3D data processing device, 3D data processing method, and program

Abstract

The objective is to provide a 3D data processing device, a 3D data processing method, and a program that can perform shape deformation on 3D point cloud data containing time-series information and efficiently create highly accurate 3D map data. [Solution] The 3D data processing device 101, which includes a preparation means 120, a division means 121, a movement amount calculation means 122, and a positioning means 123, can perform shape deformation on 3D point cloud data that has time-series information, and can automatically perform shape deformation on 3D point cloud data that has been divided at arbitrary distances based on the time-series information, thus contributing to the efficient and highly accurate creation of 3D map data.

Description

Technical Field

[0001] The present invention relates to a three-dimensional data processing apparatus, a three-dimensional data processing method, and a program for creating high-precision three-dimensional map data.

Background Art

[0002] It is known that position information data acquired by GNSS (Global Navigation Satellite System) has a measurement error depending on the surrounding situation such as satellites. Since the three-dimensional point cloud data with position information acquired by LiDAR using this position information data has problems in position accuracy, two-dimensional and three-dimensional map data with high position accuracy is used to perform shape deformation of each point cloud of the three-dimensional point cloud data. In order to perform shape deformation efficiently, the three-dimensional point cloud data is divided at predetermined distances, and shape deformation is performed for each divided region. However, since the amount of deformation is different for each divided region, there is a problem that the boundary part looks inappropriate as a map, such as having a gap when simply combining the divided regions after performing shape deformation.

[0003] In Patent Document 1, a shape deformation device for deforming a wide area into a smooth shape in order to smooth the surface of a three-dimensional shape is disclosed.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] [ However, in the invention of Patent Document 1, the shape of the object to be processed is divided into multiple deformation target regions (spheres) that overlap each other, and the destination position is determined by calculating a weighted average based on a weighting function that becomes smaller the further away from the center point of the sphere. Therefore, it is not possible to reflect time-series information such as the direction of travel of the vehicle in the destination position. Consequently, in the invention of Patent Document 1, if the purpose is to create a map, it may not be possible to perform positioning that takes into account measurement errors for each time series, and there is a risk that the amount of movement cannot be calculated with good accuracy.

[0006] This invention was proposed in view of the above circumstances, and aims to provide a 3D data processing device, a 3D data processing method, and a program that can perform shape deformation on 3D point cloud data containing time-series information and efficiently create highly accurate 3D map data. [Means for solving the problem]

[0007] To achieve the above objective, the 3D data processing device according to the present invention includes: preparation means for creating corresponding points between 3D point cloud information measured by a 3D point cloud measuring device and map information acquired from a map information storage unit; division means for dividing the 3D point cloud information with respect to the direction of travel of the 3D point cloud measuring device based on time-series information acquired by the 3D point cloud measuring device to create divided regions, wherein the divided regions include a first divided region and a second divided region, and the first divided region and the second divided region each include overlapping regions; and a map of the first divided region. The system includes a movement amount calculation means for calculating a movement amount for an overlapping region based on a first movement amount based on correspondence points with information and a second movement amount based on correspondence points with map information of a second divided region, and a positioning means for aligning the first divided region and the second divided region to create aligned 3D point cloud information, wherein the positioning of the overlapping region is performed based on the movement amount of the overlapping region, the positioning of the first divided region other than the overlapping region is performed based on the first movement amount, and the positioning of the second divided region other than the overlapping region is performed based on the second movement amount. [Effects of the Invention]

[0008] According to the present invention, shape deformation can be performed on 3D point cloud data containing time-series information acquired by LiDAR, and the shape deformation of 3D point cloud data divided at arbitrary distances based on the time-series information can be automatically performed, thus contributing to the efficient and highly accurate creation of 3D map data. [Brief explanation of the drawing]

[0009] [Figure 1] This is a configuration diagram of a three-dimensional data processing system according to an embodiment of the present invention. [Figure 2] This figure shows an example configuration of a three-dimensional data processing device according to an embodiment of the present invention. [Figure 3] This is a flowchart illustrating the preparation means for a three-dimensional data processing apparatus according to an embodiment of the present invention. [Figure 4] This is a flowchart illustrating the division means of a three-dimensional data processing device according to an embodiment of the present invention. [Figure 5] This is a flowchart illustrating the first movement amount calculation means of a three-dimensional data processing device according to an embodiment of the present invention. [Figure 6] This is a flowchart illustrating the second movement amount calculation means of a three-dimensional data processing device according to an embodiment of the present invention. [Figure 7] This is a flowchart illustrating the means for calculating the amount of movement of a three-dimensional data processing device according to an embodiment of the present invention. [Figure 8] This is an explanatory diagram illustrating the alignment means of a three-dimensional data processing device according to an embodiment of the present invention. [Figure 9] This is an explanatory diagram illustrating the division means of a three-dimensional data processing device according to an embodiment of the present invention. [Figure 10] This is an explanatory diagram illustrating the preparation means for a three-dimensional data processing apparatus according to an embodiment of the present invention. [Figure 11] This is an explanatory diagram illustrating the means for calculating the amount of movement of a three-dimensional data processing device according to an embodiment of the present invention. [Modes for carrying out the invention]

[0010] Embodiments of the present invention will be described below with reference to the drawings.

[0011] Referring to Figure 1, an example of the overall configuration of a 3D data processing system 100 in an embodiment of the present invention will be described. The 3D data processing system 100 includes a 3D data processing device 101 and a 3D point cloud measurement device 103. The 3D point cloud measurement device 103 is mounted on various objects such as a vehicle 102, a drone 107, or a person 108 for gait measurement. The objects on which the 3D point cloud measurement device 103 is mounted are not limited.

[0012] The 3D point cloud data acquired by the 3D point cloud measurement device 103 may be transmitted to the 3D data processing device 101 from the measurement company's terminal 106 or the like.

[0013] Although Figure 1 shows only one 3D data processing device 101 for convenience, multiple 3D data processing devices 101 may exist. For example, the 3D data processing device 101 and the measurement company's terminal 106 are interconnected via a network 104. Here, the network 104 may be the Internet, a LAN (Local Area Network), or a WAN (Wide Area Network), and is not particularly limited.

[0014] The 3D data processing device 101 is a computer device that performs the processing according to this embodiment. The 3D data processing device 101 may be a laptop computer device, a note computer device, a smartphone, or a tablet terminal. The 3D data processing device 101 may be implemented by a single computer device or by multiple computer devices. The components and functions of the 3D data processing device 101 will be described later.

[0015] The three-dimensional point cloud measurement device 103 is a MMS (Mobile Mapping System), and the MMS combines sensors such as a laser scanner using LiDAR technology, a digital camera, and a GNSS / IMU. As an example, when the vehicle 102 is traveling on the road 105, the mounted three-dimensional point cloud measurement device 103 acquires the spatial information of the vehicle 102 and surrounding objects. With the MMS, three-dimensional point cloud data, digital camera images, and panoramic images can be acquired. The three-dimensional point cloud data will be described later.

[0016] Next, referring to FIG. 2, an example of the detailed components of the three-dimensional data processing device 101 will be described.

[0017] The three-dimensional data processing device 101 includes a control unit 110, an input unit 111, an output unit 112, a memory unit 113, a transceiver unit 114, and a storage unit 116, and each of these elements is coupled by a system bus 115. Each component of the storage unit 116 will be described later.

[0018] The control unit 110 is also referred to as a processor and refers to, for example, a CPU. The control unit 110 controls the above-described components and executes data operations. The control unit 110 reads and executes a program from the memory unit 113 in order to execute each process according to the present embodiment. The program is a program for implementing the functions executed by the three-dimensional data processing device 101 and is stored in the storage unit 116.

[0019] The input unit 111 receives an operation instruction from the outside. Examples of the input unit 111 include a keyboard and a touch panel. The output unit 112 outputs the data stored in the storage unit 116 and the result of the arithmetic processing. Examples of the output unit 112 include a display and a printer.

[0020] The memory unit 113 is a volatile data storage device that stores data on the 3D data processing device 101, computer-executable instructions, and the like. The memory unit 113 is implemented using RAM such as DRAM (Dynamic Random Access Memory) or SRAM (Static Random Access Memory).

[0021] The transmitting / receiving unit 114 communicates with the 3D data processing device 101 and an external party, such as a measurement company's terminal 106, via the network 104.

[0022] The storage unit 116 is implemented by any storage device such as a ROM, HDD, or SSD. The storage unit 116 includes preparation means 120, division means 121, movement amount calculation means 122, and alignment means 123 related to processing performed by the 3D data processing device 101. Furthermore, the storage unit 116 includes a point cloud storage unit 130 for storing data handled by the 3D data processing device 101, a map information storage unit 131, a point cloud storage unit 132 for subsequent processing, a divided region storage unit 133, a movement amount storage unit 134, and a post-alignment point cloud storage unit 135.

[0023] It should be noted that the hardware components described in the above embodiment are merely illustrative, and other configurations are also possible.

[0024] The data stored in the point cloud storage unit 130, map information storage unit 131, subsequent processing point cloud storage unit 132, divided region storage unit 133, movement amount storage unit 134, and post-alignment point cloud storage unit 135 within the storage unit 116 of the 3D data processing device 101 will be explained below.

[0025] The point cloud storage unit 130 is a database that stores 3D point cloud data received from the 3D data processing device 101. For example, if the 3D point cloud measurement device 103 is mounted on the vehicle 102, the 3D point cloud data represents 3D point cloud information, which is 3D position information of the vehicle 102 and its surrounding objects acquired via the 3D point cloud measurement device 103.

[0026] The map information storage unit 131 stores highly accurate 3D data as map data. This map data is used to create a highly accurate map by aligning 3D point cloud data that has measurement errors. In this embodiment, for example, orthomosaic images are used as map data. Any 2D or 3D data, such as 2D map data consisting of road edges, 3D map data, or point cloud data, may be used as long as it has high positional accuracy.

[0027] The point cloud storage unit 132 for subsequent processing stores 3D point cloud data that has corresponding point pairs with map data. The corresponding point pairs will be described later.

[0028] The partitioned area storage unit 133 stores the first partitioned area data, the second partitioned area data, and the overlapping area data created by the partitioning means 121. The partitioning means 121, the first partitioned area data, the second partitioned area data, and the overlapping area data will be described later.

[0029] The movement amount storage unit 134 stores data indicating the movement amount of the overlapping area data, data indicating the movement amount of the first divided area data, and data indicating the movement amount of the second divided area data. The movement amounts of the overlapping area data, the first divided area data, and the second divided area data will be described later.

[0030] The point cloud storage unit 135 stores the 3D point cloud data after shape deformation has been performed and alignment with the map data has been carried out.

[0031] Next, referring to Figures 2 to 11, the preparation means 120, division means 121, movement amount calculation means 122, and alignment means 123 within the storage unit 116 of the 3D data processing device 101 will be described.

[0032] The processing of the preparation means 120 will be explained with reference to Figure 3.

[0033] The preparation means 120 receives 3D point cloud data from the measurement company's terminal 106 or the like and stores it in the point cloud storage unit 130 (step S301).

[0034] For example, a vehicle 102 equipped with a 3D point cloud measurement device 103 uses a laser scanner to acquire 3D point cloud data of the vehicle 102 and its surroundings for all areas where a map is to be created, while it is in motion. The vehicle 102 is not the only vehicle on which the 3D point cloud measurement device 103 can be installed.

[0035] In step S301, the 3D point cloud data transmitted by the measurement company's terminal 106 is received, but the means by which the preparation means 120 acquires the 3D point cloud data are not limited to this. The 3D point cloud data acquired by the 3D point cloud measurement device 103 may be transmitted to the 3D data processing device 101 in real time.

[0036] The 3D point cloud data is accompanied by time-series information. This time-series information includes time-series data indicating the time when the 3D point cloud measurement device 103 acquired the 3D point cloud data, and position data indicating the position of each point from which the 3D point cloud data was acquired, based on the measurement results of the GNSS / IMU. The time-series data and position data will be described later. The 3D point cloud measurement device 103 may also acquire continuous image data of surrounding objects using a digital camera while acquiring the 3D point cloud data.

[0037] The preparation means 120 obtains map data for the same location as the 3D point cloud data received in step S301 from the map information storage unit 131 in Figure 2 (step S302).

[0038] The preparation means 120 creates corresponding point pairs, which are positionally corresponding points, from the 3D point cloud data acquired from the point cloud storage unit 130 using map data, and stores the 3D point cloud data after the creation of the corresponding point pairs in the point cloud storage unit 132 for subsequent processing (step S303).

[0039] Figure 10 shows 3D point cloud data 1000 and map data 1001 of the same location. Compared to the photographic map data 1001, the 3D point cloud data 1000 has some issues with positional accuracy, and it is necessary to correlate the 3D point cloud data 1000 with the map data 1001. In the 3D point cloud data 1000, time-series data 1005a and 1005b (hereinafter referred to as "time-series data 1005") indicating the date and time the 3D point cloud data 1000 was acquired, and position data 1004a and 1004b (hereinafter referred to as "position data 1004") of the point where the 3D point cloud measurement device 103 of the vehicle 102 acquired the 3D point cloud data are shown for each scan line 1002a and 1002b (hereinafter referred to as "scan line 1002").

[0040] For example, in the 3D point cloud data 1000, the time series data 1005a, which indicates the time when the 3D point cloud data was acquired at scanline 1002a, is represented as "YYYY / MM / DD 13:05", and the position data 1004a, which indicates the point where the 3D point cloud data was acquired, is represented by the position coordinates "P1(X1,Y1,Z1)". Similarly, the time series data 1005b at scanline 1002b is represented as "YYYY / MM / DD 13:10", and the position data 1004b at point P2 is represented by the position coordinates "P2(X2,Y2,Z2)". From the time and position coordinates, it can be seen that the direction of travel of the vehicle 102 equipped with the 3D point cloud measurement device 103 is in the direction of the arrow.

[0041] Note that in Figure 10, for convenience, only two instances each of position data 1004 and time series data 1005 are shown, but the number of position data 1004 and time series data 1005 can be arbitrarily set according to the number of scan lines 1002.

[0042] For example, if the corner of a pedestrian crossing is used as a corresponding point, the corresponding point in the 3D point cloud data 1000 is indicated by "a", and the corresponding point in the map data 1001 is indicated by "a'". Corresponding points a and a' are each represented by position coordinates (x, y, z). In the case of Figure 10, the preparation means 120 creates corresponding points a and a' as a "corresponding point pair". The corresponding point pair is matched by superimposing the corresponding point a of the 3D point cloud data 1000 onto the corresponding point a' of the map data 1001 in three dimensions. Note that the corresponding point pairs may be managed in a CSV file or similar.

[0043] When the preparation means 120 creates corresponding point pairs, it is easy to identify corresponding parts and create corresponding point pairs at corners, such as the corner of the pedestrian crossing in Figure 10, but it is not possible to create many corresponding point pairs using only corners. Therefore, the preparation means 120 can create corresponding point pairs not only between points but also between points and lines, such as creating a corresponding point pair between the corresponding point "b" in the 3D point cloud data 1000 of the road, which is the straight line portion of Figure 10, and the corresponding point "b'" in the map data 1001, and the corresponding point "b" and the line connecting the corresponding point "b" and the corresponding point "b'".

[0044] Furthermore, corresponding point pairs can be weighted. For example, a corresponding point pair that is highly reliable as a map may be given a higher weight. Also, for example, if you want to investigate a specific object such as a manhole, you may give a higher weight to a corresponding point pair that includes a manhole, as it is considered a particularly important pair.

[0045] The processing of the dividing means 121 will be explained with reference to Figure 4.

[0046] The division means 121 reads the subsequent processing point cloud storage unit 132 shown in Figure 2 and acquires 3D point cloud data (step S401). Since the positional information of the 3D point cloud data may contain localized distortions, the 3D point cloud is divided before performing shape deformation in order to improve the accuracy of alignment.

[0047] The division means 121 determines the distance for dividing the 3D point cloud data with respect to the direction of travel of the object on which the 3D point cloud measurement device 103 is mounted (step S402).

[0048] For example, if the object on which the 3D point cloud measurement device 103 is mounted is a vehicle 102, the division means 121 grasps the passage of time from the time-series data 1005 of the read 3D point cloud data (Figure 10) and the passage of position from the position data 1004 to determine the direction of travel of the vehicle 102 on the 3D point cloud data. The division means 121 can arbitrarily set the division distance, such as every 500m.

[0049] The division means 121 divides the 3D point cloud data at arbitrary distances determined in step S402 with respect to the direction of travel of the object on which the 3D point cloud measurement device 103 is mounted, and creates first division region data, which is the first division region (step S403).

[0050] For example, in Figure 9, the direction of travel of the vehicle 102 is represented by an arrow, and the division means 121 performs division in the direction of the arrow. If the distance to be divided is 500m, the division means 121 divides the 3D point cloud data at the first division point 901, which is 500m from the first starting point 900, and creates the first divided region data.

[0051] Next, the division means 121 creates a second division region data, which is the second division region, while having an overlapping region with the first division region data.

[0052] In Figure 9, the division means 121 divides the 3D point cloud data at a second division point 903, which is 500m advanced from the second division point 902, which is set back relative to the direction of travel from the first division point 901, and creates a second divided region data.

[0053] At this point, overlapping region data is generated where the first and second divided region data overlap. If the divided regions are simply joined together, the amount of deformation differs for each divided region, resulting in gaps at the boundaries and making the map unsuitable. Therefore, overlapping region data is provided to create a highly accurate map by performing smooth shape deformation. The distance of the overlapping region data can be set arbitrarily.

[0054] The division means 121 stores the created first division region data, second division region data, and overlapping region data in the division region storage unit 133 in Figure 2 (step S404).

[0055] The processing of the movement amount calculation means 122 will be explained with reference to Figures 5 to 7. The division means 121 includes a first movement amount calculation means and a second movement amount calculation means.

[0056] The processing of the first displacement calculation means will be explained with reference to Figure 5.

[0057] The first movement amount calculation means reads the divided area storage unit 133 in Figure 2 and obtains the first divided area data (step S501).

[0058] The first movement amount calculation means calculates the movement amount of the first divided region (step S502).

[0059] In Figure 11, there is a reference point 1100b, which is one of the points in the 3D point cloud data before movement in the first divided region. The "shift" between reference point 1100b and its corresponding point on the map data, i.e., the amount of movement a', is generated as the "first amount of movement based on the corresponding point with the map information of the first divided region" data. Here, point 1104 is a point moved from reference point 1100b based on the amount of movement a'. The calculation of the first amount of movement can be performed using various existing methods.

[0060] Similarly, for the reference point 1100a in the overlapping area within the first divided area, a movement amount a, which is "a first movement amount based on the corresponding point with the map information of the first divided area," is generated, and point 1101 becomes a point moved from reference point 1100a based on movement amount a.

[0061] The first movement amount calculation means stores the "first movement amount based on the correspondence point with the map information of the first divided area" data in the movement amount storage unit 134 of Figure 2 as the movement amount data of the first divided area.

[0062] In Figure 11, for convenience, only two data points for the "first movement amount based on corresponding points with map information of the first divided region" are shown. However, the "first movement amount based on corresponding points with map information of the first divided region" can be calculated for any number of corresponding point pairs within the first divided region data, including overlapping regions.

[0063] The processing of the second movement amount calculation means will be explained with reference to Figure 6.

[0064] The second movement amount calculation means reads the divided area storage unit 133 in Figure 2 and acquires the second divided area data (step S601).

[0065] The second movement amount calculation means calculates the movement amount of the second divided region (step S602).

[0066] In Figure 11, the amount of movement b' relative to reference point 1100c (hereinafter referred to as "reference point 1100"), which is one of the points in the 3D point cloud data before movement in the second divided region, is generated as "second amount of movement based on the corresponding point with the map information of the second divided region" data. Here, point 1105 is a point moved from reference point 1100c based on the amount of movement b'.

[0067] Similarly, for the reference point 1100a in the overlapping area within the second divided region, a movement amount b is generated, which is "second movement amount data based on the corresponding point with the map information of the second divided region," and point 1102 becomes a point moved from reference point 1100a based on movement amount b. The calculation of the second movement amount can be performed using various existing methods.

[0068] The second movement amount calculation means stores the "second movement amount based on the correspondence point with the map information of the second divided area" data in the movement amount storage unit 134 of Figure 2 as the movement amount data for the second divided area.

[0069] In Figure 11, for convenience, only two data points for "second movement amount based on corresponding points with map information of the second divided region" are shown. However, "second movement amount based on corresponding points with map information of the second divided region" can be calculated for any number of corresponding point pairs within the second divided region data, including overlapping regions.

[0070] The process of the movement amount calculation means 122 will be explained with reference to Figure 7.

[0071] The movement amount calculation means 122 reads the movement amount storage unit 134 and obtains "first movement amount based on the correspondence point with the map information of the first divided area" data (step S701).

[0072] The movement amount calculation means 122 reads the movement amount storage unit 134 and obtains "second movement amount based on the correspondence point with the map information of the second divided area" data (step S702).

[0073] The movement amount calculation means 122 performs weighting in the overlapping region (step S703).

[0074] One method of weighting is to weight the reference point 1100a within the overlapping region based on whether it is closer to the first divided region data or the second divided region data.

[0075] For example, in Figure 11, reference point 1100a is shifted 0.2 towards the first divided region data and 0.8 towards the second divided region data within the overlapping region data. Therefore, the weighting on the first divided region data side is "0.2", and the weighting on the second divided region data side is "0.8".

[0076] A second weighting method involves weighting the data based on the ratio of the number of corresponding point pairs used in the first and second divided region data sets, assuming that a larger number of corresponding point pairs leads to more accurate alignment.

[0077] For example, in Figure 11, if the first divided region contains 70 corresponding point pairs and the second divided region contains 30 corresponding point pairs, the weighting of the overlapping region on the first divided region data side will be "0.7" and the weighting on the second divided region data side will be "0.3".

[0078] A third weighting method involves weighting based on the ratio of the number of corresponding point pairs near the reference point, under the assumption that the displacement applied to corresponding point pairs near the reference point should also be applied to the reference point.

[0079] For example, in Figure 11, if within a radius of 50m from the reference point, the first divided region data contains 30 corresponding point pairs and the second divided region data contains 70 corresponding point pairs, then the weighting of the overlapping region on the first divided region data side will be "0.3" and the weighting on the second divided region data side will be "0.7".

[0080] A fourth method of weighting is to combine the first to third weighting methods described above. For example, the weights of the first divided region data determined by the first weighting method, the weights of the first divided region data determined by the second weighting method, and the weights of the first divided region data determined by the third weighting method are added together, and the average is calculated by dividing by 3.

[0081] The movement amount calculation means 122 multiplies the movement amount a of the overlapping area obtained in step S502 and the movement amount b of the overlapping area obtained in step S602 by the weight calculated in step S703 to calculate a "first movement amount based on correspondence points with map information of the first divided area" including the weighting, and a "second movement amount based on correspondence points with map information of the second divided area" including the weighting.

[0082] For example, when using the first weighting method, the "first movement amount based on the corresponding points with map information of the first divided region," including the weighting, will be the result of multiplying the weight "0.2" by the movement amount a. Similarly, the "second movement amount based on the corresponding points with map information of the second divided region," including the weighting, will be the result of multiplying the weight "0.8" by the movement amount b.

[0083] The movement amount calculation means 122 calculates the movement amount of the overlapping area data based on "first movement amount based on corresponding points with map information of the first divided area" data including weighting and "second movement amount based on corresponding points with map information of the second divided area" data including weighting, and stores it as overlapping area movement amount data in the movement amount storage unit 134 in Figure 2 (step S704).

[0084] In Figure 11, the amount of movement c for the overlapping area is calculated by combining the "first amount of movement based on the corresponding points with the map information of the first divided area" (weighted movement a) and the "second amount of movement based on the corresponding points with the map information of the second divided area" (weighted movement b), which also includes weighting. Point 1103 is the point moved from the reference point 1100a based on movement c.

[0085] The process of the alignment means 123 will be explained with reference to Figure 8.

[0086] The alignment means 123 reads the divided area storage unit 133 in Figure 2 and obtains the first divided area data, the second divided area data, and the overlapping area data (step S801).

[0087] The alignment means 123 reads the movement amount storage unit 134 in Figure 2 to obtain the movement amount of the first divided region, the movement amount of the second divided region, and the movement amount of the overlapping region (step S802).

[0088] The alignment means 123 aligns the 3D point cloud data of the overlapping region data with the corresponding point pairs in the map data based on the amount of movement of the overlapping region acquired in step S802. Similarly, the alignment means 123 aligns the corresponding point pairs of the first divided regions other than the overlapping region based on the amount of movement of the first divided region, and aligns the corresponding point pairs of the second divided regions other than the overlapping region based on the amount of movement of the second divided region. As an example, in Figure 11, the alignment of the corresponding point pairs of the first divided regions other than the overlapping region is performed based on the amount of movement a', and the alignment of the corresponding point pairs of the second divided regions other than the overlapping region is performed based on the amount of movement b'.

[0089] When aligning corresponding point pairs in overlapping region data based on the amount of movement of the overlapping region, a transformation matrix for the corresponding points in the 3D point cloud data is calculated so that the sum of the amounts of movement of all overlapping regions is minimized. When aligning the first division region (excluding the overlapping region) and the second division region (excluding the overlapping region), a matrix with a combination of three elements—scaling, translation, and rotation around the coordinate axes—is applied to the point cloud of the 3D point cloud data to perform the alignment. Here, if the corresponding point pairs are weighted using the preparation means 120 as described above, a transformation matrix is ​​calculated that prioritizes the corresponding point pairs with higher weights.

[0090] The alignment means 123 stores the aligned 3D point cloud data in the post-alignment point cloud storage unit 135 shown in Figure 2 (step S803). The aligned 3D point cloud data may be used for driver assistance, including autonomous driving.

[0091] The order of processing described in the above embodiments does not necessarily have to be followed; it may be performed in any order, or multiple processes may be performed simultaneously. Furthermore, additional processing may be added without departing from the basic concepts of the present invention.

[0092] As described above, the 3D data processing device 101 according to this embodiment is configured as described. Next, the effects of the 3D data processing device 101 will be explained.

[0093] According to this embodiment, the division means 121 divides the 3D point cloud measurement device at arbitrary distances relative to the direction of travel to create divided regions, so that the division can be performed with an appropriate size according to the file size of the 3D point cloud data.

[0094] According to this embodiment, the amount of movement in the overlapping area is calculated based on a first amount of movement including weighting and a second amount of movement including weighting. Since the weighting is performed based on at least one of an arbitrary reference point and a corresponding point within the overlapping area, the weighting method can be freely set using the reference point, the corresponding point, or both within the overlapping area. By selecting the weighting method, high-precision 3D map data can be created efficiently or with even higher precision.

[0095] According to this embodiment, the movement amount calculation means 122 includes a first movement amount calculation means that calculates a first movement amount based on corresponding points with map information of the first divided area, and a second movement amount calculation means that calculates a second movement amount based on corresponding points with map information of the second divided area. Therefore, it is possible to align not only the overlapping area but also the first divided area other than the overlapping area and the second divided area other than the overlapping area.

[0096] Although embodiments of the present invention have been described above, it goes without saying that the present invention is not limited to these embodiments and can be implemented in various ways without departing from its spirit. For example, the roles of the means 120 to 123 of the storage unit 116 of the 3D data processing device 101 are not limited to the examples described above. [Explanation of symbols]

[0097] 100 3D Data Processing Systems 101 3D Data Processing Device 102 vehicles 103 3D Point Cloud Measurement Device 104 Network 105 Road 106 Measurement company's terminal 107 Drones 108 humans 110 Control Unit 111 Input Section 112 Output section 113 Memory section 114 Transmitter / Receiver 115 System Bus 116 Storage section 120 Preparation Means 121 Division means 122 Movement amount calculation means 123 Alignment means 130 Point cloud storage 131 Map Information Storage Unit 132 Point cloud storage unit for subsequent processing 133 Divided area storage unit 134 Movement amount storage section 135 Point cloud storage unit after alignment

Claims

1. A preparatory means for creating correspondence points between 3D point cloud information measured by a 3D point cloud measurement device and map information acquired from a map information storage unit, A division means for dividing the three-dimensional point cloud information into division regions in the direction of travel of the three-dimensional point cloud measuring device based on time-series information acquired by the three-dimensional point cloud measuring device, wherein the division regions include a first division region and a second division region, and each of the first division region and the second division region includes an overlapping region, Movement amount calculation means for calculating the movement amount of the overlapping area based on a first movement amount based on the corresponding points with the map information of the first divided area and a second movement amount based on the corresponding points with the map information of the second divided area, A three-dimensional data processing device comprising: alignment means for aligning the first divided region and the second divided region to create the aligned three-dimensional point cloud information, wherein the alignment of the overlapping region is performed based on the amount of movement of the overlapping region, the alignment of the first divided region other than the overlapping region is performed based on the first amount of movement, and the alignment of the second divided region other than the overlapping region is performed based on the second amount of movement.

2. The three-dimensional data processing device according to claim 1, wherein the division means divides the three-dimensional point cloud measuring device at arbitrary distances relative to the direction of travel to create divided regions.

3. The 3D data processing apparatus according to claim 1, wherein the amount of movement of the overlapping region is calculated based on a first amount of movement including weighting and a second amount of movement including weighting, and the weighting is performed based on at least one of an arbitrary reference point in the overlapping region and the corresponding point.

4. The aforementioned means for calculating the amount of movement is: A first movement amount calculation means calculates the first movement amount based on the correspondence points with the map information of the first divided area, A second movement amount calculation means calculates the second movement amount based on the correspondence points with the map information of the second divided region. A three-dimensional data processing apparatus according to claim 1 or 3, including the above.

5. A three-dimensional data processing method performed by a three-dimensional data processing device, The steps include creating correspondence points between 3D point cloud information measured by a 3D point cloud measurement device and map information acquired from a map information storage unit, A step of dividing the three-dimensional point cloud information into divided regions in the direction of travel of the three-dimensional data processing device based on time-series information acquired by the three-dimensional point cloud measuring device, wherein the divided regions include a first divided region and a second divided region, and the first divided region and the second divided region each include an overlapping region, A step of calculating the amount of movement of the overlapping area based on a first amount of movement based on the corresponding points with the map information of the first divided area and a second amount of movement based on the corresponding points with the map information of the second divided area, A method comprising the steps of: creating three-dimensional point cloud information after alignment by aligning the first divided region and the second divided region, wherein the alignment of the overlapping region is performed based on the amount of movement of the overlapping region, the alignment of the first divided region other than the overlapping region is performed based on the first amount of movement, and the alignment of the second divided region other than the overlapping region is performed based on the second amount of movement.

6. A program for causing a computer to perform the method described in claim 5.

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