CN111899331B - Three-dimensional reconstruction quality control method based on aerial photography of unmanned aerial vehicle - Google Patents
Three-dimensional reconstruction quality control method based on aerial photography of unmanned aerial vehicle Download PDFInfo
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- CN111899331B CN111899331B CN202010759876.XA CN202010759876A CN111899331B CN 111899331 B CN111899331 B CN 111899331B CN 202010759876 A CN202010759876 A CN 202010759876A CN 111899331 B CN111899331 B CN 111899331B
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- 238000003908 quality control method Methods 0.000 title claims abstract description 16
- 238000012216 screening Methods 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 12
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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, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
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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 device; screening the received photos; judging whether a photo which does not reach the standard exists or not; if the pictures which do not reach the standard exist, obtaining regional parameters of the subregions corresponding to the pictures which do not reach the standard; generating a supplementary shooting route according to the acquired regional parameters; the unmanned aerial vehicle executes a photographing task according to the supplementary photographing route and sends the supplementary photographed pictures back to the handheld intelligent device; judging whether the pictures which are not up to standard exist again until all the pictures are up to standard; and generating a three-dimensional model through all the up-to-standard photos. According to the three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography, before three-dimensional reconstruction, whether each photo meets the standard is verified, and re-shooting and acquisition are carried out on the photo which does not meet the standard, so that the quality of 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 for three-dimensional reconstruction is increasing. In recent years, a method for shooting a real object by using non-contact shooting equipment and reconstructing a model is mature, and is successfully applied to the fields of architecture, precision industrial measurement, object identification, military and the like.
However, due to shooting means and technical reasons, the quality of the photo is often not guaranteed, the model accuracy is poor, and the quality and content requirements of the multi-image three-dimensional reconstruction are high, so that a method for controlling the quality of the three-dimensional reconstruction is 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 area and flight parameters through a handheld intelligent device;
the handheld intelligent device automatically generates a route according to the reconstruction area and the flight parameters and sends the route to the unmanned aerial vehicle;
The unmanned aerial vehicle executes a photographing task according to the route and sends the photographed photograph back to the handheld intelligent device;
screening the received photos through a handheld intelligent device;
Judging whether a photo which does not reach the standard exists or not;
if the pictures which do not reach the standard exist, obtaining regional parameters of the subregions corresponding to the pictures which do not reach the standard;
Generating a supplementary shooting route according to the acquired regional parameters and sending the supplementary shooting route to the unmanned aerial vehicle;
The unmanned aerial vehicle executes a photographing task according to the supplementary photographing route and sends the supplementary photographed pictures back to the handheld intelligent device;
Judging whether the pictures which are not up to standard exist again until all the pictures are up to standard;
And generating a three-dimensional model through all the up-to-standard photos.
Further, the flight parameters include altitude, speed, coverage threshold, and slap angle;
further, the coverage threshold includes a heading coverage threshold and a sideways coverage threshold.
Further, the heading coverage threshold is 60%;
The bypass coverage threshold is 40%.
Further, the specific method for judging whether the unqualified photo exists is as follows:
Selecting one photo from the photos as a target photo;
finding photos of which the corresponding sub-regions are intersected with the sub-regions corresponding to the selected target photos;
Calculating the overlapping rate of the sub-region corresponding to the target photo and the sub-regions corresponding to other photos intersected with the sub-region to obtain the range coverage of the sub-region corresponding to the target photo;
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;
if one of the front coverage and the rear coverage is less than the heading coverage threshold or one of the left coverage and the right coverage is less than the sideways coverage threshold, the photograph does not reach the standard.
Further, when the overlapping rate of the sub-region corresponding to the target photo and the sub-regions corresponding to other photos intersected with the target photo is calculated to obtain the range coverage of the sub-region corresponding to the target photo, the sub-region with the trapezoid shape is corrected, and the divergent part of the trapezoid sub-region is removed.
Further, the specific method for removing the divergent part of the trapezoid subregion is as follows:
and cutting one end of the long side of the trapezoid sub-region, wherein the cutting line is parallel to the long side.
Further, after one end of the long side of the trapezoid sub-area is cut out,
And cutting out triangular areas on two sides of the residual trapezoidal subareas, and reserving a rectangular effective part.
Further, the specific method for screening the received photos through the handheld intelligent device comprises the following steps:
Edge photos are removed from the photos.
The three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography has the advantages that before three-dimensional reconstruction, whether each photo meets the standard is verified, and re-shooting and acquisition are carried out on the photo which does not meet the standard, so that the quality of three-dimensional reconstruction is ensured.
Drawings
FIG. 1 is a schematic diagram of a three-dimensional reconstruction quality control method based on aerial photography of an unmanned aerial vehicle of the present invention;
FIG. 2 is a schematic illustration of the cutting of trapezoidal subregions of the present invention;
fig. 3 is a schematic diagram of another embodiment of the invention for clipping a trapezoidal shaped sub-area.
Detailed Description
The invention is described in detail below with reference to the drawings and the specific embodiments.
As shown in fig. 1, the three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography mainly comprises the following steps: s1: the reconstruction area and the flight parameters are set by the handheld intelligent device. S2: the handheld intelligent device automatically generates a route according to the reconstruction area and the flight parameters and sends the route to the unmanned aerial vehicle. S3: and the unmanned aerial vehicle executes a photographing task according to the route and sends the photographed photos back to the handheld intelligent device. S4: and screening the received photos through the handheld intelligent device. S5: judging whether the pictures which do not reach the standard exist. S6: and if the substandard photo exists, acquiring the regional parameter of the subarea corresponding to the substandard photo. S7: and generating a supplementary shooting route according to the acquired regional parameters and sending the supplementary shooting route to the unmanned aerial vehicle. S8: and the unmanned aerial vehicle executes a photographing task according to the supplementary photographing route and sends the supplementary photographed pictures back to the handheld intelligent device. And (5) judging whether the pictures which do not reach the standard exist again through the step (S5) until all the pictures reach the standard. S9: and generating a three-dimensional model through all the up-to-standard photos. Through the steps, before three-dimensional reconstruction, whether each photo meets the standard is verified, and the photo which does not meet the standard is taken again for acquisition, so that the quality of three-dimensional reconstruction is ensured. The above steps are specifically described below.
For step S1: the reconstruction area and the flight parameters are set by the handheld intelligent device.
Specifically, the method is operated in the map application of the handheld intelligent device, a reconstruction area to be shot is selected, and then flight parameters of the unmanned aerial vehicle are set. Flight parameters include altitude, speed, coverage threshold, and skew angle. Wherein the coverage threshold comprises a heading coverage threshold and a sideways coverage threshold. Specifically, the heading coverage threshold is 60%. The bypass coverage threshold is 40%.
For step S2: the handheld intelligent device automatically generates a route according to the reconstruction area 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 area and flight parameters, generates a route according to the photographing points, and then wirelessly transmits the route to the unmanned aerial vehicle.
For step S3: and the unmanned aerial vehicle executes a photographing task according to the route and sends the photographed photos back to the handheld intelligent device.
The unmanned aerial vehicle executes a photographing task according to the received route, flies to a corresponding photographing point, photographs according to the set parameters, and sends the photographed photograph back to the intelligent handheld device.
For step S4: and screening the received photos through the handheld intelligent device.
The photographs taken by the unmanned aerial vehicle contain edge photographs that do not intersect with the area to be reconstructed, which are removed in order to increase the speed of operation.
Specifically, if the sub-region corresponding to the current photo is located on the north side of the center point of the reconstruction region, judging whether the photo exists on the northward edge of the photo according to the spatial position relation, and if the photo does not exist, recognizing the current photo as an edge photo and removing the edge photo. And the like, all edge photos are removed.
For step S5: judging whether the pictures which do not reach the standard exist.
Reconstructing the three-dimensional model through the pictures with unqualified quality can directly influence the quality of the built three-dimensional model. Therefore, in the present invention, when reconstructing a three-dimensional model, it is first determined whether there are substandard photographs, and these substandard photographs are processed.
Specifically, in step S5, the specific method for determining whether there is a photo that does not reach the standard is: one of the photos is selected as a target photo. And finding photos where the corresponding sub-region intersects with the sub-region corresponding to the selected target photo. And calculating the overlapping rate of the sub-region corresponding to the target photo and the sub-regions corresponding to other photos intersected with the target photo to obtain the range coverage of the sub-region corresponding to the target photo. If the range coverage does not meet the coverage threshold, the photograph does not reach the standard. And repeating the steps for each photo to judge. Wherein, the range coverage does not satisfy the coverage threshold means that the range coverage is not within the range of the coverage threshold.
Wherein, the coverage of the range comprises: front coverage, rear coverage, left coverage, and right coverage. If one of the front coverage and the rear coverage is less than the heading coverage threshold or one of the left coverage and the right coverage is less than the sideways coverage threshold, the photograph does not reach the standard.
Specifically, the sub-area of the target photo is A, the sub-area range corresponding to the photo taken at the previous stage of the target photo by the unmanned aerial vehicle in the direction of navigation is A 1, the sub-area range corresponding to the photo taken at the later stage of the target photo is A 2, the sub-area range corresponding to the photo taken at the left side of the target photo is A 3, the sub-area range corresponding to the photo taken at the right side of the target photo is A 4, the sub-areas A and A 1 overlap area OA 1, the sub-areas A and A 2 overlap area OA 2, the sub-areas A and A 3 overlap area OA 3, and the sub-areas A and A 4 overlap area OA 4,
Front coverage C Front part ＝OA1/a 100%,
Post-coverage C Rear part (S) ＝OA2/a 100%,
Left coverage C Left side ＝OA3/a 100%,
The right coverage C Right side ＝OA4/a is 100%,
In the present invention, if either one of the front coverage C Front part and the rear coverage C Rear part (S) is less than 60%, the photograph does not reach the standard. Likewise, if either of the left coverage C Left side and the right coverage C Right side is less than 40%, the photograph does not reach the standard.
It can be understood that the photographed pictures comprise forward photographing and oblique photographing, and for the obliquely photographed pictures, the sub-regions corresponding to the pictures are trapezoidal, and according to the principle of the divergence phenomenon, the farther from the divergence center point, the larger the error is caused. Therefore, in order to reduce the distortion of the photo, in the invention, the subregion in the shape of a trapezoid is corrected to remove the divergent part of the trapezoid subregion.
As a preferred embodiment, the specific method for removing the divergent portion of the trapezoidal subregion is: and cutting one end of the long side of the trapezoid sub-region, wherein the cutting line is parallel to the long side. As shown in fig. 2, a hatched portion C1 is reserved. In the present invention, the width of the cut long side portion occupies 10% of the height of the trapezoid. It is understood that the ratio of the clipping can be adjusted according to the actual situation.
More preferably, after one end of the long side of the trapezoid sub-area is cut out, triangular areas on both sides of the remaining trapezoid sub-area are also cut out, and the effective rectangular part is reserved. As shown in fig. 3, the hatched portion C2 portion is reserved.
For step S6: and if the substandard photo exists, acquiring the regional parameter of the subarea corresponding to the substandard photo.
And (5) finding all the unqualified photos through the step (S5), and obtaining the regional parameters of the subregions corresponding to the unqualified photos. Specifically, the regional parameters of the subregion corresponding to the photo which does not reach the standard are output as a shp vector format file.
For step S7: and generating a supplementary shooting route according to the acquired regional parameters and sending the supplementary shooting route to the unmanned aerial vehicle.
And generating a new complement taking route according to the regional parameters of the subregion corresponding to the photo which does not reach the standard, and sending the new complement taking route to the unmanned aerial vehicle.
For step S8: and the unmanned aerial vehicle executes a photographing task according to the supplementary photographing route and sends the supplementary photographed pictures back to the handheld intelligent device.
And the unmanned aerial vehicle executes a new photographing task and performs supplementary photographing to the appointed place. Specifically, the normal photo complement shooting is performed when each specified place is reached.
And (5) judging whether the pictures which do not reach the standard exist again through the step (S5) until all the pictures reach the standard, and entering the step (S9).
For step S9: and generating a three-dimensional model through all the up-to-standard photos.
And after all the photos reach the standard, the handheld device generates a three-dimensional model through all the photos reaching the standard. At this time, the quality of the generated three-dimensional model is high.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be appreciated by persons skilled in the art that the above embodiments are not intended to limit the invention in any way, and that all technical solutions obtained by means of equivalent substitutions or equivalent transformations fall within the scope of the invention.
Claims (6)
1. The three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography is characterized by comprising the following steps of:
Setting a reconstruction area and flight parameters through a handheld intelligent device;
the handheld intelligent device automatically generates a route according to the reconstruction area and the flight parameters and sends the route to the unmanned aerial vehicle;
the unmanned aerial vehicle executes a photographing task according to the route and sends the photographed photo back to the handheld intelligent device;
Screening the received photos through the handheld intelligent device;
Judging whether a photo which does not reach the standard exists or not;
if the pictures which do not reach the standard exist, obtaining regional parameters of the subregions corresponding to the pictures which do not reach the standard;
Generating a supplementary shooting route according to the acquired regional parameters and sending the supplementary shooting route to the unmanned aerial vehicle;
The unmanned aerial vehicle executes a photographing task according to the supplementary photographing route and sends the supplementary photographed pictures back to the handheld intelligent device;
Judging whether the pictures which are not up to standard exist again until all the pictures are up to standard;
Generating a three-dimensional model through all the photos reaching standards;
the flight parameters include altitude, speed, coverage threshold and slapping angle;
The coverage threshold includes a heading coverage threshold and a sideways coverage threshold;
the heading coverage threshold is 60%;
the bypass coverage threshold is 40%;
the specific method for judging whether the unqualified photo exists comprises the following steps:
Selecting one photo from the photos as a target photo;
finding photos of which the corresponding sub-regions are intersected with the sub-regions corresponding to the selected target photos;
Calculating the overlapping rate of the sub-region corresponding to the target photo and the sub-regions corresponding to other photos intersected with the sub-region to obtain the range coverage of the sub-region corresponding to the target photo;
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.
2. The three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography according to claim 1, wherein,
The range coverage includes: front coverage, rear coverage, left coverage, and right coverage;
if one of the front coverage and the rear coverage is less than the heading coverage threshold or one of the left coverage and the right coverage is less than the sideways coverage threshold, the photograph does not reach the standard.
3. The three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography according to claim 2, wherein,
When the overlapping rate of the sub-region corresponding to the target photo and the sub-regions corresponding to other photos intersected with the sub-region is calculated to obtain the range coverage of the sub-region corresponding to the target photo, correcting the sub-region with the trapezoid shape, and removing the divergence part of the trapezoid sub-region.
4. The three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography according to claim 3, wherein the specific method for removing the divergent portion of the trapezoid sub-region is as follows:
and cutting one end of the long side of the trapezoid sub-region, wherein the cutting line is parallel to the long side.
5. The method for three-dimensional reconstruction quality control based on aerial photography of an unmanned aerial vehicle according to claim 4, wherein after cutting off one end of the long side of the trapezoid sub-region,
And cutting out triangular areas on two sides of the residual trapezoidal subareas, and reserving a rectangular effective part.
6. The three-dimensional reconstruction quality control method based on unmanned aerial vehicle aerial photography according to claim 1, wherein the specific method for screening the received photos by the handheld intelligent device is as follows:
Edge photos are removed from the photos.
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| CN107514993A (en) * | 2017-09-25 | 2017-12-26 | 同济大学 | Data acquisition method and system for single building modeling based on UAV |
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| JP4331358B2 (en) * | 1999-11-17 | 2009-09-16 | アジア航測株式会社 | Aerial photogrammetry check method using triplet method |
| CN105049812B (en) * | 2015-08-07 | 2018-06-15 | 清华大学深圳研究生院 | A kind of unmanned plane portable type ground station processing method and system |
| JP7079125B2 (en) * | 2018-03-20 | 2022-06-01 | 株式会社パスコ | Aerial photography management system and program |
| KR101988212B1 (en) * | 2018-04-05 | 2019-06-12 | 주식회사 드로미 | Surveying system using unmanned aerial vehicle and method using the same |
| CN110940320A (en) * | 2019-07-19 | 2020-03-31 | 华北电力大学（保定） | An open-air stockyard monitoring system based on drone cruise |
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| CN102855482A (en) * | 2012-08-16 | 2013-01-02 | 东莞宇龙通信科技有限公司 | Image processing method and device |
| CN107514993A (en) * | 2017-09-25 | 2017-12-26 | 同济大学 | Data acquisition method and system for single building modeling based on UAV |
| CN111189433A (en) * | 2019-12-02 | 2020-05-22 | 中国地质科学院岩溶地质研究所 | A method for measuring karst peak forest landform parameters based on UAV aerial photography |
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