Point cloud data processing method and point cloud data processing device

A trajectory of a measuring device is calculated based on measurement data acquired by the measuring device that is moving along a measurement route, and on a vertical plane orthogonal to the trajectory, a two-dimensional figure is identified by designating an extraction angle range around an intersection of the trajectory and the vertical plane based on a perpendicular drawn down to a horizontal plane from the intersection and an extraction distance range based on the intersection on the vertical plane, and a region obtained by extending the two-dimensional figure along the trajectory is set as an extraction region, and point cloud data in a region including a specific analysis target is extracted as extracted point cloud data from entire circumference point cloud data acquired by scanning the circumference of the measuring device and included in the measurement data.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority under 35 U.S.C. § 119 to the Japanese Patent Application No. 2019-064792 filed Mar. 28, 2019. The contents of this application are incorporated herein by reference in their entirely.

TECHNICAL FIELD

The present disclosure relates to a point cloud data processing method and a point cloud data processing device.

BACKGROUND

Conventionally, an MMS (Mobile Mapping System) has been known for acquiring three-dimensional positional information of the peripheries of a road, such as building and road shapes, signs, and guardrails, etc., highly accurately and efficiently while moving. The MMS is configured by installing a measuring device including a GNSS (Global Navigation Satellite System), an antenna, an IMU (Inertial Measuring Unit), a camera, a laser scanner, etc., in a mobile body such as a vehicle.

An MMS is a system intended to execute post-processing of data. Therefore, first, while traveling in a measurement section on a target road by vehicle, the MMS acquires data on its own position based on a GNSS navigation signal (hereinafter, referred to as “satellite positioning data”), three-dimensional acceleration and angular velocity data obtained by the IMU (hereinafter, referred to as “inertial positioning data”), and measurement data at each point of scanning light by the laser scanner (hereinafter, referred to as “measurement point cloud data”), and next, calculates a trajectory of the vehicle on the basis of the satellite positioning data and the inertial positioning data, and synthesizes the calculated trajectory of the vehicle and the point cloud data to generate three-dimensional point cloud data as a result.

The laser scanner acquires a point cloud over the entire circumference, so that acquired point cloud data includes a large number of point data due to structural objects other than an analysis target. Therefore, in order to analyze an analysis target, a portion including the analysis target needs to be manually extracted, and the extraction operation is troublesome.

In order to solve this problem, International Publication WO 2018/159690 discloses a point cloud data processing method in which a columnar region and a parallelepiped region disposed at predetermined positions on a lower side of the trajectory are set as extraction regions, and point data belonging to these extraction regions are extracted as target point cloud data.

However, to perform an analysis of various structural objects, a new point cloud data processing method for extracting target point cloud data from entire circumference point cloud data has been required.

SUMMARY OF INVENTION

Technical Problem

The present invention was made in view of the above-described circumstances, and an object thereof is to provide a point cloud data processing method and a point cloud data processing device capable of easily extracting an analysis target from entire circumference point cloud data acquired by a measuring device that is moving along a measurement route.

Solution to Problem

In order to achieve the above-described object, a point cloud data processing method according to an aspect of the present invention includes the steps of: (a) calculating a trajectory of a measuring device based on measurement data acquired by the measuring device that is moving along a measurement route; (b) identifying, on a vertical plane orthogonal to the trajectory, a two-dimensional figure by designating an extraction angle range around an intersection P of the trajectory and the vertical plane based on a perpendicular drawn down to a horizontal plane from the intersection, and an extraction distance range based on the intersection on the vertical plane, and setting a region obtained by extending the two-dimensional figure along the trajectory, as an extraction region; and (c) extracting point cloud data in a region including a specific analysis target as extracted point cloud data from entire circumference point cloud data acquired by scanning the circumference of the measuring device and included in the measurement data.

In the aspect described above, it is also preferable that the two-dimensional figure is a trapezoid having a height in the extraction distance range.

In the aspect described above, it is also preferable that the two-dimensional figure is an annular arc shape with a predetermined width in a radial direction.

In the aspect described above, it is also preferable that, in the step (b), a shortest distance between a point whose shortest distance from the trajectory is the longest and the trajectory is designated as an extraction reference distance.

In the aspect described above, it is also preferable that in the step (b), a plane rectangular coordinate system is sectioned so that cubes each having a predetermined size are continuous, cubes each including point data whose density is not less than a predetermined value are extracted, and among the cubes including points at densities not less than the predetermined value, a cube whose shortest distance from the trajectory is the longest is identified, and a center of the cube is identified as a point that is furthest from or nearest the trajectory T.

In the aspect described above, it is also preferable that the point cloud data processing method includes the steps of:

(d) selecting one extraction mode from a plurality of extraction modes set in advance; and

(e) setting parameters corresponding to the selected one extraction mode, wherein

the steps (a) to (c) are executed by using the set parameters.

A point cloud data processing device according to another aspect of the present invention includes: a trajectory calculating unit configured to calculate a trajectory of a measuring device based on measurement data acquired by the measuring device that is moving along a measurement route; an extraction region setting unit configured to identify, on a vertical plane orthogonal to the trajectory, a two-dimensional figure by designating an extraction angle range around an intersection P of the trajectory and the vertical plane based on a perpendicular drawn down to a horizontal plane from the intersection, and an extraction distance range based on the intersection on the vertical plane, and set a region obtained by extending the two-dimensional figure along the trajectory, as an extraction region; and an extracted point cloud data generating unit configured to extract point cloud data in a region including a specific analysis target as extracted point cloud data from entire circumference point cloud data acquired by scanning the circumference of the measuring device and included in the measurement data.

Benefit of Invention

With the point cloud data processing method and the point cloud data processing device according to the aspects described above of the present invention, an analysis target can be easily extracted from entire circumference point cloud data acquired by a measuring device that is moving along a measurement route.

DESCRIPTION OF EMBODIMENT(S)

Hereinafter, a preferred embodiment of the present invention will be described with reference to the drawings, however, the present invention is not limited to this. In the following description of the embodiment, the same components are provided with the same reference sign, the same components are provided with the same name, and overlapping description is omitted as appropriate.

Configuration of Measuring Device

A point cloud data processing device according to the embodiment is a device to execute post-processing of measurement data acquired by, for example, a measuring device20illustrated inFIG. 1. First, the measuring device20will be described.

The measuring device20is a so-called MMS (Mobile Mapping System). The measuring device20includes a GNSS device21, an IMU22, a camera23, a laser scanner24, a rotary encoder25, and a synchronization control device26, installed in a vehicle27.

The GNSS device21is a receiving device that receives a navigation signal from a navigation satellite28such as a GNSS satellite. Based on the navigation signal, the GNSS device21acquires satellite positioning data, that is, a plan position and an altitude of the measuring device20. For example, the GNSS device21acquires coordinates of the measuring device20at intervals of ten times/second.

The IMU22is an inertial measuring device, includes a three axis gyroscope and a 3-directional accelerometer, and acquires inertial positioning data.

The camera23is a 360-degree camera consisting of a plurality of cameras, and shoots a video of the entire circumference (2π space) including an upper direction. Although not described herein, video image data shot by the camera23is used for construction of three-dimensional information of the surroundings in combination with point cloud data measured by the laser scanner24.

Exterior orientation elements (positions and postures) of the camera23and the laser scanner24with respect to the measuring device20(in this embodiment, a position of the IMU) are measured in advance, and such information is known.

As illustrated inFIG. 1B, the laser scanner24spirally irradiates a scanning light La onto the entire circumference (2π space), and receives a reflected light Lb (FIG. 1B) from a structural object such as a road30, a building, and a tree. Based on a time from emission of the scanning light La until reception of the reflected light Lb, the laser scanner24obtains a three-dimensional position of each reflection point, to acquire point cloud data over the entire circumference of the laser scanner24.

Along with movement of the vehicle27, the laser scanner24acquires measurement point cloud data as the entire circumference point cloud data of a scanning range32along a measurement route.

In the illustrated example, the laser scanner24is one in number. However, the number is not limited to this. The measuring device20may include a plurality of laser scanners24such as three or five. When the number of laser scanners24is increased, the point cloud density increases, and shadow portions can be minimized, so that a measurement with higher accuracy can be made.

The rotary encoder25is attached to a wheel29of the vehicle27, and acquires vehicle moving distance data from a rotation speed and a rotation angle of the wheel29.

The synchronization control device26is connected via cables, etc., or wirelessly to the GNSS device21, the IMU22, the camera23, the laser scanner24, and the rotary encoder25.

The synchronization control device26synchronizes a time of inertial positioning data acquisition by the IMU22, a time of image data acquisition by the camera23, a time of point cloud data acquisition by the laser scanner24, and a time of acquisition of moving distance data of the wheel29by the rotary encoder25.

While moving along a measurement route, the measuring device20acquires satellite positioning data, inertial positioning data, measurement point cloud data, and moving distance data respectively by the GNSS device21, the IMU22, the camera23, the laser scanner24, and the rotary encoder25. Hereinafter, these data are collectively referred to as “measurement data”.

Embodiment

Hereinafter, a point cloud data generating method and a point cloud data processing device100according to an embodiment of the present invention will be described. The point cloud data processing device100generates three-dimensional point cloud data (resultant point cloud data) around a measurement route by using measurement data acquired by the measuring device20.

The point cloud data processing device100is a so-called personal computer. The point cloud data processing device100includes hardware such as a CPU (Central Processing Unit) as a processor, a RAM (Random Access Memory) and a ROM (Read-Only Memory) as a main storage device, and a HDD (Hard Disk Drive) as an auxiliary storage device, and a liquid crystal display as a display device, etc.

The point cloud data processing device100is configured to be connectable to the GNSS device21, the IMU22, the camera23, the laser scanner24, and the rotary encoder25via the synchronization control device26. The point cloud data processing device100may be located outside the vehicle or may be located inside the vehicle. In the present description, for the sake of convenience, the point cloud data processing device is assumed to be located outside the vehicle.

FIG. 2is a functional block diagram of the point cloud data processing device100. The point cloud data processing device100includes: various function units including a data acquiring unit111, a trajectory calculating unit112, a primary point cloud data generating unit113, an extraction region setting unit114, an extracted point cloud data generating unit115, a measurement ground control point detecting unit116, a measurement ground control point adjusting unit117, a reciprocation completion determining unit118, a noise determining unit119, and a resultant point cloud data generating unit120; a storage unit130; a display unit140; and an input unit150.

The respective function units implement respective functions by executing programs by the CPU. The programs to implement the functions of the respective function units may be stored in a storage medium such as a magnetic disc, a flexible disc, an optical disc, a compact disc, a Blu-ray (registered trademark) disc, a DVD, or the like.

The data acquiring unit111accepts measurement data via an input interface (not illustrated) and stores the measurement data in the storage unit130.

The input interface is a port to be connected to the synchronization control device26. The input interface is, for example, a USB (Universal Serial Bus) terminal. Alternatively, the input interface may be a port to be connected to a LAN (Local Area Network).

The trajectory calculating unit112receives satellite positioning data and inertial positioning data from the storage unit130, and calculates a trajectory of the measuring device20in a plane rectangular coordinate system by processing using a Kalman filter.

As described above, a positional relationship between the laser scanner24and the measuring device20(IMU device) is known, and associated with each other. That is, in the present description, “trajectory of the measuring device20” is associated with “trajectory of the center of the laser scanner24.”

The primary point cloud data generating unit113generates primary point cloud data in a plane rectangular coordinate system by using the measurement point cloud data and the calculated trajectory point data.

Based on respective trajectory points acquired by the trajectory calculating unit112, the extraction region setting unit114sets an extraction angle range and an extraction distance range to set an extraction region.

The extracted point cloud data generating unit115generates extracted point cloud data by extracting point cloud data disposed in the region set by the extraction region setting unit114from the primary point cloud data. And, outputs the extracted point cloud data to the display unit140, and stores the extracted point cloud data in the storage unit130.

The measurement ground control point detecting unit116detects measurement ground control points41from the extracted point cloud data displayed on the display unit140.

The measurement ground control point adjusting unit117re-calculates a trajectory based on the measurement ground control points41detected by the measurement ground control point detecting unit116and known ground control point coordinates. Then, based on the re-calculated trajectory and the extracted point cloud data, the measurement ground control point adjusting unit117generates adjusting point cloud data.

The reciprocation completion determining unit118determines whether the generation of adjusted point cloud data has been completed for both of a forward route and a return route of the measurement route.

The noise determination unit119compares adjusted point cloud data for the forward route and the return route of the measurement route, and determines data included in only one route of the forward and return route as noise.

The resultant point cloud data generating unit120deletes data determined as noise by the noise determining unit119, generates resultant point cloud data by synthesizing point cloud data for the forward route and the return route, displays the data on the display unit140, and stores the data in the storage unit130.

The storage unit130stores measurement data, data calculated in the respective function units, and various programs and settings for making the data processing device100implement the functions. The storage unit130is realized by a main storage device and an auxiliary storage device, however, may be realized only by a main storage device or only by an auxiliary storage device.

The display unit140displays extracted point cloud data and resultant point cloud data, etc. The display unit140is realized by a display device.

The input unit150is a user interface for inputting various commands for, for example, the start of processing from a user. An example of the input unit150may include a keyboard, a mouse, etc.

Point Cloud Data Processing Method

Next, a point cloud data processing method according to the present embodiment is described.

As a detailed example, measurement data acquired by the measuring device20through reciprocation on a predetermined measurement route is used, and description is given on the assumption that an analysis target is ground control points40set at predetermined intervals on a road30on the measurement route as illustrated inFIG. 3. Each ground control point40is provided with a reflection sheets, and the like. The ground control points40are measured by a total station and their coordinates are made known in advance.

FIG. 4is a flowchart of the point cloud data processing method. When the processing is started, in Step S101, the trajectory calculating unit112receives satellite positioning data and inertial positioning data for one route from the storage unit130, and by processing using a Kalman filter, calculates a trajectory in a plane rectangular coordinate system.

Next, in Step S102, the primary point cloud data generating unit113converts measurement point cloud data relating to the same route into plane rectangular coordinate system data by use of the trajectory calculated in Step S101to generate primary point cloud data of the entire circumference.

Next, in Step S103, the extraction region setting unit114sets an extraction angle range and an extraction distance range to set an extraction region. Details of setting of the extraction angle range and setting of the extraction distance range will be described later.

Next, in Step S104, the extracted point cloud data generating unit115extracts point cloud data in the region determined by the extraction region setting unit114from the primary point cloud data, to generate extracted point cloud data. And, the extracted point cloud data generating unit115displays the extracted point cloud data on the display unit140, and stores the extracted point cloud data in the storage unit130.

Next, in Step S105, the measurement ground control point detecting unit116detects points or regions with high reflection intensity from the extracted point cloud data displayed on the display unit140, and designates the points or regions as ground control points40. On the ground control points40set on the road, reflection sheets or the like are provided, so that the measurement ground control points41in the point cloud data appear as points or regions with high reflection intensities.

Detection and designation of ground control points may be realized by a configuration made such that a user can recognize portions having high reflection intensities and specific shapes as ground control points in the extracted point cloud data displayed on the display unit and can select the points in order with a mouse pointer, etc. Alternatively, another configuration is also possible in which ground control points can be automatically extracted from point cloud data based on reflection intensities and shapes.

Next, in Step S106, the measurement ground control point adjusting unit117re-calculates a trajectory based on the detected measurement ground control points and known ground control point coordinates. Based on the re-calculated trajectory, the measurement ground control point adjusting unit117generates adjusted point cloud data.

Next, in Step S107, the reciprocation completion determining unit118determines whether the generation of adjusted point cloud data has been completed for both of the forward route and the return route of the measurement route.

When the generation has been completed only for one route (for example, the forward route) (No), the processing returns to Step S101, and the processing in Steps S101to S107is repeated for the other route (for example, the return route).

On the other hand, in Step S107, when the generation is completed for both routes (Yes), in Step S108, the noise determining unit119compares the adjusted point cloud data for forward route and the return route to determine whether there is data included only in one route.

In Step S108, when there is data included only in one route (Yes), the noise determining unit119determines the data included only in one route as noise, and in Step S109, deletes this noise point cloud data. Then, the processing shifts to Step S110.

On the other hand, in Step S108, when there is no data included only in one route (No), the noise determining unit119determines the adjusted point cloud data in question as data without noise, and the processing shifts to Step S110.

Then, in Step S110, the resultant point cloud data generating unit120synthesizes adjusted point cloud data from which noise was deleted in S109or adjusted point cloud data determined as data without noise in Step S108for the forward route and the return route to generate resultant point cloud data. The resultant point cloud data is then output to the display unit140and stored in the storage unit130, and the processing is ended.

Next, the setting of an extraction region in Step S103is described with reference toFIGS. 5 to 7.FIG. 5is a detailed flowchart of Step S103.

When the setting of an extraction region is started, in Step S201, the extraction region setting unit114sets an arbitrary vertical plane Viorthogonal to the trajectory T of the measuring device20as illustrated inFIG. 6A.FIG. 6Aillustrates the trajectory calculated in Step S101represented in a plane rectangular coordinate system. For the sake of convenience, the measuring device20is assumed to have moved eastward on the E axis.

Next, as illustrated inFIG. 6B, in Step S202, on the vertical plane Vi, based on a perpendicular Lidrawn down to the horizontal plane H from an intersection Piof the trajectory T and the vertical plane Vi, the extraction region setting unit114designates an extraction angle range Θ as −θ1≤Θ≤+θ2around the intersection Pi.FIG. 6Bis a diagram of an arbitrary vertical plane Viorthogonal to the trajectory T as viewed in the moving direction of the measuring device20.

Values of the extraction angular widths θiand θ2are set in advance. Generally, the ground control points40are set at the center of the road as illustrated inFIG. 3or at predetermined positions such as positions on the road shoulder. Therefore, the values of the extraction angular widths θ1and θ2can be predicted to some extent from, for example, a relationship between the height of the intersection P from the road and a road width or set position. Based on this prediction, for example, setting is made such that, when the ground control points are set at the center of a one-lane road, θ1=θ2=40°, and when the ground control points are set, for example, on the center line of a two-lane road, θ1=60° and θ2=40°.

Next, in Step S203, the extraction region setting unit114sets an extraction reference distance1. The extraction reference distance1is set as a distance in the vertical direction from the intersection Piof the trajectory T and an arbitrary vertical plane Vion the vertical plane Vias illustrated inFIG. 7A.

A value of the extraction reference distance1may be set in advance. For example, the distance from a road surface to the trajectory T is estimated to be substantially equal to a height h of the measuring device20. The height h of the measuring device20from the road surface is known. Therefore, as illustrated inFIG. 7B, the height h of the measuring device20can be set as the extraction reference distance1.

In a measurement using the measuring device20, a structural object measured in the vertically downward direction is mainly the road30, so that setting the height h of the measuring device20as the extraction reference distance1enables easy and proper setting of the extraction reference distance1.

Next, in Step S204, by using extraction widths d1and d2determined in advance, an extraction distance range D is designated as 1−d1≤D≤1+d2from an intersection Pxwith the trajectory.

The extraction distance widths d1and d2can be set such that d1, d2=30 cm when it is desired to extract, for example, points around a road as in this e. The extraction distance widths d1and d2may be equal to each other, or different from each other.

Next, in Step S205, as illustrated inFIG. 7A, the extraction region setting unit114identifies a trapezoid two-dimensional figure S on the vertical plane Viby using the designated extraction angle range Θ and extraction distance range D.

Next, in Step S206, as illustrated inFIG. 7C, the extraction region setting unit114sets a region obtained by extending the two-dimensional figure S along the trajectory, as an extraction region A, and the processing shifts to Step S104.

An example of extracted point cloud data extracted in this way is illustrated inFIGS. 8A and 8B.FIG. 8Aillustrates primary point cloud data before extraction processing, andFIG. 8Bis a bird's-eye view of the same route, illustrating point cloud data after extraction processing.FIG. 9Ais a plan view enlarging the white-line quadrangular portion illustrated inFIG. 8Aof the same primary point cloud data as inFIG. 8A, andFIG. 9Bis a view in the arrow direction inFIG. 9Aas viewed from a point of view on the road.

As illustrated inFIG. 8A, in the primary point cloud data before the extraction processing, the road30is covered by point cloud data based on the tree50and other structural objects. Therefore, it is difficult to visually identify the measurement ground control points41from the displayed point cloud data. By changing the display direction as inFIG. 9B, the measurement ground control points41can be detected. However, magnification and a change in display region need to be repeated for detection, so that the detection operation becomes troublesome.

On the other hand, as illustrated inFIG. 8B, after the extraction processing, the road30and the measurement ground control points41can be easily detected.

In this way, by the point cloud data generating method according to the present embodiment, point cloud data on an analysis target can be easily extracted from point cloud data acquired over the entire circumference of the measuring device for a measurement route.

When structural objects such as trees and buildings are present over the traveling route of the measuring device20, it is difficult to detect ground control points from primary point cloud data, as inFIG. 8A. Such situation means that the area over the measuring device20is shielded at the time of measurement. In this case, a reception condition of the GNSS device21deteriorates, and a trajectory error easily becomes large.

Therefore, in order to acquire highly accurate three-dimensional point cloud data, it is important to adjust the trajectory and the point cloud data with the measurement ground control points41. In this way, easy detection of ground control points by setting the analysis target as the ground control points and extracting point cloud data makes easier adjustments of trajectory and point cloud data, and this is particularly advantageous.

On the other hand, the point cloud data processing device100may be configured as a point cloud data extracting device that does not include the measurement ground control point detecting unit116, the measurement ground control point adjusting unit117, the reciprocation completion determining unit118, the noise determining unit119, and the resultant point cloud data generating unit120, and executes only the processing of Steps S101to S104.

In this case, as primary point cloud data, entire circumference point cloud data in various stages such as point cloud data adjusted by using a trajectory that has already been adjusted, can be used.

In this embodiment, a description is given by assuming an analysis target as ground control points40set on the road30. However, in this method, not only the ground control points40but also a road surface shape of the road30or a structural object installed near the road surface, etc. can be used as an analysis target.

The setting of the extraction reference distance1in Step S203may be not only setting in advance but also be automatic setting as follows. As an example, as illustrated inFIG. 10A, in point data included in the extraction angle range Θ, a furthest point Qxfrom the trajectory T is identified, and a distance between the point Qxand the trajectory T may be set as the extraction reference distance1.FIG. 10Ais a view in the traveling direction from a vertical plane V0at the starting point P0of the trajectory T.

In this case, simply, the shortest distances from the respective points Q1, Q2, Q3, . . . to the trajectory T are calculated and compared with each other, and a point having the longest distance is identified as the furthest point Qx. Then, a shortest distance between the point Qxand the trajectory T is set as the extraction reference distance1.

In a measurement using the measuring device20, a structural object measured in the vertically downward direction is mainly the road30, and there is a high possibility that the furthest point Qxis present on a road surface of the road30or in the surroundings of the road surface. Therefore, by designating a range with predetermined extraction distance widths d1and d2upward and downward from the point Qxset as a reference as the extraction distance range D, an extraction region for accurate extraction of point cloud data based on structural objects around the road surface of the road30can be set.

Alternatively, as the furthest point Qx, a lowest point may be identified, simply. This is because the lowest point is considered to be present near the furthest point Qx, and when considering a measurement of the road surface, the road surface is highly likely to include the lowest point. InFIG. 10A, the furthest point Qxmatches the lowest point. In this way, even by designating a range with predetermined extraction distance widths d1and d2upward and downward from the lowest point set as a reference, an extraction region for accurate extraction of point cloud data based on structural objects around the road surface of the road30can be set.

Alternatively, as another example, the extraction reference distance1may be set as follows.

First, as illustrated inFIG. 11A, in the extraction angle range Θ in the plane rectangular coordinate system, cubes C1, C2, . . . with predetermined dimensions are stacked, and cubes each including points whose density is not less than a predetermined value are extracted. InFIG. 11A, shaded portions are cubes including points at densities not less than the predetermined value.

Next, the extraction region setting unit114identifies a cube Cxwhose shortest distance from the trajectory T is the longest among the cubes including points at densities not less than the predetermined value. A center of this cube is identified as a furthest point Qxfrom the trajectory T.

Next, the shortest distance between the point Qxand the trajectory T, that is, a distance between the point Qxand the intersection Pxon the vertical plane Vxpassing through the point Qxillustrated inFIG. 11B, is calculated as the extraction reference distance1.

In this way, by identifying the furthest point Qxby comparing the distances of the cubes having predetermined dimensions and including point data at a predetermined density or more from the trajectory, the influences of noise such as dust on the point data can be eliminated, so that a more accurate extraction reference distance1can be set.

As a still another modification, the two-dimensional figure identified by the extraction angle range Θ and the extraction distance range D may be not a trapezoid as described above but an annular arc shape S1whose width is the extraction distance range D and whose central angle is the extraction angle range Θ as illustrated inFIG. 10B.

As a yet another modification, the point cloud data processing device100according to the embodiment may be configured to be capable of switching an extraction mode according to a position of an analysis target, etc.FIG. 12is a functional block diagram of a point cloud data processing device100aaccording to this modification.

The point cloud data processing device100ais a personal computer including the same hardware as in the point cloud data processing device100. However, the point cloud data processing device100aincludes an extraction mode selecting unit121and a selected mode setting unit122in addition to the point cloud data processing device100.

The extraction mode selecting unit121displays display for a user to select an extraction mode on the display unit140. Selection of an extraction mode by a user by using the input unit150is enabled.

The selected mode setting unit122sets parameters (extraction angular widths θ1and θ2, an extraction reference distance1, and extraction distance widths d1and d2) corresponding to a selected extraction mode.

FIG. 13is a flowchart of point cloud data processing of the point cloud data processing device200. When the processing is started, first, in Step S401, the extraction mode setting unit121displays a menu window80for selecting an extraction mode on the display unit140as illustrated inFIG. 14, and a user selects an extraction mode.

In the example illustrated inFIG. 14, there are three modes for a road surface1, a road surface2, and arbitrary setting on the menu window80. The road surface1mode corresponds to a case where the road is a one-lane road and ground control points are set at the center of the road. The road surface2mode corresponds to a case where the road is a two-lane road and ground control points are set on the center line. When a user selects arbitrary setting mode, the user can set the extraction angular widths θ1and θ2, the extraction reference distance1, and the extraction distance widths d1and d2by inputting arbitrary values. The configuration is made such that a user can select a mode by turning a radio button on with the mouse pointer81.FIG. 14illustrates a state where the mode for the road surface1is selected.

In the storage unit130, the extraction angular widths θ1and θ2, the extraction reference distance1, and the extraction distance widths d1and d2corresponding to each extraction mode as illustrated in Table 1 are set in advance, and are stored in the form of, for example, a table, etc.

When a user selects an extraction mode, the processing shifts to Step S402, and the selected mode setting unit122sets the extraction angular widths θ1and θ2, the extraction reference distance1, and the extraction distance widths d1an d2set for each extraction mode, respectively.

Next, the processing shifts to Step S403, and in subsequent Steps S403to S412, the same processing as in Steps S101to S110is executed based on the values set in Step S402.

In this way, configuring a single data processing device to be capable of executing a plurality of extraction modes enables easy setting of proper extraction conditions according to positions, etc., of ground control points on a road.

Preferred embodiments of the present invention are described above, however, the above-described embodiment and modifications are just examples of the present invention, and can be combined based on knowledge of a person skilled in the art, and such a combined one is also included in the scope of the present invention.

REFERENCE SIGNS LIST