CN113487732A - Indoor underground emergency scene three-dimensional modeling method based on mobile phone crowdsourcing imaging terminal - Google Patents
Indoor underground emergency scene three-dimensional modeling method based on mobile phone crowdsourcing imaging terminal Download PDFInfo
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Abstract
The invention provides an indoor underground emergency scene three-dimensional modeling method based on a mobile phone crowdsourcing imaging terminal, which comprises the following steps of: developing and designing crowdsourcing image data acquisition software at a mobile phone end to form a mobile phone crowdsourcing imaging terminal; the method comprises the steps that crowdsourcing image data and feature point real scale information of an indoor underground scene are collected through a mobile phone crowdsourcing imaging terminal, and the collected information is uploaded to a data processing server; building a three-dimensional point cloud data processing operation platform at a data processing server end, and generating crowdsourcing image data into a first crowdsourcing three-dimensional point cloud; and giving the characteristic point scale information to the first crowdsourcing three-dimensional point cloud, finally generating a second crowdsourcing three-dimensional point cloud with a real scale, displaying the second crowdsourcing three-dimensional point cloud at the data processing server side, and measuring and applying. The method supports the transmission, aggregation, matching and fusion of crowdsourcing sensing data, realizes the cooperative sensing and three-dimensional imaging of the indoor underground scene, and provides centimeter-level measurable scene information for emergency treatment of indoor underground emergency.
Description
Technical Field
The invention relates to the field of data transmission and three-dimensional reconstruction, in particular to an indoor underground emergency scene three-dimensional modeling method based on a mobile phone crowdsourcing imaging terminal.
Background
With global changes and high-intensity socioeconomic development, fire disasters, waterlogging and other sudden events occur in various cities of China. When the urban fire collapses locally or wholly, the large span of the building causes great difficulty for the organization and command decision of the on-site commander. Urban inland inundation can not only destroy buildings and traffic facilities to cause casualties of different degrees, but also bring harm of different degrees to urban construction, environmental safety and social stability, economic loss and the like. These emergencies cause the decline of functions of the economic society, and cause the continuous deterioration of the living environment of cities, thereby becoming a 'stubborn disease' which hinders the development of the cities.
Due to uncertainty and urgency of urban emergencies, various powerful devices in a management mechanism cannot arrive in time, and personnel in urban functional departments cannot process, command and make decisions on the emergencies in time. In addition, the emergency is caused by some irresistible external force factors and some human factors which can be controlled originally, so that people cannot only rely on corresponding management mechanisms, and vast citizens in cities also need to participate in the emergency, and the citizens need to be made clear what they should be able to do in the process.
In order to obtain the optimal and fastest solution for urban emergencies and minimize the influence on cities, a management organization must acquire the conditions of the urban emergencies through citizens at the first time. Therefore, how to design an indoor underground emergency scene three-dimensional modeling method based on a mobile phone crowdsourcing imaging terminal to deal with urban emergencies is an urgent problem to be solved.
Disclosure of Invention
In order to solve the problems, the invention provides an indoor underground emergency scene three-dimensional modeling method based on a mobile phone crowdsourcing imaging terminal, which mainly comprises the following steps:
s1, developing and designing crowdsourcing image data acquisition software at a smart phone end to form a mobile phone crowdsourcing imaging terminal, wherein the crowdsourcing image data acquisition software has an image data acquisition function, an AR ranging function, a feature point marking function, a data uploading function and a data downloading function;
s2, collecting crowdsourcing image data and feature point real scale information of an indoor underground scene based on the mobile phone crowdsourcing imaging terminal, and uploading the crowdsourcing image data and the feature point real scale information to a data processing server side;
s3, building a three-dimensional point cloud data processing operation platform at the data processing server end, and generating a first crowdsourcing three-dimensional point cloud from the crowdsourcing image data based on the three-dimensional point cloud data processing operation platform;
s4, giving the feature point real scale information to the first crowdsourcing three-dimensional point cloud, and generating a second crowdsourcing three-dimensional point cloud with a real scale;
and S5, displaying, measuring and applying the second crowdsourced three-dimensional point cloud at the data processing server side, and providing basic data information for emergency events in indoor underground scenes.
The method mainly realizes the cooperative perception and three-dimensional imaging of the indoor underground scene, and provides indoor underground centimeter-level measurable scene information for emergency treatment of emergencies (urban fire, illegal buildings, urban waterlogging and the like).
Preferably, in step S1, the crowdsourcing image data acquisition software is developed and designed on a smartphone side with a depth of field camera (tof) or lidar camera, so as to form the mobile phone crowdsourcing imaging terminal.
Preferably, in step S1, the functions of the crowdsourced image data collection software are developed and completed on an Android Studio which is an Android integrated development tool introduced by ***.
Preferably, in step S2, the crowdsourced image data collection software reminds the user that a camera mode (aperture, exposure, lens focus, etc.) needs to be set when taking a picture, and the overlapping degree of the acquired images is more than 80%, and at the same time, the shutter speed is controlled to avoid the blur of the picture.
Preferably, in step S2, when using the AR ranging function, the crowdsourced image data acquisition software prompts the user to select a position with good corners and light, and the mobile phone is kept moving stably during measurement, so as to ensure the accuracy of the measurement result.
Preferably, the three-dimensional point cloud data processing operation platform is built on an Ubuntu18.04 system, and colop open source software is installed in the Ubuntu18.04 system and used for generating the crowd-sourced image data into the first crowd-sourced three-dimensional point cloud in the ply format.
Preferably, in step S3, a colop algorithm of the colop open source software is used to generate the crowd-sourced image data into a first crowd-sourced three-dimensional point cloud, where the colop algorithm includes: SFM algorithm and MVS algorithm.
Preferably, the specific step of generating the crowdsourced image data into the first crowdsourced three-dimensional point cloud by using the colomap algorithm of the colomap open source software includes:
inputting the acquired crowdsourcing image data into a colomap algorithm, and calculating by an SFM algorithm to obtain sparse point cloud and camera parameters, wherein the camera parameters comprise: viewing angle, pose, internal reference and common view relation;
and inputting the acquired crowdsourcing image data, the sparse point cloud and the camera parameters into an MVS algorithm to generate a dense three-dimensional point cloud, so as to obtain a first crowdsourcing three-dimensional point cloud.
Preferably, step S4 specifically includes: and giving the real scale information of the feature points to the first crowdsourcing three-dimensional point cloud, obtaining a transformation matrix by utilizing the corresponding relation between the feature points, finishing the point cloud scale conversion work by using transformpointCloud in a pci library according to the transformation matrix, obtaining the real scale of the first crowdsourcing three-dimensional point cloud, and generating a second crowdsourcing three-dimensional point cloud.
Preferably, in step S5, a meshlab open source software is installed at the data processing server, and the meshlab open source software is used to view, measure and acquire the required information for the second crowdsourced three-dimensional point cloud.
Preferably, the meshlab software is an open-source, portable and expandable three-dimensional geometric processing system and is mainly used for interactive processing and unstructured editing of the three-dimensional triangular mesh, the meshlab open-source software mainly provides a set of tools for editing, cleaning, healing, checking, rendering and converting the mesh, and the software is mainly used for editing, checking and measuring finally generated second crowdsourced three-dimensional point cloud to obtain required information.
The method can obtain scene crowdsourcing image data by aiming at emergencies (urban fire, illegal buildings, urban waterlogging and the like), and then quickly process the crowdsourcing image data to obtain a scene three-dimensional model with an absolute scale, so that centimeter-level and measurable scene information is provided for indoor underground emergencies.
The technical scheme provided by the invention has the beneficial effects that: the invention discloses an indoor underground emergency scene three-dimensional modeling method based on a mobile phone crowdsourcing imaging terminal, which is characterized in that when emergencies such as urban fire, illegal buildings, urban inland inundation and the like occur in a city, citizens can acquire live crowdsourcing image data in time by using handheld devices such as mobile phones, pads and the like and transmit the live crowdsourcing image data to a data processing server end, and after the data processing server end is rapidly processed by technicians, a measurable three-dimensional point cloud scene with the precision reaching the centimeter level can be obtained, so that workers in various departments in the city can rapidly acquire the live emergencies.
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The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a flowchart of an indoor underground emergency scene three-dimensional modeling method based on a mobile phone crowdsourcing imaging terminal in the embodiment of the invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
The embodiment of the invention provides an indoor underground emergency scene three-dimensional modeling method based on a mobile phone crowdsourcing imaging terminal.
Referring to fig. 1, fig. 1 is a flowchart of an indoor underground emergency scene three-dimensional modeling method based on a mobile phone crowdsourcing imaging terminal in an embodiment of the present invention;
a three-dimensional modeling method for an indoor underground emergency scene based on a mobile phone crowdsourcing imaging terminal specifically comprises the following steps:
s1, crowdsourcing image data acquisition software is developed and designed at the smart phone end to form a crowdsourcing imaging terminal of the mobile phone, and the crowdsourcing image data acquisition software has an image data acquisition function, an AR ranging function, a feature point marking function, a data uploading function and a data downloading function.
In this embodiment, crowdsourcing image data acquisition software is developed and designed on a smart phone with a tof (depth of field) camera or a lidar camera to form the crowdsourcing imaging terminal of the mobile phone, and the crowdsourcing image data acquisition software is developed and completed on an Android Studio which is an Android integrated development tool proposed by *** and needs to realize functions of image data acquisition, AR ranging, feature point marking, data uploading, data downloading and the like.
The image data acquisition function of the developed crowdsourcing image data acquisition software is mainly to call the photographing function of the mobile phone to acquire scene crowdsourcing image data; note that some cautions need to be prompted to the user when image data is acquired, including setting a camera mode (aperture, exposure, lens focusing, etc.), overlapping degree of adjacent images should reach more than 80%, and taking care to control shutter speed, avoid blurring pictures, etc. The AR ranging function is mainly used for acquiring characteristic point information by calling the AR ranging function of the mobile phone, and a user selects two points in a scene at the mobile phone end to measure; the user can select one or more pairs of feature points to measure at the mobile phone end, and the user is prompted to select angular points, such as door frames, included angles of wallpaper and good light positions, as much as possible. The characteristic point marking function is to mark characteristic points on the mobile phone terminal image; the data uploading function is mainly to upload the picture data and the distance data of the mobile phone end to the data processing server end; after the scene image data are collected, a user can select shot scene image data to upload the image data in the jpg format, and after the scale information of the scene feature points is measured, the feature point scale information is input to upload a file in the txt format; the data downloading function is mainly to download data of the data processing server end to the mobile phone for users to check and use.
The method for developing crowdsourced image data acquisition software by using Android Studio mainly comprises the following steps:
s11, firstly, creating a blank Android project, and adding a plurality of click buttons in the layout to realize triggering corresponding functions;
s12, acquiring related rights such as photographing and network transmission storage in the configuration file;
s13, calling a camera of the mobile phone to continuously take photos and store the photos in a system photo album by using a photo button;
s14, selecting a button to realize multiple-item selection of images in the mobile phone system album, and acquiring uri and a path of the images for subsequent uploading;
s14, the uploading button realizes batch uploading of the images selected in the previous step and stores the first picture name for subsequent use;
s15, a download button is used for selecting the processed image needing to be marked on the data processing server side and storing the image to be marked on the smart phone side for marking the feature points;
s16, marking the button, adding feature points on the image downloaded in the previous step for punctuation processing, and storing the pixel coordinate position for uploading;
and S17, the last button realizes the distance measuring function of the anchor point of the mobile phone by calling related components in the arcore and uploads the distance storage file.
S2, collecting crowdsourcing image data and feature point real scale information of an indoor underground scene based on the mobile phone crowdsourcing imaging terminal, and uploading the crowdsourcing image data and the feature point real scale information to a data processing server side.
The step S2 specifically operates as follows:
s21, opening crowdsourcing image data acquisition software of the crowdsourcing imaging terminal of the mobile phone, selecting a photographing button, photographing the scene, and automatically storing the image into the mobile phone;
s22, selecting an uploading button to enter a picture selection interface, selecting all pictures shot in the previous step, clicking to upload after the selection is finished, and uploading crowdsourced image data to a data processing server side;
and S23, selecting an AR ranging button, selecting two to three pairs of proper feature points in the scene for ranging, filling the measured result into an input box, and uploading the input box to a data processing server.
When the image data acquisition function is used, a user needs to be reminded of setting a camera mode (aperture, exposure, lens focusing and the like) when taking a picture, and the overlapping degree of the acquired images needs to reach more than 80%. Meanwhile, the shutter speed is controlled, so that the phenomenon that pictures are blurred and subsequent three-dimensional modeling work is influenced is avoided. When the distance measuring function is used, a user needs to be prompted to select angular points, such as a door frame, a wallpaper included angle and a position with good light, so that the mobile phone can be kept moving stably during measurement, and the accuracy of a measuring result is guaranteed.
S3, building a three-dimensional point cloud data processing operation platform at the data processing server end, and generating a first crowdsourcing three-dimensional point cloud from the crowdsourcing image data based on the three-dimensional point cloud data processing operation platform;
in this embodiment, a three-dimensional point cloud data processing operation platform of the data processing server is built on an Ubuntu18.04 system, and open source software such as colop and meshlab is sequentially installed in the system, wherein the colop software mainly generates scene image data acquired by equipment into ply-format three-dimensional point cloud data, and the meshlab open source software is mainly used in displaying and measuring a second crowdsourced three-dimensional point cloud in the subsequent process. Adopting a colomap algorithm to generate crowdsourced image data into a first crowdsourced three-dimensional point cloud, wherein the colomap algorithm comprises the following steps: the SFM algorithm and the MVS algorithm adopt a colomap algorithm to obtain a first crowdsourced three-dimensional point cloud, and the specific steps are as follows:
s31, data preparation: organizing the collected crowdsourced image data into a project format of a colop;
s32, starting reconstruction: the colomap provides an automatic reconstruction command, and can also carry out reconstruction step by step, and the main steps comprise feature extraction, feature point matching, sparse reconstruction, image distortion removal, dense reconstruction and fusion.
S33, visualization: the reconstructed results are visualized using the COLMAP GUI.
S4, giving the feature point real scale information to the first crowdsourcing three-dimensional point cloud, and generating a second crowdsourcing three-dimensional point cloud;
step S4 specifically includes:
and giving the real scale information of the feature points to the first crowdsourcing three-dimensional point cloud, obtaining a transformation matrix by utilizing the corresponding relation between the feature points, finishing the point cloud scale conversion work by using transformpointCloud in a pci library according to the transformation matrix, obtaining the real scale of the first crowdsourcing three-dimensional point cloud, and generating a second crowdsourcing three-dimensional point cloud.
And S5, displaying, measuring and applying the second crowdsourced three-dimensional point cloud at the data processing server side, and providing basic data information for emergency events in indoor underground scenes.
In this example, the finally generated second crowdsourced three-dimensional point cloud can be displayed at the data processing server side, and can be measured to provide available information for an emergency. The meshlab is an open-source, portable and expandable three-dimensional geometric processing system, is mainly used for interactive processing and unstructured editing of three-dimensional triangular meshes, and open-source software of the meshlab mainly provides a set of tools for editing, cleaning, healing, checking, rendering and mesh conversion, and is mainly used for editing, checking and measuring point clouds to acquire required information.
The invention can acquire scene crowdsourcing images through the designed mobile phone crowdsourcing imaging terminal aiming at emergencies (urban fire, illegal buildings, urban inland inundation and the like), and then quickly process image data to obtain a scene three-dimensional model with absolute scale, thereby providing centimeter-level measurable scene information for the indoor underground emergencies.
The technical scheme provided by the invention has the beneficial effects that: the invention discloses an indoor underground emergency scene three-dimensional modeling method based on a mobile phone crowdsourcing imaging terminal, which is characterized in that when emergencies such as urban fire, illegal buildings, urban waterlogging and the like occur in a city, citizens can acquire live data in time by using handheld devices such as mobile phones and pads and upload the live data, and after the data is rapidly processed by technicians at a data processing server end, a measurable three-dimensional point cloud scene with the precision reaching the centimeter level can be established, so that workers at various departments in the city can rapidly acquire the live emergencies.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. An indoor underground emergency scene three-dimensional modeling method based on a mobile phone crowdsourcing imaging terminal comprises the following steps:
s1, developing and designing crowdsourcing image data acquisition software at a smart phone end to form a mobile phone crowdsourcing imaging terminal, wherein the crowdsourcing image data acquisition software has an image data acquisition function, an AR ranging function, a feature point marking function, a data uploading function and a data downloading function;
s2, collecting crowdsourcing image data and feature point real scale information of an indoor underground scene based on the mobile phone crowdsourcing imaging terminal, and uploading the crowdsourcing image data and the feature point real scale information to a data processing server side;
s3, building a three-dimensional point cloud data processing operation platform at the data processing server end, and generating a first crowdsourcing three-dimensional point cloud from the crowdsourcing image data based on the three-dimensional point cloud data processing operation platform;
s4, giving the feature point real scale information to the first crowdsourcing three-dimensional point cloud, and generating a second crowdsourcing three-dimensional point cloud with a real scale;
and S5, displaying, measuring and applying the second crowdsourced three-dimensional point cloud at the data processing server side, and providing centimeter-level measurable scene information for emergency treatment of emergency in indoor underground scenes.
2. The indoor underground emergency scene three-dimensional modeling method according to claim 1, wherein in step S1, the crowdsourcing image data acquisition software is developed and designed on a smartphone terminal with a depth camera or a lidar camera to form the mobile phone crowdsourcing imaging terminal.
3. The method for three-dimensional modeling of an indoor underground emergency scene as claimed in claim 1, wherein in step S1, the functions of the crowdsourced image data acquisition software are developed and completed on an Android Studio which is an Android integrated development tool introduced by ***.
4. The method for three-dimensional modeling of an indoor underground emergency scene as claimed in claim 1, wherein in step S2, the crowdsourced image data acquisition software prompts the user to set a camera mode when taking a picture and to control a shutter speed to avoid a blur of the picture when acquiring an image with an image overlapping degree of 80% or more when using an image data acquisition function.
5. The indoor underground emergency scene three-dimensional modeling method according to claim 1, wherein in step S2, the crowdsourced image data acquisition software prompts a user to select a position with good corners and light when using an AR ranging function, and a mobile phone is kept moving smoothly during measurement to ensure accuracy of a measurement result.
6. The indoor underground emergency scene three-dimensional modeling method according to claim 1, wherein in step S3, the three-dimensional point cloud data processing operation platform is built on a Ubuntu18.04 system, and a colomap open source software is installed in the Ubuntu18.04 system, and the colomap open source software is used for generating the crowd-sourced image data into the first crowd-sourced three-dimensional point cloud in the ply format.
7. The indoor underground emergency scene three-dimensional modeling method according to claim 1, wherein in step S3, a colop algorithm of colop open source software is used to generate the crowd-sourced image data into a first crowd-sourced three-dimensional point cloud, and the colop algorithm includes: SFM algorithm and MVS algorithm.
8. The method for three-dimensional modeling of an indoor underground emergency scene as claimed in claim 7, wherein the specific step of generating the crowdsourced image data into a first crowdsourced three-dimensional point cloud by using a colop algorithm of colop open source software comprises:
inputting the acquired crowdsourcing image data into a colomap algorithm, and calculating by an SFM algorithm to obtain sparse point cloud and camera parameters, wherein the camera parameters comprise: viewing angle, pose, internal reference and common view relation;
and inputting the acquired crowdsourcing image data, the sparse point cloud and the camera parameters into an MVS algorithm to generate a dense three-dimensional point cloud, so as to obtain a first crowdsourcing three-dimensional point cloud.
9. The indoor underground emergency scene three-dimensional modeling method according to claim 1, wherein the step S4 specifically includes:
and giving the real scale information of the feature points to the first crowdsourcing three-dimensional point cloud, obtaining a transformation matrix by utilizing the corresponding relation between the feature points, finishing the point cloud scale conversion work by using transformpointCloud in a pci library according to the transformation matrix, obtaining the real scale of the first crowdsourcing three-dimensional point cloud, and generating a second crowdsourcing three-dimensional point cloud.
10. The indoor underground emergency scene three-dimensional modeling method according to claim 1, wherein in step S5, a meshlab open source software is installed on the data processing server, and the meshlab open source software is used to view, measure and acquire the required information for the second crowdsourced three-dimensional point cloud.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9466143B1 (en) * | 2013-05-03 | 2016-10-11 | Exelis, Inc. | Geoaccurate three-dimensional reconstruction via image-based geometry |
| CN106576225A (en) * | 2014-08-28 | 2017-04-19 | 高通股份有限公司 | Selective crowdsourcing of location-related data |
| CN107507274A (en) * | 2017-08-30 | 2017-12-22 | 北京图航科技有限公司 | A kind of quick restoring method of public security criminal-scene three-dimensional live based on cloud computing |
| CN108064397A (en) * | 2017-08-11 | 2018-05-22 | 深圳前海达闼云端智能科技有限公司 | Establish method, crowdsourcing platform and the system of object three-dimensional image model |
| WO2018119889A1 (en) * | 2016-12-29 | 2018-07-05 | 深圳前海达闼云端智能科技有限公司 | Three-dimensional scene positioning method and device |
| CN109446783A (en) * | 2018-11-16 | 2019-03-08 | 济南浪潮高新科技投资发展有限公司 | A kind of efficient sample collection method and system of image recognition based on machine crowdsourcing |
| CN109523499A (en) * | 2018-11-16 | 2019-03-26 | 济南浪潮高新科技投资发展有限公司 | A kind of multi-source fusion full-view modeling method based on crowdsourcing |
| WO2020192354A1 (en) * | 2019-03-28 | 2020-10-01 | 东南大学 | Blended urban design scene simulation method and system |
| CN112132950A (en) * | 2020-08-13 | 2020-12-25 | 中国地质大学（武汉） | Three-dimensional point cloud scene updating method based on crowdsourcing image |
| CN112580744A (en) * | 2020-12-29 | 2021-03-30 | 武汉中海庭数据技术有限公司 | Optimized fusion method for measuring data of same lane line in crowdsourced data road segment |
-
2021
- 2021-04-21 CN CN202110427891.9A patent/CN113487732B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9466143B1 (en) * | 2013-05-03 | 2016-10-11 | Exelis, Inc. | Geoaccurate three-dimensional reconstruction via image-based geometry |
| CN106576225A (en) * | 2014-08-28 | 2017-04-19 | 高通股份有限公司 | Selective crowdsourcing of location-related data |
| WO2018119889A1 (en) * | 2016-12-29 | 2018-07-05 | 深圳前海达闼云端智能科技有限公司 | Three-dimensional scene positioning method and device |
| CN108064397A (en) * | 2017-08-11 | 2018-05-22 | 深圳前海达闼云端智能科技有限公司 | Establish method, crowdsourcing platform and the system of object three-dimensional image model |
| WO2019028853A1 (en) * | 2017-08-11 | 2019-02-14 | 深圳前海达闼云端智能科技有限公司 | Method, crowdsourcing platform and system for building three-dimensional image model of object |
| CN107507274A (en) * | 2017-08-30 | 2017-12-22 | 北京图航科技有限公司 | A kind of quick restoring method of public security criminal-scene three-dimensional live based on cloud computing |
| CN109446783A (en) * | 2018-11-16 | 2019-03-08 | 济南浪潮高新科技投资发展有限公司 | A kind of efficient sample collection method and system of image recognition based on machine crowdsourcing |
| CN109523499A (en) * | 2018-11-16 | 2019-03-26 | 济南浪潮高新科技投资发展有限公司 | A kind of multi-source fusion full-view modeling method based on crowdsourcing |
| WO2020192354A1 (en) * | 2019-03-28 | 2020-10-01 | 东南大学 | Blended urban design scene simulation method and system |
| CN112132950A (en) * | 2020-08-13 | 2020-12-25 | 中国地质大学（武汉） | Three-dimensional point cloud scene updating method based on crowdsourcing image |
| CN112580744A (en) * | 2020-12-29 | 2021-03-30 | 武汉中海庭数据技术有限公司 | Optimized fusion method for measuring data of same lane line in crowdsourced data road segment |
Non-Patent Citations (2)
| Title |
|---|
| YONGLIN SHEN: "A Gaussian Kernel-Based Spatiotemporal Fusion Model for Agricultural Remote Sensing Monitoring", 《IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING》 * |
| 马龙称等: "基于B/S架构的可量测全景平台设计与实现", 《北京测绘》 * |
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