WO2011066602A1 - Procédés d'extraction - Google Patents

Procédés d'extraction Download PDF

Info

Publication number
WO2011066602A1
WO2011066602A1 PCT/AU2010/001602 AU2010001602W WO2011066602A1 WO 2011066602 A1 WO2011066602 A1 WO 2011066602A1 AU 2010001602 W AU2010001602 W AU 2010001602W WO 2011066602 A1 WO2011066602 A1 WO 2011066602A1
Authority
WO
WIPO (PCT)
Prior art keywords
cell
values
value
sub
cells
Prior art date
Application number
PCT/AU2010/001602
Other languages
English (en)
Inventor
Bertrand Douillard
James Underwood
Original Assignee
The University Of Sydney
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by The University Of Sydney filed Critical The University Of Sydney
Publication of WO2011066602A1 publication Critical patent/WO2011066602A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/24—Character recognition characterised by the processing or recognition method
    • G06V30/248—Character recognition characterised by the processing or recognition method involving plural approaches, e.g. verification by template match; Resolving confusion among similar patterns, e.g. "O" versus "Q"
    • G06V30/2504—Coarse or fine approaches, e.g. resolution of ambiguities or multiscale approaches

Definitions

  • the present invention relates to extraction, extraction processes, extraction algorithms, and the like.
  • Data corresponding to the geometry of an area of terrain and any natural and/or artificial features or objects o the area may be generated.
  • a laser scanner such as a Riegl laser scanner, may be used to scan the area of terrain and generate 3D point cloud data corresponding to the terrain and the features.
  • 3D point cloud dat of a terrain area Various algorithms for processing 3D point cloud dat of a terrain area are known. Such algorithms are typically use to construct 3D terrain models of the terrain area for use in, for example, path planning or analysing mining environments.
  • the terrain models conventionally used include the Mean Elevation Map, the Min-Max Elevation Map, the Multi-Level Elevation Map, the Volumetric Density Map, Ground Modelling via Plane Extraction, and Surface Based Segmentation.
  • Mean Elevation Maps are commonly classified as 2 1 / 2 D models because the third dimension (height) is only partially modelled.
  • the terrain is represented by a grid having a number of cells.
  • the height of the laser scanner returns falling in each grid cell is averaged to produce a single height value for each cell.
  • An advantage of averaging th height of the laser returns is that noisy returns can be filtered out.
  • this technique cannot capture overhangin structures, such as tree canopies.
  • Min-Max Elevation Maps are also used to capture the height of the returns in each grid cell. The difference between the maximum and the minimum height of the laser scanner returns falling in a cell are computed. A cell is declared occupied if its calculated height difference exceeds a pre-defined
  • Multi-Level Elevation Maps are an extension of elevation maps. Such algorithms are capable of capturing overhanging structures by discretising the vertical dimension. They also allow for the generation of large scale 3D maps by recursively registering local maps. Typically however, the discrete classes chosen for the vertical dimension may not facilitate segmentation. Also, typically the ground is not used as a reference for vertical height.
  • Volumetric Density Maps discriminate between soft and hard obstacles. This technique breaks the terrain area into a set of voxels and counts in each voxel the number of hits and misses sensor data. A hit corresponds to a return that
  • a miss corresponds to a laser beam going through a voxel.
  • Regions containing soft obstacles, such as vegetation, correspond to a small ratio of hits over misses.
  • Regions containing hard obstacles correspond to a large ratio of hits over misses. While this technique does allow the
  • a Ground Modelling via Plane Extraction approach is suitable for extracting multi-resolution planar surfaces. This involves discretising the area terrain into two superimposed 2D grids of different resolutions, i.e. one grid has larger cells than the other. Each grid cell in each of the two grids is represented by a plane fitted to the corresponding laser returns via least square regression. A least square error for each plane in the each grid is computed. By comparing the different error values, several types of regions can be identified. In
  • the values are both small in sections corresponding to the ground.
  • the error value of the larger celled plane is small while error values of the smaller plane is large in areas containing a flat surface with a spike (e.g. a thin pole for instance) .
  • both error values are large in areas containing an obstacle. This method is able to identify the ground while not averaging out thin vertical obstacles (unlike a Mean Elevation Map) . However, it is not able to represent overhanging structures.
  • the mesh is generated by exploiting the physical ordering of the measurements which implies that longer edges in the mesh or more acute angles formed by two consecutive edges directly correspond to surface discontinuities. While this approach performs 3D segmentation, it does not identify the ground surface.
  • segmentation of 3D point cloud data that jointly provides a representation of the ground, and representations of objects.
  • the present invention provides an extraction process for extracting an object or terrain feature, the extraction process comprising: defining an area to be processed; dividing the area into a plurality of cells;
  • identifying a cell as corresponding only to a particular object or terrain feature if the determined function value for that cell is in a range of values that corresponds to the particular object or terrain feature; defining, for the cells that are not identified as corresponding only to a particular object or terrain feature, one or more sub-cells, each sub-cell having in it at least one of the plurality of different locations; and identifying a sub-cell as corresponding at least in part to the particular object or terrain feature if one or more of the measured parameter values for the at least one of the plurality of different locations in that sub-cell is in the range of values .
  • the step of identifying a sub-cell as corresponding at least in part to the particular object or terrain feature may comprise: identifying a sub-cell as corresponding only to the particular object or terrain feature if the measured parameter value for each of the at least one of the plurality of different locations in that sub-cell is in the range of values; and identifying a sub-cell as corresponding in part to the
  • the particular object or terrain feature if one or more of the measured parameter values for the at least one of the plurality of different locations in that sub-cell is in the range of values and if one or more of the measured parameter values for the at least one of the plurality of different locations in that sub-cell is not in the range of values.
  • the method may further comprise identifying a sub- cell as corresponding at least in part to a different object or terrain feature if one or more of the measured parameter values for the at least one of the plurality of different locations in that sub-cell is not in the range of values.
  • the method may further comprise identifying a sub- cell as corresponding only to a different object or terrain feature if each of the measured parameter values for the at least one of the plurality of different locations in that sub- cell is not in the range of values.
  • the step of determining a value of a function of the measured parameter values in that cell may comprise:
  • the step of determining a value of a function of the measured parameter values in that cell may further comprise: determining a gradient value for each cell using the determined average value for that cell and determined average values for each of the cells surrounding that cell.
  • the step of determining a value of a function of the measured parameter values in that cell may further comprise: grouping together directly adjacent cells having a gradient value below a first threshold value to form one or more clusters of cells.
  • the step of determining a value of a function of the measured parameter values in that cell may further comprise:
  • the step of identifying a cell as corresponding only to a particular object or terrain feature if the determined function value for that cell is in a range of values may comprise: identifying the cluster of cells having the largest number of cells; and determining the range of values using the determined average value of the largest cluster and a second threshold value.
  • the method may further comprise: identifying noisy measured values of the parameter; and disregarding the noisy measured values .
  • the parameter may be a height of the terrain.
  • the terrain feature may be a ground surface of the terrain.
  • the present invention provides an apparatus for generating a model of terrain in accordance with the method of the above aspect, the apparatus comprising a scanning and measuring apparatus for measuring the plurality of values of a parameter, and a processor arranged to perform the processing steps of the above aspect.
  • the present invention provides a computer program or plurality of computer programs arranged such that when executed by a computer system it/they cause the computer system to operate in accordance with a method of any of the above aspects.
  • the present invention provides a machine readable storage medium storing a computer program or at least one of the plurality of computer programs according to the above aspect.
  • Figure 1 is a schematic illustration of an example terrain modelling scenario in which a laser scanner is used to scan a terrain area;
  • Figure 2 is a process flowchart showing certain steps of a terrain modelling algorithm performed by a processor
  • Figure 3 is a process flowchart showing certain steps of the ground extraction process of step s2 of the algorithm
  • Figure 4 is a schematic illustration of three cells of a grid of a Mean Elevation Map
  • Figure 5 is a process flowchart showing certain steps of the object segmentation process of step s4 of the algorithm
  • Figure 6 is a schematic illustration of a first cell, a second cell, and a third cell of the grid and the range of height values assigned to each of these cells in a Mean
  • Figure 7 is a schematic illustration of the first cell, the second cell, and the third cell of the Mean Elevation Map grid, after performing step s28;
  • Figure 8 is a schematic illustration of the laser scanner scanning an object that is hidden behind a further obj ect .
  • the terminology "object” is used herein to refer to any objects or structures that exist above (or below) this surface.
  • the underlying supporting surface may, for example, include surfaces such as the underlying geological terrain in a rural setting, or the artificial support surface in an urban setting, either indoors or outdoors.
  • the geometric configuration of other objects or structures above this surface may, for example, include naturally occurring objects such as trees or people, or artificial objects such as buildings or cars.
  • terrain and objects are as follows: rural terrain having hills, cliffs, and plains, together with object such as rivers, trees, fences, buildings, and dams;
  • outdoor urban terrain having roads and footpaths, together with buildings, lampposts, traffic lights, cars, and people; outdoor urban terrain such as a construction site having partially laid foundations, together with objects such as partially constructed buildings, people and construction equipment; and indoor terrain having a floor, together with objects such as walls, ceiling, people and furniture.
  • Figure 1 is a schematic illustration of an example terrain modelling scenario in which a laser scanner 2 is used to scan a terrain area 4.
  • the laser scanner 2 used to scan a terrain area 4 is a Riegl laser scanner.
  • the laser scanner 2 generates dense 3D point cloud data for the terrain area 4 in a conventional way. This data is sent from the laser scanner 2 to a processor 3.
  • the terrain area 4 comprises an area of ground 6 (or terrain surface), and two objects, namely a building 8 and a tree 10.
  • the generated 3D point cloud data for the terrain area 4 is processed by the processor 3 using an embodiment of a novel terrain modelling algorithm, hereinafter referred to as the "segmentation algorithm".
  • the segmentation algorithm advantageously tends to provide a representation of the ground 6, as well as representations of the various objects 8, 10 above the ground 6, and also to the refinements that can be made to the representation of the ground 6 using the representations of the objects 8, 10, as described in more detail later below.
  • Figure 2 is a process flowchart showing certain steps of an embodiment of a process implemented by the segmentation algorithm performed by the processor 3.
  • a ground extraction process is performed on the 3D point cloud data.
  • the ground extraction process explicitly separates 3D point cloud data corresponding to the ground 6 from that corresponding to the other objects, i.e. here the building 8, and the tree 10 and is described in more detail later below with reference to Figure 3.
  • an object segmentation process is performed on the 3D point cloud data.
  • the object segmentation process segments the 3D point cloud data such that each segment of data corresponds to a single object, as described in more detail later below with reference to Figure 5.
  • Figure 3 is a process flowchart showing certain steps of the ground extraction process of step s2 of the segmentation algorithm .
  • a Mean Elevation Map of the terrain area 4 is computed. This is a conventional Mean Elevation Map.
  • the resolution of a grid underlying the map may be any appropriate value .
  • the Mean Elevation Map is a grid having a plurality of cells. Each cell has assigned to it a height value determined from height values corresponding to laser sensor returns from that cell. In this embodiment, the height value for a cell is the average of the height values corresponding to laser sensor returns from that cell.
  • a surface gradient value is computed for each cell in the grid.
  • a surface gradient value for a particular cell is obtained by first computing the gradients between that cell and each of the surrounding cells. The gradient with the largest absolute value is retained as the gradient at the particular cell.
  • step slO cells corresponding to relatively flat surfaces are identified. In this embodiment, this is achieved by selecting cells having a surface gradient value below a gradient-threshold value.
  • the gradient- threshold value is 0.5. This corresponds to a slope angle of 27 degrees. However, in other embodiments a different gradient- threshold value is used.
  • the cells identified as corresponding to the relatively flat surfaces i.e. the cells that have a surface gradient value below the gradient-threshold, are grouped together with any adjacent cells having a surface gradient value below the gradient-threshold value. This forms clusters of cells that correspond to relatively flat areas.
  • the largest cluster of cells that correspond to a relatively flat area i.e. the cluster formed at step sl2 containing the largest number of cells, is identified.
  • the identified largest cluster is used as a reference cluster with respect to which it can be
  • the reference cluster is used because locally smooth clusters that do not correspond to the ground 6 may exist. Thus, these cases are filtered out using the reference to the ground 6 provided by the largest ground cluster.
  • the identified largest cluster is assumed to correspond to the ground 6.
  • any of the smaller clusters of cells, the cells of which have substantially smaller or larger height values than those of the largest cluster are assumed not to correspond to the ground 6.
  • the cells corresponding to the ground 6 is defined to be the union of the largest cluster of cells, with a surface gradient value below the gradient-threshold, and the other clusters of cells, also containing a surface gradient value below the gradient-threshold, in which the absolute value of the average height of the cells minus the average height of the cells in the largest cluster is smaller than a height- threshold.
  • this height-threshold is 0.2m.
  • step sl8 a correction of errors generated during the computations of the surface gradient values is performed.
  • One source of such errors and the correction of those errors will now be explained with reference to Figure 4.
  • Figure 4 is a schematic illustration of three cells of the grid of the Mean Elevation Map, namely the first cell 12, the second cell 14, and the third cell 16.
  • the height value for the first cell 12 i.e. the average of the height values corresponding to laser sensor returns from the first cell 12, is hereinafter referred to as the "first height value 18".
  • the height value for the second cell 14, i.e. the average of the height values corresponding to laser sensor returns from the second cell 14, is hereinafter referred to as the "second height value 20".
  • the height value for the third cell 16 i.e. the average of the height values corresponding to laser sensor returns from the third cell 16, is hereinafter referred to as the "third height value 22".
  • first height value 18 and the second height value 20 are substantially equal. Also, the third height value 22 is substantially greater than the first height value 18 and the second height value 20.
  • the surface gradient value for the second cell 14, which is determined at step s8 as described above, is obtained by first computing the gradients between that cell and each of the surrounding cells. The gradient with the largest absolute value is retained as the gradient at the particular cell.
  • the surface gradient value for the second cell 14 is the slope between the height levels of the second cell 14 and the third cell 16 (since the gradient between the first cell 12 and the second cell 14 is zero) .
  • This gradient is indicated in Figure 4 by the reference numeral 24.
  • the second cell has a relatively large surface gradient value.
  • the service gradient value of the second cell 14 is above the gradient-threshold.
  • the second cell 14 is included in the same cluster of cells as the first cell 12 despite the second height value 20 being substantially equal to the first height value 18.
  • Such errors are corrected at step sl8 of the ground extraction process as follows.
  • Each cell identified as not belonging to the ground is inspected.
  • the neighbour cells of the cell being inspected that correspond to the ground 6 are identified, and their average height is computed. If the absolute value of the difference between this average height and the height in the inspected cell is less than a correction- threshold value, the inspected cell is identified as
  • the first cell 12 corresponds to the ground 6
  • the third cell 16 corresponds to an object 8, 10.
  • the difference between the height of the first cell 12, i.e. the first height value 18, and the height of the second cell 14, i.e. the second height value, is zero.
  • the correction-threshold is 0.1m.
  • the second cell 14 is identified as corresponding to the ground 6.
  • step s20 the steps sl2 and sl4 as described above are repeated.
  • the correction of errors carried out at step sl8 modifies the cluster of cells that correspond to the ground.
  • steps sl2 and sl4 i.e. the forming clusters of cells that correspond to areas of relatively flat terrain and the identification of the largest cluster of cells, are repeated after performing the function of step sl8 to accommodate for the changes made.
  • the correction steps sl8 and s20 allow for the reconstructing of a larger portion of the ground 6 of the terrain area 4. This is because a reconstruction of the ground 6 obtained without this correction comprises a number of "holes" that are not identified as either the ground 6 or an obstacle 8, 10.
  • This may, for example, allow a path planner to find paths going through areas of the map previously marked as containing obstacles.
  • step s2 the ground extraction process of step s2 is performed.
  • this ground extraction process is followed by the object segmentation process of s4.
  • Figure 5 is a process flowchart showing certain steps of the object segmentation process of step s4 of the
  • a Min-Max Elevation Map of the terrain area 4 is computed.
  • This is a conventional Min-Max Elevation Map.
  • the resolution of a grid underlying the map may be any appropriate value.
  • the grid of the Min-Max Elevation Map is the same as that of the Mean Elevation Map.
  • This Min-Max Elevation Map of the terrain area 4 is hereinafter referred to as the global map.
  • the Min-Max Elevation Map is a grid having a plurality of cells. Each cell has assigned to it a range of height values. The range of height values assigned to a particular cell ranges from the minimum to the maximum height values corresponding to laser sensor returns from that cell.
  • Figure 6 is a schematic illustration of the first cell 12, the second cell 14, and the third cell 16 of the grid and the range of height values assigned to each of these cells in the Mean Elevation Map.
  • the first cell 12 is assigned a range of height values, hereinafter referred to as the "first range 26".
  • the first range 26 has a minimum, indicated in Figure 6 by the reference numeral 260, and a maximum, indicated in Figure 6 be the reference numeral 262.
  • the second cell 14 is assigned a range of height values, hereinafter referred to as the "second range 28".
  • the second range 28 has a minimum, indicated in Figure 6 by the reference numeral 280, and a maximum, indicated in Figure 6 be the reference numeral 282.
  • the third cell 16 is assigned a range of height values, hereinafter referred to as the "third range 30".
  • the third range 30 has a minimum, indicated in Figure 6 by the reference numeral 300, and a maximum, indicated in Figure 6 be the reference numeral 302.
  • the first, second, and third cells 12, 14, 16 each have a volume assigned to them that represents the range of the heights corresponding to the laser returns from that cell.
  • step s24 adjacent cells corresponding to an object 8, 10, i.e. the sets of cells not identified as
  • a second Min-Max Elevation Map is built from the laser returns contained in that cluster.
  • These second Min-Max Elevation Map are hereinafter referred to as "local maps".
  • the local maps have higher resolution than the global map generated at step s22.
  • the cell size in the local maps is 0.2m by 0.2m
  • the cell size in the global map is 0.4m by 0.4m.
  • the range of height values of each cell in the local map is divided into segments, or voxels.
  • Each voxel for a cell corresponds to a sub-range of the range of height values.
  • the height of each voxel is 0.2m. However, in other embodiments a different voxel height is used.
  • Each voxel contain the height values within the corresponding sub-range of the laser returns from that cell. Voxels that do not contain any laser returns are disregarded. Also, voxels of a particular cell are merged with other voxels of that cell, if they are in contact with those other voxels.
  • Figure 7 is a schematic illustration of the first cell 12, the second cell 14, and the third cell 16 of the Mean Elevation Map grid, after performing step s28.
  • the third cell 16 was identified as
  • a higher resolution grid is defined over the third cell 16, and the range of values of laser returns in each of the cells of the higher resolution grid is divided into voxels, as shown in Figure 6.
  • each of the cells of the higher resolution grid of the third cell 16 contain the same data.
  • only the voxels corresponding to the higher height values in the third range 30 and the lower height values in the third range 30 contain any laser scanner returns.
  • Voxels in the middle of the third range 30 do not contain any laser scanner returns.
  • each of the cells of the higher resolution grid of the third cell 16 contains two voxels, one containing laser scanner returns corresponding to relatively lower height values, and the other containing laser scanner returns corresponding to relatively higher height values.
  • the voxels corresponding to lower height values are hereinafter referred to as the "lower voxels" and are indicated in Figure 7 by the reference numeral 38.
  • the voxels corresponding to the ground 6 are identified. The identification of these voxels is
  • the lowest voxels in the third cell 16 are the lower voxels 36.
  • the second cell 14 may be identified as a closest cell that corresponds to the ground 6 to the third cell 16.
  • the mean height of the second voxel 28 and the lower voxels 36 are substantially the same, i.e. the difference between these values is below a voxel-threshold value of, for example, 0.2m.
  • the lower voxels 36 is identified as corresponding to the ground 6.
  • This process advantageously tends to allow for the reconstruction of the ground 6 under overhanging structures, for example the canopy of the tree 10.
  • This process also advantageously allows the reconstruction of the ground 6 that was generated at step s2 as described above with reference to Figures 2 and 3, to be refined. This is carried out at step s32.
  • the reconstruction of the ground 6 is refined.
  • the fact that a voxel from a local map corresponds to the ground 6 is used to update the Mean Elevation Map generated in the ground extraction process of s2.
  • the cell in the Mean Elevation Map which most closely corresponds to the cell in the local map that contains the voxel corresponding to the ground 6 is identified.
  • the identified cell is then updated by re-computing the mean height in that cell computed using only the laser returns that falls into the voxel corresponding to the ground 6.
  • contacting voxels are grouped together to form voxel clusters.
  • voxels identified as belonging to the ground are interpreted as separators between clusters .
  • noisy laser scanner returns are identified.
  • voxels which contain noisy returns are assumed to satisfy the following conditions.
  • the voxel belongs to a cluster which is not in contact with a cell or voxel corresponding to the ground 6.
  • the size of the cluster (in each of the x-, y-, and z- directions) that the voxel belongs to is smaller than a
  • the noise threshold is 0.1m.
  • the identified noisy returns are removed or withdrawn from the map.
  • a further advantage of the segmentation algorithm is that fine details tend to be conserved. For example, frames of windows of the building 8 are conserved by the segmentation algorithm .
  • the segmentation algorithm advantageously tends to benefit from the advantages of the Mean Elevation Map approach.
  • the segmentation algorithm tends to be able to generate smooth surfaces by filtering out noisy returns.
  • the segmentation algorithm advantageously tends to benefit from the advantages of the Min-Max Elevation Map approach.
  • the segmentation algorithm does not make an approximation of the height corresponding to the laser scanner return when separating the objects above the ground.
  • the local maps have higher resolution than the global map which tends to allow efficient reasoning in the ground
  • a further advantage provided by the above described segmentation algorithm is that it tends to be able to achieve the following tasks. Firstly, the explicit extraction of the surface of ground 6 is performed, as opposed to extracting 3- dimensional surfaces without explicitly specifying which of those surfaces correspond to the ground 6. Secondly, overhanging structures, such as the canopy of the tree 10, are represented. Thirdly, full 3-dimensional segmentation of the objects 8, 10 is performed. Conventional algorithms do not jointly perform all of these tasks.
  • a further advantage of the segmentation algorithm is that errors that occur when generating 3-dimensional surfaces corresponding to the ground 6 tend to be minimised. This is due to the ability of the ground-object approach implemented by the segmentation algorithm to separate the objects above the ground.
  • a further advantage is that by separately classifying terrain features, the terrain model produced by performing the segmentation algorithm tends to reduce the complexity of, for example, path planning operations. Also, high-resolution terrain navigation and obstacle avoidance, particularly those obstacles with overhangs, is provided. Moreover, the
  • segmentation algorithm tends to allow for planning operations to be performed efficiently in a reduced, i.e. 2-dimensional workspace .
  • the provided segmentation algorithm allows a path-planner to take advantage of the segmented ground model. For example, clearance around obstacles with complex geometry can be determined. This allows for better navigation through regions with overhanging features .
  • the average value of the measured plurality of values of cells is used to determine clusters and further process those clusters, and various other processes.
  • other functions may be used instead of average value, for example an average value of parameter values that remain after certain extreme values have been filtered out, or for example statistical measures other than an average as such .
  • the measured parameter is the height of the terrain and/or objects above the ground.
  • any other suitable parameter may be used instead, for example
  • Apparatus including the processor 3, for implementing the above arrangement, and performing the method steps described above, may be provided by configuring or adapting any suitable apparatus, for example one or more computers or other processing apparatus or processors, and/or providing additional modules.
  • the apparatus may comprise a computer, a network of computers, or one or more processors, for implementing instructions and using data, including instructions and data in the form of a computer program or plurality of computer programs stored in or on a machine readable storage medium such as computer memory, a computer disk, ROM, PROM etc., or any combination of these or other storage media.
  • the 3-dimensional point cloud data for the terrain area was provided by a Riegl laser scanner.
  • the laser data is provided by a different means, for example by SICK and Velodyne sensors.
  • the data on the terrain area is not laser scanner data and is instead a different appropriate type of data, for example data generated by an infrared camera.
  • the terrain area is outdoors and comprises a building and a tree.
  • the terrain area is a different appropriate area comprising any number of terrain features.
  • the terrain features are not limited to trees and buildings.
  • the segmentation algorithm is performed by performing each of the above described method steps in the above provided order.
  • certain method steps may be omitted.
  • steps s36 and s38 of the segmentation algorithm may be omitted, however the resulting terrain model would tend to be less accurate than if these steps were included.
  • the segmentation algorithm does not take into account occluded, or partially hidden, objects.
  • provision is made for partially hidden objects as will now be described in more detail with reference to Figure 8.
  • FIG 8 is a schematic illustration of the laser scanner 2 scanning an object that is hidden behind a further object.
  • the object being scanned by the laser scanner is hereinafter referred to as the "hidden object 40", and the object partially hiding, or occluding, the hidden object 40 is hereinafter referred to as the "non-hidden object 42".
  • the hidden object 40 can only be partially imaged by the laser scanner 2.
  • a height of the hidden object observed by the laser scanner hereinafter referred to as the "observed height 44" does not correspond to the actual object height 46.
  • Accurate estimation of the ground height ideally considers occlusions such as these.
  • an estimation of the ground height is preferably based on non-occluded cells.
  • a cell can be assessed as non-occluded using a ray-tracing process.
  • a set of cells, or a trace is computed to best approximate a straight line joining two given cells. If any of the cells in the trace do not correspond to the ground, the end cell of the trace is occluded.
  • Using a ray-tracing process tends to allow for occlusions to be taken into account and reliable estimates of the ground height to be computed.
  • the ground is extracted by applying a threshold on the computed surface gradients.
  • a threshold on the computed surface gradients.
  • smoothness constraints is used to mean that the variation of height between two neighbour ground cells is limited. Thus, the closest ground cell to an obstacle will provide a reliable local estimate of the ground height, i.e. a given ground cell is connected (via "smoothness

Abstract

L'invention porte sur un procédé et un appareil d'extraction d'un objet (8, 10) ou d'une caractéristique de terrain (6), lequel procédé consiste à : définir un zone (4); diviser la zone (4) en cellules (12, 14, 16); mesurer une valeur d'un paramètre (par exemple la hauteur) à différents emplacements dans chaque cellule (12, 14, 16); pour chaque cellule (12, 14, 16), déterminer une valeur d'une fonction des valeurs de paramètre; identifier une cellule comme correspondant uniquement à un objet (8, 10) ou une caractéristique de terrain (6) si la valeur de fonction pour cette cellule est comprise dans une certaine plage de valeurs; définir, pour les cellules non identifiées comme correspondant uniquement à l'objet (8, 10) ou la caractéristique de terrain (6), une ou plusieurs sous-cellules comprenant chacune au moins un des différents emplacements; et identifier une sous-cellule comme correspondant au moins en partie à l'objet (8, 10) ou la caractéristique de terrain (6) si une ou plusieurs des valeurs de paramètre dans cette cellule sont comprises dans la plage de valeurs.
PCT/AU2010/001602 2009-12-04 2010-11-30 Procédés d'extraction WO2011066602A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
AU2009243521 2009-12-04
AU2009243521A AU2009243521A1 (en) 2009-12-04 2009-12-04 Extraction process

Publications (1)

Publication Number Publication Date
WO2011066602A1 true WO2011066602A1 (fr) 2011-06-09

Family

ID=44114545

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/AU2010/001602 WO2011066602A1 (fr) 2009-12-04 2010-11-30 Procédés d'extraction

Country Status (2)

Country Link
AU (1) AU2009243521A1 (fr)
WO (1) WO2011066602A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8565958B1 (en) * 2011-06-02 2013-10-22 Google Inc. Removing extraneous objects from maps
WO2015173005A1 (fr) * 2014-05-13 2015-11-19 Bayerische Motoren Werke Aktiengesellschaft Carte d'environnement pour surfaces de circulation ayant un profil de hauteur quelconque
EP3349041A1 (fr) * 2017-01-17 2018-07-18 Delphi Technologies LLC Système de détection d'objet

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050100220A1 (en) * 2002-11-06 2005-05-12 Keaton Patricia A. Method and apparatus for automatically extracting geospatial features from multispectral imagery suitable for fast and robust extraction of landmarks

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050100220A1 (en) * 2002-11-06 2005-05-12 Keaton Patricia A. Method and apparatus for automatically extracting geospatial features from multispectral imagery suitable for fast and robust extraction of landmarks

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
HAILIANG ET AL.: "Research on the Algorithm of Extracting Ridge and Valley Lines from Contour Data", GEO-SPATIAL INFORMATION SCIENCE, vol. 8, no. ISSUE, December 2005 (2005-12-01), pages 282 - 286 *
JENSON ET AL.: "Extracting Topographie Structure from Digital Elevation Data for Geographic Information System Analysis", PHOTOGRAMMETRIE ENGINEERING & REMOTE SENSING, vol. 54, no. 11, November 1988 (1988-11-01), pages 1593 - 1600 *
KWEON ET AL.: "Extracting Topographie Terrain Features from Elevation Maps", CVGIP: IMAGE UNDERSTANDING, vol. 59, no. 2, March 1994 (1994-03-01), pages 171 - 182 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8565958B1 (en) * 2011-06-02 2013-10-22 Google Inc. Removing extraneous objects from maps
WO2015173005A1 (fr) * 2014-05-13 2015-11-19 Bayerische Motoren Werke Aktiengesellschaft Carte d'environnement pour surfaces de circulation ayant un profil de hauteur quelconque
US10286901B2 (en) 2014-05-13 2019-05-14 Bayerische Motoren Werke Aktiengesellschaft Map of the surroundings for driving areas with random altitude profile
EP3349041A1 (fr) * 2017-01-17 2018-07-18 Delphi Technologies LLC Système de détection d'objet
US10838067B2 (en) 2017-01-17 2020-11-17 Aptiv Technologies Limited Object detection system

Also Published As

Publication number Publication date
AU2009243521A1 (en) 2011-06-23

Similar Documents

Publication Publication Date Title
Forlani et al. C omplete classification of raw LIDAR data and 3D reconstruction of buildings
Rottensteiner et al. The ISPRS benchmark on urban object classification and 3D building reconstruction
Kada et al. 3D building reconstruction from LiDAR based on a cell decomposition approach
Zhang et al. Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification
Lafarge et al. Creating large-scale city models from 3D-point clouds: a robust approach with hybrid representation
Sohn et al. Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction
Pu et al. Extracting windows from terrestrial laser scanning
Alexander et al. Integrating building footprints and LiDAR elevation data to classify roof structures and visualise buildings
US20130096886A1 (en) System and Method for Extracting Features from Data Having Spatial Coordinates
Truong-Hong et al. Octree-based, automatic building facade generation from LiDAR data
CN109242862A (zh) 一种实时的数字表面模型生成方法
WO2011085435A1 (fr) Procédé de classification d'un objet ou d'une caractéristique de terrain après extraction
Huber et al. Fusion of LIDAR data and aerial imagery for automatic reconstruction of building surfaces
WO2011085434A1 (fr) Procédés d'extraction
Li et al. New methodologies for precise building boundary extraction from LiDAR data and high resolution image
Sánchez-Lopera et al. Classification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier
WO2011085433A1 (fr) Acceptation/rejet d'une classification d'un objet ou d'une caractéristique de terrain
WO2011085437A1 (fr) Procédés d'extraction
AU2012229873A1 (en) Extraction processes
WO2011066602A1 (fr) Procédés d'extraction
Pfeifer et al. Extraction of building footprints from airborne laser scanning: Comparison and validation techniques
WO2011085436A1 (fr) Procédés d'extraction
Soderman et al. Three-dimensional environment models from airborne laser radar data
Erener et al. An approach to urban building height and floor estimation by using LiDAR data
Rutzinger et al. Object detection in airborne laser scanning data-an integrative approach on object-based image and point cloud analysis

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 10834082

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 10834082

Country of ref document: EP

Kind code of ref document: A1