JPWO2018146279A5 - - Google Patents

Description

The present invention relates to devices and methods for obtaining geometric shapes and material properties of buildings , building environments and / or environmental areas , as well as generation and representation of their respective 3D models. Furthermore, material properties such as the degree of deterioration of the surveyed object such as the building, building environment, and environmental area can be determined very efficiently and quickly.

Web mapping services can characterize maps of different locations, such as cities and towns, natural landscapes , and more. In particular, a map image of a location may include a bird's eye perspective that directly integrates a three-dimensional landscape with topographical features. For example, a textured 3D building model can be displayed in a map representation.

In addition, these web mapping services can also provide street -level views of different locations. In particular, a panorama of stitched images can be displayed on request, providing the user with a 360 degree panoramic street level view of the selected map location .

In addition, advanced driver assistance systems for vehicles avoid collisions and accidents by providing drivers with technology to alert them to potential problems, implement safety devices to take over vehicle control, and collide. To avoid. In particular, the driver assistance system may be configured to recognize an object near the vehicle and warn the driver in the event of an emergency collision or automatically perform an action.

However, the level of detailed information provided to the 3D structural model of the web mapping service as well as the driving assistance system is limited. In particular, current systems provide sufficient information about the composition of the object (eg, the type of material contained in the facade of the displayed building) or the material properties of the indicated object, such as material degradation or contamination. Do not provide.

It is an object of the present invention to provide a device and a method for analyzing an object including a building, a building environment, and / or an environmental area, which can effectively and quickly determine the material properties of these objects. To do.

In addition, devices and methods for object analysis and device modeling that improve the level of available information on the object under investigation and enable the generation of more accurate and detailed models of the object more quickly. To make a suggestion.

In order to solve the technical object described above, a device for analyzing an object according to the feature according to claim 1 and a method according to claim 12 are proposed. Dependent claims relate to preferred embodiments of the invention.

The present invention can be applied to web mapping service charts (eg Google Maps, etc.) or driving assistance systems. However, it should be noted that aspects and embodiments of the present invention may also be applied in different areas such as maintenance, agriculture or landscaping work. Further, specific embodiments and embodiments of the present invention can be implemented in nautical environments such as at sea, such as monitoring the deterioration state of ships, boats or oil rigs.

Hereinafter, the advantages are described by providing support for the features and embodiments of the exemplary embodiments. Further advantages and features will be apparent from the drawings and the more detailed description of the associated exemplary embodiments.

According to one aspect, a device for analyzing an object is provided. The object includes or has at least one of a building, a building environment, or an environmental area. That is, the object may include or constitute a building, a building environment, and / or an environmental area.

For example, the object may include roads, highways, railroads, bridges or any other infrastructure element. In addition, the object may include a house, skyscraper, industrial facility, or natural landscape .

In some embodiments, the device for analyzing an object may include imaging means. The image pickup means may be configured to detect the image pickup data point of the object.

For example, the imaging means may be any sensor or sensor system (eg, a camera or camera system) capable of detecting or receiving photons, i.e. light waves, reflected or emitted by the object to be investigated . Such object signals, i.e. radiation signals associated with the object to be investigated , can be classified as imaging data points.

Further, the device may include an allocation means configured to assign spatial coordinates to each imaging data point. In particular, the object data points may be acquired via the assignment of spatial coordinates to the respective imaging data points.

That is, the device for analyzing an object may be provided with an allocation means for assigning spatial coordinates to each of the imaging data points in order to acquire the object data points.

For example, the allocation means may include a device or any other means for determining a position in the Global Positioning System / space . Therefore, the collected imaging data points may be georeferenced by a device for analyzing the object, and the position in space may be assigned to the information collected by the imaging means, i.e., the imaging data points. .. Further, the allocation means may include one or more computing devices associated with the Global Positioning System or device. Therefore, georeferencing may be enabled via data processing performed on the one or more computing devices.

Further, the device for analyzing the object may be provided with means for determining at least the material properties of the object based on the object data points.

The determining means may refer to any device or device capable of identifying or detecting measurable characteristics of the object. For example, the determining means may refer to the ability to measure or determine temperature. In addition, the determination means may include one or more computing devices for data processing of the measured or determined data. Thus, the property may refer , for example, to the object temperature, eg, the object temperature via a thermal image. However, properties may refer to physical, structural , chemical, and / or biological material properties of any kind. Further, the property may refer , for example, to the geometry and / or size of the object to be investigated . Further, the property may also refer to a difference in composition, such as, for example, a difference in the formation of an object or a difference in the substances contained in the object .

In embodiments, at least spectral library data may be used in the determination. In addition, the spectral library data may include a set of material spectral properties corresponding to physical, structural , chemical and / or biological material properties.

For example, spectral library data can be stored in hardware (eg, a memory device) associated with the device for analyzing the object. In particular, spectral library data may refer to a set or set of pre-recorded or predetermined spectral characteristics associated with each material. For example, spectral library data may include reflection and / or emission spectra, eg, wavelength reflectance distribution dependencies , for different materials under certain external conditions, such as different lighting or humidity conditions . In addition, spectral library data include spectral signatures (eg, functional relationships between radiance and / or reflectance and wavelength) associated with different degradation states or different physical and chemical states of many materials . good. For example, the spectral library data may indicate whether a particular material contains water, ice, snow, or other material (rust, moss, microscopic organisms, etc.). A structure or structural property may refer to a particular structural feature of an object. For example, in relation to a building, structure may refer to differences in the material and geometric composition and / or arrangement of different building segments. In particular, the roof of the building may be constructed using materials such as wood and / or steel, and the building façade may be made of concrete or brick. Moreover, the geometry and / or features of the roof and foundation of a building usually show various differences . Thus, structural or structural properties can explain such various geometric and material properties.

Therefore, object characterization can be compared and / or other processing (eg, images) of acquired object data points, eg, recorded spectral information or signatures, to spectral information stored in spectral library data. It may be executed by a processing technique). In particular, if the recorded spectral information matches the spectral characteristics contained in the spectral library data , the corresponding material properties may be assigned. For example, if the recorded spectral signature matches or matches spectral library data indicating glass as a material, the device analyzing the object outputs that the object being tested contains or consists of glass. It's okay. In addition, the spectral library data may also include the respective spectral characteristics when the glass is covered with water and / or snow. Upon detecting the corresponding spectral signature, the device for analyzing the object may determine that the object tested contains or consists of glass covered with water and / or snow. .. Similar decisions can be made in view of substances covered with plants (eg, moss, etc.) or other substances (eg, rust, soil, etc.).

Corresponding comparison and / or other processing and allocation steps may be performed automatically, for example by a computing device or the like.

The above characterization may be performed for all materials shown in the spectral library data using their corresponding spectral traits .

As a result, the device for analyzing the object is associated with each object signal as well as information about the physical, structural , chemical and / or biological material properties of the object being analyzed. You can collect or collect object data points that indicate the spatial location of each of the objects or elements of the object. Therefore, detailed information can be provided to accurately investigate or monitor the current state of the object (eg, characteristics, etc.) and to generate a 3D object model.

This information can also be used to increase safety and reduce costs associated with maintenance or repair of buildings , building environments, and / or environmental areas. For example, the damaged or deteriorated part of each building can be recognized quickly and easily. In addition, devices for analyzing objects include liquid or frozen water (ice, snow), organic matter, or dust, paint, graffiti, minerals, etc. in parts of the building (eg, entrance doors, windows, etc.). It can reliably detect whether it is covered with other contaminants.

In addition, the level of structural detail in web mapping services can be significantly improved. In particular, devices that analyze objects enable highly accurate modeling of buildings, building environments and environmental areas with bird-eye views and / or street-level views of web mapping services. Therefore, navigation and orientation for web mapping services will be much easier.

The material properties may include at least the degree of deterioration of the object.

For example, the degree of deterioration is the structural integrity of the material, that is, the material is damaged , worn , cracked, covered with other materials, or has non-uniform thermal conductivity . It may refer to the structural integrity of materials such as heat.

Preferably, the material properties may include at least coverage, contamination or humidity of the object. That is, the material properties may include coverage, contamination, and / or humidity of the object.

For example, coverage may indicate the amount of coverage of an object with another substance, such as liquid or frozen water, a chemical composition (eg, rust, etc.) or a biological material (eg, microorganism or plant, etc.). .. Degree of contamination may refer to the accumulation of dirt, dust or other contaminants on the object. Humidity may refer to different levels of wettability of the material of the object.

Material property information can be greatly influenced by the above-mentioned degree of influence. For example, the spectral or thermal information of a material can vary significantly depending on whether the material is covered with water. Similarly, the spectral and / or thermal properties of a material may change over time. Therefore, by considering the above degree of influence, more accurate determination of the material and confidence in the material condition, for example, whether the material is deteriorated, covered with another substance, contaminated, etc. Highly reliable detection is possible. In addition, some materials may be recognized as materials that have been built and / or used only for a specific period of time. For example, recognition or detection of dangerous components such as asbestos may be enabled.

Therefore, a more accurate analysis procedure becomes possible. In addition, the generation of 3D models in web mapping service charts may be adapted in response to changing environmental conditions. For example, seasonal effects (eg, winter buildings, building environments and snow in environmental areas) may be indicated in bird-eye or street views of web mapping service charts. This can significantly improve the level of detail in the web mapping service chart.

The spectrum library data may include predetermined spectral information. Predetermined spectral information may be associated with various spatial, temporal, atmospheric and lighting conditions and compositional differences of each of the plurality of materials.

As mentioned above, the spectral library data may be stored in a memory device associated with the device for analyzing the object. Thus, spectral information associated with a plurality of different materials and environmental conditions may be generated via spectral signature measurements (eg, reflectance and / or radiance dependence on wavelength). For example, for different materials, the signal recording angle (eg, the angle between the object or the surface of the object and the imaging means of the device that analyzes the object), the time (eg, morning, noon, afternoon, evening or night). ), Meteorological conditions (eg rain, fog, sunlight, snowfall) and lighting conditions (eg shadow intensity when the object is in the shadow of another structure), record spectral characteristics. You may.

This provides the advantage of distinguishing a wide variety of different materials and material properties under multiple environmental conditions. Therefore, maintenance and / or repair work and model building can be facilitated.

One or more spectral characteristics related to the object may be acquired and compared with predetermined spectral information. The material properties of the object may be determined based on the agreement between the acquired spectral properties (ie, the measured radiation signal generated or reflected from the object) and the predetermined spectral information.

That is, predetermined spectral information that takes into account various spatial, temporal, atmospheric and irradiation conditions is generated for any of the materials and combined to form spectral library data. Good . Therefore, the measurement signal (eg, reflected or radiated light beam) resulting from the object may then be compared to the respective spectral characteristics of some or all conserved materials . The material properties of the object (eg, the composition or state of the object) are then identified based on the matching between the recorded spectral properties and the spectral library data.

Therefore, the process of identifying materials and material properties can be structured more efficiently. In addition, misidentification of materials and material properties can be prevented or suppressed. Therefore, the reliability of the analysis and determination procedure of the object is further increased.

Preferably, the object data points may be mapped on at least one reference coordinate of a predetermined land registration chart or web mapping service chart. That is, the object data points may be mapped to the reference coordinates of the predetermined land registration chart and / or the web mapping service chart.

For example, a computing device associated with a device for analyzing an object may perform the above mapping of object data points to such reference coordinates. Land registration charts can be obtained from their respective data libraries (eg, from the city hall) and may be made available for digital processing of computing devices associated with the device for analyzing objects. Web mapping service charts such as Google Maps may be available via the Internet (World Wide Web). Thus, the object data points, that is, the georeferenced imaging data points of the survey object, can be mapped to the layout or floor plan contained in the land registration chart or web mapping service chart. Therefore, the acquired object data points can be associated with the positions of the objects analyzed by the land registration chart or the web mapping service chart. That is, the object data points can be assigned so that each position reflects the position of the object according to the land registry map or the web mapping service chart.

Therefore, the device for analyzing the object allows the matching of the object data points with the specific reference system indicated by the land registration chart or the web mapping service chart. Accordingly, the present invention provides the advantage of producing highly accurate maps and / or floor plans.

In some embodiments, the mapped object data points (voxels) may be used to generate a 3D model of the object. Further, the 3D original object model may be scaled with respect to a predetermined chart size .

Therefore, object data points that are mapped to a particular reference system in a land registration chart or web mapping service chart may be arranged to generate a 3D model of the analyzed object. For example, a computing device associated with a device for analyzing an object may perform the above-mentioned arrangement to generate a three-dimensional model. A computing device may be associated with a device for analyzing an object if data can be processed and / or exchanged between the device for analyzing the object and the computing device. For example, the device for analyzing an object may include a computing device and may be configured to allow data processing and exchange of data with the computing device.

The size of the 3D object model so that the modeled object follows the length dimensions specified in the predetermined chart so that the 3D object model fits into a predetermined chart scale or scale . May be adjusted or scaled.

Therefore, the present invention provides a very accurate 3D object model and enables placement or positioning of the object model in a coordinate system specified by a predetermined chart. Therefore, a very realistic map can be realized. Therefore, navigation and direction according to the web mapping service chart become easy. Furthermore, according to each 3D house model, house renovation and construction planning can be made more efficient.

That is, the construction or restoration of the building structure can be planned more accurately, resulting in cost savings.

Devices for analyzing an object may be included in autonomous and / or non-autonomous mobile entities . That is, the device for analyzing the object may be included in at least one of the autonomous and non-autonomous mobile entities .

For example, an autonomous mobile entity may refer to any kind of automated or remote controlled robot or robot vehicle. A robot may refer to a machine (for example, a robot rover, a robot humanoid, an industrial robot, a robot flying drone, etc.) that can automatically perform a complex series of movements (for example, move along a space orbit). In addition, the robot or robot vehicle can be remotely guided by an external control device. Alternatively, the control may be incorporated within the robot. That is, the robot or robot vehicle may function, for example, as a non-autonomous moving entity . Alternatively, the autonomous mobile entity may point to a person or user carrying a device for analyzing an object along a specified spatial trajectory.

The autonomous and / or non-autonomous moving entity may be at least one of an aerial vehicle and a land-based vehicle. That is, the device for analyzing an object may be included in at least one of an autonomous and non-autonomous mobile entity that is an air transport vehicle and / or a land-based transport vehicle .

For example, the airlift may be any type or type of flyable device, such as a drone, helicopter, airplane, balloon, or airship. The land-based vehicle can be any kind of device that can move on the ground, such as a car or a moving frame . In addition, frames such as tripods can be classified as land-based vehicles. Further, in the framework of the present invention, a device for analyzing an object hand-held or carried by a user or an operator can also be classified as a land-based vehicle. In particular, land-based vehicles may be configured to adapt the height position or altitude of the device for analyzing an object to the ground surface . For example, a device for analyzing an object may be included in a framework or a tripod-movable platform (ie, a platform or tripod that can move along a spatial orbit).

This offers the advantage of being able to generate data from different perspectives and perspectives. Therefore, the level of detail of the accumulated data is improved.

Preferably, the imaging means may include at least one of a laser scanner, an optical camera, an infrared camera, an imaging spectrometer, an inertial measurement unit, an IPS sensor (indoor positioning system sensor), an IBeacon and / or a GPS sensor. ..

A laser scanner is a device for controlling the deflection of a visible or invisible laser beam. Laser scanners can enhance the scanning process and reduce data acquisition errors. Therefore, you can save time and money.

An optical camera is an optical remote sensing device for detecting an object without physical contact. The optical camera may operate in other parts of the visible or electromagnetic spectrum. Therefore, characteristics such as color or contrast of the object may be captured using an optical camera. Therefore, it is possible to generate a three-dimensional model by realistically expressing the analyzed object.

An infrared camera or thermography camera is a device that uses infrared light to generate an image. Therefore, the temperature characteristics of the building structure can be recorded. For example, this also allows analysis of building structures with respect to insulation properties. In addition, thermal radiation information may be used to identify various structural elements of the building structure. For example, thermal radiation information may be used to determine or recognize organic organisms (mold, moss, etc.) attached to the elements of the object to be analyzed.

An indoor positioning system (IPS) is a system that locates objects in a building using IPS sensors that can receive and / or process radio waves, magnetic fields, acoustic signals, or other sensing information.

An inertial measurement unit (IMU) is an electronic device that uses a combination of accelerometer, gyroscope, and magnetometer to measure and report forces, angular velocities, and possibly magnetic fields.

A GPS sensor is a receiver with an antenna or receiving means and is configured to use a satellite navigation system along with a satellite network in Earth's orbit to provide position, velocity, and timing information.

The image spectrometer may collect information as a set of data units. Each data unit represents a narrow wavelength range of the electromagnetic spectrum, also known as the spectral band. These data units may be combined to form spectral data cubes (N pairs) for processing and analysis. The N-1 component of the N set represents the spatial dimension, and the Nth composition represents the spectral dimension including the wavelength range. Therefore, the spectral and spatial information of the analyzed object can be efficiently represented, and the material can be determined based on the spectral characteristics.

Therefore, by using the above-mentioned imaging means, a more realistic object model and map generation become possible. Further, it is possible to easily recognize the deteriorated influence of mold and the like on the building structure. Therefore, it is possible to more efficiently recognize and prevent adverse health effects or dangerous conditions for the inhabitants of the building.

The object data points may be represented as a set of 3D graphic information units. Each 3D graphic information unit may indicate at least one of the spectral, RGB (red, green, blue) and thermal data information associated with its spatial coordinates. That is, each 3D graphic information unit may indicate spectral and / or thermal data information associated with its spatial coordinates.

Therefore, each 3D graphic information unit (voxel) may include a spatial information component. Therefore, the construction of a three-dimensional model can be easily performed. In addition, spectral information makes it possible to determine object properties such as optical appearance (eg, color, etc.) and / or identification of object material composition. Furthermore, the thermal data information can be used to express the thermal characteristics of the object model such as heat insulation characteristics.

That is, the information contained in the object data points can be efficiently expressed and / or evaluated. For example, the acquired information may be displayed on a computer-generated image or screen. This facilitates the construction of a full-scale 3D model.

Preferably, the object data points may be projected onto a plane. The plane may be parallel to the X, Y planes associated with the space X, Y coordinates of the object data points. Identification of each geometric element of the object may be assisted by an evaluation of the data point cluster density distribution in the plane.

The three-dimensional properties of the analyzed object are reflected in the spatial information of the projected object data points. Therefore, suppression of the spatial component of each object data point results in the projection of all object data points onto a two-dimensional surface or plane. Therefore, the relative spatial arrangement of each object data point is reflected in the data point density distribution or data point cluster density distribution on its projection plane or surface. For example, in a three-dimensional original configuration in which each object data point is associated with coordinates (X, Y, Z), the Z component of each object data point is set to zero to project onto a two-dimensional surface. Can be done. Therefore, all data points are "projected" on the X, Y planes. As such, the projected object data points form clusters of varying densities in the plane. That is, the projected data points generate a data point (cluster) density distribution in the plane.

That is, the identification of the geometric elements and / or structures of the analyzed object may be supported or facilitated by the evaluation of the data point density distribution on the projection plane.

Preferably, the identification may be realized only by considering the density exceeding the predetermined density threshold. Therefore, it is possible to identify the element and / or the structure more quickly.

Preferably, the device may be configured to extract fine object structures from the object data. In particular, the device may be configured to identify one or more object surfaces from the density distribution. In addition, the device may be configured to estimate the external contour of the surface of the object. In addition, the device may be configured to extract contour feature lines. In addition, the device can be configured to determine the geometric two-dimensional properties of the characteristic lines and to extract all lines with the same characteristics regardless of their density. Further , the device may be configured to determine a region having a geometric property in the same range as compared to the determined geometric property to construct a set of linearly dependent 2D points. The order of the above procedures may be exchanged in consideration of procedural requirements.

Therefore, there are two main steps to creating a 3D model.

In the first step, a (building) point cloud is generated from the image data. After that, the point cloud is divided into meaningful building structures. Therefore, some segments of the point cloud may be labeled as façade , roof, floor, etc.

Below is a more detailed description of the segmentation process described above. First, the building point cloud can be divided into two , for example. One part contains points associated with the vertical structure of the building under investigation (such as the façade ). Another part contains all other points.

Next, the point cloud associated with the vertical structure is projected onto the XY plane. All parallel lines are detected and extracted from this projected image. In general, there are more points on the line corresponding to the façade . That is, the point density on the projection plane for the points corresponding to the façade is higher . These lines are detected by a density criterion and are called major lines or characteristic lines. In general, other lines that are not dense enough to be automatically detected or extracted (eg by computational means) but are still associated with the final result have the same direction as the main or characteristic lines . For example, roof windows, stairs, and chimneys usually have parallel planes to at least one façade . Therefore, these lines can be classified as important based on their direction. In particular, the potential stairs are about the same length and may be detected as a group of parallel lines located at the same or matching distance (usually less than 60 cm) from each other. Therefore, features corresponding to potential stairs and the like can be easily identified within the point cloud using the projected point cloud density profile.

As mentioned above, each line (ie, the line identified as having an increasing density distribution of projected data points in the plane) corresponds to the vertical structure projected onto the XY plane. These lines project in the Z direction. Next, for each line , a grid is created with a height corresponding to the height of the building. After that, a search in the building point cloud is performed across all points in the grid. In particular, for each point , it is checked whether the normal of each point, that is, the normal vector associated with the point, corresponds to the normal vector of the grid plane. An image of the façade is created using the grid and search results, which can later be used to relate to the spectral characteristics of the building. Therefore, material information associated with spectral properties can be labeled at all points on the façade . This step can be performed for each vertical structure identified in the point cloud data.

In the second step for detecting the roof plane, the points corresponding to the roof plane are identified. In order to identify such roof plane points, it is generally utilized that all façades of the building intersect the roof. That is, points that belong to or are located at the intersection of the façade segment and the roof belong to both, that is, the façade and the roof. Therefore, it is possible to identify the points corresponding to the roof surface.

Next, a boundary line, that is, a roof line embedded in a set of roof plane points, is defined. For example, a roof line can be specified at a point at the intersection of a segment of the façade and the roof. However, you can use any line of the contour of the façade . Subsequently, all the points in the point cloud of the roof located in the predetermined neighborhood are extracted from the roof line. For example, the distance from the roof contour may be 80 cm. Then, the plane is fitted through these extracted points, and the plane coefficients (a, b, c, d) are obtained by the equation aX + bY + cZ + d = 0 (equation a).

Then you will find all the points on this plane. That is, all the points that match the approximation of the roof plane obtained by the equation a are confirmed.

In addition, the plane of the newly found point is refitted . Therefore, a new coefficient (a', b', c', d') by the formula a'X + b'Y + c'Z + d'= 0 (formula b) is acquired.

Repeat the steps of finding all the points on the plane and refitting the plane with the newly found points until all the points corresponding to this roof plane are found.

Therefore, the roof plane can be extracted efficiently and easily.

In the final step, the identified roof planes, façade , and extracted building geometry are assembled to generate a 3D schematic model of the building . Additional information obtained from spectral , optical, and / or thermal imaging devices can be incorporated into the model to generate a realistic 3D object model .

Evaluation of the data point cluster density distribution may be used to support the generation of at least one of an internal microstructure, an external microstructure, or a structure of a 3D object model . That is, the evaluation of the data point cluster density distribution may be used to support internal and external microstructure and / or structure generation of the 3D object model .

Therefore, by using the information contained in the data point density distribution, it is possible to identify the layout or aspect of the internal construction (such as stairs) and the subtle details of the external construction (such as gables and bay windows) of the building . Therefore, it is possible to raise the level of detail and generate a three-dimensional model.

According to another aspect, a method for analyzing an object may be provided. The object may include at least one of a building, a building environment, and an environmental area. That is, the object may include a building, a building environment and / or an environmental area. Further, this method may include a step of detecting an imaging data point of the object. Further, this method may include a step of assigning spatial coordinates to each imaging data point in order to acquire the object data point. The method may further include determining the characteristics of the object based on the object data points. Preferably, the method may utilize at least spectral library data for the determination. The spectral library data may include a set of material spectral properties corresponding to at least one of physical, structural, chemical and biological material properties. That is, the spectral library data may include a set of material spectral properties corresponding to physical, structural , chemical and / or biological material properties.

As a result, the method of analyzing an object is to collect information about the physical, chemical and / or biological material properties of the analyzed object and the object data points indicating the spatial position of each of the object elements. Can be collected or collected. That is, this method can provide data for generating a very detailed 3D object model. In addition, this method further enables investigation or monitoring procedures to determine the condition of the object.

Preferably, the object data points may be mapped on at least one reference coordinate of a predetermined land registration chart and web mapping service chart. That is, the object data points may be mapped to the reference coordinates of the predetermined land registration chart and / or the web mapping service chart. In addition, the mapped object data points may be used to generate a 3D model of the object. In addition, the 3D object model may be scaled to a predetermined chart size .

Therefore, a method is provided that enables the generation of highly accurate and realistic 3D models embedded in predetermined maps and / or charts.

Hereinafter, the invention will be further described based on at least one preferred example of the invention with reference to the accompanying exemplary drawings.

An exemplary imaging means for scanning and / or analyzing an object is schematically shown.

The survey or data accumulation status of the device for analyzing the object is outlined.

It illustrates a situation where an object data point is acquired, mapped, and scaled based on predetermined reference coordinates.

The set of spectral signatures contained in the spectral library is outlined here, where different spectral distributions are associated with different materials.

Illustrates the collected spectral data associated with the surface of the building and the evaluation of material properties according to the collected spectral data. Illustrates the collected spectral data associated with the surface of the building and the evaluation of material properties according to the collected spectral data. Illustrates the collected spectral data associated with the surface of the building and the evaluation of material properties according to the collected spectral data.

The steps of projecting spatial data onto a plane and the identification of object elements by evaluating the data point cluster density distribution in the plane are shown schematically.

A method for analyzing an object and determining material properties is schematically shown.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the accompanying drawings. The same or similar features in different drawings are indicated by similar reference numbers. It should be understood that the following detailed description of the various preferred embodiments of the invention does not imply limiting the scope of the invention.

FIG. 1 illustrates an exemplary device 100 for analyzing an object. In particular, the device 100 has been shown to include a laser scanner 100A, a camera 100B, and a spectrum sensor 100C. The device 100 for analyzing an object is such that the laser scanner 100A, the camera 100B and the spectral sensor (image spectrometer) 100C are spatially rotatable so that they can be aligned to receive signals from all directions. good. In the illustrated example, the geometry or geometry of the object 200 investigated or analyzed (eg, a building or building structure) can be determined utilizing the laser scanner 100A and / or the camera 100B. In particular, the camera may be used to provide an optical image of the object to be investigated. That is, the optical image or photograph may be provided via the camera 100B. To provide spatial (three-dimensional) information on the surveyed object, it is possible to combine duplicate photographs taken from different locations on the device 100 with respect to the surveyed object, eg, via a related computing device. can. That is, it is possible to generate a photogrammetric point cloud containing optical and spatial information about the object to be investigated.

The laser scanner 100A may be configured to emit laser light toward the object to be investigated and detect the laser light returned or reflected from the object to be investigated. Therefore, the distance between the device 100 and the object to be investigated or a particular element or segment of the object to be investigated can be accurately determined via signal execution time, i.e., laser scanning. That is, the geometric shape of the object to be investigated can be accurately determined. Therefore, it is possible to generate a laser scanning point cloud containing spatial information about the object to be investigated. In addition, the laser scanner may be equipped with an inertial measurement unit that allows the GNSS sensor (Global Navigation Satellite System) and / or the device 100 and / or the laser scanner 100A to determine its position and / or spatial alignment. ..

Further, as a representative of the device 100 for analyzing an object, a spectrum sensor 100C configured for a spectrum imaging process is included. Spectrum imaging collects and processes information from the entire electromagnetic spectrum. The purpose of spectrum imaging is to acquire the spectrum of each pixel in the image for the purpose of finding the features and materials of the object . In spectral imaging, the recorded spectrum has a predetermined length of resolution and covers a wide range of wavelengths. In particular, the spectrum sensor collects information as a set of objects to be imaged. Each imaging object represents a narrow wavelength range of the electron spectrum (spectral band). These imaging objects are combined to form a 3D spectral data cube for processing and analysis. For example, each 3D spectral data cube may have components (X, Y, λ). The X and Y components correspond to the spatial dimensions of the surface of the object (for example, the surface coordinates of the object to be investigated), and λ represents the spectral dimension including the wavelength range. That is, detailed spectral information of the surveyed object can be collected. This spectral information can be combined with the information collected by the laser scanner 100A and / or the camera 100B of the device 100. That is, it is possible to generate an imaging data point of the surveyed object, that is, a data point cloud containing information on the geometric shape and spectral characteristics of the surveyed object.

In addition, the device 100 may also include geographical positioning means such as a global navigation satellite system (eg GPS system), an IPS system (indoor positioning system) and / or an inertial measurement unit. Therefore, the data collected for the survey object can be georeferenced . That is, all points in the set of imaging data points, that is, point cloud data, can be associated with accurate position information (geolocation) . Therefore, it is possible to generate a set of object data points , which enables accurate three-dimensional modeling of the survey object associated with predetermined reference coordinates such as a land registration chart and a web mapping service chart.

FIG. 2 illustrates the process of data acquisition or investigation of an object 200 (eg, a building) via device 100. In particular, the device 100 can be attached to an aerial vehicle (eg, a remotely controlled or autonomously driven drone, etc.) or to a mobile vehicle or frame for ground use. Therefore, an object (eg, a building) is continuously scanned or investigated from a variety of different locations 30A, 30B, 30C. The accumulated data is then collected by device 100 (eg, via a contained computing device (not shown)) or by an external computing device (not shown) to generate an object data point. Combined . That is, the imaging means (eg, camera 100, laser scanner 100A and / or spectral sensor 100C) produces imaging data points for the building under investigation, i.e., optical, geometric and spectral information for the building under investigation. Used for. Further, the georeferencing means (not shown) of the device 100 georeferences the imaging data points, and as a result, the object data points are generated. That is, the imaging data points that include the optical, geometric, and spectral information of the object to be investigated (eg, the building) are georeferenced , i.e., the respective geolocation information is provided.

FIG. 3 exemplifies the generation of a 3D model 300 of a survey object 200 (eg, a building) from the accumulated data of the device 100. The generation of the three-dimensional model 300 of the object 200 can be performed using a computing device (not shown). Further, the generated 3D model 300 can be displayed or output by any display means (for example, a TV or a computer screen (not shown)).

As shown in FIG. 3, the device 100 performs a survey of the object 200. In particular, the building 200 is investigated by the laser scanner 101A and the photographic image 101B. Further, the device 100 is also configured to record spectral data for the building 200 (not shown). Therefore, the distance between the surface of the object 200 and the device 100 can be determined, and the optical features as well as the geometric features or shapes of the building 200 can be identified. In particular, structures such as the geometry of building walls, windows 202 or building roof 203 can be identified. In addition, differences in color and material composition can be extracted. From the accumulated data, a three-dimensional building model 300 is generated based on optical information , spatial information , and spectral information, that is, object data points. Further, the three-dimensional object model 300 can be mapped to a predetermined reference system . In particular, for such a mapping procedure, all object data points (ie, georeferenced imaging data points) are provided with their respective coordinates of a predetermined reference system . A three-dimensional object model 300 is generated based on the information contained in each object data point. In particular, spectral library data ( which may be stored in the database or memory associated with device 100) is used to identify the material or material properties of the building 200 and reflect these structures in the 3D object model 300. do. In addition, these data can be used to improve position accuracy. For example, with the information contained in the spectral library data (not shown) , the 3D object model 300 reflects different structures and materials such as modeled glass windows 302, stairs 301, roof boundaries 303, etc. can do. In addition, based on the spectral library data information, the 3D object model 300 also considers whether the surface of the building facade is covered with water, snow, ice, contaminants, or organic materials (not shown). be able to.

The three-dimensional object model 300 is scaled according to a predetermined scale . For example, when the 3D object model 300 is mapped to specific coordinates in the web mapping service chart, the object model 300 is scaled accordingly. That is, the 3D object model 300 can be realistically inserted into the display of the web mapping service chart.

FIG. 4 shows an example of a set of spectral signatures contained in the spectral library data. Spectral library data may be stored in memory or storage equipment (eg, a database) accessible by a computing device configured to process spectral information. In particular, such computing devices can be configured to compare the collected spectral data with spectral library data and determine their respective material properties from such comparisons . The computing device may be included in the device 100 for analyzing an object. Alternatively, the device 100 for analyzing the object may be associated with the computing device and the imaging data may be made accessible to the computing device. For example, the device 100 for analyzing an object may be connected to a computing device by a communication link.

Specifically, FIG. 4 shows a set of eight materials contained in the exemplary spectral library data and their corresponding spectral characteristics (ie, reflectance dependence over the wavelength range of 350-1050 nm ). Is shown as an example. In particular, each item is related to:
(1) Gravel sand wall and related spectral behavior,
(2) Organic substances (plans or moss) growing on the wall and their related spectral behavior,
(3) Brick wall and related spectral behavior,
(4) Iron fence and related spectral behavior,
(5) White plaster and related spectral behavior,
(6) White paint on a wooden frame and related spectral behavior,
(7) Metal rain of pipes and related spectral behavior,
(8) Roof tiles (brick or concrete) and related spectral behavior.

In fact, the corresponding spectral data can be collected and stored for a variety of different materials . In addition, for each material , spectral characteristics that reflect different environmental conditions, such as different irradiation levels (different times, shadow levels, etc.) or different atmospheric conditions (fog, rain, etc.) can be collected. Therefore, spectral library data can be used to make accurate and reliable determinations of the material or material properties of the study object.

FIG. 5A shows a process that utilizes spectral library data to determine the material or material properties of the study object 200 (eg, building 200). In particular, FIG. 5A shows an image of a building 200 with different regions 202, 202A, 204A, 204B, 205 and 206. In addition, a related spectral characteristic diagram is shown. As can be seen from FIG. 5A, each material represents a different spectral signature . Therefore, the differences in the various spectral characteristics shown are related to the different spectral characteristics associated with each material. Therefore, it is possible to reliably determine a specific material composition in the study area by utilizing the difference in spectral characteristics.

For example, as can be seen from FIG. 5A, the brick wall 204 of the building 200 includes segments of dry brick wall 204B and wet brick wall 204A. As can be seen in the spectral distribution graph, the spectra of the dry brick wall region 204B and the wet brick wall region 204A are similar, but reflect small differences over the wavelength range shown . Therefore, the information contained in the spectral library data can be used to determine that the material of the building wall 204 is the same in regions 204A and 204B. Further, it can be inferred that the materials of the two regions 204A and 204B, namely the brick stone, have different levels of wetness or humidity.

The image of the building 200 displays the area 202 of the brick wall 204 corresponding to the opening of the window. In addition, in another segment of the building 200, the area 202A adjacent to the brick wall 204 can be recognized. According to the information available in the spectral distribution graphs (204B, 204A, 205, etc.), the spectral characteristics of regions 202 and 202A are the same. Therefore, it can be determined that the areas 202 and 202A shown are made of the same material (window glass).

Further, the building 200 includes a region 205 made of steel according to the corresponding spectral data. As can be inferred from the photograph, the area 206 containing plants can be seen in front of the building 200.

Therefore, reliable identification of the material and its physical condition (eg, wet or dry, etc.) can be made.

FIG. 5B details how information about the material composition and material state of the study object 200 can be used to build a realistic model. In particular, the photographs of the building 200 are classified according to the information received via the spectral image. As seen in FIG. 5B, regions with the same spectral characteristics are assigned the same material and material state. For example, region 204B is characterized as a dry brick according to a comparison of spectral library data with recorded spectral characteristics. Similarly, area 204A is classified as a damp brick wall. Further, the region 205 can be identified as being made of steel. Further, the region 205 is identified as made of glass. In addition, plants (eg trees and bushes) are identified as region 206.

Therefore, it is possible to generate an accurate model of the object 200 to be investigated, which reflects not only the composition of the material but also the state of the material.

FIG. 5C schematically illustrates the process of identifying material properties (eg, material and / or material state) by comparison with predetermined spectral library data. In particular, FIG. 5C shows a spectral profile or reflectance graph 500 (ie, the functional relationship between wavelength and reflectance). The reflectance of the surface of the material is the effect of reflecting radiant energy. In particular, the reflectance is the ratio of the incident electromagnetic force reflected on the surface of the object. As shown in FIG. 5C, the reflectance spectrum or the spectral reflectance curve is a plot of reflectance with wavelength as a function. The wavelength range shown is in the range of about 500-900 nm. However, different wavelength regions or wavelength ranges can also be selected. Further, the graph 500 displays a set of spectra or reflection profiles 500A, 500B. In particular, the illustrated spectral profile 500A corresponds to spectrally recorded or measured spectra of a particular material recorded or measured under various external conditions (wet brickstone in this example). For example, each of the illustrated spectral profiles 500A corresponds to reflected radiant energy (ie, light or electromagnetic waves) recorded from different angles and / or irradiation levels of wet bricks. Similarly, the spectral profile 500B corresponds to the reflected radiant energy recorded from different angles and / or irradiation levels of the dried brick stone.

A specific process for identifying the material and / or material state of the object to be investigated by the device for analyzing the object will be described in detail according to Graph 600. Graph 600 shows an enlarged portion of Graph 500. In particular, the graph 600 shows an enlarged portion of the spectral profiles 600A and 600B (the enlarged illustrated portion of the spectral profiles 600A and 600B corresponds to the respective spectral profiles 500A and 500B). The spectral range of the expanded spectral profile segment extends from about 690 to 750 nm (wavelength). As can be seen from Graph 600, the particular course or function of the spectral profile of Graph 600B corresponds or matches to some extent within the wavelength range of about 690-750 nm. Similarly, the functional distribution of Graph 600A is somewhat or exactly matched in each wavelength region.

Therefore, the material of the object or the material state of the object is identified in association with the predetermined spectral characteristics or spectral information associated with the brick stone. In particular, the spectral library data contains information about the spectral profile distribution of each material in one or more wavelength regions. In addition, the spectral library data also contains information about the spectral profile distribution of the material for external conditions such as different physical conditions and / or varying degrees of irradiation. For example, the spectral behavior of dry brickstone in multiple wavelength ranges or wavelength regions can be preserved . Similarly, the spectral behavior of wet bricks for different wavelength regions can be stored in the spectral library data. Recording measurements , or reflected radiant energies , allows a device for analyzing an object to match the recorded spectral characteristics with predetermined spectral information in the spectral library data. In particular, the predetermined spectral behavior in a given wavelength region is compared with the recorded spectral information in the corresponding wavelength region. If the spectral distribution or spectral profile of the recorded spectral profile matches or corresponds to the distribution contained in the spectral library data within the same wavelength region, the device for analyzing the object associates with the spectral information of the spectral library data. Allocate the specified material. Similarly, the physical state can be determined. In particular, spectral library data includes spectral properties associated with various material properties (eg, the physical state of the material). Recording the reflected radiant energy allows the measured amount of spectral characteristics to be compared with the information in the spectral library data. Corresponding material properties or material states are assigned depending on the particular distribution of the spectral distribution in one or more wavelength regions. For example, as seen in Graph 600, the spectral graph 600A exhibits similar functional behavior in the wavelength range of about 690-750 nm. Therefore, based on predetermined spectral library data, the device for analyzing the object determines that the spectral graph 600A corresponds to a brick in a damp condition or state. Similarly, a device for analyzing an object can determine that the spectral graph 600B corresponds to a dry or situational brick.

Differences between the respective spectral profiles 600A and 600B are due to different illumination (irradiation) conditions and / or different measurement angles and different material compositions . However, the process of determining a particular material or material property is based on a particular (predetermined) spectral distribution or spectral pattern within a particular wavelength region. Therefore, error-prone identification of materials and / or material properties (wet or dry brick stones in the illustrated example) can be made reliably .

FIG. 6 schematically shows the projections of the 3D building model 300 and the 3D building model 300 onto the 2D surface. In particular, this model is generated using multiple object data points. That is, each data point contains optical and spatial or geolocation information .

Therefore, all data points contain three-dimensional information, for example, associating a tuple of (X, Y, Z) coordinates in a predetermined reference frame with each data point. Projection onto the plane 400 (two-dimensional surface) is performed by setting the Z coordinate of each data point to zero. Therefore, the data points that generate the 3D building model 300 " fit " in the plane . That is, all data points are mapped to their respective positions within the plane (projection plane) 400. As a result, the data point (cluster) density distribution 500 or a plurality thereof is generated in the plane 400. The density distribution 500 is formed by the data points on the plane 400.

Thus, vertical structures associated with clusters or accumulations of three-dimensional data points, such as gables 310, can be identified by their respective density profiles 510 within the projection plane 500. Therefore, the object structure can be easily identified by using the above-mentioned density distribution evaluation. For this purpose, the surface of the section on which the object data points are projected can be adjusted in any direction. The condition illustrated in FIG. 6 is only an example of a particular choice of section surface or projection surface adjustment . The projection surface 400 of FIG. 6, or preferably its surface normal , can be rotated by any spatial angle to perform the corresponding projection of the object data points onto such a newly adjusted projection surface 400. Various data point cluster density distribution profiles may be obtained . By using surface normals , the calculation time can be further reduced. Further, the resolution of the three-dimensional structure of the corresponding object can be improved.

That is, even in a situation where the data provided by the imaging means, that is, the imaging data points, is inaccurate, the features or structures of the object can be identified. Therefore, knowledge of each geometric element of the object using the relationship of the data point cluster density distribution in the projection plane functions as a noise reduction or error avoidance means. Therefore, the quality of the three-dimensional model of the object 200 can be significantly improved.

FIG. 7 schematically illustrates a method or spectral data analysis workflow for analyzing an object and / or object properties.

This method starts from the data acquisition step 710. In particular, object data is collected or measured in step 710. In addition, the object data includes geolocation information. Geolocation or georeference information may be provided by GPS sensor means, IPS sensor means, and IMU means. In step 720, the accumulated object data, that is , the information associated with the survey object is preprocessed. For example, object data preprocessing may be subject to one or more of object data cleaning, normalization, or transformation. Further, the pretreatment may include an extraction and / or selection step of a specific object function. The pretreatment step can be performed utilizing a computing device (not shown) associated with the device for analyzing the object.

In step 730, a noise reduction step is performed. In general, noise reduction is the process of removing noise from a signal. Noise is random or non-coherent white noise, or coherent noise generated by a device or pretreatment process for analyzing an object.

In step 740, spectral matching is performed. That is, by using the information contained in the spectrum library, the object data (which has undergone the noise removal step in advance) can be associated with the specific object information contained in the spectrum library (step 745). The spectrum library can be implemented, for example, in a storage device associated with a computing device. In addition, the computing device may be included in or associated with the device for analyzing the object. In particular, spectral library data may include spectral information or spectral properties that allow identification of the object material or material properties based on the spectral properties contained in the reflected or radiated object radiation. Therefore, the spectral portion of the object data is matched with the information contained in the spectral library and assigned the respective object material or material properties of the object. That is, a spectral matching process or step 740 that identifies each object material and / or material properties can be performed.

Subsequently, in step 750, various process steps can be performed. For example, a dataset containing object data associated with each object material and / or property may be mapped to a predetermined reference system such as a web mapping service chart (eg, Google Maps). In addition, a three-dimensional model of the object to be investigated may be generated at the respective coordinates of a predetermined reference system (not shown). In addition, additional processing steps such as unmixing and matching filtering can be performed. For example, the temperature information contained in the object data can be deleted or filtered to emphasize the information contained in the visible region (about 400-700 nm) of the electromagnetic spectrum. That is, object model information can be specified or displayed according to different wavelengths or spectral ranges.

The above describes a particular sequence of operations performed by a particular embodiment of the invention, but in another embodiment the actions are performed in a different order, the particular actions are combined, and the particular actions are duplicated. It should be understood that such an order is exemplary, such as letting. The above examples, embodiments, and / or embodiments of the present invention may be combined in some way, partially or entirely.

Further note that the steps of the particular method described above and the components of the system described can be performed by a programmed computer. As used herein, some embodiments are intended to cover machine or computer readable and also program storage devices that encode machine-executable or computer-executable program instructions, such as Digital Data Storage media. The instruction is to perform some or all of the steps in the method. The program memory device may be, for example, a digital memory, a magnetic storage medium such as a magnetic disk and a magnetic tape, a hard drive, an optically readable digital data storage medium, or an internet cloud solution. The embodiments are also intended to cover a computer programmed to perform the steps of the method described above.

Further, it should be noted that the functionality of the various elements described herein can be provided through the use of dedicated hardware and hardware that can run the software in connection with the appropriate software. When provided by a processor, the functionality may be provided by a single dedicated processor, a single shared processor, or multiple individual processors in which some are shared. In addition, the explicit use of the term "processor" or "controller" should not be construed as referring only to hardware capable of running software, including, but not limited to, digital signal processor (DSP) hardware, network processors. Also implicitly includes application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), read-only memory (ROMs) for storing software, random access memory (RAMs), and non-volatile storage. Other traditional and / or custom hardware, such as a graphical card processor, can also be included.

Finally, it should be noted that the block diagram herein represents a conceptual diagram of an exemplary circuit that embodies the principles of the invention. Similarly, every flow chart, flow diagram, state transition diagram, pseudocode, etc. represents different steps and is virtually represented on a computer-readable medium, whether or not the computer or processor is explicitly shown. It should be understood that it is performed by a computer or processor.

Claims (18)

A device for analyzing objects, including buildings, building environments, and / or environmental areas.
An imaging means for detecting an imaging data point of the object, and an imaging means.
An allocation means for assigning spatial coordinates to each of the imaging data points in order to acquire an object data point, and
A determination means for at least determining the material properties of the object based on the object data points.
At least spectral library data is used for the determination, which comprises a set of spectral properties of the material corresponding to physical, structural , chemical and / or biological material properties.
The determination of the material characteristics of the object is associated with the spectral characteristics stored in association with the material contained in the spectral library data, and the spectral characteristics detected by the imaging means are referred to as the spectral library data. The stored spectral characteristics in a given wavelength region are compared to the detected spectral characteristics in the corresponding wavelength region, performed by matching with the stored spectral characteristics within.
The determination of the material properties of the object is based on a particular spectrum distribution or pattern within a particular wavelength region.
The object data points are projected onto the projection surface to generate a data point cluster density distribution and each density profile to obtain the structure of the object.
The projected surface is rotated by an arbitrary spatial angle to obtain a plurality of data point cluster density distributions.
A three-dimensional model of the object is generated based on the spatial position of each element of the object and the object data points showing information about the physical, structural, chemical and / or biological material properties. Be done,
device. To reduce the noise generated by the device of the radiation signal associated with the object before matching the spectral characteristics detected by the imaging means with the stored spectral characteristics in the spectral library data. The device according to claim 1, further comprising noise reducing means. The device according to claim 1 or 2 , wherein the material properties include at least the degree of deterioration of the object. The device according to any one of claims 1 to 3 , wherein the material properties include at least the degree of coverage, the degree of contamination and / or the humidity of the object. The spectral library data includes predetermined spectral information associated with various spatial, temporal, atmospheric and irradiation conditions and compositional differences for each of the plurality of materials. Item 5. The device according to any one of Items 1 to 4 . The one or more spectral properties associated with the object are acquired and compared with the predetermined spectral information, and the material properties of the object are the acquired one or more spectral characteristics and the said. The device of claim 5 , which is determined based on a match with predetermined spectral information. The device according to any one of claims 1 to 6 , wherein the object data point is mapped on reference coordinates of a predetermined land registration chart and / or a web mapping service chart. The mapped object data points (voxels) are used to generate a 3D model of the object, which is the predetermined land registration chart and / or The device of claim 7 , scaled with respect to the size of the web mapping service chart . The device according to any one of claims 1 to 8 , wherein the device is included in an autonomous and / or non-autonomous mobile entity . The device of claim 9 , wherein the autonomous and / or non-autonomous mobile entity is an air transport vehicle and / or a land-based transport vehicle . The image pickup means according to any one of claims 1 to 10 , further comprising at least one of a laser scanner, an optical camera, an infrared camera, an image pickup spectrometer, an inertial measurement unit, an IPS sensor and / or a GPS sensor. Device. The object data points are represented as a set of 3D graphic information units (voxels), each 3D graphic information unit containing spectral, RGB, and / or thermal data information associated with its spatial coordinates. The device according to any one of claims 1 to 11 , as shown. The object data points are projected onto a plane, and the identification of each geometric element of the object is in any one of claims 1-12 based on the evaluation of the data point cluster density distribution in the plane. The device described. 13. The device of claim 13 , wherein the identification can be achieved by considering only densities that exceed a predetermined density threshold. Identify one or more object surfaces from the data point cluster density distribution and
External contours on the surface of the one or more objects are extrapolated.
The characteristic line of the external contour is extracted and
The geometric two-dimensional characteristics of the characteristic lines are determined, and all the characteristic lines having the same characteristics regardless of their densities are extracted.
A set of linearly dependent 2D points is constructed by determining the region having the geometric property in the same range as compared with the determined geometric 2D property.
13. The device of claim 13 . One of claims 13-15 , wherein the evaluation of the data point cluster density distribution is utilized to assist in the generation of fine construction and / or structure inside and outside the three-dimensional model of the object. The device described in the section. A method of analyzing an object, including a building, building environment, and / or environmental area.
The stage of detecting the imaging data point of the object and
In order to acquire the object data points, the stage of assigning spatial coordinates to each of the imaging data points, and
At least at the stage of determining the material properties of the object based on the object data points, at least the spectral library data is used for the determination, and the spectral library data is physically and structurally . A method comprising steps, having a set of spectral properties of a material corresponding to chemical and / or biological material properties .
The determination of the material property of the object is associated with the spectral property stored in association with the material contained in the spectral library data, and the detected spectral property is stored in the spectral library data. Performed by matching with the spectral characteristics obtained, the stored spectral characteristics in a given wavelength region are compared to the detected spectral characteristics in the corresponding wavelength region.
The determination of the material properties of the object is based on a particular spectrum distribution or pattern within a particular wavelength region.
The object data points are projected onto the projection surface to generate a data point cluster density distribution and each density profile to obtain the structure of the object.
The projected surface is rotated by an arbitrary spatial angle to obtain a plurality of data point cluster density distributions.
A three-dimensional model of the object is generated based on the spatial position of each element of the object and the object data points showing information about the physical, structural, chemical and / or biological material properties. How to be done . The object data points are mapped to reference coordinates in a predetermined land registration chart and / or web mapping service chart, and the mapped object data points are used to generate a three-dimensional model of the object. 17. The method of claim 17, wherein the 3D model of the object is utilized and scaled with respect to a predetermined size of a land registration chart and / or the web mapping service chart .