TWI676151B - Method and apparatus for improving accuracy of point clouds - Google Patents

Abstract

A method and device for improving the accuracy of a point cloud. The method includes: inputting a plurality of groups of point clouds in a measurement area into the device; selecting two sets of known points, wherein the two sets of known points are different and uniform. Point clouds with spatial differences outside an allowable range; coordinate transformation to reduce system errors and elimination of point clouds with spatial differences outside another allowable range; and points that are not excluded The clouds assign weights to merge the point clouds.

Description

Method and device for improving accuracy of point cloud

The invention relates to the processing of point clouds, in particular to a method and device for improving the accuracy of point clouds.

Point clouds can be generated by image parallax or calculation of electromagnetic wave transmission and reception. The accuracy of the point cloud calculated from the known image parallax is limited by the imaging equipment itself, the image quality, the intersection angle of the captured image, the photo density, the angle of the observed object, the level of the surface of the object, and so on; and the calculated point cloud of the electromagnetic wave emission and reception Accuracy is limited by electromagnetic wave transmitting and receiving equipment, positioning equipment or environmental interference. Therefore, in the past, increasing accuracy would involve improving the instrument, trying to control the experimental environment, or training the operator. However, in the real environment, the number of better instruments and equipment is small and more costs are needed. Therefore, a method and device that can improve the accuracy by using the original instruments are needed.

An object of the present invention is to provide a method and device for improving the accuracy of a point cloud, so as to solve the above problems.

Another object of the present invention is to provide a method and a device for improving the accuracy of a point cloud, so as to improve the accuracy of the point cloud without side effects or less likely to cause side effects.

At least one embodiment of the present invention provides a method for improving the accuracy of a point cloud. The method may include: (a) multi-group point clouds input in a measurement area; (b) selecting a coordinate conversion mode between each of the multi-group point clouds and a reference coordinate; (c) selecting A set of first known points A set of second known points of known points, wherein the set of first known points includes a plurality of first known points on a reference coordinate and the plurality of first known points in each of the multi-point cloud A known point, and the set of second known points includes a plurality of second known points on the reference coordinate and the plurality of second known points in each of the clusters of point clouds; ( d) Calculate the spatial difference between the first plurality of known points of each of the plurality of point clouds and the plurality of first known points on the reference coordinate, respectively. The spatial difference of any one of the plurality of first known points is outside a first allowable range, then the point cloud is eliminated, and according to the coordinate conversion method selected in step (b), it is not removed respectively. The point cloud is converted to the reference coordinate to generate a converted point cloud; (e) According to the coordinate conversion method selected in step (b), the plurality of point clouds are respectively converted to the reference coordinate to generate the converted points. Point cloud, and respectively calculate the number of the plurality of first known points of each of the converted point clouds and the plurality of first known points on the reference coordinate The spatial difference of any of the plurality of first known points in a converted point cloud is outside a second allowable range, the converted point cloud is excluded; (f) collection The converted point clouds not removed in steps (d) and (e) are used as the remaining converted point clouds; and (g) the plurality of each of the remaining converted point clouds are respectively calculated. The spatial difference between the second known point and the plurality of second known points on the reference coordinate, and the spatial difference between any of the plurality of second known points in the point cloud after a residual transformation If the quantity is outside a third allowable range, then the remaining transformed point clouds are excluded.

At least one embodiment of the present invention provides an analysis device that operates according to the method described above. The analysis device may include a processing circuit for executing a set of codes corresponding to the method to control the analysis device to operate according to the method.

One of the advantages of the present invention is that the present invention can improve the accuracy of the point cloud itself, and effectively improve the accuracy of the point cloud or give suggestions for re-testing according to different settings. In addition, implementation in accordance with the related embodiments of the present invention does not add many additional costs. Therefore, the problems of related technologies can be solved without the overall cost increasing too much. Compared with the related art, the present invention can improve the accuracy of the point cloud without any side effects or less likely to cause side effects.

100‧‧‧analytical device

110‧‧‧Processing circuit

112C‧‧‧Code

120‧‧‧Storage device

2000‧‧‧Workflow

2100, 2200 ‧‧‧ Sub-process

202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236‧‧‧‧

300‧‧‧ reference coordinates

310 ₁ , 310 ₂ , 310 ₃ , 310 ₄ , 310 ₅ , 310 ₆ , 310 ₇ , ..., 310 _n , 410 ₁ , 410 ₂ , 410 ₃ , 410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ , 410 ₈ , 410 ₉ , 410 ₁₀ , ..., 410 _n ‧‧‧point cloud

Ar ₁ , Ar ₂ , Ar ₃ , Br ₁ , Br ₂ , Br ₃ , A _1,1 , A _1,2 , A _1,3 , B _1,1 , B _1,2 , B _1,3 , A _{2 , 1} , A _2,2 , A _2,3 , B _2,1 , B _2,2 , B _2,3 , ..., A _{n, 1} , A _{n, 2} , A _{n, 3} , B _{n, 1} , B _{n, 2} , B _{n, 3} , Ar _1,1 , Ar _1,2 , Ar _1,3 , Br _1,1 , Br _1,2 , Br _1,3 , Ar _2,1 , Ar _{2, 2} , Ar _2,3 , Br _2,1 , Br _2,2 , Br _2,3 , ..., Ar _{n, 1} , Ar _{n, 2} , Ar _{n, 3} , Br _{n, 1} , Br _{n, 2} , Br _{n, 3} ‧‧‧known points

FIG. 1 is a schematic diagram of an analysis device according to an embodiment of the present invention.

FIG. 2 is a flowchart of the working process of the method in an embodiment of the present invention.

FIG. 3 is a sub-flow of the workflow shown in FIG. 2 according to an embodiment of the present invention.

FIG. 4 is a sub-flow in the workflow shown in FIG. 2 according to an embodiment of the present invention.

FIG. 5 is a schematic diagram of a multi-group point cloud and a reference coordinate according to an embodiment of the present invention.

FIG. 6 is a schematic diagram of a point cloud at the reference coordinate after multi-group conversion according to an embodiment of the present invention.

FIG. 7 is a schematic diagram of a converted point cloud generated by converting an unremoved point cloud to the reference coordinate according to an embodiment of the present invention.

FIG. 8 is a schematic diagram of the remaining transformed point cloud on the reference coordinate according to an embodiment of the present invention.

FIG. 1 is a schematic diagram of an analysis apparatus 100 according to an embodiment of the present invention. The analysis device 100 may include a processing circuit 110 (which may include at least a processor, a memory, a chipset, etc., and at least a part (for example, a part or all) of the branch components may be coupled to each other through a bus) and At least one storage device 120 (eg, one or more storage devices, such as a portable memory device, a solid state drive, and / or various embedded storage devices). In particular, the processing circuit 110 can be used to execute a set of codes 112C to control the analysis device 100 to operate according to the method proposed by the present invention, but the present invention is not limited thereto. Examples of the analysis device 100 may include, but are not limited to, personal computers (such as desktop and laptop computers) and servers.

FIG. 2 is a flowchart of the method in a working flow 2000 of an embodiment of the present invention, and FIG. 3 is a sub-flow 2100 in the working flow 2000 shown in FIG. 2 of an embodiment of the present invention, And FIG. 4 is a sub-process 2200 in the workflow 2000 shown in FIG. 2 according to an embodiment of the present invention.

Please refer to FIG. 2 and FIG. 5 together, where FIG. 5 is a multi-group point cloud (such as a point cloud {310 ₁ , 310 ₂ , ..., 310 _n ) according to an embodiment of the present invention, where n is A positive integer) and a coordinate (coordinate) such as the schematic diagram of the reference coordinate 300. In step 202, a user may input a plurality of groups of point clouds (such as point clouds {310 ₁ , 310 ₂ , ..., 310 _n }) in a measurement area to the analysis device 100 and proceed to step 204, where the The multi-group point cloud can be a multi-group point cloud for the measurement area from different observation devices, or the multi-group point cloud can be generated through multiple photo pairs of the measurement area, for example, an observation device can target the measurement area. Ten photos are generated, and any two of the ten photos can generate a group of point clouds. Therefore, the ten photos can generate a total of 45 group of point clouds, but the present invention is not limited thereto. Please note that the method of inputting the multi-group point cloud into the analysis device 100 is not limited to manual input by the user. For example, an image generator (or scanning device) may be coupled to the analysis device 100 and generated by the image generator. After one or more clusters of point clouds, the analysis device 100 may automatically (and / or synchronously) store the one or more clusters of points in a storage device (such as storage device 120) for subsequent steps, but the present invention Not limited to this.

In step 204, the analysis device 100 (or the user) may select a coordinate conversion mode between each of the point clouds {310 ₁ , 310 ₂ , ..., 310 _n } and the reference coordinate 300 and enter Step 206, where the example of the coordinate conversion method may include (but not limited to): 6-parameter positive conversion, 7-parameter positive conversion, 9-parameter affine conversion, and 12-parameter affine conversion, of which 7-parameter positive conversion can be used A preset coordinate conversion method, but the present invention is not limited thereto.

In step 206, the user (or analysis device 100) may select a set of first known points and a set of second known points different from the set of first known points (where the set of first known points) And the second known points are evenly distributed and cover the measurement area as much as possible). The set of first known points may include a plurality of first known points (such as known points {Ar ₁ , Ar ₂ , Ar ₃ )) at reference coordinate 300 and each of the multi-point cloud the first plurality of known points (such as at a known point 310 on the cloud point _{1 {a 1,1, a 1,2, a} 1,3}, in the _two known points on the point cloud 310 {a _2,1 , A _2,2 , A _2,3 }, ... and the known points {A _{n, 1} , A _{n, 2} , A _{n, 3} } on the point cloud 310 _n , And the set of second known points may include a plurality of second known points (such as known points {Br ₁ , Br ₂ , Br ₃ }) on the reference coordinate 300 and each of the plurality of point clouds. the second plurality of points of known persons (such as a known point in the point cloud 310. _{1 {B 1,1, B 1,2, B} 1,3}, to a known point 310 on the cloud point of ₂ { B _2,1 , B _2,2 , B _2,3 }, ... and known points {B _{n, 1} , B _{n, 2} , B _{n, 3} } on point cloud 310 _n ) Then, enter sub-flow 2100, but the present invention is not limited to this. Please note that the method of selecting the first known point in the group and the second known point in the group may include (but not limited to): before obtaining the multi-group point cloud, the user may place a known object on the measurement The area is regarded as a known point; after obtaining the multi-group point cloud, the analysis device 100 may use image analysis techniques (for example, according to the gray or color of the image, etc.) to identify the boundary point of an object as a (Known points) identify data points corresponding to the same position in point clouds from different groups (such as known points A _1,1 , known points A _2,1 , ... that correspond to the same position in the survey area) Known point N _{n, 1} ), and use the one with higher recognition (for example, the one with relatively large difference in gray or color) as a known point for subsequent analysis; a scanning device sets a laser light in different directions After hitting an object at an angle (or laser light on the object by scanning devices at different positions), the analysis device 100 can identify different groups of point clouds based on information such as the intensity or color of the reflected laser light. Correspond to data points at the same location, and use the one with higher recognition (for example: (Or) as a known point for subsequent analysis; and, through the aforementioned reflected laser light, the analysis device 100 may determine the surface characteristics of the object (such as material, texture, continuity, etc.), but not limited thereto ) Identify the data points corresponding to the same position in the point clouds of different groups, and use the higher recognition as the known point for subsequent analysis.

In addition, through the same (or similar) image analysis technology, the analysis device 100 can identify at least a part (a part or all) of the data points of each of the plurality of point cloud positions corresponding to the positions in the measurement area, respectively. (Or object) for subsequent steps, details will be explained in subsequent paragraphs.

In this embodiment, the number of the first known points in each of the multi-point cloud and the plurality of second known points in each of the multi-point cloud The quantity requirements must meet the minimum requirements of the aforementioned coordinate conversion method. For example: 6-parameter positive conversion requires at least two points, 7-parameter positive conversion requires at least three points, and 9-parameter affine conversion requires at least three points. , And the 12-parameter affine transformation requires at least four points. In the embodiment of the present invention, each of the point clouds {310 ₁ , 310 ₂ , ..., 310 _n } includes three first known points and three second known points, for example: known The points {A _1,1 , A _1,2 , A _1,3 } can represent the three first known points in the point cloud 310 ₁ and the known points {B _1,1 , B _1,2 , B _{1 , 3} } can represent the three second known points in the point cloud 310 ₁ , the point cloud {310 ₂ , ..., 310 _n } and so on, each of which contains three first known points and Three second known points, where the known points {Ar ₁ , Ar ₂ , Ar ₃ } (three first known points) on the reference coordinate 300 and the known points {Br ₁ , Br ₂ , Br ₃ } (Three second known points) correspond to six known points (three first known points and three second known points) in each of the point clouds {310 ₂ , ..., 310 _n } (Known points), this is for the purpose of illustration only, and is not a limitation on the present invention.

Please refer to FIG. 5 and FIG. 6 together, where FIG. 6 is a schematic diagram of multiple group point clouds at the reference coordinates according to an embodiment of the present invention. Using the aforementioned coordinate conversion method, the analysis device 100 can convert the point cloud {310 ₁ , 310 ₂ , ..., 310 _n } shown in FIG. 5 to a reference coordinate 300 to generate a converted point cloud (such as the sixth The point cloud {410 ₁ , 410 ₂ , ..., 410 _n }) shown in the figure, in which the analysis device 100 can convert the known points on the point cloud 310 ₁ shown in FIG. 5 through the coordinate transformation method described above. _{{a 1,1, a 1,2, a} 1,3, B 1,1, B 1,2, B 1,3} 300 to coordinate conversion to generate the reference shown in Fig. 6 of the point cloud ₄₁₀₁ Given the points {Ar _1,1 , Ar _1,2 , Ar _1,3 , Br _1,1 , Br _1,2 , Br _1,3 }, the point cloud {310 ₂ , ..., 310 _n } Similar operations are performed for each of the multiple known points, and the details will be explained in the subsequent paragraphs.

於本實施例中，點雲{310₁,310₂,...,310_n}中的每一者包含有三個第一已知點，因此分析裝置100可透過計算得到點雲{310₁,310₂,...,310_n}中的每一者的三組空間差量，例如：針對點雲310₁，分析裝置100可透過計算得到已知點A_1,1與已知點Ar₁的空間差量D_1,1、已知點A_1,2與已知點Ar₂的空間差量D_1,2、以及已知點A_1,3與已知點Ar₃的空間差量D_1,3；針對點雲310₂，分析裝置100可透過計算得到已知點A_2,1與已知點Ar₁的空間差量D_2,1、已知點A_2,2與已知點Ar₂的空間差量D_2,2、以及已知點A_2,3與已知點Ar₃的空間差量D_2,3；以此類推，針對點雲310_n，分析裝置100可透過計算得到已知點A_n,1與已知點Ar₁的空間差量D_n,1、已知點A_n,2與已知點Ar₂的空間差量D_n,2、以及已知點A_n,3與已知點Ar₃的空間差量D_n,3。接著，進入步驟210。 Please refer to FIG. 3 and FIG. 5 together. In step 208, the analysis device 100 may calculate the plurality of numbers of each of the point clouds {310 ₁ , 310 ₂ , ..., 310 _n }. A known point (such as a known point {A _1,1 , A _1,2 , A _1,3 } on point cloud 310 ₁ ), a known point {A _2,1 , A ₂ on point cloud 3102 _{, 2} , A _2,3 }, ... and the known point {A _{n, 1} , A _{n, 2} , A _{n, 3} }) on the point cloud 310 _n with respect to the reference coordinate 300 The spatial difference between the corresponding known points {Ar ₁ , Ar ₂ , Ar ₃ }. The way to calculate these spatial differences may include (but is not limited to): When the known point coordinates on a point cloud are (X, Y, Z) and the corresponding known point coordinates on a reference coordinate are (X _r , Y _r , Z _r ), then the spatial difference between a known point on the point cloud and a known point on the reference coordinate can be calculated by:

In this embodiment, each of the point clouds {310 ₁ , 310 ₂ , ..., 310 _n } includes three first known points, so the analysis device 100 can obtain the point cloud {310 ₁ by calculation 310 ₂ , ..., 310 _n } for each of the three sets of spatial differences, for example, for the point cloud 310 ₁ , the analysis device 100 can calculate the known point A _1,1 and the known point Ar ₁ through calculation. the spatial difference D _1,1, a _1,2 known point and the known point difference space Ar ₂ D _1,2, a _1,3 known point and the point Ar known spatial difference of D _{3 1,3} ; For the point cloud 310 ₂ , the analysis device 100 can calculate the spatial difference D _2,1 between the known point A _2,1 and the known point Ar ₁ , and the known point A _2,2 and the known point. Ar spatial difference D _{2 2,2, 2,3} and the known point a and the point Ar known spatial difference of ₃ D _3; and so on, for the point cloud 310 _n, 100 may be analyzed by calculating the means _n-known point a to _give, Ar ₁ point and the known spatial difference D _{n 1} is _{1, n-known} point _a, point ₂ Ar known spatial difference D _{n 2} is _2, and the known point a _{n, 3} and Ar known point spatial difference D _{n 3,} and _3. Next, the process proceeds to step 210.

其中D_avg為空間差量{D₁,D₂,...D_m,}的平均值。接著，使用者可依據各自的需求 設定該第一容許範圍，例如：將該第一容許範圍設定為(D_avg-2* σ)與(D_avg+2* σ)之間的區間；又例如：將該第一容許範圍設定為(D_avg-3* σ)與(D_avg+3* σ)之間的區間。但本發明不限於此。 In step 210, when the corresponding spatial difference of any one of the plurality of first known points in a point cloud is outside a first allowable range, the analysis device 100 may eliminate the point cloud. In this embodiment, the first allowable range can be determined by calculating the standard deviation and the average value of the spatial differences, for example, the analysis device 100 calculates the spatial differences {D ₁ , D ₂ , ... D _{After m} ,} (where m is a positive integer), the standard deviation σ of the space difference {D ₁ , D ₂ , ... D _m ,} can be calculated by the following two methods:

Where D _avg is the average of the spatial differences {D ₁ , D ₂ , ... D _m ,}. Then, the user can set the first allowable range according to their respective needs, for example: set the first allowable range as an interval between (D _avg -2 * σ) and (D _avg + 2 * σ); for example : This first allowable range is set to an interval between (D _avg -3 * σ) and (D _avg + 3 * σ). However, the present invention is not limited to this.

In step 212, the analysis device 100 (or the user) may decide whether to change the first allowable range according to a quantity threshold NTH1. When the analysis device 100 (or the user) determines that the first allowable range needs to be changed to increase the After the number of removed point clouds is changed (not shown in the figure), the process proceeds to step 210; otherwise, the process proceeds to step 214. For example, the user may set the quantity threshold NTH1 to 10, assuming that the number of point clouds that have not been removed in step 210 in this situation is 8, the user (or the analysis device 100) may change the first allowable range (for example: from (D _avg -2 * σ) and (D _avg + 2 * σ) changed to the interval between (D _avg -3 * σ) and (D _avg + 3 * σ)) to increase The number of point clouds.

In step 214, the analysis device 100 may convert the point cloud that has not been removed in step 210 to the reference coordinate 300 according to the aforementioned coordinate conversion method to generate a converted point cloud. Please refer to FIG. 7, which is a schematic diagram of a converted point cloud generated by converting an unremoved point cloud to a reference coordinate 300 according to an embodiment of the present invention. It is assumed that the point cloud not removed in step 210 includes the point cloud 310 ₁ , the point cloud 310 ₂ , the point cloud 310 ₃ , the point cloud 310 ₄ , the point cloud 310 ₅ , the point cloud 310 _6, and the points shown in FIG. 7. Cloud 310 ₇ and the analysis device 100 (or user) decides not to change the first allowable range in step 212, the analysis device 100 may generate a point on the reference coordinate 300 as shown in FIG. 7 in step 214 Cloud {410 ₁ , 410 ₂ , 410 ₃ , 410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ }. Then, enter sub-flow 2200. Please note that for each of point cloud {410 ₂ , 410 ₃ , 410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ } and point cloud {310 ₂ , 310 ₃ , 310 ₄ , 310 ₅ , 310 ₆ , 310 ₇ } The plurality of first known points and the plurality of second known points in one are omitted for brevity and are not shown in the drawings.

Please refer to FIG. 4 and FIG. 6 together, where FIG. 6 is a point cloud (such as point cloud {410 ₁ , 410 ₂ , ..., 410 _n )) after multi-group conversion according to an embodiment of the present invention. Reference is made to the diagram of coordinates 300. In step 216, the analysis device 100 can respectively transform the multi-group point cloud (such as the point cloud {310 ₁ , 310 ₂ , ..., 310 _n }) shown in FIG. 5 according to the aforementioned coordinate transformation method. To the reference coordinate 300 to generate the multi-group transformed point cloud (such as the point cloud {410 ₁ , 410 ₂ , ..., 410 _n } shown in FIG. 6), wherein the analysis device 100 can convert the coordinate through the above-mentioned coordinate The known points {A _1,1 , A _1,2 , A _1,3 , B _1,1 , B _1,2 , B _1,3 } on the point cloud 310 ₁ shown in Figure 5 Known points on point cloud 310 ₂ {A _2,1 , A _2,2 , A _2,3 , B _2,1 , B _2,2 , B _2,3 }, ... Known points {A _{n, 1} , A _{n, 2} , A _{n, 3} , B _{n, 1} , B _{n, 2} , B _{n, 3} } on cloud 310 _n are transformed to reference coordinates 300 to produce the location shown in Figure 6 the point cloud 410 shows a known point on the _{1 {Ar 1,1, Ar 1,2,} Ar 1,3, Br 1,1, Br 1,2, Br 1,3}, at the point cloud 410 ₂ It has been known points on the _{{Ar 2,1, Ar 2,2, Ar} 2,3, Br 2,1, Br 2,2, Br 2,3}, ...... and 410 _n in the point cloud Knowing points {Ar _{n, 1} , Ar _{n, 2} , Ar _{n, 3} , Br _{n, 1} , Br _{n, 2} , Br _{n, 3} }. However, the present invention is not limited to this.

In step 218, analysis device 100 can calculate in a known point on the point cloud _{410 1 {Ar 1,1, Ar 1,2} , Ar 1,3}, at a known point on the point cloud 410 ₂ {Ar _2,1 , Ar _2,2 , Ar _2,3 }, ... and the known points {Ar _{n, 1} , Ar _{n, 2} , Ar _{n, 3} } on the point cloud 410 _n and The spatial difference between the corresponding known points {Ar ₁ , Ar ₂ , Ar ₃ } at the reference coordinate 300. For example, for the point cloud 410 ₁ , the analysis device 100 can obtain the known points Ar _1,1 through calculation. spatial difference Dr _1,1 known point of Ar _1, Ar _1,2 known point and the point Ar known spatial difference of ₂ Dr _{1,2, l, 3} and Ar known points and known points Ar spatial difference Dr _{3 1,3;} point cloud for _4102, the analysis device 100 may be obtained with the known points Ar _2, Ar known point spatial difference of _2,1 Dr _1, Ar known point by calculating _{2 ,} Ar ₂ and known point spatial difference of ₂ Dr _{2,2, 2,3} and the known spatial point difference Ar known point Ar ₃ of Dr _2,3; so, 410 _n for the point cloud, analysis device 100 can be obtained by calculating the known point _n-Ar, Ar ₁ point and the known spatial difference of Dr _{n 1, 1,} known point _n-Ar, Ar ₂ and the known spatial points difference Dr _{n 2 of 2} and a known point Ar _{n, 3} and have Ar spatial point difference Dr _{n 3,} and _3. Then, the process proceeds to step 220.

In step 220, when the corresponding spatial difference of any one of the plurality of first known points in a transformed point cloud is outside a second allowable range, the analysis device 100 may remove the transformed point cloud. In this embodiment, the second allowable range can be determined in a manner similar to that in determining the first allowable range. For the sake of brevity, the content overlapping with the foregoing steps will not be repeated here.

In step 222, the analysis device 100 (or the user) may determine a threshold value NTH2 Decide whether to change the second allowable range. When the analysis device 100 (or the user) determines that the second allowable range needs to be changed to increase the number of point clouds that have not been removed and converted, then change (not shown in the illustration) ) And then proceed to step 220, otherwise, proceed to step 224. For the sake of brevity, the content overlapping with the previous steps will not be repeated here.

Please refer to FIG. 2 and FIG. 8 together, where FIG. 8 is a schematic diagram of the remaining transformed point cloud on the reference coordinate 300 according to an embodiment of the present invention. In step 224, the analysis device 100 may collect the converted point cloud generated in the sub-process 2100 and the converted point cloud not removed in the sub-process 2200 as the remaining converted point cloud. For example: Assume that the transformed point cloud generated in sub-process 2100 contains the point cloud {410 ₁ , 410 ₂ , 410 ₃ , 410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ }, and it is not removed in sub-process 2200 The point cloud after conversion includes the point cloud {410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ , 410 ₈ , 410 ₉ , 410 ₁₀ }, then the analysis device 100 may collect the above-mentioned converted point cloud as the remaining converted point cloud, including Point cloud {410 ₁ , 410 ₂ , 410 ₃ , 410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ , 410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ , 410 ₈ , 410 ₉ , 410 ₁₀ }, please note that among them The point cloud {410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ } repeatedly appears in the transformed point cloud generated in the sub-process 2100 and the converted point cloud in the sub-process 2200 that has not been removed. In order to avoid the same point cloud being repeatedly adopted The analysis device 100 may delete the duplicate remaining converted point clouds to adjust the remaining converted point clouds to the point clouds shown in FIG. 8 {410 ₁ , 410 ₂ , 410 ₃ , 410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ , 410 ₈ , 410 ₉ , 410 ₁₀ }, and proceed to step 226. Please note that there are multiple first known points in each of the point clouds {410 ₂ , 410 ₃ , 410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ , 4108,410 ₉ , 410 ₁₀ } and multiple The second known point is omitted here for brevity and not shown in the figure.

In step 226, the analysis device 100 may use the second known points selected in step 206 to separately calculate a plurality of second known points in each of the remaining transformed point clouds in step 224. The spatial difference between the known points {Br ₁ , Br ₂ , Br ₃ } corresponding to the reference coordinates 300. For example: Assume that the remaining transformed point clouds collected in step 224 include the point cloud shown in Figure 8 {410 ₁ , 410 ₂ , 410 ₃ , 410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ , 410 _{8, 4109, 41010}} for the point cloud _4101, the analysis device 100 may be obtained by calculating the known point and the known point Br Br _1,1 spatial difference ₁ Drb _1,1, Br known points _{1, 2} with a space known point difference Br ₂ Drb _1,2, Br and _{l, 3} known points with known spatial point difference Br ₃ Drb _1,3; point cloud for _4102, the analysis device 100 may be permeable known points calculated point Br Br _2,1 known spatial difference of ₁ Drb _{2,1, 2,2 &} known point Br Br known spatial point difference of ₂ Drb _2,2, and known points The spatial difference between Br _2,3 and the known point Br ₃ , Drb _2,3 ; similarly, for the point cloud {410 ₃ , 410 ₄ , 410 ₅ , 410 ₆ , 410 ₇ , 410 ₈ , 410 ₉ , 410 ₁₀ } Each of the analysis devices 100 can obtain corresponding spatial differences through calculation. Then, it progresses to step 228.

In step 228, when the corresponding spatial difference of any one of the plurality of second known points in the residual point cloud is outside a third allowable range, the analysis device 100 may eliminate the residual conversion. After the point cloud. In this embodiment, the third allowable range can be determined in a manner similar to that when determining the first allowable range and the second allowable range. For the sake of brevity, the content overlapping with the foregoing steps will not be repeated here. .

In step 230, the analysis device 100 (or the user) may decide whether to change the third allowable range according to a quantity threshold NTH3. When the analysis device 100 (or the user) determines that the third allowable range needs to be changed to increase the The number of remaining converted point clouds after being eliminated is changed (not shown in the figure) and then proceeds to step 228, otherwise, proceeds to step 232. For the sake of brevity, the content overlapping with the previous steps will not be repeated here.

In step 232, when the number of remaining converted point clouds that have not been removed is 0, the process proceeds to step 234 to obtain a new multi-group point cloud different from the multi-group point cloud input in step 202, and then, returns Go to step 202; otherwise, go to step 236.

In step 236, the analysis device 100 may merge the remaining converted point clouds that have not been removed to generate a point cloud with improved accuracy. For example, the analysis device 100 may allocate corresponding weights to the remaining un-removed converted point clouds based on the spatial differences calculated in step 226 of the remaining un-removed converted point clouds. Subsequent point cloud mergers (for example: weighted average). For example, the spatial differences corresponding to the plurality of second known points in the point cloud 410 ₁ are smaller than the spatial differences corresponding to the plurality of second known points in the point cloud 410 _2. Therefore, the analysis device 100 may allocate The higher weight is assigned to the point cloud 410 ₁ , and the lower weight is assigned to the point cloud 410 ₂ ; for another example, the analysis device 100 may be based on the aforementioned standard deviation of the spatial difference, and the remaining converted point clouds that have not been removed. Ground sample distance of the source image, the accuracy (such as photo resolution) or error of the source images of the remaining converted point clouds that have not been removed, or an instrument and the measurement area or an object The weighting ratio is used to allocate weights, but the present invention is not limited to this.

In this embodiment, the analysis device 100 has been able to identify at least a part (a part or all) of the data points of each of the plurality of point cloud in the previous step (such as step 206) respectively in the measurement area. Location (or object), therefore, those skilled in this art should be able to fully understand the operation details of point cloud merging (such as weighted average), and will not repeat them here.

Please note that as long as it does not hinder the implementation of the present invention, one or more steps can be modified, added or deleted in the workflow. For example, in step 212, step 222, or step 230, the user can modify the related subsequent steps of the quantity threshold NTH1, the quantity threshold NTH2, and the quantity threshold NTH3 according to their respective needs (for example, when the step 210 is not removed) If the number of point clouds is less than the number threshold NTH1 (for example: 5), then enter step 234 to re-acquire multiple clusters of point clouds and return to step 202; for example, when the number of unremoved point clouds in step 210 is equal to 0 , Then go to step 234 to re-acquire the multi-group point cloud and return to step 202); for another example, the execution order of sub-process 2100 and sub-process 2200 in the work flow 2000 does not have to follow the order shown in FIG. 2 In other words, the analysis apparatus 100 may perform the steps in the sub-process 2200 first, and then perform the steps in the sub-process 2100, or the analysis apparatus 100 may perform the sub-process 2100 and the sub-process 2200 in parallel. Another example: According to this embodiment, the operation of deleting the duplicated collected point cloud in step 224 may be performed before the point cloud merge is performed in step 236, or the aforementioned operation of deleting the duplicated collected point cloud after conversion is performed. It can be performed when any one of steps 224 to 236 is performed. As the person skilled in this art can understand the operation of each step shown in Figure 2 after reading the above paragraphs, for the sake of brevity, the relevant details will not be repeated here.

Compared with the prior art, the method proposed by the present invention can improve the accuracy of the point cloud itself. Degrees, without being limited to the use of image parallax calculations due to the imaging equipment itself, image quality, shooting image intersection angle, photo density, observing object angle, object surface flatness, etc., and the use of advanced computer science The user can easily input a large amount of point cloud information into the analysis device 100 to quickly complete the above-mentioned point cloud processing. In addition, the implementation according to the related embodiments of the present invention does not increase many additional costs. Therefore, the problems of related technologies can be solved without the overall cost increasing too much. Compared with the related art, the present invention can improve the accuracy of the point cloud without any side effects or less likely to cause side effects.

The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the scope of patent application of the present invention shall fall within the scope of the present invention.

Claims (10)

A method for improving the accuracy of a point cloud, comprising: (a) multiple groups of point clouds input in a measurement area; (b) selecting a coordinate between each of the multiple group of point clouds and a reference coordinate Conversion mode; (c) selecting a group of first known points and a group of second known points different from the group of first known points, wherein the group of first known points includes a plurality of reference coordinates A first known point and the plurality of first known points in each of the clusters of point clouds, and the set of second known points includes a plurality of second known points on the reference coordinate, and The plurality of second known points at each of the plurality of point clouds; (d) calculating the plurality of first known points and the reference coordinates of each of the plurality of point clouds separately The spatial difference between the plurality of first known points, wherein when the spatial difference of any one of the plurality of first known points in a point cloud is outside a first allowable range, the Point cloud, and according to the coordinate conversion method selected in step (b), convert the point cloud that has not been removed to the reference coordinate to generate a converted point cloud; (e) According to step (b) The selected coordinate transformation method converts the plurality of point clouds to the reference coordinates to generate the transformed point clouds, and calculates the plurality of first known ones of each of the transformed point clouds, respectively. The spatial difference between the point and the plurality of first known points on the reference coordinate, when the spatial difference between any one of the plurality of first known points in the point cloud is at a second Outside the allowable range, the converted point cloud is eliminated; (f) the converted point clouds collected in steps (d) and (e) are not removed as the remaining converted point clouds; and (g) Calculate the space difference between the plurality of second known points of each of the remaining transformed point clouds and the plurality of second known points on the reference coordinate respectively. If the spatial difference of any one of the plurality of second known points is outside a third allowable range, the remaining transformed point clouds are excluded. The method according to item 1 of the scope of patent application, further comprising: performing weight allocation on the remaining converted point clouds that are not excluded in step (g) to perform point cloud merging. The method according to item 2 of the scope of patent application, wherein the weight assignment of the remaining converted point clouds that are not excluded in step (g) to perform point cloud merger is based on step ( The space difference calculated in d), the space difference calculated in step (e), the space difference calculated in step (g), and the source images of the remaining converted point clouds that have not been removed Ground sample distance, the accuracy or error of the source images of the remaining converted point clouds that have not been removed, or the distance measurement ratio between an instrument and the measurement area or an object. The method according to item 1 of the scope of patent application, wherein the minimum number of known points in the set of first known points and the set of second known points in a point cloud selected in step (c) is determined by The coordinate conversion method selected in step (b) is determined. The method according to item 1 of the scope of patent application, wherein when the number of converted point clouds generated in step (d) is 0 and the number of the converted point clouds not removed in step (e) is At 0, the multi-group point cloud is reacquired and the process returns to step (a). The method according to item 1 of the scope of patent application, wherein when the number of the remaining transformed point clouds that are not removed in step (g) is 0, the multi-group point clouds are reacquired and the process returns to step (a) . The method according to item 1 of the scope of patent application, wherein when the number of point clouds not removed in step (d) is lower than a number threshold, the first allowable range is selectively changed according to a first user setting Or re-acquire multiple clusters of point clouds and return to step (a). The method according to item 1 of the scope of patent application, wherein when the number of the converted point clouds that are not removed in step (e) is lower than a number threshold, the second user setting is selectively changed according to a second user setting. The second allowable range, or re-acquire multiple clusters of point clouds and return to step (a). The method according to item 1 of the scope of patent application, wherein when the number of the remaining converted point clouds not removed in step (g) is lower than a number threshold, it is selectively changed according to a third user setting This third allowable range, or re-acquire multiple clusters of point clouds, and return to step (a). An analysis device that operates in accordance with the method described in item 1 of the scope of patent application, the analysis device includes: a processing circuit for executing a set of codes corresponding to the method to control the analysis device to perform according to the method Operation.