CN111899331A - Three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography - Google Patents
Three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography Download PDFInfo
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- CN111899331A CN111899331A CN202010759876.XA CN202010759876A CN111899331A CN 111899331 A CN111899331 A CN 111899331A CN 202010759876 A CN202010759876 A CN 202010759876A CN 111899331 A CN111899331 A CN 111899331A
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- 238000003908 quality control method Methods 0.000 title claims abstract description 20
- 230000000295 complement effect Effects 0.000 claims abstract description 22
- 238000012216 screening Methods 0.000 claims abstract description 7
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
- G01C11/04—Interpretation of pictures
- G01C11/06—Interpretation of pictures by comparison of two or more pictures of the same area
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/08—Indexing scheme for image data processing or generation, in general involving all processing steps from image acquisition to 3D model generation
Abstract
The invention discloses a three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography, which comprises the following steps: setting a reconstruction area and flight parameters; automatically generating a route; executing a photographing task and sending the photographed picture back to the handheld intelligent equipment; screening the received photos; judging whether the picture which does not reach the standard exists; if the unqualified photos exist, acquiring the regional parameters of the sub-regions corresponding to the unqualified photos; generating a complementary shooting route according to the acquired region parameters; the unmanned aerial vehicle executes a photographing task according to the complementary photographing route and sends the complementary photographed picture back to the handheld intelligent equipment; judging whether the photos which do not reach the standard exist again until all the photos reach the standard; and generating a three-dimensional model through all the qualified photos. According to the three-dimensional reconstruction quality control method based on the unmanned aerial vehicle aerial photography, whether each photo meets the standard or not is verified before three-dimensional reconstruction, shooting and collecting are carried out again aiming at the photos which do not meet the standard, and the quality of the three-dimensional reconstruction is ensured.
Description
Technical Field
The invention relates to a three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography.
Background
With the continuous development of the information age, the demand of three-dimensional reconstruction is more and more. In recent years, non-contact photographing equipment is used for photographing real objects, and a method for reconstructing a model is gradually mature and is successfully applied to the fields of architecture, precision industrial measurement, object identification, military and the like.
However, due to the shooting means and the technical reasons, the quality of the pictures is often not guaranteed in the conventional three-dimensional reconstruction, the accuracy of the model is poor, and the requirements of the multi-image three-dimensional reconstruction on the quality and the content of the pictures are high, so that a method for controlling the quality of the three-dimensional reconstruction is urgently needed.
Disclosure of Invention
The invention provides a three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography, which adopts the following technical scheme:
a three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography comprises the following steps:
setting a reconstruction region and flight parameters through handheld intelligent equipment;
the handheld intelligent equipment automatically generates a route according to the reconstruction region and the flight parameters and sends the route to the unmanned aerial vehicle;
the unmanned aerial vehicle executes a photographing task according to the air route and sends the photographed picture back to the handheld intelligent equipment;
screening the received photos through the handheld intelligent equipment;
judging whether the picture which does not reach the standard exists;
if the unqualified photos exist, acquiring the regional parameters of the sub-regions corresponding to the unqualified photos;
generating a complementary shooting route according to the acquired region parameters and sending the complementary shooting route to the unmanned aerial vehicle;
the unmanned aerial vehicle executes a photographing task according to the complementary photographing route and sends the complementary photographed picture back to the handheld intelligent equipment;
judging whether the photos which do not reach the standard exist again until all the photos reach the standard;
and generating a three-dimensional model through all the qualified photos.
Further, flight parameters include altitude, speed, coverage threshold, and tilt angle;
further, the coverage threshold includes a heading coverage threshold and a sideway coverage threshold.
Further, the heading coverage threshold is 60%;
the lateral coverage threshold is 40%.
Further, the specific method for judging whether the picture which does not reach the standard exists is as follows:
selecting one of the photos as a target photo;
finding out the photo of which the corresponding sub-region is intersected with the sub-region corresponding to the selected target photo;
calculating the overlapping rate of the sub-region corresponding to the target picture and the sub-regions corresponding to other pictures intersected with the target picture to obtain the range coverage of the sub-region corresponding to the target picture;
if the range coverage does not meet the coverage threshold, the photo does not reach the standard;
and repeating the steps for each photo to judge.
Further, the range coverage includes: front coverage, rear coverage, left coverage and right coverage;
and if one of the front coverage and the rear coverage is smaller than the course coverage threshold or one of the left coverage and the right coverage is smaller than the side direction coverage threshold, the picture does not reach the standard.
Further, when the overlapping rate of the sub-area corresponding to the target photo and the sub-areas corresponding to the other photos intersected with the target photo is calculated to obtain the range coverage of the sub-area corresponding to the target photo, the sub-area with the trapezoidal shape is corrected, and the divergent part of the trapezoidal sub-area is removed.
Further, a specific method for removing the divergent part of the trapezoidal sub-region is as follows:
and cutting one end of the long edge of the trapezoidal subregion, wherein the cutting line is parallel to the long edge.
Further, after one end of the long side of the trapezoidal sub-area is cut off,
and cutting to remove triangular areas on two sides of the residual trapezoidal sub-area and reserving a rectangular effective part.
Further, a specific method for screening the received photos through the handheld intelligent device is as follows:
and removing the edge photos from the photos.
The unmanned aerial vehicle aerial photography-based three-dimensional reconstruction quality control method has the advantages that before three-dimensional reconstruction, whether each photo meets the standard is verified, shooting and collecting are carried out again on the photos which do not meet the standard, and the quality of the three-dimensional reconstruction is ensured.
Drawings
FIG. 1 is a schematic diagram of a three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography of the present invention;
FIG. 2 is a schematic diagram of the present invention for clipping trapezoidal sub-regions;
FIG. 3 is a schematic diagram of another embodiment of clipping trapezoidal sub-regions according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the embodiments.
As shown in fig. 1, a three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography mainly includes the following steps: s1: and setting a reconstruction region and flight parameters through the handheld intelligent equipment. S2: and the handheld intelligent equipment automatically generates a route according to the reconstruction region and the flight parameters and sends the route to the unmanned aerial vehicle. S3: and the unmanned aerial vehicle executes the photographing task according to the air route and sends the photographed picture back to the handheld intelligent equipment. S4: and screening the received photos through the handheld intelligent equipment. S5: and judging whether the picture which does not reach the standard exists or not. S6: and if the unqualified photos exist, acquiring the regional parameters of the sub-regions corresponding to the unqualified photos. S7: and generating a complementary shooting route according to the acquired region parameters and sending the complementary shooting route to the unmanned aerial vehicle. S8: and the unmanned aerial vehicle executes the photographing task according to the complementary photographing route and sends the complementary photographed picture back to the handheld intelligent equipment. Through step S5, it is determined again whether there are any photos that do not meet the standard until all photos meet the standard. S9: and generating a three-dimensional model through all the qualified photos. Through the steps, before three-dimensional reconstruction, whether each picture meets the standard or not is verified, shooting and collecting are carried out again aiming at the pictures which do not meet the standard, and the quality of three-dimensional reconstruction is ensured. The above steps are specifically described below.
For step S1: and setting a reconstruction region and flight parameters through the handheld intelligent equipment.
Specifically, the method is operated in the map application of the handheld intelligent device, the reconstruction area to be shot is selected, and then the flight parameters of the unmanned aerial vehicle are set. Flight parameters include altitude, speed, coverage threshold, and tilt angle. The coverage threshold comprises a heading coverage threshold and a sideway coverage threshold. Specifically, the heading coverage threshold is 60%. The lateral coverage threshold is 40%.
For step S2: and the handheld intelligent equipment automatically generates a route according to the reconstruction region and the flight parameters and sends the route to the unmanned aerial vehicle.
The handheld intelligent device automatically generates a plurality of photographing points according to the defined reconstruction region and the flight parameters, generates a route according to the photographing points, and wirelessly transmits the route to the unmanned aerial vehicle.
For step S3: and the unmanned aerial vehicle executes the photographing task according to the air route and sends the photographed picture back to the handheld intelligent equipment.
The unmanned aerial vehicle executes a photographing task according to the received air route, flies to a corresponding photographing point, photographs according to set parameters, and sends the photographed pictures back to the intelligent handheld device.
For step S4: and screening the received photos through the handheld intelligent equipment.
The photos taken by the unmanned aerial vehicle comprise edge photos which do not intersect with the region to be reconstructed, and the edge photos are removed in order to improve the operation speed.
Specifically, if the sub-region corresponding to the current photo is located on the north side of the central point of the reconstruction region, whether the photo exists on the northward edge of the photo is judged according to the spatial position relationship, and if the photo does not exist, the current photo is determined to be an edge photo and removed. And in the same way, removing all edge photos.
For step S5: and judging whether the picture which does not reach the standard exists or not.
The quality of the built three-dimensional model can be directly influenced by reconstructing the three-dimensional model through the photos with unqualified quality. Therefore, in the present invention, when reconstructing the three-dimensional model, it is first determined whether there are any unsatisfactory pictures, and these unsatisfactory pictures are processed.
Specifically, in step S5, the specific method for determining whether there is an unqualified photo includes: and selecting one of the photos as a target photo. And finding the photo of which the corresponding sub area is intersected with the sub area corresponding to the selected target photo. And calculating the overlapping rate of the sub-area corresponding to the target picture and the sub-areas corresponding to other pictures intersected with the target picture to obtain the range coverage of the sub-area corresponding to the target picture. If the range coverage does not meet the coverage threshold, the photo does not meet the standard. And repeating the steps for each photo to judge. Wherein the coverage not meeting the coverage threshold means that the coverage is not within the coverage threshold.
Wherein the range coverage comprises: front coverage, back coverage, left coverage, and right coverage. And if one of the front coverage and the rear coverage is smaller than the course coverage threshold or one of the left coverage and the right coverage is smaller than the side direction coverage threshold, the picture does not reach the standard.
Specifically, the sub-area of the target picture is A, and the range of the sub-area corresponding to the picture shot by the unmanned aerial vehicle at the previous stage of the target picture in the heading direction is A1The sub-area range corresponding to the picture shot at the later stage of the target picture is A2The picture taken at the position beside the target picture corresponds to the target pictureHas a subregion range of A3The sub-area range of the corresponding picture taken at the lateral right position of the target picture is A4Subregions A and A1Overlap area OA1Subregions A and A2Overlap area OA2Subregions A and A3Overlap area OA3Subregions A and A4Overlap area OA4Then, the first step is executed,
front coverage CFront side＝OA1/A*100％，
Rear coverage CRear end＝OA2/A*100％，
Left coverage CLeft side of＝OA3/A*100％，
Right coverage CRight side＝OA4/A*100％，
In the present invention, the front coverage CFront sideAnd degree of after-coverage CRear endIf any one of the numbers is less than 60%, the picture does not reach the standard. Likewise, left coverage CLeft side ofAnd right coverage CRight sideLess than 40% of any, the picture is likewise substandard.
It can be understood that the taken photos include positive photos and oblique photos, and for oblique photos, the sub-regions corresponding to the photos are trapezoidal, and according to the principle of divergence, the farther away from the divergence center point, the larger the error caused. Therefore, in order to reduce the distortion of the photograph, in the present invention, the sub-region having a trapezoidal shape is corrected to remove the divergent portion of the trapezoidal sub-region.
As a preferred embodiment, a specific method for removing the divergent part of the trapezoidal sub-region is as follows: and cutting one end of the long edge of the trapezoidal subregion, wherein the cutting line is parallel to the long edge. As shown in fig. 2, the shaded portion C1 remains. In the present invention, the width of the trimmed long side portion occupies 10% of the height of the trapezoid. It can be understood that the cutting proportion can be adjusted according to actual conditions.
More preferably, after one end of the long side of the trapezoidal sub-region is cut out, the triangular regions on both sides of the remaining trapezoidal sub-region are also cut out, and the rectangular effective portion is retained. As shown in fig. 3, the hatched portion C2 remains.
For step S6: and if the unqualified photos exist, acquiring the regional parameters of the sub-regions corresponding to the unqualified photos.
All the photos which do not reach the standard are found through the step S5, and the area parameters of the sub-areas corresponding to the photos which do not reach the standard are obtained. Specifically, the region parameters of the sub-regions corresponding to the photos which do not reach the standard are output as the shp vector format file.
For step S7: and generating a complementary shooting route according to the acquired region parameters and sending the complementary shooting route to the unmanned aerial vehicle.
And generating a new complementary shooting route according to the regional parameters of the sub-region corresponding to the picture which does not reach the standard, and sending the new complementary shooting route to the unmanned aerial vehicle.
For step S8: and the unmanned aerial vehicle executes the photographing task according to the complementary photographing route and sends the complementary photographed picture back to the handheld intelligent equipment.
And the unmanned aerial vehicle executes a new photographing task and carries out a complementary photographing at a specified place. Specifically, the orthophotos is taken after arriving at each designated location.
In step S5, it is determined again whether there are any photos that do not meet the standard, and the process proceeds to step S9 until all photos meet the standard.
For step S9: and generating a three-dimensional model through all the qualified photos.
And when all the photos reach the standard, the handheld device generates a three-dimensional model through all the photos reaching the standard. In this case, the quality of the generated three-dimensional model is high.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It should be understood by those skilled in the art that the above embodiments do not limit the present invention in any way, and all technical solutions obtained by using equivalent alternatives or equivalent variations fall within the scope of the present invention.
Claims (10)
1. A three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography is characterized by comprising the following steps:
setting a reconstruction region and flight parameters through handheld intelligent equipment;
the handheld intelligent equipment automatically generates a route according to the reconstruction region and the flight parameters and sends the route to the unmanned aerial vehicle;
the unmanned aerial vehicle executes a photographing task according to the air route and sends the photographed picture back to the handheld intelligent equipment;
screening the received photos through the handheld intelligent equipment;
judging whether the picture which does not reach the standard exists;
if the unqualified photos exist, acquiring the regional parameters of the sub-regions corresponding to the unqualified photos;
generating a complementary shooting route according to the acquired region parameters and sending the complementary shooting route to the unmanned aerial vehicle;
the unmanned aerial vehicle executes a photographing task according to the complementary photographing route and sends the complementary photographed picture back to the handheld intelligent equipment;
judging whether the photos which do not reach the standard exist again until all the photos reach the standard;
and generating a three-dimensional model through all the qualified photos.
2. The unmanned aerial vehicle aerial photography-based three-dimensional reconstruction quality control method according to claim 1,
the flight parameters include altitude, speed, coverage threshold, and tilt angle.
3. The unmanned aerial vehicle aerial photography-based three-dimensional reconstruction quality control method according to claim 2,
the coverage threshold comprises a heading coverage threshold and a sidewise coverage threshold.
4. The unmanned aerial vehicle aerial photography-based three-dimensional reconstruction quality control method according to claim 3,
the heading coverage threshold is 60%;
the lateral coverage threshold is 40%.
5. The unmanned aerial vehicle aerial photography-based three-dimensional reconstruction quality control method according to claim 3,
the specific method for judging whether the picture which does not reach the standard exists comprises the following steps:
selecting one of the photos as a target photo;
finding out the photo of which the corresponding sub-region is intersected with the sub-region corresponding to the selected target photo;
calculating the overlapping rate of the sub-region corresponding to the target picture and the sub-regions corresponding to other pictures intersected with the target picture to obtain the range coverage of the sub-region corresponding to the target picture;
if the range coverage does not meet the coverage threshold, the photo does not reach the standard;
and repeating the steps for each photo to judge.
6. The unmanned aerial vehicle aerial photography-based three-dimensional reconstruction quality control method according to claim 5,
the range coverage includes: front coverage, rear coverage, left coverage and right coverage;
and if one of the front coverage and the rear coverage is smaller than the course coverage threshold or one of the left coverage and the right coverage is smaller than the side coverage threshold, the picture does not reach the standard.
7. The unmanned aerial vehicle aerial photography-based three-dimensional reconstruction quality control method according to claim 6,
and when the overlapping rate of the sub-region corresponding to the target picture and the sub-regions corresponding to other pictures intersected with the target picture is calculated to obtain the range coverage of the sub-region corresponding to the target picture, correcting the sub-region with the trapezoidal shape, and removing the divergent part of the trapezoidal sub-region.
8. The unmanned aerial vehicle aerial photography-based three-dimensional reconstruction quality control method according to claim 7,
the specific method for removing the divergent part of the trapezoidal sub-region comprises the following steps:
and cutting one end of the long edge of the trapezoidal subregion, wherein the cutting line is parallel to the long edge.
9. The unmanned aerial vehicle aerial photography-based three-dimensional reconstruction quality control method according to claim 8,
after one end of the long side of the trapezoidal sub-region is cut off,
and cutting to remove triangular areas on two sides of the residual trapezoidal sub-area and reserving a rectangular effective part.
10. The unmanned aerial vehicle aerial photography-based three-dimensional reconstruction quality control method according to claim 1,
the specific method for screening the received photos through the handheld intelligent device comprises the following steps:
and removing the edge photos from the photos.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112822478A (en) * | 2020-12-31 | 2021-05-18 | 杭州电子科技大学 | High-quality photo sequence acquisition method for three-dimensional reconstruction |
| CN113421332A (en) * | 2021-06-30 | 2021-09-21 | 广州极飞科技股份有限公司 | Three-dimensional reconstruction method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001141454A (en) * | 1999-11-17 | 2001-05-25 | Asia Air Survey Co Ltd | Method for checking aerial photogrammetry by using triplet technique |
| CN102855482A (en) * | 2012-08-16 | 2013-01-02 | 东莞宇龙通信科技有限公司 | Method and device for processing picture |
| CN107514993A (en) * | 2017-09-25 | 2017-12-26 | 同济大学 | The collecting method and system towards single building modeling based on unmanned plane |
| KR101988212B1 (en) * | 2018-04-05 | 2019-06-12 | 주식회사 드로미 | Surveying system using unmanned aerial vehicle and method using the same |
| CN111189433A (en) * | 2019-12-02 | 2020-05-22 | 中国地质科学院岩溶地质研究所 | Karst peak forest landform parameter measuring method based on unmanned aerial vehicle aerial photography |
-
2020
- 2020-07-31 CN CN202010759876.XA patent/CN111899331A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001141454A (en) * | 1999-11-17 | 2001-05-25 | Asia Air Survey Co Ltd | Method for checking aerial photogrammetry by using triplet technique |
| CN102855482A (en) * | 2012-08-16 | 2013-01-02 | 东莞宇龙通信科技有限公司 | Method and device for processing picture |
| CN107514993A (en) * | 2017-09-25 | 2017-12-26 | 同济大学 | The collecting method and system towards single building modeling based on unmanned plane |
| KR101988212B1 (en) * | 2018-04-05 | 2019-06-12 | 주식회사 드로미 | Surveying system using unmanned aerial vehicle and method using the same |
| CN111189433A (en) * | 2019-12-02 | 2020-05-22 | 中国地质科学院岩溶地质研究所 | Karst peak forest landform parameter measuring method based on unmanned aerial vehicle aerial photography |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112822478A (en) * | 2020-12-31 | 2021-05-18 | 杭州电子科技大学 | High-quality photo sequence acquisition method for three-dimensional reconstruction |
| CN113421332A (en) * | 2021-06-30 | 2021-09-21 | 广州极飞科技股份有限公司 | Three-dimensional reconstruction method and device, electronic equipment and storage medium |
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Inventor after: He Yuefei Inventor after: Liang Xiaofeng Inventor after: Shi Saiqun Inventor after: Wen Jingyi Inventor after: He Yusheng Inventor after: Yang Jiangchuan Inventor after: Fang Peng Inventor before: He Yusheng Inventor before: Yang Jiangchuan Inventor before: Shi Saiqun Inventor before: Wen Jingyi Inventor before: Fang Peng |