CN106154287A - A kind of map constructing method based on two-wheel speedometer Yu laser radar - Google Patents
A kind of map constructing method based on two-wheel speedometer Yu laser radar Download PDFInfo
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- CN106154287A CN106154287A CN201610856937.8A CN201610856937A CN106154287A CN 106154287 A CN106154287 A CN 106154287A CN 201610856937 A CN201610856937 A CN 201610856937A CN 106154287 A CN106154287 A CN 106154287A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
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- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The application relates to a kind of map constructing method based on two-wheel speedometer Yu laser radar, and combines figure optimization, improves the precision of map structuring, reduces the distortion sense of map.The method uses the dual sensor of two-wheel speedometer and laser radar to build map simultaneously, owing to two-wheel speedometer provides pose change substantially so that when laser radar data carries out ICP splicing, the calculating time is greatly shortened.
Description
Technical field
The present invention relates to a kind of map constructing method, be specifically related to a kind of indoor combining speedometer and laser radar and put down
Face map constructing method.
Background technology
Indoor map based on laser radar builds the most extensively to be sent out in robot field's application.Common are 3d laser thunder
Reach and 2d laser radar.The 3d model of the whole environment of 3d Laser Radar Scanning, 2d laser radar as robot bottom, surface sweeping put down
Face base map.Above two method is in accordance with key frame splicing and realizes base map structure.This base map construction method is due to right
The location estimation of sensor is the change in displacement obtained by the splicing of adjacent two pins, and when indoor large area scanning, two is biphase
The integration that ortho position is moved can produce cumulative error so that final base map builds poor effect.Especially for corridor situation, long and narrow
Corridor along with scanning propelling and error accumulation can bent, other large area situations there will be map entirety distortion feelings
Condition.
Summary of the invention
Traditional indoor map construction method based on 2d laser radar is combined lifting by the present invention with two-wheel speedometer
The precision of map structuring, reduces the distortion sense of map.Particular content is as follows:
A kind of map constructing method based on two-wheel speedometer Yu laser radar, comprises the following steps:
1) scanning platform is built
Platform is made up of with wheel speedometer double drivewheel chassis, laser radar；
2) scan data
Double drivewheel mobile platforms carry laser radar, and remote-control laser radar is scanned, and obtains scan data；Simultaneously double masters
Speedometer is installed on driving wheel, reads mileage information；
3) interframe splicing
Two adjacent frames are taked ICP closest approach alternative manner to obtain the pose of platform by the data gone out for Laser Radar Scanning
Change；
4) closed loop detection
Extract all of scanning frame characteristic point and be stored in characteristics dictionary, to each new frame, characteristics dictionary is searched coupling, if
It is made into merit and then completes closed loop detection；
5) figure optimization
Between the series of frames obtaining step 3, the pose of splicing carries out figure optimization, with the pose of each frame observation moment is wherein
Node, the pose of adjacent two pins is changed to limit and builds optimization figure, is calculated in optimization figure input isam optimization method storehouse, is optimized
The central point pose of each frame gone out, and comprise the optimization figure after the process of this central point pose；
6) speedometer pose is added
In optimization figure after the treatment, the wheel speed letter read in conjunction with moment of each frame laser radar data collection
Breath, these two velocity informations also can calculate the pose change of adjacent two frames, between the node corresponding to these adjacent two frames
Add the pose change obtained by mileage information, if having Bian Zebian to have two between every two nodes of the optimization figure so constituted
Bar；
7) quadratic diagram optimization
Optimization figure after above-mentioned process is reruned the central point pose that figure optimization method obtains each frame of optimization.
8) render
Obtained the central point pose of each frame by step 7) after, opengl is used to draw out occupancy grid map, wherein
Sampled point is labeled as black, and the line from sampled point to center position is plotted as white.
The method uses the dual sensor of two-wheel speedometer and laser radar to build map, owing to two-wheel speedometer carries
Supply pose change substantially so that when laser radar data carries out ICP splicing, the calculating time is greatly shortened.
Detailed description of the invention
A kind of map constructing method based on two-wheel speedometer Yu laser radar, comprises the following steps:
1) scanning platform is built
Platform is made up of with wheel speedometer double drivewheel chassis, laser radar；
2) scan data
Double drivewheel mobile platforms carry laser radar, and remote-control laser radar is scanned, and obtains scan data；Simultaneously double masters
Speedometer is installed on driving wheel, reads mileage information；
3) interframe splicing
Two adjacent frames are taked ICP closest approach alternative manner to obtain the pose of platform by the data gone out for Laser Radar Scanning
Change；
4) closed loop detection
Extract all of scanning frame characteristic point and be stored in characteristics dictionary, to each new frame, characteristics dictionary is searched coupling, if
It is made into merit and then completes closed loop detection；
5) figure optimization
Between the series of frames obtaining step 3), the pose of splicing carries out figure optimization, with the pose of each frame observation moment is wherein
Node, the pose of adjacent two pins is changed to limit and builds optimization figure, is calculated in optimization figure input isam optimization method storehouse, is optimized
The central point pose of each frame gone out, and comprise the optimization figure after the process of this central point pose；
6) speedometer pose is added
In optimization figure after the treatment, the wheel speed letter read in conjunction with moment of each frame laser radar data collection
Breath, these two velocity informations also can calculate the pose change of adjacent two frames, between the node corresponding to these adjacent two frames
Add the pose change obtained by mileage information, if having Bian Zebian to have two between every two nodes of the optimization figure so constituted
Bar；
7) quadratic diagram optimization
Optimization figure after above-mentioned process is reruned the central point pose that figure optimization method obtains each frame of optimization；
8) render
Obtained the central point pose of each frame by step 7) after, opengl is used to draw out occupancy grid map, wherein
Sampled point is labeled as black, and the line from sampled point to center position is plotted as white.
Claims (1)
1. a map constructing method based on two-wheel speedometer Yu laser radar, comprises the following steps:
1) scanning platform is built
Platform is made up of with wheel speedometer double drivewheel chassis, laser radar；
2) scan data
Double drivewheel mobile platforms carry laser radar, and remote-control laser radar is scanned, and obtains scan data；Simultaneously double masters
Speedometer is installed on driving wheel, reads mileage information；
3) interframe splicing
Two adjacent frames are taked ICP closest approach alternative manner to obtain the pose of platform by the data gone out for Laser Radar Scanning
Change；
4) closed loop detection
Extract all of scanning frame characteristic point and be stored in characteristics dictionary, to each new frame, characteristics dictionary is searched coupling, if
It is made into merit and then completes closed loop detection；
5) figure optimization
Between the series of frames obtaining step 3, the pose of splicing carries out figure optimization, with the pose of each frame observation moment is wherein
Node, the pose of adjacent two pins is changed to limit and builds optimization figure, is calculated in optimization figure input isam optimization method storehouse, is optimized
The central point pose of each frame gone out, and comprise the optimization figure after the process of this central point pose；
6) speedometer pose is added
In optimization figure after the treatment, the wheel speed letter read in conjunction with moment of each frame laser radar data collection
Breath, these two velocity informations also can calculate the pose change of adjacent two frames, between the node corresponding to these adjacent two frames
Add the pose change obtained by mileage information, if having Bian Zebian to have two between every two nodes of the optimization figure so constituted
Bar；
7) quadratic diagram optimization
Optimization figure after above-mentioned process is reruned the central point pose that figure optimization method obtains each frame of optimization.
8) render
Obtained the central point pose of each frame by step 7) after, opengl is used to draw out occupancy grid map, wherein
Sampled point is labeled as black, and the line from sampled point to center position is plotted as white.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610856937.8A CN106154287A (en) | 2016-09-28 | 2016-09-28 | A kind of map constructing method based on two-wheel speedometer Yu laser radar |
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| CN201610856937.8A CN106154287A (en) | 2016-09-28 | 2016-09-28 | A kind of map constructing method based on two-wheel speedometer Yu laser radar |
Publications (1)
| Publication Number | Publication Date |
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| CN106154287A true CN106154287A (en) | 2016-11-23 |
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ID=57340949
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| Application Number | Title | Priority Date | Filing Date |
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| CN201610856937.8A Pending CN106154287A (en) | 2016-09-28 | 2016-09-28 | A kind of map constructing method based on two-wheel speedometer Yu laser radar |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106767827A (en) * | 2016-12-29 | 2017-05-31 | 浙江大学 | A kind of mobile robot point cloud map creating method based on laser data |
| CN107515891A (en) * | 2017-07-06 | 2017-12-26 | 杭州南江机器人股份有限公司 | A kind of robot cartography method, apparatus and storage medium |
| CN108332759A (en) * | 2018-01-12 | 2018-07-27 | 浙江国自机器人技术有限公司 | A kind of map constructing method and system based on 3D laser |
| CN109118940A (en) * | 2018-09-14 | 2019-01-01 | 杭州国辰机器人科技有限公司 | A kind of mobile robot composition based on map splicing |
| CN109211251A (en) * | 2018-09-21 | 2019-01-15 | 北京理工大学 | A kind of instant positioning and map constructing method based on laser and two dimensional code fusion |
| CN111398984A (en) * | 2020-03-22 | 2020-07-10 | 华南理工大学 | Self-adaptive laser radar point cloud correction and positioning method based on sweeping robot |
| CN111679663A (en) * | 2019-02-25 | 2020-09-18 | 北京奇虎科技有限公司 | Three-dimensional map construction method, sweeping robot and electronic equipment |
| CN112119326A (en) * | 2019-07-31 | 2020-12-22 | 深圳市大疆创新科技有限公司 | Data correction method, mobile platform and nonvolatile computer readable storage medium |
| CN114742310A (en) * | 2022-04-22 | 2022-07-12 | 山东省人工智能研究院 | Terrain trafficability map construction method based on wheel-ground interaction |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106767827B (en) * | 2016-12-29 | 2020-02-28 | 浙江大学 | Mobile robot point cloud map creation method based on laser data |
| CN106767827A (en) * | 2016-12-29 | 2017-05-31 | 浙江大学 | A kind of mobile robot point cloud map creating method based on laser data |
| CN107515891A (en) * | 2017-07-06 | 2017-12-26 | 杭州南江机器人股份有限公司 | A kind of robot cartography method, apparatus and storage medium |
| CN108332759A (en) * | 2018-01-12 | 2018-07-27 | 浙江国自机器人技术有限公司 | A kind of map constructing method and system based on 3D laser |
| CN109118940A (en) * | 2018-09-14 | 2019-01-01 | 杭州国辰机器人科技有限公司 | A kind of mobile robot composition based on map splicing |
| CN109211251B (en) * | 2018-09-21 | 2022-01-11 | 北京理工大学 | Instant positioning and map construction method based on laser and two-dimensional code fusion |
| CN109211251A (en) * | 2018-09-21 | 2019-01-15 | 北京理工大学 | A kind of instant positioning and map constructing method based on laser and two dimensional code fusion |
| CN111679663A (en) * | 2019-02-25 | 2020-09-18 | 北京奇虎科技有限公司 | Three-dimensional map construction method, sweeping robot and electronic equipment |
| CN112119326A (en) * | 2019-07-31 | 2020-12-22 | 深圳市大疆创新科技有限公司 | Data correction method, mobile platform and nonvolatile computer readable storage medium |
| CN111398984A (en) * | 2020-03-22 | 2020-07-10 | 华南理工大学 | Self-adaptive laser radar point cloud correction and positioning method based on sweeping robot |
| CN111398984B (en) * | 2020-03-22 | 2022-03-29 | 华南理工大学 | Self-adaptive laser radar point cloud correction and positioning method based on sweeping robot |
| CN114742310A (en) * | 2022-04-22 | 2022-07-12 | 山东省人工智能研究院 | Terrain trafficability map construction method based on wheel-ground interaction |
| CN114742310B (en) * | 2022-04-22 | 2022-09-16 | 山东省人工智能研究院 | Terrain trafficability map construction method based on wheel-ground interaction |
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Application publication date: 20161123 |
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