly whether an area of interest is actually an area of interest or not.
Claims
CLAIMS
1. System (1) for constructing a map of an environment, the system (1) comprising: - a measuring device (10) comprising at least one lidar (101) configured to acquire a point cloud representative of said environment in which the measuring device (10) moves, - a processor (11) configured to 'performing a preselection of a plurality of initial point sets in the point cloud, each initial point set of the plurality of initial point sets being representative of a potential area of interest in the environment, 'for each initial set of points, search for a local correction to be made to a movement of the measuring device along each potential area of interest represented by the initial set of points considered in order to determine whether each initial set of points actually represents an area of interest, 'jointly correct the sets of initial points effectively representing an area of interest, by applying a global correction to a movement of the measuring device in the environment, 'build the environment map with the corrected initial point sets actually representing an area of interest and the initial point sets not representing an area of interest.
2. The system (1) of claim 1, wherein the processor (11) is configured to iterate the steps of preselection, local correction and joint correction until a predefined criterion is met.
3. System (1) according to claim 1 or 2, in which the processor (11) is configured to carry out the local correction searches to be made to each initial set of points in parallel.
4. System (1) according to one of claims 1 to 3, in which the local correction to be made to a movement of the measuring device (10) comprises the correction of a local trajectory of the measuring device (10) along each potential area of interest and in which the joint correction comprises the correction of a global trajectory of the measuring device in the environment.
5. System (1) according to one of claims 1 to 4, wherein the local correction to be made to a movement of the measuring device (10) comprises the correction of a local speed of the measuring device (10) along each potential area of interest and wherein the joint correction comprises the correction of a global trajectory of the measuring device in the environment.
6. System (1) according to one of claims 1 to 5, in which the global correction to be made to a movement of the measuring device (10) in the environment is determined by solving an optimization problem.
7. System (1) according to claim 5, wherein the areas of interest comprise a flat area such as a road, a traffic sign or a wall, the processor (11) being configured, for each potential area of interest corresponding to a potential flat area, to calculate a minimum thickness C' of said potential flat area obtained after the local correction of the movement of the measuring device, said minimum thickness being obtained by direct resolution of: N is the number of points x t in the initial set of points considered, x = “ iXj is the average of the points x t of the initial set of points, is the average of the instants associated with said points of the initial set of points, e s t intermediate calculation, )(%; - x) T is the covariance matrix, and the function A(-) returns the smallest eigenvalue of the matrix given as argument, and where the processor (11) is configured to determine that the potential flat area is indeed an area of interest if C' is less than a predefined threshold.
8. System (1) according to one of the preceding claims, in which the areas of interest comprise a cylindrical area such as a post, the processor (11) being configured, for each potential area of interest corresponding to a potential cylindrical area, to determine whether there is a correction of a local movement (Amouv) of the measuring device for which the distance is minimal between each of the N points xi of the initial set of points considered and a cylinder of center c, axis u and radius r according to a double minimization: min min- Amouv u,c,r N where Amouv is a variation of the movement of the measuring device 10 at each point x, and where Xi=i, 2, (Amouv) is the position of the point i after applying a variation of movement Amouv, the processor being further configured to determine that a potential cylindrical area of interest is indeed a cylindrical area of interest if a result of the double minimization is less than a predefined threshold.
9. System (1) according to one of the preceding claims, in which for each initial set of points effectively representing an area of interest, the processor (11) is configured to delete points present in the initial set of points but not belonging to said area of interest.
10. A method of constructing a map of an environment, the method comprising: - The acquisition, by a measuring device (10) comprising at least one lidar (101), of a point cloud representative of said environment in which the measuring device moves, - Implementation, by a processor (1): *A preselection of a plurality of initial point sets in the point cloud, each initial point set of the plurality of initial point sets being representative of a potential area of interest in the environment, *for each initial set of points, a search for a local correction to be made to a movement of the measuring device (10) along each potential area of interest represented by the initial set of points considered in order to determine whether each initial set of points actually represents an area of interest, *a joint correction of the sets of initial points effectively representing an area of interest, by applying a global correction to a movement of the measuring device (10) in the environment, *a construction of the environment map with the corrected initial point sets actually representing an area of interest and the initial point sets not representing an area of interest.