WO2013175022A1

WO2013175022A1 - Systems and methods for topography and three-dimensional reconstruction from a scatter plot and computer storage media for said systems and methods

Info

Publication number: WO2013175022A1
Application number: PCT/EP2013/060898
Authority: WO
Inventors: Houman Borouchaki; Patrick MUFFAT-JOLY
Original assignee: Universite De Technologies De Troyes; Illico Presto Carto
Priority date: 2012-05-25
Filing date: 2013-05-27
Publication date: 2013-11-28
Also published as: FR2991090B1; FR2991090A1

Abstract

The invention relates to a topography and a three-dimensional reconstruction of an environment from data representative of at least one scatter plot created by means of topographical measurement by wave scanning, characterised in that they comprise a determination (51) of consecutive sections comprising a plurality of points classified according to at least one index; a determination (52) of a polygonal line joining all of the consecutive points in each of the sections; and a triangulation (53) of the strips between the polygonal lines, by at least one iterative advancing front of defined successive triangles, from a segment connecting a polygonal line to the following polygonal line, forming a triangle with the point that follows - according to said chronology - one of said two points of the two polygonal lines that is the least furthest away from the polygonal line to which it does not belong.

Description

The present invention relates to the field of topography, that is to say of the precise measurement (without the use of a point cloud and computer storage media for these systems and methods. representation bias in contrast to environmental mapping (eg, land, urban or rural or industrial landscapes, seabed, etc.). The present invention more particularly relates to a system and a method for three-dimensional reconstruction (and extraction of technical characteristics in some embodiments) of an environment from data representative of at least one cloud of points from a survey. topographic record in the environment by means of wave-scan measurement. The invention is particularly suitable, but not limited, to data such as data of the LIDAR type ("Llght Detection And Ranging" according to the English terminology) for example. In general, the invention applies to any type of data representative of a point reading of an environment produced by at least one wave scan measurement means. Such a wave scan measurement means may be a scanning laser, a sonar, a radar (for example a ground penetrating radar) or any other device. The present invention also relates to a surveying system and method implemented directly in the surveyed environment.

In the fields of cartography and topography, surveyors traditionally made plans for the environment, especially roads, from measurements resulting from the use of theodolite (instrument of geodesy completed with an optical instrument , for measuring angles in the two horizontal and vertical planes to determine a direction) and GPS ("Global Positioning System" in the English terminology), generally centimeter (whose accuracy is of the order of a centimeter) . The objects of the plan were drawn from the points raised by these devices and the totality of the measurements necessary to draft the plan were done in the field. The terms terrain or environment will be used in the present context to designate the object of the topographic survey.

A new process in the field, known as Anglo-Saxon "mobile mapping" (cartography or mobile topography, in French). According to this method, a vehicle embeds point recording means comprising means for measuring by wave scanning, such as scanning laser scanners, for example 2 to 5 LIDAR, sometimes associated with image-capturing means (such as cameras for example). This type of method has many advantages, but also has drawbacks because it generates data files that are very large, both for point clouds and recorded images (of the order of 300 MB of data per hour). ). Moreover, this type of process poses the technical problem of the readability of the point clouds which proves to be very delicate. In particular, large files require very large capacities and processing times, but most importantly, three-dimensional reconstruction is difficult because the harvested data represent point clouds associated with various types of information and listed according to the spatial coordinates of the data. points and the time at which the return of the transmitted wave was received to raise them (laser return in the case of LIDARs).

On the other hand, it is known in the prior art various point measurement solutions by means of measurement by wave scanning, such as for example sonars, radars (particularly georadar) which all allow surveying an environment by moving there (possibly in depth as in the case of the georadar for example) by raising clouds of points. Similarly, there are various types of surveys using fixed measurement means (ie, which do not move in the environment) to measure the topographical variations of a surveyed environment or object. Finally, there are also various types of readings using moving means of measurement, but whose movement is controlled with respect to the object or environment being surveyed (for example, using rails or other guidance systems), unlike the "mobile mapping" in which the movement of the vehicle is free (within the limits of its capabilities and within the limits of the exploitation of data that can be made according to the routes taken) . All these types of surveys generally pose the problem of the treatment of the cloud of points thus obtained to obtain a reliable and faithful three-dimensional reconstruction (that is to say the closest to the possible reality) and generally have at least one of the disadvantages detailed above.

In this context, it is interesting to propose a solution for performing a three-dimensional reconstruction from the readings resulting from the techniques of the prior art.

The present invention therefore aims to overcome at least one of the disadvantages of the prior art by providing a three-dimensional reconstruction method to obtain a faithful reconstruction and / or fast and / or inexpensive in time and / or capacity of calculation.

This object is achieved by a method of three-dimensional reconstruction of an environment from data representative of at least one cloud of points taken by means of topographic measurement by wave scanning, the method being implemented in a system comprising data processing means accessing storage means containing said data whose points are indexed at least according to at least one index and their spatial coordinates in a three-dimensional space, characterized in that it comprises:

determining, from the cloud of points, consecutive sections comprising a plurality of consecutive points, classified according to at least one of the indices of each point of the cloud;

determining, for each of the consecutive sections, a polygonal line joining all the consecutive points of the section in the three-dimensional space;

a triangulation of the bands between the polygonal lines of each of the consecutive sections, by at least one iterative advance of front of successive triangles according to at least one temporal chronology, each of the successive triangular fronts being defined, starting from a segment connecting a point of the polygonal line of a section to a point of the polygonal line of the following section, forming a triangle with the following point, in the said temporal chronology, one of these two points of the two polygonal lines and which is the least distant, in terms of distance in three-dimensional space, from the polygonal line to which it belongs not, this triangulation making it possible to obtain a map comprising a plurality of triangles whose vertices are formed by said points of the data,

Other features and advantages of certain embodiments of this type of process are detailed in the present application.

In addition, the present invention aims to overcome at least one of the disadvantages of the prior art by providing a three-dimensional reconstruction system to obtain a faithful reconstruction and / or fast and / or inexpensive time and / or computing capabilities.

This object is achieved by a three-dimensional reconstruction system based on data representative of at least one cloud of points recorded by wave-scan topographic measurement means, comprising data processing means accessing storage means containing said data representative of at least one cloud of at least one indexed points and their spatial coordinates in a three-dimensional space, characterized in that the processing means execute at least one software configured for implementing the method according to various embodiments of the invention.

Other features and advantages of some embodiments of this type of system are detailed in this application. On the other hand, survey techniques have the difficult processing disadvantages of the points listed and often pose a problem sorting data, which aggravates the difficulty of dealing with point clouds.

The present invention therefore aims to overcome at least one of the drawbacks of the prior art by providing a method of topography that is fast and reliable, to obtain a faithful reconstruction and / or fast and / or inexpensive time and / or in computing capabilities.

This object is achieved by a topography method comprising at least one point recording by at least one wave scanning measurement means scanning the points of the environment by a determined scan in a plane of the three-dimensional space, said reading. listing the points at least according to at least one index and according to their spatial coordinates in the three-dimensional space, characterized in that the survey provides at least one reference point among the points recorded and that the method is implemented by a system which comprises data processing means performing an acquisition of data representative of the points recorded by the measuring means and sorting said data, during the acquisition, by classifying the points in increasing order of at least one index, by means of at least a buffer memory for temporarily storing, pending the return of the wave for a point of a given index, the index points sup to the given index, until the return of the wave is received for the point corresponding to this given index and allows to order the different points according to their index in the buffer memory and / or storage means of the system, this sorting during the acquisition of data allowing the implementation of the method according to various embodiments of the invention, in real time from the acquisition of data corresponding to at least two consecutive sections.

Other features and advantages of certain embodiments of this type of process are detailed in the present application. In addition, the present invention aims to overcome at least one of the disadvantages of the prior art by providing a topography system that is fast and reliable, to obtain a faithful reconstruction and / or fast and / or inexpensive in time and / or in computing capabilities. This object is achieved by a topography system comprising at least one wave scan topographic measuring means scanning the points of an environment by a determined scan in a three-dimensional space plane, and listing the points at least according to the minus one index and according to their spatial coordinates in the three-dimensional space, characterized in that the processing means execute at least one software configured for the implementation of the method according to various embodiments of the invention, as and when of data acquisition.

Other features and advantages of some embodiments of this type of system are detailed in this application.

Other features and advantages of the present invention will appear more clearly on reading the description below, made with reference to the accompanying drawings, in which:

FIG. 1 shows a schematic representation of an example of a cloud of points obtained by means of topographic measurement by wave scanning, according to various embodiments;

FIG. 2 shows a schematic representation of an example of a plurality of sections determined from a scatterplot, with the average surface of each of the sections and a reference point, according to various embodiments;

FIG. 3 shows a schematic representation of an example of polygonal lines joining all the points of each of the sections, according to various embodiments;

FIG. 4 shows a schematic representation of an example of successive triangular fronts for the triangulation of the bands between the polygonal lines of the successive sections, according to various embodiments;

FIG. 5 shows a schematic representation of an example of a triangulation map obtained from the polygonal lines on a cloud of points, according to various embodiments;

FIG. 6 shows a schematic representation of an example of a simplified map obtained from a triangulation map, according to various embodiments;

FIG. 7 shows a schematic representation of an example of deletion, during simplification according to various embodiments;

FIG. 8 shows a schematic representation of an optimization example, during simplification according to various embodiments;

FIG. 9 shows an example of data representative of a scatter plot, in a LAS format;

FIG. 10 shows a schematic representation of an optimization example, during simplification according to various embodiments;

- Figure 1 1 shows a schematic representation of a system according to various embodiments;

FIG. 12 shows a schematic representation of a method according to various embodiments;

FIG. 13A shows an example of data representative of a point cloud corresponding to the survey of an environment by topographic measurement means, and on the basis of these data, FIG. 13B shows the result of the determination of the sections, FIG. 13C shows the result of the triangulation and FIG. 13D shows an enlargement of a portion of FIG. 13C, FIGS. 13E and 13F each show the result of the determination of a characteristic, respectively, geometric and physical, FIG. 13G shows the result of the simplification and Figure 13H shows an enlargement of a portion of Figure 13G. The present invention relates to a system and method for three-dimensional reconstruction of an environment, and a system and a topography process. Thus, the invention relates to three-dimensional reconstruction (possibly with the extraction of technical characteristics and / or the detection of at least one element) of an environment (or an object) from data (D) representative of at least one cloud of points (P _n ) recorded by means of topographic measurement (12) by wave scanning, but the invention thus also relates to the topographic survey itself.

The terms "wave scan means of topographic measurement" are used in the present description in their meaning meaning that at least one device for transmitting a wave sweeping a field makes it possible to record points (and thus "to measure the shape ") Of an object or an environment. These terms must not be interpreted in a limiting manner, and must not be interpreted in particular as being limited to topography in the classical sense of the term (terrain topography) because the present invention may be implemented on a "topographied" object. by means of measurement by scanning wave moving around the object or fixed with respect to the object which undergoes variations of shapes, under the effect of constraints for example (case of a dam for example). The term environment refers to any type of place whose topography is recorded by these measuring means and must not be interpreted in a limiting manner and the term object refers to any entity whose various parameters such as shape (but also color, reflectance, heat or any other parameter or parameter variation over time) can be measured by these measuring means. As mentioned in the preamble of the present application, the invention applies to any type of data representative of a reading of points of an environment, made by at least one wave scan measurement means. Such a wave scan measurement means may be a scanning laser, a sonar, a radar (for example a ground penetrating radar) or any other device. This application details various embodiments of the invention with reference to the example of the "mobile mapping" method mentioned in the preamble, but is not limited to this particular application. Indeed, the invention can be applied to fixed measuring means or measuring means placed on any type of moving support (for example a rolling or flying or floating vehicle, etc.) insofar as the technology of Wave-scan measurement is used to obtain a cloud of points recorded in the environment or on the object whose three-dimensional reconstruction is desired (ie, "modeling").

Moreover, even in the case of mobile mapping, it is possible to obtain varying degrees of precision with respect to the spatial coordinates of the points, ranging from a precision typically of the order of one meter or slightly lower in the Geographical Information System (GIS) to a centimeter-precision for computer-aided design (CAD), in passing through an intermediate precision of the order of ten centimeters for some intermediate systems. For GIS-type precision, a global satellite navigation system (GNSS) is preferable (and preferably a real-time processing), whereas for precision intermediate, post-processing is generally required from the data provided by an inertial unit, and preferably from the relatively fast GNSS base stations, but for the accuracy of CAD type, of the order of a centimeter, it is downright all these means (differential and inertial) with local base stations and accurate treatment of the location of the measuring means. Thus, depending on the type of precision that is desired for the modeled environment, it will be possible to provide a topography system integrating, in addition to the means for measuring by wave scanning, all or part of the following means:

- Digital camera (s) or camera - GNSS (Global Navigation Satellite System)

- inertial navigation system (inertial center)

- Distance measuring instruments

- Multiplexer (precise synchronization of devices)

- Differential GNSS receivers

- Data capture software

For accuracy, the Differential Global Positioning System (DGPS or Differential GNSS or GPS Differential) is a GPS enhancement that provides improved location accuracy, from 15 meters GPS at nominal accuracy to about 10 cm in the case of best implementations . The DGPS uses a network of fixed, ground-based reference stations to diffuse the difference between the positions indicated by the satellite systems and fixed known positions. These stations diffuse the difference between the pseudo-distances ("pseudorange" according to the English terminology) to the measured and real satellites (calculated internally), and the reception stations can correct their pseudo-distances by the same quantity. The digital correction signal is generally broadcast locally on a field using short-range transmitters.

As regards the means for measuring by wave scanning, in the case of mobile mapping, scanning laser scanners, such as for example 2 to 5 LIDAR, are generally provided. These measuring means are often associated with positioning means, for example 2 to 3 GPS, supplemented or not with positioning correction means such as an inertial unit and / or a DGPS system ("Differential Global Positioning System"). According to the English terminology). The reading means frequently include image-capturing means, for example 2 to 8 cameras. By traversing the environment to be met, this vehicle performs a scan and obtains a collection, called "cloud of points", composed of points listed by their spatial coordinates (for example 500 to 1000 points per square meter), preferably referenced temporally for the implementation of the present invention. This collection of points is possibly associated with a collection of georeferenced images (for example a point of view every 2 meters). The term "georeferenced" refers in the present application to the fact that reference is made to spatial coordinates, as understood by the general definition of this term, but preferably that reference is also made to at least one index, such as the time for example, for implementation by the present invention. It is known in the prior art to correct the GPS coordinates of the measuring means during their displacement to obtain accurate readings of points whose measured coordinates are transposable in the Earth's coordinate system and no details will be provided on this aspect. in this application. Then, from these data, an operator can digitize topographic objects in the images obtained. Laser scanning (or other survey methods within the scope of the present invention) is a tool for creating information (ie, point clouds are not a deliverable). Thanks to these tools, we can generate information on:

- Data Capture for Spatial Imaging: 3D Data, Laser Scanning + Imaging, Support for all Software Features, Scattered or Dense Point Cloud Options.

- GIS data capture: Data collection for geographic information systems: manual extraction. Specialized functions and high productivity (eg automated extraction of road signs).

- Capture data for imaging: Location of photos, Processing capacity for geographic information systems: manual extraction.

- Studies: Target detection control, point cloud recording, edge detection, DEM (digital elevation model) / TIN

(triangulated irregular network), LandXML export (a data format specialized, containing data of civil engineering measures and study, commonly used in urban or rural development and in the transport industry).

- Signaling: signal detection, pole detection, marking detection, photogrammetry / inventory by laser scanner.

- Pavement: generation of mosaic, report of defects of the pavement, plans of cuts of the roads.

For precision, a triangulated irregular network (TIN) is a digital data structure used in a geographic information system (GIS) for the representation of a surface. A TIN is a vector representation of the physical land surface or seabed, consisting of irregularly distributed nodes and lines with three-dimensional coordinates (x, y, and z) that are arranged in a network of non-overlapping triangles. TINs are often derived from elevation data of a digital elevation (DEM) data model.

Thus, the present invention finds many applications in various fields. In particular, the invention allows modeling of an environment or an object, but also allows the detection of elements (from particular shapes or characteristics) in the environment or on the object. For example, certain embodiments of the invention may be used to characterize linear objects (arcs) and areal objects that describe the environment, including (in an illustrative and non-limiting manner) the urban landscape of the street:

• curbs, water lines, roadway, curbs of traffic islands,

• entrance doors, thresholds, windows of buildings along the street,

• the outcrops of the networks:

- sanitation and sewers: round or square buffers, drains, gully, etc. - gas and electricity: counting boxes, supports, overhead cables, junction box on the ground, etc.

- water supply: locks for special valves, inspection frame, floor counting boxes, etc.

- telecom: cabinet of brewing, hatch of the rooms of visit and draw, boxes of connection to the ground and frontage.

In mobile mapping, each laser generates a semblance of terrain cut because it generates a plurality of raised points at the intersection between the surface of the environment and the scanning plane of the laser. Each section is generally spaced from the previous one about 2 to 5 cm depending on the speed of movement of the vehicle on which the lasers are embedded (these distances are therefore given by way of illustrative and non-limiting example). Inside the field, the points will for example be separated by 2 to 3 cm and aligned, to the sound, on a line almost straight but deformed helically in the direction of the trajectory of the vehicle. However, the data is "disorganized" and delivered as a scatterplot whose tricky processing is facilitated by various embodiments of the present invention.

Thus, certain embodiments of the invention relate to a method for three-dimensional reconstruction of an environment from data (D) representative of at least one cloud of points (P _n ) recorded (measured, recorded) by means of topographic measurement (12) by wave scanning. This method is generally implemented in a system (1), for example such as a computer, comprising data processing means (10), such as at least one processor, accessing storage means (1 1 ), such as storage disks and / or RAMs for example, containing said data (D) whose points (P _n ) are listed at least according to at least one index (IT, A, n) and their spatial coordinates (x, y, z) in a three-dimensional space. As explained above, the measuring means (12) are generally georeferenced, that is to say that their spatial coordinates are determined. In addition, as some embodiments relate to measuring means moving in the environment or with respect to the surveyed object, the term "georeferencing" can be understood in the present application as also indicating a time referencing. In addition, the points recorded are also georeferenced, ie their spatial coordinates are determined by the survey, but the time is also recorded for each of the points, whether it is a survey by measuring means moving or not, because in the case of fixed measuring means (12), it is precisely the displacement of the environment or the surveyed object that is monitored over time. It should be noted, however, that if the representative data in the survey uses other survey indices, referencing could use these indices, as detailed below. On the other hand, it should be noted that since the invention may apply to an entity such as an environment or an object, the term "environment" used in the present application will have to be interpreted as being able to designate an object or any entity, to unless it is explicitly mentioned that it is only an environment (for example in which the measuring means are moving).

The method preferably comprises:

a determination (51), from the point cloud (P _n ), of sections

(Si) consecutive comprising a plurality of consecutive points (P _n ), classified according to at least one of the indices (IT, A, n) of each of the points (P _n ) of the cloud;

a determination (52) for each of the consecutive sections (S) of a polygonal line (LP) joining all the consecutive points (P _n ) of the section (S) in the three-dimensional space;

a triangulation (53) of the strips (B) between the polygonal lines (LPi) of each of the consecutive sections (Si, S2, ..., Si) by at least one successive iterative advance of successive triangles (FT) according to at least one temporal chronology (Ch), each successive triangular fronts (FT) being defined, starting from a segment (ST) connecting a point (P _j ) of the line polygonal (LP) of a section (S,) at a point (P _k ) of the polygonal line (LP _{i +} i) of the next section (Si + i), forming a triangle (T) with the point ( P _n ) which follows one of these two points (Pj, P _k ) of these two polygonal lines (LPi, LP _{i +} i) which is the least distant, in terms of distance (d) in the three-dimensional space, from the polygonal line (LP _{i +} i, LP,) to which it does not belong, this triangulation (53) making it possible to obtain a card (CT) comprising a plurality of triangles (T) whose vertices are formed by said points ( P _n ) data (D).

It is understood from the foregoing that a mesh of triangles (T) is constructed from a cloud of points (P _n ) so as to reconstruct, at least in three dimensions, an environment or object from the data (D) of the points (P _n ) of the cloud. It is also understood that the reconstruction uses triangular fronts and is based on a selection criterion between the two possible points to form the successive triangles based on the distance of a given point (P _n ) with the polygonal line (LP _{i +} i , LPj.i) adjacent to the line (LP,) with which this point (P _n ) is associated. The indices j and k are used here arbitrarily to identify a point (P _n ) on each of the two lines forming the band to be triangulated. Indeed, there is a segment formed by the point (P _j ) on the line (LP) and the point (P _k ) on the line (LP _{i +} i). From this segment (ST), you need a criterion to choose whether the triangle should be formed with the point (P _{j +} i) or the point (P _k + i). The criterion is to compare the distance between the point (P _{j +} i) and the polygonal line (LP _{i +} i) with the distance between the point (P _k + i) and the polygonal line (LP,) and to choose the point for which distance is the smallest. Thus, we obtain a triangle that best matches the real geometry, even if the polygonal lines describe left curves and are actually complex geometries. Numerous methods are known in the prior art for choosing how to form such a triangle during a three-dimensional reconstruction, but none of the known methods has been shown to be sufficiently effective in the case of very diverse data (D). Indeed, many methods are irrelevant and methods that have been tested, such as the method of maximizing the minimum angle or the method of the minimum distance between the 2 points (or the 2 choices), did not prove effective because the reconstruction was unsatisfactory, or even completely erroneous. The present invention therefore preferably uses this distance criterion to obtain a fast, coherent and efficient three-dimensional reconstruction because it provides a mesh that is as close as possible to the real geometry of the environment or object corresponding to the scatterplot.

FIG. 12 shows an illustrative example of a succession of treatments that can be implemented by means of the present invention, but it should not be interpreted in a limiting manner since it will be understood from the present application that various embodiments may comprise all or part of treatments illustrated in this figure 12.

It is therefore understood that from data comprising at least one cloud, even dense, points defined at least by spatial coordinates and at least one index (for example temporal), the method can generate a triangulation of the space of in a reliable manner and faithful to the three-dimensional distribution of the points, even if the points contained in the data (D) are not classified or ordered, for example according to their temporal index or their spatial coordinates. Note, however, that the term "point cloud" is used here to refer to a set of points that are not distributed in a completely random manner, as some general or mathematical meanings might suggest. Here, the data (D) come from a reading of points by means of periodic scanning measurement (using waves, for example light or sound) and the cloud of points has an organization, certainly relative because of the return times of the wave, which makes the points are relatively ordered in the scan plane (the successive positions scanned at each scan) and the scan path (the periodicity or direction of scanning) of the measuring means. Figures 1 and 13A show examples of point clouds (of the order of 1 to 3 million points for Figure 13A). In practice, the method preferably applies to data of the LIDAR type but can be applied to various types of measurements (laser or sonar or radar) and data in various formats. For example, the LIDAR type data has been acquired by at least one laser scanning the points according to a determined scan in a plane of the space moving with the laser in the three-dimensional space, according to a coordinate referenced displacement mode ( georeferenced, preferably with a GPS for spatial coordinates, but the invention also provides at least one temporal referencing). The data may, by way of a preferred but nonlimiting example, be of the .las type (an illustrative and nonlimiting example of which is shown in FIG. 9) because this format is particularly suitable for reading a plurality of parameters for points. from space. For example, the .las format is particularly relevant and widely used, but it is possible to use other types of formats. These parameters may in particular comprise, for each point, spatial coordinates, a temporal index (such as the time at which the point is measured), a scanning angle of the measurement means by wave scanning (laser for example), an intensity the return of the wave as the brightness of the laser return for example (representative of physical properties of the surface to which the point belongs), etc. Indeed, by way of example, it is known in the field of mapping, or topography, systems comprising a vehicle equipped with at least one laser, at least one means for measuring the coordinates of the laser transmitter (GPS and / or central inertia) and at least one temporal measurement means (GPS time for example). This type of system, with the vehicle moving in an environment, allows the laser to scan the environment and then record a representation of it by listing the points scanned by the laser according to a plurality of parameters such as those described above.

As indicated above, the means for topographic measurement (12) by scanning waves can for example be at least one scanning laser or at least one sonar or at least one radar or any type of topographic measuring means that sweeps the environment to survey with waves (sound or light for example). In some embodiments, the points are ranked according to at least one index to determine sections. In practice, these sections correspond to a scan plane of the topographic measurement means. Unfortunately, the known means of measurement are points and the data from the surveys are in the form of a cloud of points that must be processed. Since the invention applies to readings taken by scanning measuring means, it is advantageously proposed to determine sections that correspond to the successive scanning planes. For example, a laser can scan the environment at 360 ° in a given time and start scanning continuously, so that successive sweeps form sections (or cuts) of the environment (possibly slightly deformed by the movement of the laser ). In another example, the measuring means can go back and forth in a range of angle values and successive scans also form sections of the environment (although 2 successive scans are performed in a direction opposite one another. the other). Some embodiments of the invention therefore propose to treat the cloud of points by already determining the successive sections which depend, as mentioned above, the plane and the scan path. Typically, the data lists points based on at least one index, but in general it is not a directly usable index. For example, as a possible index, there is often a datum corresponding to the time of return of the wave (the time, often the GPS time, at which the wave return took place, laser return time for example), which depends the distance to which the raised point is located with respect to the means that emitted the wave (the optical center of the laser, for example). Another data that is often listed, especially for a laser or a radar (GPR, for example), is the angle of emission of the laser for each of the points raised. It is therefore possible to reconstruct the sections from at least one of these indices of each of the points: the time or the angle. For such a reconstruction, a maximum threshold (time interval or angular spacing for example) is used. So we understand that if the data included at least one index arbitrary (n and / or i) for each point (P _n ) and / or the section (S,) of each point, it would be enough to use this index (n, i) to determine the sections. Since the present measurement means (12) do not record such an index in the data, some embodiments provide for a determination of the sections based on at least one index different from an index relative to the section and / or to the ordering of the points during the measurement (since even the time index generally corresponds to the return time of the wave and is therefore not usable to indicate which point was measured before the other).

In some embodiments, said determination (51) of the sections (S,) is performed by selecting the points as a function of a threshold (DT) maximum between at least one of the indices (IT, A, n) of each of the points (P _n ) of the cloud, each of said sections (S,) then comprising a plurality of consecutive points (P _n ), classified according to this index (IT, A, n) and whose spatial coordinates (x, y, z) belong to at a mean surface (PM) defined around at least one reference point (Pr). In practice, the average surface of the sections is quasi-plane, therefore designated as mean plane (PM), but it can in fact be a left surface (since the points are conventionally aligned on a helically deformed line in the direction of movement of the vehicle). It should be noted that the term surface and plane is used here, whereas the invention may allow a certain margin of error in the alignment of the coordinates and that these terms must not be interpreted in a limiting way since they may in fact designate volumes (which are approximated to the average surface). Thus, in certain embodiments, particularly when the data have the disadvantage of not ordering the dots according to an easy-to-use index, as is often the case in the systems and methods of the prior art, the data processing means (10) ) implement a sorting (50) of the data (D) to classify the points (P _n ) according to at least one index, for example (and preferably) their temporal index (TI) which corresponds to the time (t _n ) at which the means (12) of measurement has received the return of the measurement wave of a given point (P _n ). Here again, it is preferably a matter of georeferenced measurement means (spatial reference at least, but preferably also time reference). The points here are preferably sorted according to the time, but if one had another type of index that the time of the return of the wave (return laser for example), such as an arbitrary index (n) for example the process would be simplified. It is also possible to organize the data directly according to a particular index to avoid these expensive sorting in terms of capacity and processing time, as detailed below. It is understood that if the data were collected, when measuring the points, using an index to order these points, the treatment would be facilitated (including allowing to determine the sections as explained above ). Thus, the invention provides various embodiments where the points are indexed according to at least one arbitrary index (n) or sorted during acquisition (during the survey in the environment or on the object). Thus, in certain embodiments, the sorting (50) is implemented prior to the determination (51) of the consecutive sections (Si), by the data processing means (10), from the data (D) which are stored in the storage means (1 1) and representative of at least one cloud of points (P _n ) whose scheduling is not a function of a usable index, for example the time index (IT). On the other hand, in other embodiments, the sorting (50) is implemented by the data processing means (10) during data acquisition (D) by means (12) of measurement by classifying the points (P _n ) in ascending order of at least one index (IT, A, n), by means of a buffer memory (1 10) for temporarily storing, while awaiting the return of the wave for a point (P _n ) of a given index (IT, A, n), the index points (P _n ) greater than this given index, until the return is received for the point corresponding to this given index , and allows to order the different points (P _n ) according to their index (IT, A, n) in the buffer memory (1 10) and / or the storage means (1 1), this sorting (50) during data acquisition (D) allowing the implementation of the process in real time from the acquisition of the data (D) corresponding to at least two consecutive sections (S). Preferably, the index may indicate the section (i.e., scan number or scan plane number) to which the points belong. In particular, reference is made here to the index (A) which corresponds to the angle (A _n ), but such an index requires at least to know the section (and even sometimes to know other information relating to the number of times this angle is scanned in the section, because in some cases, the data indicate several passages of the measuring means at a given angle, as detailed below in one of the exemplary embodiments). In practice, in order to perform the sorting and the determination of the sections when they have not been made during the acquisition, the invention provides for locating the sections by means of a starting point, for example such as the reference point. (Pr), then order the points in the sections by the difference of their value for at least one index (eg time). On the other hand, it is also possible to determine the sections according to other methods based on knowledge of the measurement methodology, for example using the scan path (periodicity and / or direction) mentioned above. For example, in the case of a continuous scan in a given direction (eg, a given direction of rotation, that it covers 360 ° or not), knowing the time that the measuring means makes to perform a scan, one can determine the periodicity of the scan. Such a scanning period makes it possible to determine, by knowing the time at which the scanning has started, the points which belong to the same section since they are received in the scanning period. With this type of determination of sections per period, some points may be associated with a section subsequent to that during which the measuring means has scanned these points (because of their return time of the wave which is higher at the time at which this scan ended), but the number of points thus underestimated would be rather limited and these points would correspond to very distant points whose reliability (measurement) can be quite reduced. One can then apply to these points a particular correction treatment or simply ignore them (note that it is also planned to ignore the orphan points for which algorithms are described in this application, in particular because the measurement of at least some of these orphan points is not necessarily very reliable). Similarly, in the case of a round-robbing scan, knowing the changes in direction of the scanning of the measuring means, it is possible to determine the successive scanning planes (which must be considered to be oriented in opposite directions, in pairs). For such a determination of the path of the measuring means, it is possible, for example, to use the time at which the change takes place and / or an index recorded in the data to indicate the direction of the scan (DS, FIG. 9) or to indicate the change in the direction of the scan. direction and / or information concerning the values of the range of angles in which the measuring means is back and forth, preferably in association with time. It will therefore be understood from the examples of methods described above that the invention provides various embodiments for determining sections from various types of information, preferably from at least some of the information contained in the data ( D), but possibly from other information (scanning period or time for a round trip, for example) or a combination of both. Conversely, the sorting during the acquisition is provided in various embodiments, even if the measuring means (12) of the points moves, but in this case a follow-up of the coordinates and the orientation of the measurement means that can also be realized in real time to deliver in real time the coordinates of the points. GPS and central inertia do not necessarily achieve this goal with sufficient accuracy (see the desired accuracy according to the applications) in all cases (tunnel, canyon urban, etc.), at least for now. Nevertheless, it is possible that these technologies evolve or that the topography system then comprises other means of controlling the position (and orientation) of the measuring means, for example such as a rail constraining its movement or a ground tracking device (theodolite / robotic complete station for example) or any other means of transposing the coordinates of the laser in space. On the other hand, the measuring means can be fixed with motorized and non-motorized vertical and horizontal rotations (theodolite or other), in certain embodiments and this real-time processing is freed from the constraint of the positioning (XYZ and orientation) of the measuring means (12). It is therefore understood that with this type of embodiment, the invention also relates to a topography method comprising at least one survey (61) of points (P _n ) by at least one means (12) for measuring by scanning of wave scanning the points (P _n ) of the environment by a determined scan in a plane of the three-dimensional space, said survey (61) listing the points (P _n ) at least according to at least one index (IT, A, n ) and according to their spatial coordinates (x, y, z) in the three-dimensional space, characterized in that the survey (61) provides at least one reference point (Pr) among the points (P _n ) recorded and that the process is implemented by a system (1) which comprises data processing means (10) performing an acquisition of data (D) representative of the points recorded by the measurement means (12) and performing a sorting (50) of said data , during the acquisition, classifying the points (P _n ) in ascending order of at least one index (IT, A n), thanks at least to a buffer memory (1 10) allowing temporary storage, pending the return of the wave for a point (P _n ) of an index (IT, A, n) given, the points (P _n ) of index greater than this given index, until the return of the wave is received for the point corresponding to this given index and allows to order the different points (P _n ) according to their index (IT, A, n) in the buffer memory (1 10) and / or storage means (1 1) of the system (1), this sorting (50) during the data acquisition (D) ) allowing the implementation of the three-dimensional reconstruction method described here, but in real time from the acquisition of data (D) corresponding to at least two consecutive sections (S,). Thus, in some embodiments of the topography method, the acquisition is performed during the displacement of the topographic measuring means (12) in the environment, identified by means (13) for measuring the position and at least an orientation of the means of measurement (12) topographic (laser, sonar or radar for example). It should be noted that reference is made here to "at least one" orientation because the measuring means is not necessarily mobile along all the axes and a measurement of the orientations is provided according to the degrees of freedom (roll, pitch, yaw or Roll, Pitch, yaw according to the Anglo-Saxon terminology).

On the other hand, as mentioned above, various embodiments relate to a three-dimensional reconstruction system (1) based on data (D) representative of at least one cloud of points (P _n ) recorded by topographic measurement means ( 12), comprising data processing means (10) accessing storage means (11) containing said data (D) representative of at least one cluster of points (P _n ) listed at least according to at least one index (IT, A, n) and their spatial coordinates (x, y, z) in a three-dimensional space. This system (1) comprises processing means (10) which execute at least one software configured for the implementation of at least one of the embodiments of the method described herein.

Moreover, as mentioned above, various embodiments relate to a topography system (1) comprising at least one means (12) for topographic measurement by scanning the waves scanning the points (P _n ) of an environment by a determined scanning in a plane of the three-dimensional space, and listing the points (P _n ) at least according to at least one index (IT, A, n) and according to their spatial coordinates (x, y, z) in the three-dimensional space. The processing means (10) of such a system execute at least one software configured for the implementation of the topography method described in the present application, as and when data acquisition.

It will be understood that the reconstruction systems may only comprise data processing means and storage means, but that topography systems also comprise measuring means (12), preferably completed by means (13). position control (x, y, z and orientation). In addition, these systems should preferably include a buffer memory (for example such as a RAM expansion) sufficient for temporary storage of data during acquisition. In a manner, the invention can be implemented using at least one software installed on at least one computer system (or more generally an application executed within a software environment) and controlled by a user of the system. to execute some or all of the algorithms described in this application. The invention therefore also relates to at least one software or application stored in storage means (any type of computer storage medium). The invention also relates to a computer storage medium (such as for example a CD, a DVD, a removable or non-removable data storage device, etc.), including data for the execution and / or installation of the device. less a software or application in a computer system, characterized in that said software or application comprises one or more algorithms for the implementation of at least one of the method embodiments described in the present application. In the case of a topography with real-time reconstruction, the data are processed immediately and it is possible to store only the data relating to the triangles, for example in the form of triangular maps. In the case of a reconstruction based on data from a topographic survey, the storage means or memories (1 1, 1 10) of the system store at least one file containing said data representative of at least one cloud of points and the processing means (10) execute the algorithms for generating the triangles. It will be noted that the data relating to triangles are referred to here as the "map", but that this term should not be interpreted in a limiting manner since the data generated comprise at least a plurality of triangles indexed by their vertices. Various types of files and storage formats can be provided, and it is possible, for example, simply to store this information under the shape of a succession of triangles each defined by a triplet of points, because the purpose of the invention is especially to generate the triangles (thanks to the edges connecting the points) to determine the surfaces that are read with the help of the cloud points from the measuring means. The invention makes it possible, in fact, from a cloud of points that is not exploitable as such to know and / or characterize the raised environment, to generate triangles whose vertices are formed by these points and indicating the surfaces underlying these raised points. Starting from unusable data, the invention thus provides usable data to know and / or characterize the environment identified, notably by means of extractions of technical characteristics or various calculations on the data obtained (possibly using the initial data, particularly in the case of physical characteristics).

In the case of the topography with a three-dimensional reconstruction in real time, examples of which are envisaged above, the position and orientation of the measuring means are preferably followed continuously and simultaneously with the measurement (survey). In known systems, to obtain the precision of the order of cm, the inertial unit imposes a calculation that takes into account both ends of the path to allow compensation. Nevertheless, it is possible to provide more frequent compensation in order to reduce the calculation time necessary for the transposition of the coordinates. In addition, other means of derivation are possible because they are potentially capable of delivering "online" (in real time) a sufficiently precise positioning, even if they are generally less rapid or precise than the inertia plants Such means of geopositioning can for example, in a manner known per se, include a theodolite (a rangefinder and horizontal and vertical reporters whose aligned centers of rotation) which is robotic (thus motorized and whose engine is controllable), enslaved on a prism (or a cube corner) for continuously determining coordinates (x, y, z). The skilled person will understand, using the functional considerations detailed above for monitoring the position of the measuring means, the various means of geopositioning which are within the scope of the invention. In some of these topographic system (1) embodiments, the acquisition is performed during the displacement of the topographic measuring means (12) in the environment, identified by means (13) for measuring the position and at least one orientation of the topographic measurement means (12) integrated in the system for mapping the environment. On the other hand, it is understood that this constraint is lifted in these surveying systems and methods when the measuring means is fixed.

In some embodiments, the points (P _n ) are also listed according to the scanning angle (A _n ) of a measuring means (12) for detecting said points (P _n ) while traveling in an environment to which the points (P _n ) in the three-dimensional space, said reference point (Pr) of each of the sections (S,) corresponding to a point listed according to a scanning angle (A _n ) of the measuring means (12) determined to be reliable for referencing the other points (P _n ) of the section (S,), depending on the orientation of the measuring means (12) moving in the environment where the points (P _n ) are scanned. In some of these embodiments, the scanning angle (A _n ) of said reference point (Pr) of each of the sections (S,) is an angle corresponding to a point at ground level of the environment in which moves the measuring means (12). In general, especially in LIDAR type data, this angle is the null angle (= 0) and is used because it corresponds to a point on the ground, which is less variable than the rest of the environment (in particularly in the case where the laser is on a land vehicle moving in the scanned environment). Nevertheless, it is sufficient to have a reliable reference that depends on the measurement method and one can choose to provide other methods to determine a reference point (Pr). In particular, the angle θ is chosen for the LIDAR data because it generally corresponds to an angle for which the laser points towards the ground and the point of reference (Pr) thus chosen will generally be reproducible from one section to another (more than would be a point measured at an angle pointing to the horizon). Nevertheless, this angle value chosen for the reference point depends on how the survey is performed and may depend on what is recorded. The value of the data used to define the reference point can therefore be parameterized by a user of the system to adapt to the case. For example, even in LIDAR data, sometimes, for at least a few sections, there is no point at angle 0 because the ground has a puddle that the laser can not measure. It is then sufficient to choose another angle, preferably also on the ground, to implement the method. On the other hand, this angle 0 does not generally correspond to the first point measured in a section for the LIDAR data, but it is conceivable that it changes and that the laser starts with a measurement on the ground. The method may then use a different index than the angle to determine a reference point. Moreover, in the case where the sections are identified by an index, the determination of the section does not require searching a reproducible reference point, but such a point could simply be the first of each section (especially if it corresponds to the soil or at least if it is sufficiently reproducible to then allow the following determinations of the process which are detailed below).

In some embodiments, particularly in the case where the angle θ serves to define the reference point and when the laser performs 360 ° scanning in which several angles θ can be found, it is intended to verify that the points angle 0 are consistent as reference points. Thus, for example, when determining (51) consecutive sections (S,), if a first reference point (Pr) is separated from a second reference point (Pr) by an index value (IT) or a distance in three-dimensional space greater than a maximum spacing threshold (ESP) is that the second reference point (second point at the angle 0) does not actually correspond to a reference point d another section that of the first point of reference. In this case, the method provides that the two consecutive sections (S ,, S, ₊ i) (for example Si and S ₂ ) are merged into a single section (S,) because they have been wrongly discriminated. This is actually an "uncertainty lift" on the reference point (Pr), that is, if the distance between two candidates at the reference point is greater than the average distance between 2 sections, then the 2 ^nd candidate is rejected and the points that surround him are merged with the section of the 1 ^st candidate. It is therefore clear that the spacing threshold may be set according to data type, to correspond to a value greater than the average distance between two sections or may be fixed arbitrarily, for example by the user of the system.

In practice, distance is generally used, but it is possible to use time since two "cuts" of the terrain are generally recorded within a limited time interval. It will be understood that the method merges the sections if the difference between their reference points is greater than a maximum threshold which corresponds either to a distance (distance in the three-dimensional space between the two reference points) or to a delay (difference of time between the two reference points). The reference point (Pr) is a characteristic point of the section in general, and preferably located in the middle of it and of which it is certain that it is in the environment (and generally reproducible from a section to the other). It is used as the calibration point of the section. It is usually delivered by the measurement system (Lidar, Sonar, Radar) as a reference for measurements. In some embodiments, the reference point is used to determine the sections and in some (non-exclusive) embodiments it serves as a starting point for the triangulation. It is understood that there will always be a reference point in the sections, at least to determine a starting point of processing, but that according to the organization of data, the various methods proposed here can solve the problems of point cloud processing. It is understood that various embodiments provide for determining (51) sections (requiring a reproducible reference point or more simply a section identifier). This determination (51) generally requires a preliminary sorting (50), especially if the points are not ordered during the acquisition. The example below is thus provided in an illustrative and nonlimiting manner to show how it is possible to rearrange the data of the point cloud when it comes for example from a survey by a laser scanning the environment at 360 ° and whose angle 0 points to the ground. The points are first sorted in ascending order of the GPS survey time. For this, one can apply various sorting algorithm and in general the "Heap Sort" algorithm of complexity N log N is preferred where N is the number of points. Indeed, this simple algorithm is efficient and fast to execute. The points thus ordered are partially partitioned into subsets of ordered points designated here as "sections. Each section contains a geometric angle reading reference point at 0 degrees (in practice, this point is the first encountered in the scanning of ordered points). In addition, in each section, the difference in survey time between two consecutive ordered points is less than a given threshold (DT). This maximum interval threshold corresponds here to a time interval, but as mentioned above, it is also possible to use other indices, whose angle (An) for example. This threshold (DT) may be parameterized by a user, like most of the values used in the algorithms detailed in this application. As the reference point is in the present example at the angle 0 and approximately in the middle of its section, each section is thus determined from its reference point according to two scans, respectively, in ascending and descending order of chronology. temporal points. A full scan of the laser defining a section or a frame may contain two 0 degree angle survey points. Thus, the uncertainty is raised on the reference point by sweeping the maps in the order of construction, and if two consecutive sections have distance reference points greater than a given threshold (ESP), they are merged into a single section having as reference point that associated with the first section.

In general, the partitioning into sections thus obtained does not cover all the points. The points, known as orphans, not belonging to any section are located, in the ordered list of points, either before the first section, or between two consecutive sections, or after the last section. To assign these points to sections, it is possible to perform the following processing. The orphan points before the first section, respectively after the last section, are associated with the first or last section respectively. The points between two consecutive sections are associated with one of the two sections according to their belonging to the average surface (or "average plane") (PM) of a section. The latter is a plan that passes at best by all points of the section. The section S thus has the average plane 77: ax + βγ + γζ = 1 with a, β and γ solutions of the linear system:

where (y Zi) scan all the points constituting the section S. Let P be an orphan point located between two consecutive maps Si and S2 of average plane respectively 771 and 77 ₂ . If d (P, 77i) <d (P, 77 ₂ ) (d (.,.) Designates the distance from a point to a plane) then P belongs to Si and otherwise P belongs to S ₂ . In practice, among the orphan points located between two consecutive sections, the first orphan point belonging to the second section is searched in chronological order of time and the other orphan points are systematically associated with the second section.

It is therefore understood that in certain embodiments, when it is necessary to sort the points in consecutive order and to determine the sections with a mean surface (MW), the method may comprise a additional treatment. For example, when determining (51) consecutive sections (S,) for points, called orphans, whose membership in a section (S,) has been excluded by the maximum time interval threshold (DT) the data processing means (10) define that:

- If an orphan point has a time index value (IT) less than the value of the smallest time index of the first (Si) consecutive sections (S,), then this orphan point belongs to this first section (Si) ; and or

- If an orphan point has a time index value (IT) greater than the value of the largest time index of the last (S,) consecutive sections, then this orphan point belongs to this last section (S,); and or

- If an orphan point has a time index value (IT) between the values of the time indices of the points of two consecutive sections

(S ,, Si + i), then this orphan point belongs to the section (S ,, S, ₊ i) corresponding to the average surface (PM) of which it is the least distant in terms of distance in the three-dimensional space, the average surface (PM) being defined by the data processing means (10) as the area (PM) which passes through the maximum number of points (P _n ) of the section (S ,, Si + i) to a neighborhood determined from said reference point (Pr) of each of the sections (S,).

We understand that in practice (and preferably for faster processing), a search algorithm in this example, the first orphan (according to an index, for example temporal IT) that joins the ^2nd section and the following are also associated with the ^2nd section. Distance comparisons were therefore iterated only for points associated with the first section and the first point associated with the ^2nd instead of also do all the points associated with the ^2nd. One can also choose to treat all these points individually instead of dealing with all the points that follow an orphan assigned to the second section as that orphan. You can also choose to ignore orphan points. In practice, the system may include a plurality of options selected to provide these various processing options by the user.

In general, in various embodiments, the data processing means (10) is configurable by a user of the system to set at least one of a plurality of parameters including a maximum index threshold (DT). and / or a spacing threshold (ESP) and / or time chronology (s) (Ch) and / or a number of parallel iterative advances and / or a maximum separation threshold (ACE) ) and / or at least one determined neighborhood defined by a neighbor number to be considered and / or a proximity threshold (SP) and / or a regularity threshold (SR) and / or a maximum variation threshold (SLR) which are detailed in this application. The invention also provides, in various embodiments, choices between several processing algorithms (ALG) to be executed in the system (see Figure 1 1). These choices offered by the system through a user interface (see Figure 1 1 for example) allow flexibility on the treatments performed. These selected algorithms allow the user to control the system for the implementation of all or part of the process steps detailed in this application, but also to control which type of processing to perform for each step among possible treatment options for each step. For example, it is possible to offer choices for the treatment of orphan points or for various processing options, for example such as options defining whether the method uses a time index, angle index, or arbitrary index (based on the type of data for example) and to refine the treatments by adjusting the various thresholds.

Starting from a cloud of points, for example cloud composed of 64456654 points, the treatment example exposed above makes it possible to obtain 39157 sections containing on average 1646 points (with a minimum of 1016 and a maximum of 2765 points). Figure 13A shows a view of this cloud restricted to 1200 sections. Figure 2 shows a schematic representation of the average areas (PM) and the determination of sections which are materialized by thick lines between the points, because they correspond to the ordered succession of points on the average surface, but not to be confused with the polygonal lines or curves detailed below, illustrated in Figure 3 and which join actually the points.

In some embodiments, the determination (52), for each of the consecutive sections (S,), of a polygonal line (LP) joining all the consecutive points (P _n ) of the section (S,) in the three-dimensional space is realized by the data processing means (10) by joining only the consecutive points (P _n ) separated by a distance, in the three-dimensional space, below a determined maximum separation threshold (ECA). For example, in these lines, the segments of length greater than a given distance threshold (1 meter for example, especially in the example of Figure 13B) are ignored. This threshold is parameterizable and makes it possible to validate the existence of a connection between two points as a function of the accuracy of the point-reading device (topographic measurement means (12)). It will be noted on the other hand that polygonal lines are referred to here between the points, but that it is possible to use curves. However, a line is generally preferred since it is simpler to determine and because the segments of these lines will form edges of the triangles during the triangulation. Nevertheless, it is possible to use a curve, especially for calculating the distance (d) used in the triangulation.

In some embodiments, said triangulation (53) of the bands (B) between each of the consecutive sections (S,) is performed by several iterative advances in parallel by the data processing means (10). Indeed, it is possible to triangulate several bands at the same time, in parallel. A schematic representation of the triangulation is illustrated by way of nonlimiting example in FIG. 4. Moreover, in some embodiments, said triangulation (53) of the bands (B) is performed according to at least one temporal chronology (Ch). defining a first triangle edge segment (T) (T) formed between the reference point (Pr) in the polygonal line (LP) of the first (S) of the two sections (S ,, S, ₊ i) consecutive lines (for example the line LPi in FIG. 3) and the point (P _n ) closest to this polygonal line (LP) of the first (S) of the two sections, in terms of distance (d) in the three-dimensional space, among the points (P _n ) in the vicinity of the reference point (Pr) of the polygonal line (LP _{i +} i) (for example the line LP ₂ in FIG. 3) of the second (S, ₊ i) two consecutive sections (S ,, S, ₊ i).

These embodiments may for example be implemented when the reference point is a point at the beginning of each section (the first point of each section preferably). In other embodiments, in particular in the case where the reference point is located elsewhere and for example in the middle of the section (as in the examples described above for LIDAR type data where the angle point 0 is about the middle of the successive points of the section, said triangulation (53) of the bands (B) is performed according to an increasing time chronology (Ch) and a decreasing time chronology (Ch):

the increasing chronology being defined by iterations in the increasing order of the temporal indices (IT), from the reference point (Pr) of the first section (S) to the segment (ST) joining the last two points ( P _n ) of the two consecutive sections (S ,, S, ₊ i);

the decreasing chronology being defined by iterations in decreasing order of the temporal indices (IT) from the reference point (Pr) of the first section (S) to the segment (ST) joining the first two points (P); _n ) two consecutive sections (S ,, S, ₊ i).

It will be appreciated that in some embodiments, the two successive and decreasing chronologies of the same band can be processed in parallel as discussed above for several bands.

It is understood from the foregoing that the reference point (Pr) serves as the starting point of the triangulation. Nevertheless, once again, this point can simply be taken arbitrarily if the organization of the data (D) the allows, as already mentioned several times. In addition, the triangulation can provide a maximum threshold of length for the edges of the triangles. Such a threshold may also be configurable by a user of the system or be fixed according to values determined to be optimal for the majority of cases.

Note that in the case of choice of threshold value or parameters offered to the user to set the system, it is possible to pre-register a plurality of values determined for each parameter as optimized according to the type of data. and / or measures. Thus, the user is guided in these choices or even has to indicate the type of data or measurement for the system uses the optimal parameters.

As above, the algorithm example below is provided in an illustrative and nonlimiting manner to show how it is possible to achieve the triangulation of points when they come for example from a survey by a laser scanning the 360 ° environment and whose angle 0 points to the ground and serves as a reference.

Strip is the area between two curves (polygonal lines) of record associated with two consecutive ordered sections. To generate the global triangulation of the surface representing the scatter plot, the triangulation of the bands is iteratively constructed. This reconstruction is based on an advanced approach of triangles front from a segment joining a section to its consecutive section. Two fronts are generated, the first advancing in the points of increasing temporal chronology and the second in the points of decreasing time chronology (when we have the point of reference defining these two chronologies). Let S1 and S2 be two ordered consecutive sections, the construction of the triangles band associated with the pair (S1, S2) is described by the following algorithm (the points of S1 are designated by P and those of S2 by Q,): • The first segment of the front is defined by [Pref (Si), Q (P _re f, S2)] where P _r e _f (Si) is the reference point in the polygonal line LP ₁ of the section Si and Q ( P _ref , S ₂ ) is the point of the polygonal line LP ₂ of the section S ₂ closest to P _ref (Si). In practice, the point Q (P _ref , S ₂ ) is determined by a search in a neighborhood of Q _ref (S ₂ ), the reference point of S ₂ . (the neighborhood being understood in the mathematical sense, as also including the reference point itself)

• Case of increasing time chronology:

o As long as the current segment of the front is different from the segment joining the last two points of sections Si and S ₂ :

^■ Let [PiQ _j ] the current segment of the front then

• If d (Pi + i, S ₂ ) <d (Qj + i, Si), d (.,.) Denotes the distance from a point to a curve (polygonal line), the triangle (PiQ _j P _{i +} i ) is formed and the current segment of the front becomes [Ρ, + iQj].

• Otherwise, the triangle (P, QjQj + i) is formed and the current edge segment becomes [P, Qj ₊ i].

• Case of the decreasing time chronology:

o As long as the current segment of the front is different from the segment joining the first two points of sections Si and S ₂ :

^■ Let [PiQ _j ] the current segment of the front then

• If d (Pi-i, S ₂ ) <d (Qj.i, Si), the triangle (PnQjPi) is formed and the current segment of the front becomes [Pi-iQj]. · Otherwise, the triangle (P, Qj-iQj) is formed and the current segment of the front becomes [PiQj-i].

As in the case of curves or lines associated with sections, in each band, triangles having an edge longer than the given distance threshold are ignored. In the previously detailed example of the point cloud, a portion of which is illustrated in FIG. 13A, 39156 triangulated strips are generally obtained with 122861840 triangles. Figure 13C shows the band triangulation for the 1200 sections of Figure 13B and Figure 13D shows an enlargement of Figure 13C (recognizable by the car present in the environment). FIG. 5 shows a schematic representation of a triangulation map (CT) obtained at the end of this treatment. In some embodiments, the data processing means (10) implement an identification (54) of at least one technical characteristic (CG, CP) of the points (P _n ) forming the vertices of the triangles resulting from the triangulation (53) from said data (D). Note that the present application details below an example of geometric characteristic of normal to a surface, but that this example is not limiting because it is possible to choose various geometric characteristics, such as the curvature between the points and / or or triangulated surfaces. In practice, the system may include a plurality of options selected to offer the user these various processing options characterizing various aspects of the environment and / or surveyed object.

In some embodiments, the data processing means (10) implement an identification (540) of at least one technical characteristic (CG, CP) of the triangles derived from the triangulation (53), by an interpolation of the values technical characteristics (CG, CP) determined during the identification (54) for the points (P _n ) forming the vertices of these triangles. In general, one simply uses a linear interpolation which is sufficient and fast to execute, but it is possible to foresee other methods to estimate the average values on the triangles. On the other hand, it is understood that the invention makes it possible, in certain embodiments, to identify not only the topology (eg, the shape) of the environment (or the object) but also the technical characteristics (CG , CP) points raised and / or to estimate these characteristics on the triangulated surfaces between the raised points. Indeed, the triangulation allows a three-dimensional reconstruction of the scanned environment (from at least one cloud of points). Thanks to the triangles obtained, it is possible to estimate the orientation of the surfaces with respect to a reference orientation, provided that the point recording is performed by providing a reliable reference orientation and allows various characterizations geometric, such as curvature for example. The invention therefore provides an identification (541) of at least one geometric characteristic (CG), for example by estimating the orientation of the triangulated surfaces. On the other hand, the scanning of the environment by a laser (12) allows for example to collect the wave intensity of the return of the measuring means (for example laser). The reflectance of a laser depends on the material and angle of the laser, so it is possible, at least in some cases, to estimate the reflectance of the material on which the laser has passed or at least to compare reflectances in the laser. 'environment. . The other measurement means (sonar and radar) also make it possible to quantify the response of the medium following the impact of the incident measurement wave. The invention therefore provides for an identification (542) of at least one physical characteristic (CP) by measuring the value of at least one datum associated with each of the points, for example to estimate this characteristic (for example the reflectance index ) on the triangulated surfaces. The example of the intensity of the return of the (for example luminous) wave must also not be interpreted in a limiting way with regard to the technical characteristics. Indeed, as topographic survey systems often incorporate other means of capture or measurement, and the data thus captured or measured are listed with reference to the points recorded by the topographic measuring means (12), it is possible to use these data in the identification of technical characteristics (SCG, CP). For example, in the case of an image capture, it is possible to "paste" a color on the points, or even triangles obtained. For example, Figure 9 shows the RGB parameters in .las formats, but it is also possible to list Infra-Red measurements or other measurements of a device associated with the scanning means (12). wave. In fact, the possibilities are only limited by the measurements made and the method and system make it possible to exploit each of the information. In particular, they also allow this information to be used as constraints in the simplification of the triangulation as detailed below. In addition, depending on the type of topographic measurement device used, The deductible characteristics may vary and the example of the reflectance relates to the laser, but other examples are also within the scope of the invention. For example, for a georadar, the signal collected is in the form of hyperbolas representative of the material encountered. Hyperbolic signal processing algorithms can therefore be used for this identification (54) of the characteristic thus measured.

Thus, various embodiments may allow objects to be detected in the environment, in particular by extracting geometric features from the environment or the object (by triangulation) but by identifying (542) at least one object. Physical feature also allows for the detection of particular objects, elements or entities based on a physical characteristic measured during the reading of the points.

In some embodiments, the data (D) includes values representative of the wave intensity returned by each of the scanned points (P _n ) allowing identification (54) of a technical characteristic (CP). For the laser, the reflectance index of a material depends on the laser return from the material and the incident angle. The underlying material can therefore sometimes be estimated by the intensity of the wave (radioelectric light, but for a sonar, the sonic wave can be used).

In some embodiments, the processing means (10) implement an identification (541) of a geometric feature, such as, for example, the technical characteristic (CG) of the angular deviation of the triangulated surfaces from a unit normal reference. This identification can be carried out by:

a calculation of a unit normal of reference in each of the sections (S,), defined as the average of the unit normals of the points of the section (S,) situated in a determined neighborhood of the reference point (Pr) of this section ( S,); then calculating the angular deviation of the normal of each of the points (P _n ) of each of the sections (S,) with respect to said normal unit of reference; and

a linear interpolation of the angular deflection value of each of the points forming the vertices of a triangle (T) to determine the average angular deviation of the normal to the surface of each of the triangles relative to said unit normal reference.

This identification of the geometry, by the angular deviation here but possibly by the curvature or another calculation, makes it possible to determine the geometry within the environment. FIG. 13E shows an example of the result and illustrates that this type of calculation makes it possible to recognize forms of the environment and / or object.

It is understood from the above examples that the present invention allows many variations to characterize the object of the survey (regardless of the measured entity). The angular deflection advantageously makes it possible to identify the orientation of the surfaces, in particular with respect to the reference frame that is the ground. It will be understood that once again the reference point is here reused advantageously but that another reference could be used to indicate a given orientation with respect to which the normals of the triangles can be compared (vertical known for example , especially in the case of an airplane or other).

As above, the algorithm example below is provided in an illustrative and nonlimiting manner to show how it is possible to perform the identification of specific characteristics.

At first, the unit normal at each vertex of the triangulation is estimated by considering an average of the normals at the elements sharing the vertex. Let P be a vertex of the triangulation. Denote by K the triangles incident to P and by 0, the vertex angles P of K,. If n (Ki) is the unit normal to K, (oriented in the direction of the vertices of K,), then the normal in P is defined as: n ⁾ llΣ. 0.n (J¾ ll where || H represents the usual Euclidean vector standard.

For each section, the unit ground normal is defined as the average of the unit normal at the points of the associated curve in a neighborhood of the section reference point. In practice, for a section S, from its reference point P _ref (S), we consider PEP (here PEP = 10) points in the increasing chronological time order and PEP points in the decreasing time chronological order and the unitary ground unit associated with the section is defined as the unit average of the unit normal at these points. The ground normal being defined for each section, we associate at each point of the section the angular deflection of its normal relative to the normal ground. This gives a discrete angular deviation field associated with the vertices of the triangulation. By linearly interpolating the field in each triangle from the field values to these vertices, a continuous field of angular deviations is defined over the entire triangulated surface.

For the cloud of points considered and illustrated in part in FIG. 13A, FIG. 13E shows the continuous field (with the problem of black and white representation near) of angular deviations in degree varying from 0 (light gray) to 90 (dark gray). associated with the sections of Figure 13B.

At each vertex of the triangulation is associated a light intensity dependent on the reflectance index of the underlying material. Likewise, this discrete field of light intensities defined at the vertices of the triangulation is made continuous by linear interpolation in each triangle from these vertices.

Figure 13F shows the continuous field (to the problem of black and white representation near) of light intensities varying in various gray scales, associated with the sections located in Figure 13B.

In some embodiments, the data processing means (10) implement a simplification (55) of the map (CT) obtained by the triangulation (53) to obtain a simplified map (CS) whose number of triangles ( T) is reduced. Indeed, the clouds of points to be treated Being very dense, the resulting surface triangulation usually includes several tens of millions of triangles. A method of simplification of the triangulation is then applied that makes it possible to reduce the number of elements, preferably by preserving at least one geometric approximation of the surfaces and / or at least one identified physical property (associated with the vertices of the triangulation). In some of these embodiments, the simplification (55) is performed with at least one constraint for preserving at least one of the values calculated during the identification (54) of the technical characteristics (CG, CP) in a range of determined value. Thus, we can simplify the map obtained without losing the relevant information that we want to preserve. The system can therefore be configurable by a user who can choose what information should not be degraded by simplification. For example, one can preserve the unit normal of the triangles (interpolated) in a range of value to avoid that the simplification does not modify too much the orientation of the surfaces. Similarly, one can preserve the return intensity of the (interpolated) wave by preventing it from being modified beyond a threshold. Various algorithms are conceivable and those presented above are more illustrative than limiting, although they are particularly advantageous in terms of preservation of information and speed of processing. FIG. 6 shows a schematic representation of a simplified map (CS) obtained by such a simplification (55) from the schematic illustration of the initial map (CT) of FIG. 5. FIG. 13G shows the result of the simplification of the triangulation of FIG. 13C and FIG. 13H shows the enlargement of the result of the simplification of the enlargement of FIG. 13D, for the point cloud of FIG. 13A and the sections of FIG. 13B.

In some embodiments, the determined value range, for the technical characteristic (CG) of the unit normal, is defined by a proximity factor (FP) indicating that the difference between the obtained (CT) map and the simplified map (CS) must be less than a proximity threshold (SP) and a Regularity factor (FR) indicating that the normal to the triangles of the simplified map (CS) must deviate from the normals at the vertices of the triangle by a value less than a regularity threshold (SR).

As before, the algorithm example below is provided in an illustrative and nonlimiting way to show how it is possible to simplify the triangulation by preserving at least one of the values (the unit normal in the example below ) calculated during the identification (54) of the technical characteristics (CG, CP) within a given range of values. The initial dense triangulation (CT) is designated by the reference triangulation discretely defining the geometric shape of the surface. Simplified triangulation (CS) must verify two fundamental properties namely: each element must be close to the surface (described by the reference triangulation) and each element must be close to the tangent planes of its three vertices, ensuring the respect of the geometry . The first so-called proximity property indicates that the gap between the simplified triangulation and the surface must be bounded. This difference represents the largest distance between an element of the simplified triangulation and the surface. The proximity factor (FP) therefore indicates that the difference between the obtained (CT) map and the simplified map (CS) must be less than a proximity threshold (SP) (bounded) meaning that the deviations, in one direction or in the other, are below a threshold). The second so-called regularity property locally translates the continuity G1 of the surface. An element of the simplified triangulation is close to the tangent planes of its vertices if the angular difference between the element and the planes tangent to its vertices is bounded. Thus, the regularity factor (FR) indicates that the normal to the triangles of the simplified map (CS) must deviate from the norms at the vertices of the triangle by a value less than a regularity threshold (SR).

To ensure the proximity property, an area of global proximity is introduced around the surface located at a Hausdorff distance δ given, fixing the bounds of the deviations, on either side of the triangulation of reference of the surface. This distance can be represented globally or as a percentage of the diagonal of the minimum box encompassing the reference triangulation. Similarly, to ensure the property of regularity, each vertex of the reference triangulation is associated with a cone of local regularity centered in this vertex, with a main axis the normal at the apex at the surface and a given aperture angle Θ .

Thus, any triangle resulting from simplification must first belong to the proximity band and secondly have a normal belonging to the regularity cones associated with its vertices. Let T _re f denote the reference triangulation (CT). Let K be a triangle resulting from simplification, K must therefore verify:

d _H (K, T _ref ) ≤ ô and Vi, {ηι (Κ), η (Κ)) ≥ cos 0 where dhi (.,.) designates the Hausdorff distance from a triangle to a triangulation, <., .> is the scalar product of two vectors, n, (K) is the unit normal to the vertex i of K and n (K) is the unit normal to K. In some embodiments, to preserve the physical characteristics (CP) from of the identification (542), the determined value range for the physical technical characteristic (CP) (such as, for example, the reflectance index) is defined by a maximum variation threshold (SLR) between the values of the points forming the vertices of the triangles of the obtained map (CT) and the values of those of the simplified map (CS). Thus, in order to preserve the physical property associated with the vertices of the triangulation, only the edges having a small variation of property (CP) between their ends are processed.

In some embodiments, the simplification (55) is done iteratively at least with a deletion (551) of edge (s) between the vertices of the triangles (T) and an optimization (552). ) triangles. In addition, it is possible to optimize the triangles with a shake (553) of points (illustrated in Figure 10 showing that this procedure makes any triangles stretch towards equilateral triangles). In practice, the deletion is preferably done by vertex merging triangles (shown in FIG. 7 showing that merging from 5 triangles results in only 3 triangles) and optimization by edge flip-flop (shown in FIG. 8 showing that, by the rocker, the common edge of 2 triangles, situated between 1 vertex of each triangle, becomes the edge connecting the two vertices of the triangles which did not share an edge).

As before, the algorithm example below is provided in an illustrative and nonlimiting manner to show how it is possible to iteratively simplify by a combination of deletions, optimizations and point movements. It will be noted that various iterative combinations are possible, even if the procedure below is advantageous because the succession of operations optimizes the reorganization of the triangles.

The method consists mainly of deleting and optimizing, in an iterative manner, the edges of the reference triangulation (CT) as much as possible. For this two operations are used: the fusion of the ends of edges and the flip-flop of edges. The first operation (deletion), although simple, proves to be quite effective insofar as the two properties of proximity and regularity can be checked explicitly. The second operation (edge optimization) is a classic edge flip operation (replacing the two triangles sharing the edge with two others) which improves the quality in the form of the triangulation and is not performed only if the triangles sharing the edge are quasi-coplanar. In this case, the geometry of the surface is automatically preserved. These globally applied deletion and edge-to-edge operations can be followed by a point-shifting procedure to increase the number of edges that can be removed and to improve the quality of the edges. shape of the triangulation (the best form being that of an equilateral triangle). The movement of a vertex of the triangulation consists of moving this vertex, step by step, towards an "optimal" point leading to a configuration of optimal triangles (in quality). For this, in each vertex of the triangulation, the surface (or the underlying geometry) is locally approximated by a quadric surface passing at best by the adjacent vertices and the optimal point is projected on this surface. Similarly, the shake is locally performed if the geometry of the surface and the quality in the form of the triangulation are preserved. These constraints can be explicitly verified as in the case of the fusion of the ends of an edge.

The simplification method thus makes it possible to construct a simplified triangulation, associated with a given control triplet (5, θ, β) (δ expressed as a percentage of the box encompassing the surface and Θ in degree), making it possible to quantify on the one hand the desired level of geometric approximation as well as the quality degradation in the form of triangulation. In practice, a relaxation of the first two parameters is considered and the operations described above are applied in an iterative manner. The simplification algorithm of the triangulation is then the following one: One initializes the current triangulation T to T _re f, Δ (resp.O) to δ ₀ ≤δ (respectively θ ₀ ≤θ), then as long as Δ≤δ and Q≤9 \

o Optimize all the edges of T

o Remove and optimize all the edges of T if the geometry (Δ, θ) and quality degradation β are preserved o Move the vertices of T if the geometry (Δ, Ο) and quality degradation β are preserved

o Increment Δ and O. Starting with an optimization (toggle) is advantageous because it allows to generate additional potential mergers for the next operation. It is understood that this algorithm uses a combination of the operations described using at least once each of the operations. Relaxation allows for progressive simplification that provides more efficient optimization and preservation of the desired factors. As detailed above, in some embodiments, said unit normal reference corresponds to the average unit average at ground level and the identification (541) of the technical characteristic (CG) of the angular deviation from a normal The unit of reference makes it possible to determine the orientation of the surfaces of the scanned environment in the three-dimensional space with respect to the average plane of the ground. It is understood that by taking the reference point on the ground directly in the data (D) of the point survey, it is possible to estimate the geometric orientation characteristic of the triangles with respect to the ground. Nevertheless, it should be noted that the reference unit normal may relate to a reference other than the ground, although the ground is the preferred reference frame in most embodiments because the angle 0, used for the reference point as detailed hereinabove. for various preferred embodiments, generally corresponds (depending on the type of topography system) to a ground measurement which is generally "reproducible" from one section to another. On the other hand, it will be noted that the normal unit reference could be provided during the acquisition instead of being calculated on the neighborhood of the reference point (Pr) within the sections (S,), since it suffices in having a measurement of a vertical (or any other reference orientation) and various measuring means can be used to determine it. The invention therefore provides various embodiments where the geometry is determined from this type of reference regardless of the source.

The present application describes various technical features and advantages with reference to the figures and / or various embodiments. Those skilled in the art will appreciate that the technical features of a given embodiment may in fact be combined with features of another embodiment unless the reverse is explicitly mentioned or it is evident that these characteristics are incompatible or that the combination does not provide a solution to at least one of the technical problems mentioned in this application. In addition, the technical features described in one embodiment given may be isolated from other features of this mode unless the reverse is explicitly mentioned.

It should be obvious to those skilled in the art that the present invention allows embodiments in many other specific forms without departing from the scope of the invention as claimed. Therefore, the present embodiments should be considered by way of illustration, but may be modified within the scope defined by the scope of the appended claims, and the invention should not be limited to the details given above.

Claims

1. Method for the three-dimensional reconstruction of an environment from data (D) representative of at least one cloud of points (P _n ) recorded by means of topographic measurement (12) by wave scanning, the method being implemented in a system (1) comprising data processing means (10) accessing storage means (1 1) containing said data (D) whose points (P _n ) are listed at least according to at least one index (IT) , A, n) and their spatial coordinates (x, y, z) in a three-dimensional space, characterized in that it comprises:

a determination (51), from the point cloud (P _n ), of consecutive sections (S,) comprising a plurality of consecutive points (P _n ), classified according to at least one of the indices (IT, A, n) of each of the points (P _n ) of the cloud;

a determination (52), for each of the consecutive sections (S,), of a polygonal line (LP) joining all the consecutive points (P _n ) of the section (S,) in the three-dimensional space;

a triangulation (53) of the strips (B) between the polygonal lines (LPi) of each of the consecutive sections (Si, S2, ..., Si), by at least one successive iterative advance of successive triangles (FT) according to minus one temporal chronology (Ch), each of the successive triangular fronts (FT) being defined, starting from a segment (ST) connecting a point (P _j ) of the polygonal line (LP) of a section (S) ,) at a point (P _k ) of the polygonal line (LP _{i +} i) of the next section (Si + i), forming a triangle (T) with the point (P _n ) which follows, in said temporal timeline ( Ch), one of these two points (Pj, P _k ) of these two polygonal lines (LP ,, LP _{i +} i) and which is the least distant, in terms of distance (d) in three-dimensional space, from the polygonal line (LP _{i +} i, LP,) to which it does not belong, this triangulation (53) making it possible to obtain a card (CT) comprising a plurality of triangles (T) whose vertices are formed by said points ( P _n ) d Data (D).

2. Method according to claim 1, characterized in that said determination (51) of the sections (S,) is performed by selecting the points as a function of a threshold (DT) maximum between at least one of the indices (IT, A, n) each of the points (P _n ) of the cloud, each of said sections (S,) then comprising a plurality of consecutive points (P _n ), classified according to this index (IT, A, n) and whose spatial coordinates (x , y, z) belong to a mean surface (PM) defined around at least one reference point (Pr).

3. Method according to one of claims 1 and 2, characterized in that said triangulation (53) of the bands (B) between each of the sections (S,) consecutive is achieved by several iterative advances in parallel by the processing means (10). of data.

4. Method according to one of claims 1 to 3, characterized in that said triangulation (53) of the strips (B) is performed according to at least one temporal chronology (Ch) defining a first segment (ST) of the front of the triangle (T) formed between the reference point (Pr) in the polygonal line (LPi) of the first (S,) of the two consecutive sections (S ,, S, ₊ i) and the point (P _n ) closest to this polygonal line (LP,) of the first (S,) of the two sections (Si), in terms of distance (d) in the three-dimensional space, from the points (P _n ) in the vicinity of the reference point (Pr) of the line polygonal (LP _{i +} i) of the second (S, ₊ i) of the two consecutive sections (S ,, Si + i).

5. Method according to one of claims 1 to 4, characterized in that said triangulation (53) of the bands (B) is performed according to an increasing time chronology (Ch) and a decreasing time chronology (Ch):

the increasing chronology being defined by iterations in the increasing order of the time indices (IT), from the reference point (Pr) of the first section (S,) to the segment (ST) joining the last two points (P) _n ) two consecutive sections (S ,, S, ₊ i); the decreasing chronology being defined by iterations in descending order of the temporal indices (IT) from the reference point (Pr) of the first section (S) to the segment (ST) joining the first two points (P _n ) of the two consecutive sections (S ,, S, ₊ i).

6. Method according to one of claims 1 to 5, characterized in that, during the determination (51) of consecutive sections (S,), for points, called orphans, whose membership in a section (S,) has excluded by the maximum time interval threshold (DT), the data processing means (10) define that:

- If an orphan point has a time index value (IT) less than the value of the smallest time index of the first of the consecutive sections (S,), then this orphan point belongs to this first section; and or

If an orphan point has a time index (IT) value greater than the value of the largest time index of the last of the consecutive (Si) sections, then that orphan point belongs to this last section (Si); and or

If an orphan point has a time index value (IT) between the values of the time indices of the points of two consecutive sections (S ,, Si ₊ i), then this orphan point belongs to the section (S,) corresponding to the mean surface (MP) from which it is least distant in terms of distance in three-dimensional space, the average surface (PM) being defined by the data processing means (10) as the area (PM) which passes through the maximum number of points (P _n ) of the section (S,) to a determined neighborhood of said reference point (Pr) of each of the sections (S,).

7. Method according to one of claims 1 to 6, characterized in that the determination (52), for each of the consecutive sections (S ,, S, ₊ i), of a polygonal line (LP) joining all the points ( P _n ) consecutive section (S,) in the three-dimensional space is performed by the data processing means (10) by joining only the points (P _n ) consecutive distances separated by a distance, in the three-dimensional space, below a determined maximum separation threshold (ECA).

8. Method according to one of claims 1 to 7, characterized in that the data processing means (10) implement an identification (54) of at least one technical characteristic (CG, CP) points (P _n ) forming the vertices of the triangles from the triangulation (53), from said data (D).

9. Method according to claim 8, characterized in that the data processing means (10) implement an identification (540) of at least one technical characteristic (CG, CP) of the triangles resulting from the triangulation (53), by interpolation of the values of the technical characteristics (CG, CP) determined for the points (P _n ) forming the vertices of these triangles.

10. Method according to one of claims 8 and 9, characterized in that the data (D) comprise values representative of the wave intensity returned by each of the points (P _n ) scanned allowing identification (542) of a physical technical characteristic (CP).

1 1. Method according to one of Claims 8 to 10, characterized in that the processing means (10) implement an identification (541) of the geometric technical characteristic (CG) of the angular deviation of the triangulated surfaces with respect to a unit normal of reference, realizing:

a calculation of a unit normal of reference in each of the sections (S,), defined as the average of the unit normals of the points of the section (S,) situated in a determined neighborhood of the reference point (Pr) of this section ( S,); then

calculating the angular deviation of the normal of each of the points (P _n ) of each of the sections (S,) with respect to said normal unit of reference; and a linear interpolation of the angular deflection value of each of the points forming the vertices of a triangle (T) to determine the average angular deviation of the normal to the surface of each of the triangles relative to said unit normal reference.

12. Method according to one of claims 8 to 11, characterized in that the data processing means (10) implement a simplification (55) of the map (CT) obtained by the triangulation (53) to obtain a map simplified (CS) whose number of triangles (T) is reduced.

13. Method according to claim 12, characterized in that said simplification (55) reduces the number of triangles, but with at least one preservation constraint of at least one of the values calculated during the identification (54) of the technical characteristics. (CG, CP) in a determined range of values.

Method according to claims 1 to 13, characterized in that the determined value range, for the technical characteristic (CG) of the unit normal, is defined by a proximity factor (FP) indicating that the difference between the obtained (CT) map and the simplified map (CS) must be less than a proximity threshold (SP) and a regularity factor (FR) indicating that the normal to the triangles of the simplified map (CS) must deviate, with respect to normal to the vertices of the triangle, less than a regularity threshold (SR).

15. Method according to one of claims 8 and 12 to 14, characterized in that the determined value range, for the physical technical characteristic (CP), is defined by a maximum variation threshold (SLR) between the values of the points forming the triangles of the obtained map (CT) and the values of those of the simplified map (CS).

16. Method according to one of claims 12 to 15, characterized in that the simplification (55) is carried out iteratively at least by means of a deletion (551) of edge (s) between the vertices of the triangles ( T) and an optimization (552) of the triangles.

17. Method according to one of claims 1 to 16, characterized in that the data processing means (10) are configurable by a user of the system, for setting at least one of a plurality of parameters comprising a maximum index threshold (DT) and / or a spacing threshold (ESP) and / or time chronology (s) (Ch) and / or a number of parallel iterative advances and / or a gap threshold maximum (ECA) and / or at least one determined neighborhood defined by a neighbor number to be considered and / or a proximity threshold (SP) and / or a regularity threshold (SR) and / or a maximum variation threshold (SLR) ).

18. Method according to one of claims 1 to 17, characterized in that the points (P _n ) are also listed according to the scanning angle (A _n ) of a measuring means (12) for recording said points (P _n ). by moving in an environment to which the points (P _n ) belong in the three-dimensional space, said reference point (Pr) of each of the sections (S,) corresponding to a point listed at a scanning angle (A _n ) of the measuring means (12) determined to be reliable for referencing the other points (P _n ) of the section (S,), depending on the orientation of the measuring means (12) moving in the environment where are scanned the points (P _n ).

19. The method of claim 18, characterized in that the scanning angle (A _n ) of said reference point (Pr) of each of the sections (S,) is an angle corresponding to a point located at the ground level of the environment in which the measuring means (12) moves.

20. Process according to claims 1 1 and 19, characterized in that said normal unit of reference corresponds to the mean unitary average at ground level and the identification (54, 541) of the technical characteristic (CG) of the angular deviation relative to a normal unit of reference makes it possible to determine the orientation of the environmental surfaces scanned in the three-dimensional space with respect to the average plane of the ground.

21. Method according to one of claims 1 to 20, characterized in that the data processing means (10) implement a sorting (50) of the data (D) to classify the points (P _n ) according to their time index ( IT) which corresponds to the time (t _n ) at which the measurement means (12) received the return of the measurement wave of a given point (P _n ).

22. Method according to claim 21, characterized in that the sorting (50) is implemented prior to the determination (51) of the consecutive sections (S,) by the data processing means (10), starting from data (D) which are stored in the storage means (1 1) and representative of at least one scatter plot (P _n ) whose scheduling is not dependent on the temporal index (IT).

23. The method as claimed in claim 21, characterized in that the sorting (50) is implemented by the data processing means (10) during the acquisition of the data (D) by the means (12). measurement method by classifying the points (P _n ) in ascending order of at least one index (IT, n), by means of a buffer (1 10) for temporarily storing, waiting for the return of the wave for a point (P _n ) of a given index (IT, n), the points (P _n ) of index greater than this given index, until the return is received for the point corresponding to this given index, and allows to order the different points (P _n ) according to their index (IT, n) in the buffer memory (1 10) and / or the storage means (1 1), this sorting (50) during the acquisition of data (D) allowing the implementation of the method in real time from the acquisition of the data (D) corresponding to at least two consecutive sections (S).

24. A topography method comprising at least one survey (61) of points (P _n ) by at least one means (12) for measuring by scanning the waves scanning the points (P _n ) of the environment by a determined scan in a plane of the three-dimensional space, said survey (61) listing the points (P _n ) at least according to at least one index (IT, A, n) and according to their spatial coordinates (x, y, z) in the space three-dimensional, characterized in that the survey (61) provides at least one reference point (Pr) among the points (P _n ) read and the method is implemented by a system (1) which comprises data processing means (10) performing a data acquisition (D) representative of the points recorded by the measuring means (12) and performing a sorting (50) said data, during the acquisition, by classifying the points (P _n ) in increasing order of at least one index (IT, n), by at least one buffer (1 10) for storing temporarily, pending the return of the wave for a point (P _n ) of a given index (IT, n), the points (P _n ) of index greater than this given index, until the return of the wave is received for the point corresponding to this given index and makes it possible to order the different points (P _n ) according to their index (IT, n) in the buffer memory (1 10) and / or storage means ( 1 1) of the system (1), this sorting (50) during data acquisition (D) allowing the implementation of the method according to one of the claims s 1 to 21, in real time from the data acquisition (D) corresponding to at least two consecutive sections (S).

25. Method of topography according to the preceding claim, characterized in that the acquisition is performed during the displacement of the topographic measuring means (12) in the environment, identified by means (13) for measuring the position and at least one orientation of the topographic measuring means (12).

26. System (1) for three-dimensional reconstruction from data (D) representative of at least one cloud of points (P _n ) recorded by means of topographic measurement measurement (12) by wave scanning, comprising processing means data store (10) accessing storage means (1 1) containing said data (D) representative of at least one cloud of points (P _n ) listed at least in at least one index (IT, A, n) and their spatial coordinates (x, y, z) in a three-dimensional space, characterized in that the processing means (10) execute at least one software or application comprising one or more algorithms (ALG) for the implementation of the process according to one of claims 1 to 23.

27. Topography system (1) comprising at least one wave scan topographic measuring means (12) scanning the points (P _n ) of an environment by a determined scan in a three-dimensional space plane, and listing the points (P _n ) at least according to at least one index (IT, A, n) and according to their spatial coordinates (x, y, z) in the three-dimensional space, characterized in that the processing means (10) execute at least one software or application comprising one or more algorithm (s) (ALG) for implementing the method according to one of claims 24 and 25, as and when data acquisition.

28. System (1) of topography according to claim 27, characterized in that the acquisition is performed during the displacement of the topographic measuring means (12) in the environment, identified by means (13) for measuring the position and at least one orientation of the topographic measurement means (12) integrated in the system for the mapping of the environment.

29. Computer storage medium, comprising data for the execution and / or installation of at least one software or application in a computer system, characterized in that said software or application comprises one or more algorithms ( ALG) for the implementation of the method according to one of claims 1 to 23.