JP2023059688A - Device, method and program for point group data processing - Google Patents

Abstract

To provide a device, a method and a program for point group data processing that optimize three-dimensional point group data of a processing object.SOLUTION: A device A for point group data processing determines whether or not loss of data exists in three-dimensional point group data of a target object, applies loss determination interpolation processing for interpolating the loss of data if it is determined that loss of data exists, determines whether or not outlier data deviating from a data value expressing the target object exists in the three-dimensional point group data after the loss determination interpolation processing, applies outlier determination elimination processing for eliminating the outlier data if it is determined that the outlier data exists, and repeatedly performs the loss determination interpolation processing and the outlier determination elimination processing until the outlier data no longer is determined to exist or until a prescribed upper limit frequency is attained.SELECTED DRAWING: Figure 1

Description

The present invention relates to a point cloud data processing device, a point cloud data processing method, and a point cloud data processing program for processing three-dimensional point cloud data to be processed.

2. Description of the Related Art In recent years, automation has progressed in various fields, and technical developments have been made. The information processing device disclosed in Patent Document 1 is an information processing device for estimating the height of dust accumulated in a dust pit. Height for calculating the height of the dust at the boundary from the positional relationship between the boundary specifying unit that specifies the boundary between the a calculating unit, wherein the height calculating unit calculates the height of the dust at the boundary between the plurality of wall surfaces of the dust pit and the dust, and calculates the height of the dust in the area inside the boundary. , the height of the dust at the boundary is calculated from the weighted average according to the distance to the boundary.

JP 2018-173248 A

By the way, it is necessary to recognize the target object for the automation, and it is known to represent the target object with three-dimensional point group data and recognize it as one of them. For example, when generating 3D point cloud data of a target object with a 3D point cloud data generation device that generates 3D point cloud data, such as LiDAR, between the 3D point cloud data generation device and the target object In addition, if some other object exists, the other object may create a blind spot in the 3D point cloud data generation device, or the 3D point cloud data may contain the data of the other object as noise with respect to the data of the target object. There is a risk that it will fall off.

The present invention has been made in view of the above circumstances, and its object is to provide a point cloud data processing device, a point cloud data processing method, and a point cloud data processing program capable of optimizing three-dimensional point cloud data to be processed. is to provide

As a result of various studies, the inventors of the present invention have found that the above object can be achieved by the present invention described below. That is, the point cloud data processing device according to one aspect of the present invention provides data for acquiring three-dimensional point cloud data in a predetermined range in a predetermined three-dimensional coordinate space generated by measuring a predetermined target object. an acquisition unit for determining whether or not there is data loss in the three-dimensional point cloud data, and loss determination interpolation for performing loss determination interpolation processing for interpolating the data loss when it is determined that there is a data loss. and determining whether or not outlier data deviating from the data value representing the target object exists in the three-dimensional point cloud data after being processed by the loss determination interpolation unit, and determining whether the outlier data exists. When it is determined that there is outlier data, an outlier determination removal unit that performs an outlier determination removal process for removing the outlier data, and until the outlier data is no longer determined to be present in the outlier determination removal unit, or a predetermined and a repetition processing unit that causes the loss determination interpolation unit and the outlier determination removal unit to repeatedly perform the loss determination interpolation processing and the outlier determination removal processing until an upper limit number of times of is reached. Preferably, in the point cloud data processing device described above, the three-dimensional point cloud data to be subjected to the loss determination interpolation process is the three-dimensional point cloud data acquired by the data acquisition unit at the first time, and The following is the three-dimensional point cloud data after being processed by the outlier determination/removal unit. Preferably, in the point cloud data processing device described above, the loss determination interpolating unit causes the loss determination interpolating unit to sequentially perform the loss determination interpolation processing on all the data within the predetermined range. Repeat up to a predetermined number of times (loop count). Preferably, in the point cloud data processing device described above, the outlier determination/removal unit determines whether or not there is an outlier by using a threshold value for determining whether or not it is outlier data. do.

Such a point cloud data processing device interpolates the missing data when it is determined that there is a missing data in the 3D point cloud data. Missing data can be interpolated even if The point cloud data processing device excludes the outlier data when it is determined that there is outlier data in the 3D point cloud data. Noise such as data caused by other objects other than the object can be removed. Therefore, the point cloud data processing device can optimize the acquired three-dimensional point cloud data to be processed.

In another aspect, in the point cloud data processing device described above, the loss determination interpolation unit determines whether or not there is at least one data loss in the three-dimensional point cloud data acquired by the data acquisition unit, When it is determined that there is at least one data defect, the data defect is interpolated, and when it is determined that there is no data defect, it is determined that there is no data defect 3 The dimensional point cloud data is assumed to be three-dimensional point cloud data after being processed by the loss determination interpolation unit.

Such a point cloud data processing device first determines whether or not there is at least one missing data in the three-dimensional point cloud data acquired by the data acquisition unit. If the point cloud data does not have any data loss, the loss determination interpolation process can be omitted.

In another aspect, in the point cloud data processing device described above, the loss determination interpolating unit includes a loss determination unit that determines whether or not each data of the three-dimensional point cloud data has a loss. and a loss interpolation unit that interpolates the loss of the data when the loss determination unit determines that the data is missing, and the loss interpolation unit surrounds the missing data with non-missing data. When there are more than half of the data, the missing data is interpolated based on the non-missing data around the missing data. Preferably, in the point cloud data processing device described above, the missing interpolation section interpolates the missing data by using the average value of the non-missing data as the data value of the missing data.

If each data is missing at each coordinate point adjacent to each other in the three-dimensional coordinate space, it may not be possible to interpolate with an appropriate data value. The point cloud data processing device interpolates the missing data based on the non-missing data around the missing data when half or more of the missing data surrounds the missing data. Can be interpolated with more appropriate data values.

In another aspect, in the above-described point cloud data processing device, the outlier determination removal unit removes each data of the three-dimensional point cloud data after being processed by the loss determination interpolation unit. By calculating the difference between each of the surrounding data and comparing the average value of each difference calculated for each of the surrounding data with a threshold value for determining whether or not it is the outlier data, the It is determined whether or not the data is the outlier data.

Since the outlier is a value that deviates from the data value representing the target object, it is considered that the change in the data value is large, and the data of the outlier is considered to deviate from the surrounding data. The above-described point cloud data processing apparatus considers the data surrounding the data to be determined when determining outlier data, so it can more appropriately determine whether or not the data is outlier data.

In another aspect, in the point cloud data processing device described above, new three-dimensional point cloud data is generated by thinning data from the three-dimensional point cloud data acquired by the data acquisition unit, and the generated new a thinning unit that converts such three-dimensional point cloud data into three-dimensional point cloud data for which the loss determination interpolation process is performed by the loss determination interpolation unit. Preferably, in the point cloud data processing device described above, the thinning section thins out data from the three-dimensional point cloud data acquired by the data acquisition section according to a predetermined rule. Preferably, in the above-described point cloud data processing device, the thinning unit arranges the data at equal intervals at predetermined intervals when viewed in an axial direction along one coordinate axis forming the three-dimensional coordinate space. Data is thinned out from the three-dimensional point cloud data acquired by the data acquisition unit.

Such a point cloud data processing device generates new three-dimensional point cloud data by thinning data from the three-dimensional point cloud data (original three-dimensional point cloud data) acquired by the data acquisition unit, and generates new three-dimensional point cloud data. Since the new three-dimensional point cloud data is processed, the amount of information processing can be reduced, and the information processing time can be shortened.

In another aspect, in the point cloud data processing device described above, the three-dimensional point cloud data is obtained by measuring the waste stored in a storage unit in an incineration facility for incinerating waste as the target object. 3D point cloud data generated by

In incineration facilities, overhead cranes, etc., may be installed to stir and transport the waste stored in the storage unit. can cause blind spots and noise. As a result, the three-dimensional point cloud data obtained by measuring the waste may include defects or outliers, and the situation of the waste may not be properly recognized. The above point cloud data processing device corrects by interpolating deficiencies and excluding outlier data, so that it is possible to appropriately recognize the state of waste.

A point cloud data processing method according to another aspect of the present invention is data for acquiring three-dimensional point cloud data in a predetermined range in a predetermined three-dimensional coordinate space generated by measuring a predetermined target object. an acquisition step, a loss determination interpolation step of determining whether or not there is data loss in the three-dimensional point cloud data, and interpolating the data loss when it is determined that there is a data loss, and the loss determination. Determining whether or not the three-dimensional point cloud data after processing in the interpolation step includes outlier data that deviates from the data value representing the target object, and if it is determined that there is outlier data, an outlier determination/removal step of removing the outlier data; and the loss determination/interpolation step until it is no longer determined that there is outlier data in the outlier determination/removal step or until a predetermined upper limit number of times is reached. and a repeating step of repeatedly performing each of the outlier determination removing steps.

A point cloud data processing program according to another aspect of the present invention causes a computer to generate three-dimensional point cloud data in a predetermined range in a predetermined three-dimensional coordinate space generated by measuring a predetermined target object. A data acquisition unit to be acquired, determining whether or not there is data loss in the three-dimensional point cloud data, and performing loss determination interpolation processing for interpolating the data loss when it is determined that there is a data loss. determining whether or not outlier data deviating from data values representing the target object exists in the three-dimensional point cloud data after being processed by the determination interpolation unit and the loss determination interpolation unit; If it is determined that there is outlier data, until it is no longer determined that there is outlier data by the outlier determination removal unit that performs outlier determination removal processing for removing the outlier data, and the outlier determination removal unit, or and a repetition processing unit that causes the loss determination interpolation unit and the outlier determination removal unit to repeatedly perform the loss determination interpolation processing and the outlier determination removal processing until a predetermined upper limit number of times is reached. It's a program.

Such a point cloud data processing method and a point cloud data processing program interpolate the missing data when it is determined that there is a missing data in the 3D point cloud data, so that, for example, the 3D point cloud data is generated. Even if a loss occurs due to a blind spot when doing so, the missing data can be interpolated. The point cloud data processing method and the point cloud data processing program exclude outlier data when it is determined that there is outlier data in the three-dimensional point cloud data, so that, for example, three-dimensional point cloud data is generated. Noise such as data generated by an object other than the target object can be removed. Therefore, the point cloud data processing method and the point cloud data processing program can optimize the acquired three-dimensional point cloud data to be processed.

A point cloud data processing device, a point cloud data processing method, and a point cloud data processing program according to the present invention can optimize three-dimensional point cloud data to be processed.

It is a block diagram which shows the structure of the point cloud data processing apparatus in embodiment. As an example, it is a schematic side view of a receiving pit. It is a schematic diagram for demonstrating the method of interpolation. FIG. 10 is a schematic diagram for explaining how to obtain an average value of differences; It is a flow chart which shows the operation of the point cloud data processing device. As an example, it is a three-dimensional graph showing the result of applying the present embodiment in an incineration facility that is actually in operation. As an example of a modified form, it is a three-dimensional graph showing the results of applying the present embodiment in an incineration facility that is actually in operation.

One or more embodiments of the invention are described below with reference to the drawings. However, the scope of the invention is not limited to the disclosed embodiments. It should be noted that the configurations denoted by the same reference numerals in each figure indicate the same configurations, and the description thereof will be omitted as appropriate. In the present specification, reference numerals with suffixes omitted are used when referring to generically, and reference numerals with suffixes are used when referring to individual configurations.

A point cloud data processing device according to an embodiment includes a data acquisition unit that acquires three-dimensional point cloud data in a predetermined range in a predetermined three-dimensional coordinate space generated by measuring a predetermined target object; a loss determination interpolation unit that determines whether or not there is data loss in dimensional point cloud data, and performs loss determination interpolation processing for interpolating the data loss when it is determined that there is a data loss; Determining whether or not the three-dimensional point cloud data processed by the interpolation unit includes outlier data that deviates from the data value representing the target object, and if it is determined that there is outlier data, an outlier determination removal unit for performing outlier determination removal processing for removing the outlier data, and until the outlier data is no longer judged to exist in the outlier determination removal unit, or a predetermined upper limit number of times is reached. a repetition processing unit that causes the loss determination interpolation unit and the outlier determination removal unit to repeatedly perform the loss determination interpolation processing and the outlier determination removal processing up to and including each of the loss determination interpolation processing and the outlier determination removal processing. Hereinafter, such a point cloud data processing device will be described more specifically.

FIG. 1 is a block diagram showing the configuration of a point cloud data processing device according to an embodiment. FIG. 2 is a schematic side view of a receiving pit as an example. FIG. 3 is a schematic diagram for explaining the method of interpolation. FIG. 3A shows a case where the coordinate position of the data to be interpolated is a corner (corner, vertex) when the predetermined range is a rectangle when viewed from the Z direction, and FIG. 3B shows the data to be interpolated. FIG. 3C shows a case where the coordinate position is an edge, and FIG. 3C shows a case where the coordinate position of the data to be interpolated is a position excluding the corner and the edge. FIG. 4 is a schematic diagram for explaining how to find the average value of differences. FIG. 4A shows a case where the predetermined range is a rectangle when viewed from the Z direction, and the coordinate position of the target data for calculating the average value of the difference is a corner (corner, vertex), and FIG. FIG. 4C shows a case where the coordinate position of the data of is an edge, and FIG. 4C shows a case where the coordinate position of the target data is a position excluding the corner and the edge.

For example, as shown in FIG. 1, the point cloud data processing apparatus A in the embodiment includes a data acquisition unit 1, a control processing unit 2, an input unit 3, an output unit 4, and an interface unit (IF unit) 5. , and a storage unit 6 .

The data acquisition unit 1 is connected to the control processing unit 2, and according to the control of the control processing unit 2, a three-dimensional point in a predetermined range in a predetermined three-dimensional coordinate space generated by measuring a predetermined target object. A device for acquiring group data.

The predetermined target object is arbitrary as long as three-dimensional point cloud data can be generated, and the predetermined range is also arbitrary. For example, the predetermined target object is the waste contained in a container in an incineration facility for incinerating waste. In such a case, the three-dimensional point cloud data are three-dimensional point cloud data generated by measuring the waste as the target object. In one example, the storage unit is a receiving pit PT that is provided in a refuse incineration facility for incinerating waste refuse and receives the refuse refuse, as shown in FIG. 2, for example. The receiving pit PT is a substantially rectangular parallelepiped empty space (recess) whose bottom surface and wall surfaces are made of concrete and whose top surface is open. The receiving pit PT is equipped with a crane CR supported by and suspended from the crane garter CG. When a three-dimensional XYZ orthogonal coordinate system is set as shown in FIG. 2, the crane CR is configured to be movable along the Z direction (vertical direction on the paper surface) with respect to the crane garter CG, and is guided by the crane garter CG. The crane garter CG is configured to be movable along the X direction (horizontal direction on the paper surface), and the crane garter CG is configured to be movable along the Y direction (front and rear direction on the paper surface) while being guided by runway rails (not shown) extending in the Y direction. be. Thereby, the crane CR is configured to be movable in each of the three-dimensional directions of the Z direction, the X direction and the Y direction. An input hopper HP is provided adjacent to the receiving pit PT. The input hopper HP is connected to an incinerator (not shown), and the waste picked up by the crane CR from the receiving pit PT is input to the input hopper HP, whereby the waste is introduced into the incinerator and incinerated. be. The crane CR of such an incineration facility is used, for example, to agitate the waste in the receiving pit PT and to transport the waste.

The data acquisition unit 1 is, for example, a measuring instrument MD that generates three-dimensional point cloud data by measuring a predetermined target object. The measurement device MD is, for example, a LiDAR (Light Detection and Ranging, three-dimensional laser scanner), a stereo camera rangefinder, or the like, and generates three-dimensional point cloud data representing each position (shape) on the target surface. . LiDAR generally obtains the distance to the surface of an object by a so-called TOF (Time of Flight) method by transmitting and receiving measurement pulse waves such as light and ultrasonic waves while scanning. A stereo camera type rangefinder generally obtains a parallax based on a pair of left and right images captured by a pair of left and right stereo cameras arranged at a base length distance so that the optical axes are parallel to each other, Based on the obtained parallax, the distance to the object surface is obtained based on the so-called triangulation principle. For example, in the case shown in FIG. 2, such a measuring meter MD is arranged above the receiving pit PT so that the entire top surface of the waste collected in the receiving pit PT can be measured from a bird's-eye view. In the example shown in FIG. 2, the measurement meter MD is arranged at the upper end of one side wall of the receiving pit PT. In the example shown in FIG. 2, the predetermined range is a rectangle when viewed from the Z direction.

Alternatively, for example, the data acquisition unit 1 may be an input unit that inputs three-dimensional point cloud data of a target object generated by measuring with such a measurement meter MD. In this case, the data acquisition unit 1 may also serve as the input unit 3, which will be described later.

Alternatively, for example, the data acquisition unit 1 may be an interface circuit for inputting and outputting data with an external device. In this case, the external device stores 3D point cloud data of the target object. It is a storage medium that The storage medium is, for example, a USB (Universal Serial Bus) memory, an SD card (registered trademark), or the like. Alternatively, for example, the data acquisition unit 1 may be a drive device that reads data from a recording medium recording the 3D point cloud data of the target object. In this case, the recording medium is, for example, a CD-ROM (Compact Disc Read Only Memory), CD-R (Compact Disc Recordable), DVD-ROM (Digital Versatile Disc Read Only Memory) and DVD-R (Digital Versatile Disc Recordable). Alternatively, for example, the data acquisition unit 1 may be a communication interface circuit that transmits and receives communication signals to and from an external device. In this case, the external device is a network (WAN (Wide Area Network, public communication network ), a LAN (Local Area Network), etc.) to manage the three-dimensional point cloud data of the target object. When the data acquisition unit 1 is the above-described interface circuit or communication interface circuit, the data acquisition unit 1 may also serve as the IF unit 5 described later.

The input unit 3 is connected to the control processing unit 2, for example, various commands such as a command to start processing, and a point cloud data processing device A such as a target object name (three-dimensional point cloud data name of the target object) It is a device that inputs various data necessary for operating the point cloud data processing device A, for example, a plurality of input switches to which predetermined functions are assigned, a keyboard, a mouse, etc. The output unit 4 is a device that is connected to the control processing unit 2 and outputs commands and data input from the input unit 3, three-dimensional point cloud data of processing results, etc. according to the control of the control processing unit 2, Examples include display devices such as CRT displays, liquid crystal displays and organic EL displays, and printing devices such as printers.

A so-called touch panel may be configured from the input unit 3 and the output unit 4 . In the case of constructing this touch panel, the input unit 3 is a position input device for detecting and inputting an operation position, such as a resistive film method or a capacitive method, and the output unit 4 is a display device. In this touch panel, the position input device is provided on the display surface of the display device, one or a plurality of input content candidates that can be input are displayed on the display device, and the input content that the user wants to input is displayed. When a position is touched, the position is detected by the position input device, and the display content displayed at the detected position is input to the point cloud data processing device A as user operation input content. With such a touch panel, the user can intuitively understand the input operation, so that the point cloud data processing device A is provided which is easy for the user to handle.

The IF unit 5 is a circuit that is connected to the control processing unit 2 and performs data input/output with an external device according to the control of the control processing unit 2. For example, an interface circuit of RS-232C which is a serial communication method. , an interface circuit using the Bluetooth (registered trademark) standard, an interface circuit for infrared communication such as the IrDA (Infrared Data Association) standard, and an interface circuit using the USB (Universal Serial Bus) standard. The IF section 5 is a circuit for communicating with an external device, and may be, for example, a data communication card or a communication interface circuit conforming to the IEEE802.11 standard.

The storage unit 6 is a circuit that is connected to the control processing unit 2 and stores various predetermined programs and various predetermined data under the control of the control processing unit 2 . The various predetermined programs include, for example, a control processing program, and the control processing program controls each unit 1, 3 to 6 of the point cloud data processing apparatus A according to the function of each unit. A program, a loss determination interpolation program for determining whether or not there is data loss in the three-dimensional point cloud data, and interpolating the data loss when it is determined that there is a data loss, and the loss determination interpolation It is determined whether or not the three-dimensional point cloud data processed by the program has outlier data that deviates from the data value representing the target object, and if it is determined that there is outlier data, the An outlier determination removal program for removing outlier data, or until the outlier data is no longer determined to exist in the outlier determination removal program, or until a predetermined upper limit number of times is reached, the loss determination interpolation program and A repetitive processing program that repeatedly executes each of the outlier determination removal programs, and a new three-dimensional point cloud data is generated by thinning data from the three-dimensional point cloud data acquired by the data acquisition unit 1, and the generated new 3D point cloud data to be 3D point cloud data used in the loss determination interpolation program. The various predetermined data include the target object name (three-dimensional point cloud data name of the target object), the predetermined upper limit number of times, and a threshold value ( data necessary for executing each of these programs, such as outlier determination threshold). Such a storage unit 6 includes, for example, a ROM (Read Only Memory) that is a non-volatile storage element, an EEPROM (Electrically Erasable Programmable Read Only Memory) that is a rewritable non-volatile storage element, and the like. The storage unit 6 includes a RAM (Random Access Memory) or the like that serves as a so-called working memory of the control processing unit 2 that stores data generated during execution of the predetermined program. Note that the storage unit 6 may include a hard disk device capable of storing a large amount of data in order to store relatively large amount of learning data.

The control processing unit 2 controls each unit 1, 3 to 6 of the point cloud data processing device A according to the function of each unit, and processes the three-dimensional point cloud data to be processed so as to optimize it. is the circuit of The control processing unit 2 is configured including, for example, a CPU (Central Processing Unit) and its peripheral circuits. The control processing unit 2 is functionally configured with a control unit 21, a thinning unit 22, a loss determination interpolation unit 23, an outlier determination removal unit 24, and a repetition processing unit 25 by executing the control processing program. .

The control unit 21 controls each unit 1, 3 to 6 of the point cloud data processing apparatus A according to the function of each unit, and controls the point cloud data processing apparatus A as a whole.

The thinning unit 22 generates new three-dimensional point cloud data by thinning data from the three-dimensional point cloud data (original three-dimensional point cloud data) acquired by the data acquisition unit 1, and extracts the generated new three-dimensional point cloud data. The dimensional point cloud data is used as three-dimensional point cloud data on which the loss determination interpolation processing is performed by the loss determination interpolation unit 23 . The thinning unit 22 thins out data from the original three-dimensional point cloud data according to a preset rule. For example, in the case shown in FIG. 2, the original three-dimensional point cloud data is data representing the top surface shape of the waste collected in the receiving pit PT. By thinning out the data from the original three-dimensional point cloud data so that the data are arranged at equal intervals at predetermined intervals when viewed from the axial direction along one of the constituting coordinate axes, for example, the Z axis, Generate new 3D point cloud data. The predetermined interval for thinning out is appropriately set in advance in consideration of, for example, the resolution (spatial resolution) of the original three-dimensional point cloud data, the resolution (spatial resolution) required for recognizing the target object, and the like.

A loss determination interpolation unit 23 determines whether or not there is a data loss in the three-dimensional point cloud data, and performs loss determination interpolation processing for interpolating the data loss when it is determined that there is a data loss. It is. The loss determination interpolation process is repeatedly executed by the repetition processing unit 25, and the three-dimensional point cloud data to be subjected to the loss determination interpolation process is acquired by the data acquisition unit 1 at the first time in the repeated execution. 3D point cloud data after being processed by the outlier determination/removal unit from the second time onward in the repeated execution. In the present embodiment, the three-dimensional point cloud data acquired by the data acquisition unit 1 is thinned out by the thinning unit 22, so that the three-dimensional point cloud data to be subjected to the loss determination interpolation process is, at the first time, The three-dimensional point cloud data acquired by the data acquisition unit 1 and thinned by the thinning unit 22 is obtained. Determination of whether or not the data is missing is determined as the data missing, for example, when the data itself does not exist. Alternatively, for example, the determination of whether or not the data is missing is performed when the data itself exists but the data value satisfies the condition (missing determination condition) for determining that the data is missing (for example, When the data value is less than a preset threshold (loss determination threshold) for determining whether or not the data is missing, the data is determined to be missing.

In this embodiment, the loss determination interpolation unit 23 further determines whether or not there is at least one data loss in the three-dimensional point cloud data acquired by the data acquisition unit 1, and determines whether or not there is at least one data loss. When it is determined that there is a missing data, the missing data is interpolated, and when it is determined that there is no missing data, the three-dimensional point cloud data determined as having no missing data is treated as an outlier. The three-dimensional point cloud data after being processed by the defect determination interpolating unit 23 is used by the determination removing unit 24 . In the present embodiment, the three-dimensional point cloud data acquired by the data acquisition unit 1 is thinned out by the thinning unit 22 . Determining whether or not there is at least one data defect in the dimensional point cloud data, interpolating the data defect when it is determined that there is at least one data defect, and determining that there is not even one data defect 3D point cloud data after processing by the loss determination interpolation unit 23, which is used in the outlier determination removal unit 24 when it is determined that there is no data loss. and

The loss determination interpolation unit 23 relates to loss determination and interpolation thereof, and more specifically, functionally includes a loss determination unit 231 and a loss interpolation unit 232 .

The loss determining unit 231 determines whether or not each data of the three-dimensional point cloud data within the predetermined range has a loss. That is, the loss determining unit 231 sequentially determines whether or not each data in the three-dimensional point cloud data within the predetermined range is missing.

The loss interpolation unit 232 interpolates the data loss when the loss determination unit 231 determines that the data loss exists. In the interpolation, the missing interpolation unit 232 interpolates the missing data based on the non-missing data around the missing data when half or more of the missing data surrounds the missing data. . More specifically, when the predetermined range is a rectangle when viewed from the Z direction, the coordinate position of the data to be interpolated (interpolation target data, that is, loss interpolation target data described later) MP1 is a corner (corner, vertex ), as shown in FIG. 3A, there are three pieces of data SP11 to SP13 around the interpolation target data MP1. , interpolate the missing data. For example, the missing interpolation unit 232 interpolates the missing data by using the average value of the non-missing data as the data value of the missing data. When the coordinate position of the interpolation target data MP2 is an edge, as shown in FIG. 3B, five pieces of data SP21 to SP25 exist around the interpolation target data MP2. When there are three or more non-missing data, the missing data is interpolated, and the average value of the non-missing data is used as the data value of the missing data to interpolate the missing data. When the coordinate position of the interpolation target data MP3 is a position excluding the corner and the edge, eight data SP31 to SP38 exist around the interpolation target data MP3 as shown in FIG. 3C. Therefore, when there are four or more pieces of non-missing data, the missing interpolation unit 232 interpolates the missing data and sets the average value of the non-missing data as the data value of the missing data. Interpolate missing data. On the other hand, when the non-missing data around the missing data is less than half, the missing interpolation unit 232 does not interpolate the missing data, and remain as

The outlier determination removal unit 24 determines whether or not the three-dimensional point cloud data processed by the loss determination interpolation unit 23 includes outlier data that deviates from the data value representing the target object. When it is determined that there is value data, an outlier determination/removal process is performed to remove the outlier data. More specifically, in the determination, the outlier determination removal unit 24 removes each data of the three-dimensional point cloud data after being processed by the loss determination interpolation unit 23, and each data surrounding the data. and comparing the average value of each difference obtained for each of the surrounding data with a threshold value (outlier determination threshold value) for determining whether or not the data is the outlier data. It is determined whether or not the data is the outlier data. The outlier determination threshold is appropriately set in advance using, for example, a plurality of samples of the three-dimensional point cloud data. More specifically, when the predetermined range is a rectangle when viewed from the Z direction, and only the Z coordinate value is focused, the data to be subjected to the calculation (difference average calculation) to obtain the average value of the differences (difference average calculation target When the coordinate position of data MP1 is a corner, as shown in FIG. When the Z coordinate value of MP1 is _z0 , and the Z coordinate values of the three data SP11 to SP13 around it are _z1 to _z3 , the average value S(z) of the differences is given by the following equation 1: expressed. When the coordinate position of the differential average calculation target data MP2 is an edge, as shown in FIG. When the Z coordinate value of the target data MP2 is _z0 , and the Z coordinate values of the five data SP21 to SP25 around it are _z1 to _z5 , the average value S(z) of the differences is given by the following equation: 2. When the coordinate position of the differential average calculation target data MP3 is a position excluding the corner and the edge, as shown in FIG. exists, and the Z coordinate value of the data MP3 for difference average calculation is z ₀ , and the Z coordinate values of the eight data SP31 to SP38 around it are z ₁ to z ₈ , the average value of the differences S (z) is represented by the following formula 3.

When the average value S(z) of the differences obtained in this manner exceeds the outlier determination threshold value, the outlier determination removing unit 24 determines whether or not the outlier data exists. If the average value S(z) of the differences is equal to or less than the outlier determination threshold value, it is determined that the target data is not outlier data.

The repetition processing unit 25 repeats the loss determination interpolation processing and the outlier determination removal processing until the outlier determination removal unit 24 no longer determines that there is outlier data, or until a predetermined upper limit number of times is reached. The missing determination interpolating unit 23 and the outlier determination removing unit 24 are each caused to perform this repeatedly. The predetermined upper limit number of times is appropriately set in advance using, for example, a plurality of samples of three-dimensional point cloud data.

These control processing unit 2, input unit 3, output unit 4, IF unit 5, and storage unit 6 can be configured by, for example, a computer such as a desktop computer or a notebook computer. In the example shown in FIG. 2, the computer that constitutes each of these units 2 to 6 may be placed, for example, in the operation room of the incineration facility and incorporated into the console (also used as the console), or It may be separate from the console.

Next, the operation of this embodiment will be described. FIG. 5 is a flow chart showing the operation of the point cloud data processing device.

When the power of the point cloud data processing apparatus A having such a configuration is turned on, it initializes each necessary part and starts its operation. In the control processing unit 2, a control unit 21, a thinning unit 22, a loss determination interpolation unit 23, an outlier determination removal unit 24, and a repetition processing unit 25 are functionally configured by executing the control processing program. The section 23 is functionally configured with a loss determination section 231 and a loss interpolation section 232 .

In FIG. 5, first, the point cloud data processing device A is initialized, and the data acquisition unit 1 acquires the 3D point cloud data (original 3D point cloud data) of the target object and stores it in the storage unit 6. (S11), and subsequently, the thinning unit 22 of the control processing unit 2 thins out the data from the three-dimensional point cloud data acquired by the data acquiring unit 1 in processing S11 to generate new three-dimensional point cloud data. Then, the generated new three-dimensional point cloud data is stored in the storage unit 6 as three-dimensional point cloud data to be used in subsequent processing (S12). In the initial setting, a counter (loop counter) RT for counting the number of times the loss determination interpolation process is performed is initialized to 0 (RT←0), and the loss determination interpolation process and the outlier determination removal process are performed. A counter (step counter) SC for counting the number of times the step has been performed is initialized to 0 (SC←0).

Subsequently, the point cloud data processing device A determines whether or not there is at least one data loss in the stored three-dimensional point cloud data by the loss determination interpolation unit 23 of the control processing unit 2 (S13). As a result of this determination, if it is determined that there is at least one missing data (Yes), the point cloud data processing device A next executes processing S14 in order to interpolate the missing data. . On the other hand, if it is determined that there is no data loss as a result of the determination (No), the point cloud data processing device A causes the loss determination interpolation unit 23 to determine that there is no data loss. The 3D point cloud data determined to be the 3D point cloud data after being processed by the loss determination interpolation unit 23, and in order to omit the current loss determination interpolation process, next, processing S22 is executed.

In this process S14, the point cloud data processing device A uses the loss determination interpolating unit 23 to select the data to be subjected to the loss determination interpolation process (loss interpolation target data) from among the data of the three-dimensional point cloud data. Set the data (start target data) for starting the judgment interpolation process. For example, in the example shown in FIG. 2, the predetermined range is a quadrangle when viewed from the Z direction, and one of the four vertices (corners) of this quadrangle (for example, the upper left vertex in plan view) is coordinated A three-dimensional XYZ coordinate system is set as the origin, and data of coordinates (0, 0, z) is set as the start target data of the missing interpolation target data.

Subsequently, the point cloud data processing apparatus A determines whether or not the loss interpolation target data is missing by the loss determination unit 231 of the loss determination interpolation unit 23 (S15). If the result of this determination is that the data subject to loss interpolation is not missing (No), the loss determination interpolating unit 23 next executes processing S17. If yes (Yes), the missing data is interpolated by the missing interpolation unit 232 (S16), and then the process S17 is executed. As described above, in the present embodiment, when the number of non-missing data around missing data is less than half, the missing data is not interpolated, and the missing data to be interpolated is the missing data. It remains missing.

In this process S17, the point cloud data processing apparatus A determines whether or not the loss interpolation target data is the last loss interpolation target data for ending the loss determination interpolation process by the loss determination interpolation unit 23 . As a result of this determination, if the missing interpolation target data is the last missing interpolation target data (Yes), the point cloud data processing device A next executes processing S19. On the other hand, if the result of the determination is that the missing interpolation target data is not the last missing interpolation target data (No), the point cloud data processing device A causes the loss determination interpolation unit 23 to replace the missing interpolation target data with Next, the data to be subjected to the loss determination interpolation process is set (S18), and then the process returns to the process S15. As a result, it is determined that the data is missing for each of all the data in the three-dimensional point cloud data from the start target data to the last loss interpolation target data, and it is determined that the data is missing. , the missing data is interpolated. In the example shown in FIG. 2, the last loss interpolation target data is, for example, when the loss interpolation target data is shifted like a so-called raster scan, it becomes the data of the vertex diagonal to the coordinate origin, and then the loss determination interpolation. In this case, the data to be processed is first shifted by one in the X direction (x←x+1), and when the last data in the X direction is reached, the first data in the X direction is shifted by one in the Y direction. data (x=0, y←y+1), and thereafter data shifted by repeating the same procedure. Note that the method of setting the loss interpolation target data is not limited to this raster scan, and any other setting method may be used as long as the loss determination interpolation process is performed for each of all the data in the three-dimensional point cloud data. good.

In the processing S19, the point cloud data processing device A determines whether or not the three-dimensional point cloud data subjected to the loss determination interpolation processing as described above is the three-dimensional point cloud data without loss by the loss determination interpolation unit 23. do. As a result of this determination, if the three-dimensional point cloud data includes the missing data (Yes), then the point cloud data processing device A sequentially performs the processes S20 and S21, and then , the process returns to process S14. As a result, the loss determination interpolation process is performed again, and the loss of the data is interpolated. On the other hand, if the result of the determination is that the three-dimensional point cloud data does not include the missing data (No), the point cloud data processing apparatus A next executes processing S22.

In the process S20, the point cloud data processing apparatus A updates the loop counter RC by incrementing it by 1 (RC←RC+1) by the loss determination interpolation unit 23 .

In the processing S21, the point cloud data processing apparatus A determines whether or not the loop counter RC has reached the upper limit by the loss determination interpolating section 23 . As a result of this determination, when the loop counter RC reaches the upper limit (Yes), the point cloud data processing apparatus A next executes processing S22. In addition, it replaces with execution of process S22, and process S30 may be performed. On the other hand, if the result of the determination is that the loop counter RC has not reached the upper limit value (No), the point cloud data processing apparatus A returns the process to step S14. These processes S20 and S21 prevent the loss determination interpolation process from being infinitely repeated. The upper limit value of the loop counter RC is appropriately set in advance using, for example, a plurality of samples of three-dimensional point cloud data.

In the process S22, the point cloud data processing device A uses the outlier determination removal unit 24 of the control processing unit 2 to calculate the average value of the differences in the outlier determination removal process (difference average calculation) target data ( Data for starting the difference average calculation (start target data) is set from each data of the three-dimensional point cloud data as the difference average calculation target data.

Subsequently, the point cloud data processing apparatus A uses the outlier determination/removal unit 24 to perform difference average calculation on the difference average calculation target data (S23).

Subsequently, the point cloud data processing apparatus A determines whether or not the difference average calculation target data is the last difference average target data for ending the difference average calculation by the outlier determination removing unit 24 . As a result of this determination, if the difference average target data is the last difference average target data (Yes), the point cloud data processing apparatus A next executes processing S26. On the other hand, if the result of the determination is that the data subject to difference average calculation is not the last data subject to difference average calculation (No), the point cloud data processing apparatus A causes the outlier determination removal unit 24 to perform difference average calculation. The target data is set as the data to be subjected to the difference average calculation next (S25), and then the process returns to the process S23. Thereby, the difference average calculation is sequentially performed for each of all the data in the three-dimensional point group data from the start target data to the last difference average calculation target data.

In this process S26, the point cloud data processing apparatus A uses the outlier determination/removal unit 24 to determine whether or not there is outlier data in the three-dimensional point cloud data. As a result of this determination, if there is outlier data in the three-dimensional point cloud data (Yes), the point cloud data processing apparatus A next executes processing S27. If there is no outlier data in the point cloud data (No), the point cloud data processing apparatus A next executes processing S30. That is, for each data in the three-dimensional point cloud data, the average value S(z) of the differences is compared with the outlier determination threshold, and the average value S(z) of the differences is the outlier determination threshold. If there is data exceeding , it is determined that there is outlier data in the three-dimensional point cloud data, and the data in which the average value S (z) of the differences exceeds the outlier determination threshold is extracted. is equal to or less than the outlier determination threshold value, it is determined that there is no outlier data in the three-dimensional point cloud data.

In the processing S27, the point cloud data processing apparatus A uses the outlier determination removing unit 24 to remove all data in which the average value S(z) of the differences exceeds the outlier determination threshold value from the three-dimensional point cloud data. .

Subsequently, the point cloud data processing apparatus A updates the step counter SC by incrementing it by 1 by the repeat processing unit 25 of the control processing unit 2 (SC←SC+1, S28).

Subsequently, the point cloud data processing apparatus A determines whether or not the step counter SC has reached the upper limit by the repetition processing unit 25 (S29). As a result of this determination, if the step counter SC has reached the upper limit (Yes), the point cloud data processing apparatus A next executes processing S30. On the other hand, if the result of the determination is that the step counter SC has not reached the upper limit value (No), the point cloud data processing apparatus A returns the process to step S14. As a result, each of the loss determination interpolation process and the outlier determination removal process is performed until the outlier determination/removal unit 24 no longer determines that there is outlier data in the process S26, or until a predetermined upper limit number of times is reached. This is repeated by the loss determination interpolation unit 23 and the outlier determination removal unit 24, respectively. The upper limit value of the step counter SC is appropriately set in advance using, for example, a plurality of samples of three-dimensional point cloud data.

In the processing S<b>30 , the point cloud data processing device A outputs the three-dimensional point cloud data of the processing result to the output unit 4 by the control unit 21 of the control processing unit 2 . The three-dimensional point cloud data resulting from the processing may be output to an external device via the IF unit 5 as required.

As described above, the point cloud data processing device A and the point cloud data processing method and the point cloud data processing program mounted thereon according to the embodiment, when it is determined that there is data loss in the three-dimensional point cloud data, Since the missing data is interpolated, the missing data can be interpolated even if the missing data is caused by a blind spot when generating the three-dimensional point group data. The point cloud data processing device A, the point cloud data processing method, and the point cloud data processing program exclude outlier data when it is determined that there is outlier data in the three-dimensional point cloud data. When generating three-dimensional point cloud data, noise such as data generated by objects other than the target object can be removed. Therefore, the point cloud data processing apparatus A, the point cloud data processing method, and the point cloud data processing program can optimize the three-dimensional point cloud data to be processed.

The point cloud data processing apparatus A, the point cloud data processing method, and the point cloud data processing program are used to obtain three-dimensional point cloud data obtained by the data obtaining unit 1, and in this embodiment, the data obtaining unit 1 obtains the point cloud data, and the thinning unit 22 obtains Since it is determined whether or not there is at least one missing data in the three-dimensional point cloud data after thinning, if there is not even one missing data in the three-dimensional point cloud data, the loss determination interpolation is performed. Processing can be omitted.

If each data is missing at each coordinate point adjacent to each other in the three-dimensional coordinate space, it may not be possible to interpolate with an appropriate data value. The point cloud data processing device A, the point cloud data processing method, and the point cloud data processing program are arranged so that when there is more than half of the data that is not missing around the missing data, there is no missing data around the missing data. Since the missing data is interpolated based on the data, more appropriate data values can be interpolated.

Since the outlier is a value that deviates from the data value representing the target object, it is considered that the change in the data value is large, and the data of the outlier is considered to deviate from the surrounding data. The point cloud data processing device A, the point cloud data processing method, and the point cloud data processing program consider the data surrounding the data to be determined when determining outlier data. can be determined more appropriately.

The point cloud data processing device A, the point cloud data processing method, and the point cloud data processing program thin out data from the three-dimensional point cloud data (original three-dimensional point cloud data) acquired by the data acquisition unit 1 to generate new data. Since three-dimensional point cloud data is generated and the generated new three-dimensional point cloud data is processed, the amount of information processing can be reduced, and the information processing time can be shortened.

In incineration facilities, overhead cranes, etc., may be installed to stir and transport the waste stored in the storage unit. can cause blind spots and noise. As a result, the three-dimensional point cloud data obtained by measuring the waste may include defects or outliers, and the situation of the waste may not be properly recognized. The point cloud data processing device A, the point cloud data processing method, and the point cloud data processing program correct the defects by interpolating the defects and excluding outlier data, so that the waste situation can be appropriately recognized. becomes possible.

Next, an example will be described. FIG. 6 is a three-dimensional graph showing, as an example, the results of applying this embodiment to an incineration facility that is actually in operation. FIG. 6A shows each data in the three-dimensional point cloud data after thinning, and FIG. 6B shows each data in the three-dimensional point cloud data after one step of performing the loss determination interpolation processing and the outlier determination removal processing once. FIG. 6C shows each data in the three-dimensional point cloud data after two steps of performing the loss determination interpolation process and the outlier determination removal process twice, and FIG. 6D shows the loss determination interpolation process and the outlier determination removal process. is performed three times, and FIG. 6E shows each data in the three-dimensional point cloud data after four steps of performing the loss determination interpolation process and the outlier determination removal process four times. Show data. In FIG. 6A to FIG. 6E, the upward right axis in plan view is the X axis, the left upward axis is the Y axis, and the vertical axis is the Z axis. Each data in the three-dimensional point group data is represented by each point (●).

The target object in this one embodiment is the waste litter deposited in the receiving pit of an incineration facility. Like the receiving pit PT shown in FIG. 2, the receiving pit is a substantially rectangular parallelepiped hollow space, but further includes a plate-like partition member extending along the X-axis direction and erected in the Z-axis direction. . The surface of dust accumulated in such receiving pits was measured from above by a sensor such as LiDAR, and three-dimensional point cloud data representing the height of the dust (original three-dimensional point cloud data) was generated. New three-dimensional point cloud data is generated by thinning out the original three-dimensional point cloud data as a noise filter so that they are arranged at equal intervals when viewed in the Z direction, and this is shown in FIG. 6A. The three-dimensional point cloud data to be processed shown in FIG. 6A has data loss due to blind spots due to the partition member, and outlier data due to the crane. When the loss determination interpolation process and the outlier determination removal process are performed once on the three-dimensional point cloud data to be processed shown in FIG. 6A, the three-dimensional point cloud data shown in FIG. 6B, and the three-dimensional point cloud data shown in FIG. 6B is subjected to one loss determination interpolation process and outlier determination removal process. becomes the three-dimensional point cloud data shown in FIG. 6C, and the three-dimensional point cloud data to be processed shown in FIG. The 3D point cloud data shown in 6C becomes the 3D point cloud data shown in FIG. 6D, and the 3D point cloud data shown in FIG. 6D undergoes one loss determination interpolation process and outlier determination removal process. Then, the three-dimensional point cloud data shown in FIG. 6D becomes three-dimensional point cloud data shown in FIG. 6E. That is, the three-dimensional point cloud data to be processed shown in FIG. 6A becomes the three-dimensional point cloud data shown in FIG. 6E by repeating the loss determination interpolation process and the outlier determination removal process four times. As can be seen from FIGS. 6A to 6E, as the loss determination interpolation process and the outlier determination removal process are repeated, the data loss is gradually interpolated, the outlier data is removed and interpolated, and the three-dimensional point shown in FIG. In the group data, the deficiencies of the data are interpolated and outlier data are removed. Thus, it can be seen that the three-dimensional point cloud data can be optimized by simultaneously processing the interpolation of missing data and the removal of outlier data. Therefore, it is possible to appropriately recognize the state of the garbage of the waste before processing.

In the above-described embodiment, if the 3D point cloud data after loss interpolation processing in the loss interpolation unit 232 is the same as the 3D point cloud data before loss interpolation processing, that is, the loss interpolation unit 232 If the interpolation processing is not performed for even one point, the loss interpolation unit 232 may temporarily relax the interpolation condition and perform the loss interpolation processing again. More specifically, in the above-described embodiment, when the coordinate position of the interpolation target data MP2 is an edge, the loss interpolation unit 232 corrects the loss of the data when there are three or more pieces of data that are not missing. The missing data is interpolated by interpolating and using the average value of the non-missing data as the data value of the missing data. When there are two or more pieces of non-missing data among the data, the interpolation condition is relaxed so as to interpolate the missing data, and the average value of the non-missing data is used as the data value of the missing data. The missing data may be interpolated by In this case, in the above-described flowchart, if no point is interpolated in the missing interpolation processing S16 until it is determined as the last target data in the processing S17, the interpolation condition is temporarily relaxed. Then, the loss interpolation process is executed again from the start target data setting S14. More specifically, when the coordinate position of the data to be interpolated is on the edge, if there are two or more pieces of non-missing data out of the five surrounding data, the interpolation condition is set so as to interpolate the missing data. The missing data is interpolated by relaxing and taking the mean value of the non-missing data as the data value of the missing data. In one example, the results shown in FIG. 7A are refined to the results shown in FIG. 7B. FIG. 7 is a three-dimensional graph showing the result of applying this embodiment to an incineration facility that is actually in operation as an example of a modified form. FIG. 7A shows the result before this variant and FIG. 7B shows the result with this variant. 6A and 7B, the axis rising to the right in plan view is the X-axis, the axis rising to the left is the Y-axis, and the vertical axis is the Z-axis. Each data in the three-dimensional point group data is represented by each point (●).

Although the present invention has been adequately and fully described above through embodiments with reference to the drawings in order to express the present invention, modifications and/or improvements to the above-described embodiments can easily be made by those skilled in the art. It should be recognized that it is possible. Therefore, to the extent that modifications or improvements made by those skilled in the art do not depart from the scope of the claims set forth in the claims, such modifications or improvements do not fall within the scope of the claims. is interpreted to be subsumed by

A point cloud data processing device 1 data acquisition unit 2 control processing unit 3 input unit 4 output unit 5 interface unit (IF unit)
6 storage unit 21 control unit 22 thinning unit 23 loss determination interpolation unit 24 outlier determination removal unit 25 repetition processing unit

Claims (8)

a data acquisition unit that acquires three-dimensional point cloud data in a predetermined range in a predetermined three-dimensional coordinate space generated by measuring a predetermined target object;
a loss determination interpolation unit that determines whether or not there is data loss in the three-dimensional point cloud data, and performs a loss determination interpolation process for interpolating the data loss when it is determined that there is a data loss;
It is determined whether or not the three-dimensional point cloud data processed by the loss determination interpolation unit includes outlier data that deviates from the data value representing the target object, and it is determined that the outlier data is present. an outlier determination removal unit that performs an outlier determination removal process for removing outlier data in the case of
The loss determination interpolation unit and the outlier determination removal unit perform the loss determination interpolation processing and the outlier determination removal processing until the outlier determination removal unit no longer determines that there is outlier data, or until a predetermined upper limit number of times is reached. a repetition processing unit that causes each of the outlier determination removal units to repeatedly perform
Point cloud data processor. The loss determination interpolation unit determines whether or not there is at least one data loss in the three-dimensional point cloud data acquired by the data acquisition unit, and if it is determined that there is at least one data loss, the After interpolating data deficiencies and determining that there are no data deficiencies, the three-dimensional point cloud data determined to have no data deficiencies is processed by the loss determination interpolation unit. Let the 3D point cloud data of
The point cloud data processing device according to claim 1. The loss determination interpolation unit is
a loss determination unit that determines whether or not each data of the three-dimensional point cloud data has a loss;
a loss interpolation unit that interpolates the loss of the data when the loss determination unit determines that there is a loss of the data;
The missing interpolation unit interpolates the missing data based on the non-missing data around the missing data when half or more of the missing data surrounds the missing data.
The point cloud data processing device according to claim 1 or 2. The outlier determination removal unit obtains a difference between each data of the three-dimensional point cloud data after being processed by the loss determination interpolation unit and each data surrounding the data, Determining whether the data is the outlier data by comparing the average value of each difference obtained for and the threshold value for determining whether it is the outlier data,
The point cloud data processing device according to any one of claims 1 to 3. New three-dimensional point cloud data is generated by thinning data from the three-dimensional point cloud data obtained by the data obtaining unit, and the generated new three-dimensional point cloud data is processed by the loss determination interpolation unit. Further comprising a thinning unit for three-dimensional point cloud data that performs judgment interpolation processing,
The point cloud data processing device according to any one of claims 1 to 4. The three-dimensional point cloud data is three-dimensional point cloud data generated by measuring, as the target object, the waste stored in a storage unit in an incineration facility that incinerates waste.
The point cloud data processing device according to any one of claims 1 to 5. a data acquisition step of acquiring three-dimensional point cloud data in a predetermined range in a predetermined three-dimensional coordinate space generated by measuring a predetermined target object;
a loss determination interpolation step of determining whether or not there is data loss in the three-dimensional point cloud data, and interpolating the data loss when it is determined that there is a data loss;
It is determined whether or not the three-dimensional point cloud data processed in the defect determination interpolation step includes outlier data that deviates from the data values representing the target object, and it is determined that the outlier data is present. an outlier determination removing step of removing the outlier data in the case of
a repeating step of repeatedly performing the loss determination interpolation step and the outlier determination removal step until it is no longer determined that there is outlier data in the outlier determination removal step, or until a predetermined upper limit number of times is reached; comprising
Point cloud data processing method. the computer,
a data acquisition unit that acquires 3D point cloud data in a predetermined range in a predetermined 3D coordinate space generated by measuring a predetermined target object;
a loss determination interpolation unit that determines whether or not there is data loss in the three-dimensional point cloud data, and performs loss determination interpolation processing for interpolating the data loss when it is determined that there is a data loss;
It is determined whether or not the three-dimensional point cloud data processed by the loss determination interpolation unit includes outlier data that deviates from the data value representing the target object, and it is determined that the outlier data is present. an outlier determination removal unit that performs an outlier determination removal process for removing data of the outlier in the case, and
The loss determination interpolation unit and the outlier determination removal unit perform the loss determination interpolation processing and the outlier determination removal processing until the outlier determination removal unit no longer determines that there is outlier data, or until a predetermined upper limit number of times is reached. a repetition processing unit that causes each of the outlier determination removal units to repeatedly perform;
A point cloud data processing program for functioning as

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