CN107944018A - A kind of automatic quality detecting method of map vector positional precision based on laser point cloud data - Google Patents
A kind of automatic quality detecting method of map vector positional precision based on laser point cloud data Download PDFInfo
- Publication number
- CN107944018A CN107944018A CN201711309135.6A CN201711309135A CN107944018A CN 107944018 A CN107944018 A CN 107944018A CN 201711309135 A CN201711309135 A CN 201711309135A CN 107944018 A CN107944018 A CN 107944018A
- Authority
- CN
- China
- Prior art keywords
- cloud data
- map vector
- data
- point cloud
- precision
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/56—Information retrieval; Database structures therefor; File system structures therefor of still image data having vectorial format
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B29/00—Maps; Plans; Charts; Diagrams, e.g. route diagram
- G09B29/003—Maps
- G09B29/004—Map manufacture or repair; Tear or ink or water resistant maps; Long-life maps
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Mathematical Physics (AREA)
- Remote Sensing (AREA)
- Processing Or Creating Images (AREA)
Abstract
The invention discloses a kind of automatic quality detecting method of map vector positional precision based on laser point cloud data, first, using laser scanner along direct of travel 360 degree of scannings, the high-precision cloud data of direct of travel both sides is obtained；Cloud data is filtered, accuracy test, after coordinate conversion with map vector to be checked is unified unites to the same coordinate system；Automatically extract the position coordinate value of earth object and calculation and object plane difference and elevation difference in map vector to be detected in a cloud object；The middle error of all detected values is calculated, judges whether to meet design threshold and provides quality inspection report automatically.Map vector is detected using this method, it is possible to reduce the field process amount in existing detection method, increase detection sample size, reduces the human factor in detection process, effectively lift the precision and efficiency of detection.
Description
Technical field
The invention belongs to map vector positional precision assessment technique field, and in particular to a kind of based on laser point cloud data
The automatic quality detecting method of map vector positional precision.
Background technology
With the fast development of China's economic society, smart city pace of construction is increasingly accelerated, and mapping geography information is existing
Basic, strategic resources are in during generation informationization, intelligent construction, are occupied importantly in review on management of modern cities
Position.The important component of geodata based on map vector, is widely used in urban planning, land management, agricultural
The every field such as generaI investigation, environmental protection, transport development, play important fundamental role.The quality of map vector is straight
Connect influence the whether advanced of engineering construction, governability decision-making whether science, its critical role is self-evident.
Currently, map vector quality examination is mainly around quality elements such as Fundamentals of Mathematics, positional precision, attribute accuracies.It is many
More scholars have done substantial amounts of research with regard to the automation aspect of quality inspection procedure, precision statistics, the quality inspection scoring of map vector quality inspection etc.,
Achieve fruitful achievement.Cai Jiande, Zhang Fuli etc. elaborate that land deeds, topographic map precision check the number of statistics program automatically
According to institutional framework and corresponding program is devised, realizes the output of the programming count and report of precision；Yu Huanju, Li Yunling
Deng proposition with the relevant data in a large amount of and locus accumulated in Process of Urban Development, the condition that checks as various dimensions checks
Data to be checked, the premise of this method is the shared and fusion of data；Liu Jianjun proposes the relation and constraints using figure
The foundation checked automatically as program, the interior industry batch for realizing data check；Meng Fanqiang, just intelligent dragon are from digital topography map position
The quality inspection efficiency and accuracy of precision are started with, and realize the automation of interior industry accuracy computation and statistics；Wang Youwu, Ma Zengyi etc. are carried
Go out digital terrain map analysis anticipation strategy, construct corresponding quality evaluation system and realize；Hou Yajuan, Ge Zhonghua are by vehicle-mounted shifting
Dynamic measuring system works applied to large scale topographical map quality inspection, compared for vehicle carried data collecting data and field operation measured data
Precision and efficiency.
Basic data of the map vector as economic construction and decision-making management, the inspection of quality of achievement is key link.It is logical
The methods of normal way is comprehensive field operation field survey, inspection and drawing interpretation, judges quality of achievement, in sample by surveying sample
This extraction, amount detection aspect have clear and definite regulation.With the development of modern measure technology, the producer of map vector
There is larger development in formula, production efficiency and achievement form, but the quantity of map vector accuracy detection, the method for detection and
The efficiency of detection does not have large change.Required precision of the project construction and precision management of current smart city to map vector
Higher and higher, this proposes the requirement of higher to the workload and precision of data inspection.The field operation of current vector map is examined on the spot
In survey, influenced be subject to manual work, the quantity of Data Detection and detection automation etc. still have greater room for improvement.
The content of the invention
Goal of the invention：For the deficiencies in the prior art, the object of the present invention is to provide one kind to be based on laser point cloud
The automatic quality detecting method of map vector positional precision of data,
Technical solution：In order to realize foregoing invention purpose, the technical solution adopted by the present invention is：
A kind of automatic quality detecting method of map vector positional precision based on laser point cloud data, first, uses laser scanning
Device obtains the high-precision cloud data of direct of travel both sides along direct of travel 360 degree of scannings；Cloud data is filtered, precision
Examine, is unified to the same coordinate system system with map vector to be checked after coordinate conversion；Automatically extract earth object in a cloud object
Position coordinate value and calculation and object plane difference and elevation difference in map vector to be detected；Calculate the middle mistake of all detected values
Difference, judges whether to meet design threshold and provides quality inspection report automatically.
The automatic quality detecting method of map vector positional precision based on laser point cloud data, comprises the following steps that；
1) map vector is in units of width, according to《Surveying and mapping result quality examination is with checking and accepting》Regulation, determines the sample checked
Quantity；
2) situation of the map sheet obtained according to sampling, design scan route, scan the cloud data of acquisition；
3) cloud data that step 2) obtains, resolves by integrated navigation, puts cloud filtering, coordinate conversion calculation procedure, obtain
The high-precision test point cloud of map sheet to be checked；According to a cloud computing as a result, using the whole world at the obvious atural object in selected section crossing
Navigational satellite system mensuration measurement portion divides control point, and the precision of cloud is relatively put according to the inspection of the precision of map vector to be checked；
4) the point cloud precision obtained based on step 3), the feature selecting function of being carried using SWDY softwares, extraction point cloud number
Characters of ground object dotted line in；
5) according to the characters of ground object of extraction, searching threshold is set, according to closest to method in same type automatic phase one by one
The mutually nearest similar culture point of search, completes the matching of characteristic point and corresponding culture point, the automatic sectional drawing of characteristic point that it fails to match
It is manually qualitative；
6) according to step 5) as a result, automatically generating the audit report of this batch detection data.
In step 1), when examining achievement sum to be more than 201, completed according to different production units, operating type, achievement
Situations such as time, survey area's concrete condition, divides, and keeps operating area, operating type, operation habit, regional characteristics relatively uniform.
In step 2), highway route design requires the custom of compound conventional detection and can play the advantage of traverse measurement, takes more
The mode that a platform is combined, it is uniform to obtain the cloud data for examining map sheet；
In step 4), the characters of ground object dotted line of extraction has vertical thick stick, street lamp, highway sideline, well lid, house corner point；Vertical thick stick,
The shaft atural object such as street lamp provides object centers, and well lid provides geometric center, and the corner point in house provides plan-position.
In step 5), automatic matching calculates the difference dS of every group of data, contrast dS and 2 times of M of error in standard0Difference,
More than 2 times M0Be rough error, error M is calculated during remaining dS is participated in, and finally calculates the M of the lot data.
In step 5), the formula for calculating the M of the lot data is：
In formula, n is number, dSiFor i-th of difference.
In step 6), the middle error M result of calculations containing data, the error distribution results of data in report.
The method that the method for the traverse measurement of the application detects map vector automatically, sampling immediately is with selecting vector to be detected
Figure, is pre-designed traverse measurement scanning route, the high accuracy for scanning route acquisition region both sides to be detected of advancing along traverse measurement
Cloud data and full-view image data, by resolving, filtering, coordinate conversion and etc. obtain high-precision dot cloud, based on a cloud from
It is dynamic to extract the feature dotted lines such as the vertical thick stick for obtaining route of travel both sides, street lamp, corner point, well lid, kerb line, Auto-matching superposition
Map vector to be detected, can calculate the middle error of characteristic point and measuring point to be checked automatically and provide corresponding report.
Beneficial effect：Compared with prior art, the present invention has the following advantages：
(1) efficiency of map vector data quality inspection is effectively increased, reduces substantial amounts of field data collection workload, is protected
The life safety of operating personnel is demonstrate,proved；Automatically extracting in post processing, reduces the work of the atural object data acquisition in cloud data
Measure；Point in conventional process becomes selection automatic matching, automatic calculating, automatic report, and larger improves efficiency.
(2) brand-new map vector quality inspection thinking is provided.Data quality checking is gradually transitioned into entirely from original sampling check
Number checks, changes the local sample examination method in existing GPS-RTK inspection methods.Check the increase of high number, Ke Yizong
Body controls topographic map data quality, but does not increase the workload of inspection personnel.
(3) precision quality of data is effectively improved, reduce further human factor influence.This method increases in inspection amount
While adding, have to the quality of inspection and be obviously improved, can find the problems such as losing leakage of data easily, can automatical one by one
Go out the vector atural object key element that error is larger or transfinites, targetedly control very much vector data quality.
Brief description of the drawings
Fig. 1 is the method detection map vector flow chart of traverse measurement；
Fig. 2 is map vector to be checked；
Fig. 3 is cloud data extraction characters of ground object design sketch；
Fig. 4 is to lose leakage inspection result figure.
Embodiment
The present invention is described further with reference to the accompanying drawings and examples.
Embodiment 1
Traverse measurement system is a kind of quick scanning system based on a variety of mobile platforms, can efficiently, fast and accurately
Obtain the stereo colour point cloud information of ground table object.Traverse measurement system is generally by laser, IMU, GPS, odometer, panorama phase
The several majors such as machine, control system are formed.Wherein, the information of laser acquisition and recording traveling process both sides, the point cloud of acquisition are close
Spend related to the point of laser frequency and gait of march.GPS and high accuracy IMU provides position and the attitude data of whole system, inner
Cheng Jike with the travel distance of registration of vehicle platform, can combined calculation lifting gps signal losing lock when integrated navigation precision.Panorama
The earth surface image that camera obtains is in addition to point cloud RGB information is provided, available for terrain object attribute information judging.According to road situation
Difference, traverse measurement system can select motor vehicle platform, motorcycle platform and manpower knapsack.
SSW used in the present embodiment (first Shi Siwei) traverse measurement system (China Surveying and Mapping Research Academy and Capital Division
Model university joint research and development, Beijing Siweiyuanjian Information Technology Co., Ltd.'s sale) support image data, point cloud number automatically
According to, position and attitude data fusion production colour point clouds data, high-acruracy survey can be rapidly completed, streetscape obtains, key element collection
Etc. measurement task.The system integration laser scanner, IMU, POS2010, Tian Bao GPS, odometer DMI, area array cameras CANON
The plurality of devices such as EOS 5D, panorama camera Ladybug5 have the distinguishing features such as speed is fast, precision is high, performance is stablized in one.
The premise that classification atural object data are follow-up precision checked operations is automatically extracted from the cloud data of acquisition.Accurately
Filtered classification result contributes to the singulation of earth object, easy to the efficient management of cloud data hierarchical classification, retrieval and display.
The SWDY softwares (2017 editions, China Surveying and Mapping Research Academy's offer) that the present embodiment uses are completed noise processed, are sorted out one by one
The atural object elements such as vertical thick stick, shade tree, road surface, kerb line, traffic mark, well lid, building wall linea angulata, easy to subsequent automated
Detect the precision of map vector.
The automatic quality detecting method of map vector positional precision based on laser point cloud data of the application, flow as shown in Figure 1,
Comprise the following steps that；
1) vector map sheet is sampled.Map vector in units of width, according to《Surveying and mapping result quality examination is with checking and accepting》Regulation, really
The sample size that regular inspection is looked into；When examining achievement sum more than 201, when being completed according to different production units, operating type, achievement
Between, survey area's concrete condition situations such as divide, be just to maintain operating area, operating type, operation habit, regional characteristics on the whole
It is relatively uniform.
2) scanning obtains point cloud.The situation of the map sheet obtained according to sampling, design scanning route.Highway route design requires compound
The custom of conventional detection and the advantage that traverse measurement can be played, the mode for taking motor vehicle, two wheeler and manpower platform to be combined,
It is comprehensively uniform to obtain the cloud data for examining map sheet.
3) Point Cloud Processing.The cloud data that step 2) obtains, resolves, point cloud filters, coordinate turns by integrated navigation
The calculation procedure such as change, obtain the high-precision test point cloud of map sheet to be checked.According to a cloud computing as a result, selected section crossing is obvious
Divide control point using Global Navigation Satellite System mensuration measurement portion at atural object, according to the precision of map vector to be checked inspection relatively point cloud
Precision.
4) Automatic Feature Extraction.The point cloud precision obtained based on step 3).The feature selecting work(carried using SWDY softwares
Can, extract the characters of ground object dotted line in cloud data.The characters of ground object dotted line of extraction have vertical thick stick, street lamp, highway sideline, well lid,
House corner point.The shaft atural objects such as vertical thick stick, street lamp provide object centers, and well lid provides geometric center, and the corner point in house provides
Plan-position.
5) Auto-matching calculates.According to the characters of ground object of extraction, searching threshold is set, according to closest to method in same type
In the nearest similar culture point of automatic mutually search one by one, complete the matching of characteristic point and corresponding culture point, the spy that it fails to match
The automatic sectional drawing of sign point is manually qualitative.Automatic matching calculates the difference dS of every group of data, contrast dS and 2 times of M of error in standard0Difference
Value, more than 2 times M0Be rough error, remaining dS participate in error M calculate, according to formula (1), finally calculate the lot data
M。
In formula, n is number, dSiFor i-th of difference.
6) precision report is provided.According to step 5) as a result, automatically generating the audit report of this batch detection data.Contain in report
There are the middle error M result of calculations of data, the error distribution results of data.
Embodiment 2
Using the method and system of embodiment 1, detected on the spot, detailed process is as follows：
1st, situation on the spot
Project survey region is 2 square kilometres of Xinghua city, include in regional extent building, road, street lamp, vegetation,
The topographic(al) features such as water system, are located in that city internal passageway is sensible, passage situation is good, vector data engineer's scale 1 to be detected in region:
1000, mapping time is 2017.
In live laser scanning operation process, road speed is maintained at 30km/h, when collection total duration 1.5 is small, total scanning
Mileage 25km, gps signal is good in gatherer process, the situation of long-time losing lock does not occur.GPS Base Station is set up in gatherer process
1, data sampling rate 1Hz.After the end of scan, inspection personnel confirms do not have along scanning checking of routing point cloud covering integrality
There is a wide range of blank caused by omitting and blocking.44 check points are measured using GPS-RTK be used for check post cloud on the spot
Data precision, check point select terrain vehicle diatom readily identified on cloud data and Li Gang edges.
2nd, collection and processing
On the spot before data acquisition, the scanning road according to the current conditional plan in survey region.Vehicle-mounted scanning platform according to
The cloud data of both sides of the road is obtained according to the route data of planning.Using SWDY points cloud processing platforms, calculate respectively vehicle-mounted
GPS, IMU, odometer and base station data obtain integrated navigation data, and three dimensional point cloud is then calculated.Full-view image number
According to resolving with obtaining colour point clouds data after point cloud data fusion, comprising information such as color, reflected intensitys, atural object is truly reflected
The state of landforms.
Using actual measurement GPS-RTK points, [Xu works, Cheng Xiao armies are moved with the corresponding points comparison check data precision in cloud data
Dynamic measuring system point cloud accuracy assessment and applied analysis [J] engineering investigations, 2013,41 (09):42-46.].Survey 44 atural objects
Characteristics of objects point, lists a cloud plane precision and checks result such as table 1.
1 cloud precision checklist of table
Technology is automatically extracted using cloud data vector characteristic, road width, the mobile vehicle in area are surveyed by pre-setting
Whether relative position in the road, have parameter information, the extraction such as greenbelt and its vegetation height, street lamp height to obtain roadside
Line, green belt, upright bar, bus station, dustbin, shade tree, isolation strip isovector data.
3rd, testing result
Using automatically extract street lamp, the data such as upright bar according to search rule complete with it is corresponding in Vector Topographic Map to be checked
The bidirectional research of vector element, detection range is arranged to 2 times of error 0.5m in the planar design precision of this batch of map vector, double
To difference 4, the automatic sectional drawing of software is as follows, qualitative one by one to lose leakage etc. for rough error, scan loss and collection.
Complete 2 times of range searchings of error threshold 0.5m in plane, the match point 201 (tables 2) of acquisition, according to formula (1)
It is 0.263 to calculate error M in the batch data, meets middle error requirements.
Errors table in 2 testing result of table
Claims (8)
1. a kind of automatic quality detecting method of map vector positional precision based on laser point cloud data, it is characterised in that first, use
Laser scanner obtains the high-precision cloud data of direct of travel both sides along direct of travel 360 degree of scannings；Cloud data is filtered
It is unified to the same coordinate system system with map vector to be checked after ripple, accuracy test, coordinate conversion；Automatically extract atural object in a cloud object
The position coordinate value of object and calculation and object plane difference and elevation difference in map vector to be detected；Calculate all detected values
Middle error, judges whether to meet design threshold and provides quality inspection report automatically.
2. the map vector positional precision automatic quality detecting method according to claim 1 based on laser point cloud data, it is special
Sign is, comprises the following steps that；
1) map vector is in units of width, according to《Surveying and mapping result quality examination is with checking and accepting》Regulation, determines the sample size checked；
2) situation of the map sheet obtained according to sampling, design scan route, scan the cloud data of acquisition；
3) cloud data that step 2) obtains, resolves by integrated navigation, puts cloud filtering, coordinate conversion calculation procedure, obtain and treat
Examine the high-precision test point cloud of map sheet；According to a cloud computing as a result, using worldwide navigation at the obvious atural object in selected section crossing
Satellite system mensuration measurement portion divides control point, and the precision of cloud is relatively put according to the inspection of the precision of map vector to be checked；
4) the point cloud precision obtained based on step 3), the feature selecting function of being carried using SWDY softwares, is extracted in cloud data
Characters of ground object dotted line；
5) according to the characters of ground object of extraction, searching threshold is set, is mutually searched automatically one by one in same type according to closest to method
The nearest similar culture point of rope, completes the matching of characteristic point and corresponding culture point, and the automatic sectional drawing of characteristic point that it fails to match is artificial
It is qualitative；
6) according to step 5) as a result, automatically generating the audit report of this batch detection data.
3. the map vector positional precision automatic quality detecting method according to claim 2 based on laser point cloud data, it is special
Sign is, in step 1), when examining achievement sum to be more than 201, is completed according to different production units, operating type, achievement
Situations such as time, survey area's concrete condition, divides, and keeps operating area, operating type, operation habit, regional characteristics relatively uniform.
4. the map vector positional precision automatic quality detecting method according to claim 2 based on laser point cloud data, it is special
Sign is, in step 2), highway route design requires the custom of compound conventional detection and can play the advantage of traverse measurement, takes multiple
The mode that platform is combined, it is uniform to obtain the cloud data for examining map sheet.
5. the map vector positional precision automatic quality detecting method according to claim 2 based on laser point cloud data, it is special
Sign is, in step 4), the characters of ground object dotted line of extraction has vertical thick stick, street lamp, highway sideline, well lid, house corner point；Vertical thick stick,
The shaft atural object such as street lamp provides object centers, and well lid provides geometric center, and the corner point in house provides plan-position.
6. the map vector positional precision automatic quality detecting method according to claim 2 based on laser point cloud data, it is special
Sign is, in step 5), automatic matching calculates the difference dS of every group of data, contrast dS and 2 times of M of error in standard0Difference, surpass
Cross 2 times of M0Be rough error, error M is calculated during remaining dS is participated in, and finally calculates the M of the lot data.
7. the map vector positional precision automatic quality detecting method according to claim 2 based on laser point cloud data, it is special
Sign is, in step 6), the middle error M result of calculations containing data, the error distribution results of data in report.
8. the map vector positional precision automatic quality detecting method according to claim 2 based on laser point cloud data, it is special
Sign is, in step 5), the formula for calculating the M of the lot data is：
In formula, n is number, dSiFor i-th of difference.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711309135.6A CN107944018A (en) | 2017-12-11 | 2017-12-11 | A kind of automatic quality detecting method of map vector positional precision based on laser point cloud data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711309135.6A CN107944018A (en) | 2017-12-11 | 2017-12-11 | A kind of automatic quality detecting method of map vector positional precision based on laser point cloud data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107944018A true CN107944018A (en) | 2018-04-20 |
Family
ID=61946494
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711309135.6A Pending CN107944018A (en) | 2017-12-11 | 2017-12-11 | A kind of automatic quality detecting method of map vector positional precision based on laser point cloud data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107944018A (en) |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109341704A (en) * | 2018-09-21 | 2019-02-15 | 宽凳（北京）科技有限公司 | A kind of accuracy of map determines method and device |
| CN110400363A (en) * | 2018-04-24 | 2019-11-01 | 北京京东尚科信息技术有限公司 | Map constructing method and device based on laser point cloud |
| CN110647591A (en) * | 2018-06-07 | 2020-01-03 | 北京京东尚科信息技术有限公司 | Method and device for testing vector map |
| CN110889222A (en) * | 2019-11-27 | 2020-03-17 | 重庆宝图科技发展有限公司 | Real estate mapping method and system based on graphical method and analytic method |
| CN111221808A (en) * | 2019-12-31 | 2020-06-02 | 武汉中海庭数据技术有限公司 | Unattended high-precision map quality inspection method and device |
| CN111664869A (en) * | 2020-05-28 | 2020-09-15 | 武汉中海庭数据技术有限公司 | High-precision map surface feature data quality evaluation method and device |
| CN111858808A (en) * | 2020-07-17 | 2020-10-30 | 重庆市勘测院 | Automatic evaluation method for topographic map characteristic ground object precision based on massive real measuring points |
| CN112116549A (en) * | 2019-06-03 | 2020-12-22 | 北京京东叁佰陆拾度电子商务有限公司 | Method and device for evaluating point cloud map precision |
| CN112256703A (en) * | 2020-10-23 | 2021-01-22 | 重庆同汇勘测规划有限公司 | ArcGIS-based method for rapidly outputting point cloud precision report |
| CN112270272A (en) * | 2020-10-31 | 2021-01-26 | 武汉中海庭数据技术有限公司 | Method and system for extracting road intersections in high-precision map making |
| CN112381940A (en) * | 2020-11-27 | 2021-02-19 | 广东电网有限责任公司肇庆供电局 | Processing method and device for generating digital elevation model from point cloud data and terminal equipment |
| CN113034685A (en) * | 2021-03-18 | 2021-06-25 | 北京百度网讯科技有限公司 | Method and device for superposing laser point cloud and high-precision map and electronic equipment |
| CN113065076A (en) * | 2021-04-25 | 2021-07-02 | 北京四维图新科技股份有限公司 | Map data processing method and device, electronic equipment and storage medium |
| CN113643236A (en) * | 2021-07-06 | 2021-11-12 | 青岛市勘察测绘研究院 | Geometric precision evaluation method for live-action three-dimensional model |
| CN114036255A (en) * | 2022-01-07 | 2022-02-11 | 北京四维图新科技股份有限公司 | High-precision map data inspection method, device and equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101702200A (en) * | 2009-11-03 | 2010-05-05 | 武汉大学 | Automatic classification method of airborne laser radar point cloud data |
| CN104913766A (en) * | 2015-06-24 | 2015-09-16 | 中交第二公路勘察设计研究院有限公司 | Laser scanning measurement method and device |
| CN106846308A (en) * | 2017-01-20 | 2017-06-13 | 广州市城市规划勘测设计研究院 | The detection method and device of the topographic map precision based on a cloud |
| CN107121064A (en) * | 2017-04-27 | 2017-09-01 | 上海华测导航技术股份有限公司 | A kind of laser scanner |
-
2017
- 2017-12-11 CN CN201711309135.6A patent/CN107944018A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101702200A (en) * | 2009-11-03 | 2010-05-05 | 武汉大学 | Automatic classification method of airborne laser radar point cloud data |
| CN104913766A (en) * | 2015-06-24 | 2015-09-16 | 中交第二公路勘察设计研究院有限公司 | Laser scanning measurement method and device |
| CN106846308A (en) * | 2017-01-20 | 2017-06-13 | 广州市城市规划勘测设计研究院 | The detection method and device of the topographic map precision based on a cloud |
| CN107121064A (en) * | 2017-04-27 | 2017-09-01 | 上海华测导航技术股份有限公司 | A kind of laser scanner |
Non-Patent Citations (1)
| Title |
|---|
| 李志林等: "《数字高程模型》", 30 November 2017, 科学出版社 * |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110400363A (en) * | 2018-04-24 | 2019-11-01 | 北京京东尚科信息技术有限公司 | Map constructing method and device based on laser point cloud |
| CN110400363B (en) * | 2018-04-24 | 2024-05-24 | 北京京东尚科信息技术有限公司 | Map construction method and device based on laser point cloud |
| CN110647591A (en) * | 2018-06-07 | 2020-01-03 | 北京京东尚科信息技术有限公司 | Method and device for testing vector map |
| CN109341704A (en) * | 2018-09-21 | 2019-02-15 | 宽凳（北京）科技有限公司 | A kind of accuracy of map determines method and device |
| CN112116549A (en) * | 2019-06-03 | 2020-12-22 | 北京京东叁佰陆拾度电子商务有限公司 | Method and device for evaluating point cloud map precision |
| CN110889222A (en) * | 2019-11-27 | 2020-03-17 | 重庆宝图科技发展有限公司 | Real estate mapping method and system based on graphical method and analytic method |
| CN110889222B (en) * | 2019-11-27 | 2023-06-02 | 重庆宝图科技发展有限公司 | Real estate mapping method and system based on graphic method and analytic method |
| CN111221808A (en) * | 2019-12-31 | 2020-06-02 | 武汉中海庭数据技术有限公司 | Unattended high-precision map quality inspection method and device |
| CN111664869A (en) * | 2020-05-28 | 2020-09-15 | 武汉中海庭数据技术有限公司 | High-precision map surface feature data quality evaluation method and device |
| CN111858808A (en) * | 2020-07-17 | 2020-10-30 | 重庆市勘测院 | Automatic evaluation method for topographic map characteristic ground object precision based on massive real measuring points |
| CN111858808B (en) * | 2020-07-17 | 2023-10-31 | 重庆市勘测院 | Automatic evaluation method for feature and ground object precision of topographic map based on massive actual measurement points |
| CN112256703A (en) * | 2020-10-23 | 2021-01-22 | 重庆同汇勘测规划有限公司 | ArcGIS-based method for rapidly outputting point cloud precision report |
| CN112270272A (en) * | 2020-10-31 | 2021-01-26 | 武汉中海庭数据技术有限公司 | Method and system for extracting road intersections in high-precision map making |
| CN112270272B (en) * | 2020-10-31 | 2022-07-29 | 武汉中海庭数据技术有限公司 | Method and system for extracting road intersections in high-precision map making |
| CN112381940A (en) * | 2020-11-27 | 2021-02-19 | 广东电网有限责任公司肇庆供电局 | Processing method and device for generating digital elevation model from point cloud data and terminal equipment |
| CN113034685A (en) * | 2021-03-18 | 2021-06-25 | 北京百度网讯科技有限公司 | Method and device for superposing laser point cloud and high-precision map and electronic equipment |
| CN113065076A (en) * | 2021-04-25 | 2021-07-02 | 北京四维图新科技股份有限公司 | Map data processing method and device, electronic equipment and storage medium |
| CN113643236B (en) * | 2021-07-06 | 2022-05-17 | 青岛市勘察测绘研究院 | Geometric precision evaluation method for live-action three-dimensional model |
| CN113643236A (en) * | 2021-07-06 | 2021-11-12 | 青岛市勘察测绘研究院 | Geometric precision evaluation method for live-action three-dimensional model |
| CN114036255B (en) * | 2022-01-07 | 2022-03-25 | 北京四维图新科技股份有限公司 | High-precision map data inspection method, device and equipment |
| CN114036255A (en) * | 2022-01-07 | 2022-02-11 | 北京四维图新科技股份有限公司 | High-precision map data inspection method, device and equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107944018A (en) | A kind of automatic quality detecting method of map vector positional precision based on laser point cloud data | |
| CN108764205B (en) | Method for urban municipal general survey based on unmanned aerial vehicle low-altitude aerial photography system | |
| CN101901244B (en) | Geological geographic information system processing method | |
| CN105371857B (en) | A kind of device and method based on bus GNSS space-time trajectory data construction road network topologies | |
| CN108288059B (en) | Construction waste monitoring method based on high-resolution remote sensing technology | |
| CN102620721B (en) | Fine digital terrain model based road surveying method | |
| CN103884321B (en) | A kind of remote sensing image becomes figure technique | |
| CN101914890B (en) | Airborne laser measurement-based highway reconstruction and expansion investigation method | |
| CN102445186B (en) | Method for generating road design surface information by laser radar scan | |
| CN114353876A (en) | Loess highway slope health monitoring method | |
| CN104376595A (en) | Method for generating three-dimensional roads on basis of airborne LiDAR (light detection and ranging) and GIS (geographic information system) cooperation | |
| CN102074047A (en) | High-fineness urban three-dimensional modeling method | |
| CN101335431A (en) | Overhead power transmission line optimized line selection method based on airborne laser radar data | |
| CN111444872B (en) | Method for measuring geomorphic parameters of Danxia | |
| CN103778681A (en) | Vehicle-mounted high-speed road inspection system and data acquisition and processing method | |
| CN112461205B (en) | Method for manufacturing cross section of existing railway line based on unmanned aerial vehicle oblique photogrammetry | |
| CN107657636A (en) | A kind of method that route topography figure elevational point is automatically extracted based on mobile lidar data | |
| CN202175906U (en) | Vehicle-mounted road surface three-dimensional measuring device | |
| CN114859374B (en) | Newly-built railway cross measurement method based on unmanned aerial vehicle laser point cloud and image fusion | |
| CN111426303A (en) | Karst slope parameter measuring method | |
| CN101929858A (en) | Precise surveying and mapping method of 0.25m contour interval of flat ground with large scale of 1:500 to 1:200 | |
| CN114283070B (en) | Method for manufacturing terrain section by fusing unmanned aerial vehicle image and laser point cloud | |
| CN104732870A (en) | Measurement method and system for manufacturing large-scale map | |
| CN110836661A (en) | Sky pit parameter measuring method | |
| Xiao et al. | A review of remote sensing applications in urban planning and management in China |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |