CN109507677A - A kind of SLAM method of combination GPS and radar odometer - Google Patents

A kind of SLAM method of combination GPS and radar odometer Download PDF

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CN109507677A
CN109507677A CN201811306455.0A CN201811306455A CN109507677A CN 109507677 A CN109507677 A CN 109507677A CN 201811306455 A CN201811306455 A CN 201811306455A CN 109507677 A CN109507677 A CN 109507677A
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data
gps
pose
angle
lidar
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CN109507677B (en
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张剑华
林瑞豪
吴佳鑫
冯宇婷
徐浚哲
陈胜勇
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Zhejiang University of Technology ZJUT
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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
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • 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
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/53—Determining attitude
    • 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
    • G09B29/005—Map projections or methods associated specifically therewith

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Theoretical Computer Science (AREA)
  • Electromagnetism (AREA)
  • Mathematical Physics (AREA)
  • Business, Economics & Management (AREA)
  • Educational Administration (AREA)
  • Educational Technology (AREA)
  • Optical Radar Systems And Details Thereof (AREA)
  • Traffic Control Systems (AREA)
  • Navigation (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Length Measuring Devices By Optical Means (AREA)

Abstract

A kind of SLAM method of combination GPS and radar odometer includes the following steps: 1) to acquire DGPS data and the point cloud data from laser radar；2) processing GPS data is displaced (X, Y, Z) and the angle posture RPY；3) point cloud data for matching GPS data and LiDAR realizes Data Matching in such a way that timestamp is aligned；4) point cloud data of the pose data and LiDAR that combine step 2) processing GPS to obtain examines the reliability of GPS data；5) (X, Y, Z) and the angle RPY are obtained using radar odometer algorithm LOAM；6) reliably local in GPS data, use the pose of GPS acquisition as final pose；In the insecure section of GPS data, final pose is obtained using the pose of the GPS pose optimization LOAM algorithm of the section beginning and end；7) under the point cloud data to world coordinate system of the pose switched laser radar exported using step 6), final global map is obtained.The present invention is suitable for the building of a wide range of city three-dimensional map.

Description

A kind of SLAM method of combination GPS and radar odometer
Technical field
The present invention relates to the SLAM of computer vision technique, especially one (simultaneous localization and Mapping, while positioning and building figure) method.
Background technique
SLAM technology refers to that robot in a strange environment, can construct map and the positioning of ambient enviroment simultaneously The technology of oneself position in the map out.SALM technology is by many applications, such as the positioning and navigation of automatic Pilot, robot Deng.
The SLAM technology of view-based access control model odometer or radar odometer cannot achieve big due to the presence of accumulated error The building of the city three-dimensional map of range.Although the SLAM technology based on GPS is without accumulated error, in urban area, by It is blocked in building, the reasons such as signal interference, some areas can not obtain reliable GPS data, thus also cannot achieve big model The building of the city three-dimensional map enclosed.
Summary of the invention
In order to realize the building of large-scale city three-dimensional map, the invention proposes one combine radar odometer and The SLAM method of GPS.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of combination GPS and radar odometer SLAM (simultaneous localization and mapping, Position and build figure simultaneously) method, the SLAM method includes the following steps:
1) data acquire
Using differential GPS acquisition longitude and latitude height, the angle RPY, timestamp (times of acquisition data) data, the angle RPY includes Roll- Roll angle, Pitch- pitch angle and Yaw- yaw angle；Point cloud data and timestamp are acquired using laser radar LiDAR；
2) processing GPS data is displaced (X, Y, Z) and the angle posture RPY
(X, Y, Z) is the displacement of the initial position LiDAR to the current location LiDAR, and what the angle RPY indicated is LiDAR present bit The posture set, wherein Z, that is, difference in height obtains the high degree of the current location LiDAR and initial position directly from DGPS data According to and ask difference to obtain, the value of X, Y is sat by the way that the longitude and latitude data of the current location LiDAR and initial position are transformed into UTM plane Mark system is lower and asks poor acquisition；The angle RPY is directly obtained from DGPS data；The calculating of (X, Y, Z) is as follows:
(X, Y, Z)=(XEnd,YEnd,ZEnd)-(XJust,YJust,ZJust)
Wherein, (XEnd,YEnd,ZEnd) that represent is the position of current radar, (XJust,YJust,ZJust) what is represented is the position of initial radar It sets；
3) point cloud data of GPS data and LiDAR is matched
We realize Data Matching in such a way that timestamp is aligned.
The time stamp T ime of the data of GPS gathersGPSIt is all seconds, the time stamp T ime of the data of laser radar acquisitionLiDARIt is Number of seconds apart from nearest integral point；In addition, TimeGPSAnd TimeLiDARThere are 18 seconds leap second is poor；In order to realize timestamp The unification of format, to TimeGPSIt pre-processes, remembers that the timestamp of pretreated GPS is TimeGPSL:
TimeGPSL=TimeGPS%3600-18
4) point cloud data of step 2) treated GPS data and LiDAR is combined to examine the reliability of GPS data
Collected for LiDAR continuous two frame (LiDAR from the collected data that turn around) data F1, F2, first use GPS data after reason converts it under world coordinate system, remembers the point cloud after converting into FW1, FW2, then, use LOAM The Feature Points Extraction of (Lidar Odometry and Mapping in Real-time) algorithm extracts FW1, FW2Angle point And millet cake, remember FW1The quantity of angle point and millet cake is C2, S2；Using the corresponding relationship matching process in LOAM algorithm in FW2Feature F is looked in point1Characteristic point corresponding relationship, note find corresponding relationship characteristic point quantity be C1, S1, calculate and find corresponding relationship Angle point and millet cake quantity accounting R1, R2:
If R1, R2, C2, S2Both greater than given threshold value, it is considered that GPS data is reliable；
5) method of radar odometer obtains the angle RPY and displacement (X, Y, Z)
Using laser radar obtain point cloud data, using high-precision radar odometer LOAM algorithm obtain the angle RPY and (X, Y,Z)；
6) pose merges
It is reliably local in GPS data, the pose for using GPS the to obtain pose final as system；GPS data can not The pose data of the section starting point obtained by GPS are first passed to LOAM algorithm as initial value, are calculated using LOAM by the section leaned on Method obtains the pose T of each frame point cloud in the section1(transformation matrix being calculated by the angle RPY and displacement (X, Y, Z) information), so Afterwards, we are input to ELCH (An using the pose data of above-mentioned pose and the road segment end obtained by GPS as input Explicit Loop Closing Technique for 6D SLAM) in algorithm, the accumulation for obtaining each frame point cloud pose misses Poor T2, finally use T2Optimize the pose from radar odometer, the pose after note optimization is T3；
T3=T2*T1
7) final global three-dimensional map is obtained
Use the fused pose T 6) obtained4Under the point cloud data to world coordinate system of switched laser radar, obtain most Whole global map；
Some coordinate of point under radar fix system is P in note point cloud1, the coordinate being transformed under world coordinate system is P2；
P2=T4*P1。
Technical concept of the invention are as follows: when GPS data is reliable, the pose of laser radar is obtained by GPS；? When GPS data is insecure, the pose of GPS is obtained by radar odometer, and using the pose of GPS to radar odometer Pose optimizes.And then realize the SLAM system being applicable in the building of a wide range of city map.
Particularly, GPS data and point cloud data are acquired first, and obtain us to the necessary processing of GPS data progress to need The pose (displacement and posture) wanted.Then, the point cloud data of GPS data and laser radar is matched.Next, in conjunction with point cloud data With the reliability of the data judging GPS data from GPS.Then, pose is obtained by radar odometer.Finally, according to GPS number Pose fusion is carried out to the pose of pose and radar odometer from GPS according to the judgement result of reliability and using fused Pose transfer point cloud obtains global map.
Beneficial effects of the present invention are mainly manifested in: effectively having been merged GPS and radar odometer, and then realized one It can be realized the SLAM system of a wide range of city map building.
Specific embodiment
The invention will be further described below.
A kind of combination GPS and radar odometer SLAM (simultaneous localization and mapping, Position and build simultaneously figure) method, include the following steps:
1) data acquire
The relative position of fixed XW-GI5651 (differential GPS mobile terminal) and VLP-16 LiDAR (a laser radar), leads to It crosses XW-GI5651 and exports GPRMC data to VLP-16 LiDAR, realize that timestamp of the two on hardware is synchronous, then acquire Data；
Using differential GPS acquisition longitude, latitude, height, (Roll- roll angle, Pitch- pitch angle and Yaw- are inclined at the angle RPY Navigate angle, expression be laser radar posture), the timestamp times of data (acquisition)；Use the collection point laser radar LiDAR cloud Data and timestamp.
2) processing GPS data is displaced (X, Y, Z) and the angle posture RPY
The GPS data returned are handled, the displacement and posture needed.
(X, Y, Z) is the displacement of the initial position LiDAR to the current location LiDAR, and what the angle RPY indicated is LiDAR present bit The posture set；The value of X, Y are by being transformed into UTM plane coordinate system for the longitude and latitude data of the current location LiDAR and initial position It descends and asks poor acquisition；Z, that is, difference in height obtains the high degree of the current location LiDAR and initial position directly from DGPS data According to and ask difference to obtain, the calculating of (X, Y, Z) is as follows:
(X, Y, Z)=(XEnd,YEnd,ZEnd)-(XJust,YJust,ZJust)
Wherein, (XEnd,YEnd,ZEnd) that represent is the position of current radar, (XJust,YJust,ZJust) what is represented is the position of initial radar It sets；
The angle RPY is directly obtained from DGPS data；
3) point cloud data of GPS data and LiDAR is matched
We realize Data Matching in such a way that timestamp is aligned
The time stamp T ime of the data of GPS gathersGPSIt is all seconds, the time stamp T ime of the data of laser radar acquisitionLiDARIt is Number of seconds apart from nearest integral point；In addition, TimeGPSAnd TimeLiDARThere are 18 seconds leap second is poor；In order to realize timestamp The unification of format, to TimeGPSIt pre-processes, remembers that the timestamp of pretreated GPS is TimeGPSL:
TimeGPSL=TimeGPS%3600-18
For TimeLiDAR=Time1Moment collected point cloud data, TimeGPSL=Time1Corresponding GPS data is exactly The data to match with it；
4) step 2) treated the GPS data matched in conjunction with the point cloud data of LiDAR examines the reliability of GPS data
Collected for LiDAR continuous two frame (LiDAR from the collected data that turn around) data F1, F2, first use GPS data after reason converts it under world coordinate system, remembers the point cloud after converting into FW1, FW2, then extract FW1, FW2Spy Point is levied, then in FW2Middle searching FW1Characteristic point corresponding relationship, if FW1, FW2There are enough characteristic points and has enough FW1Characteristic point have found corresponding relationship and then think that GPS data is reliable, otherwise, GPS data is unreliable；
Use the Feature Points Extraction of LOAM (Lidar Odometry and Mapping in Real-time) algorithm Extract FW1, FW2Angle point and millet cake, remember FW1The quantity of angle point and millet cake is C2, S2；Use the corresponding relationship in LOAM algorithm Method of completing the square is in FW2Characteristic point in look for F1Characteristic point corresponding relationship, note find corresponding relationship characteristic point quantity be C1, S1。 Calculate the quantity accounting R of the angle point and millet cake that find corresponding relationship1, R2:
If R1, R2, C2, S2Both greater than given threshold value, it is considered that GPS data is reliable, C2, S2Sufficiently large explanation FW1, FW2There are enough characteristic points, R1, R2It is sufficiently large, illustrate enough FW1Characteristic point have found corresponding relationship；
5) method of radar odometer obtains the angle RPY and displacement (X, Y, Z)
The angle RPY and displacement (X, Y, Z) are obtained using radar odometer LOAM；
Only using the point cloud data of laser radar as input, RPY is obtained using high-precision radar odometer LOAM algorithm Angle and (X, Y, Z)；
6) pose merges
It is reliably local in GPS data, the pose for using GPS the to obtain pose final as system；GPS data can not The section leaned on, Optimization Steps 5) obtained pose is as final pose；
In the insecure section of GPS data, the pose data of the section starting point obtained by GPS are first passed into LOAM and are calculated Method obtains the pose T of each frame point cloud in the section using LOAM algorithm as initial value1(by the angle RPY and displacement (X, Y, Z) information The transformation matrix being calculated), then, using the pose data of above-mentioned pose and the road segment end obtained by GPS as inputting, It is input in ELCH (An Explicit Loop Closing Technique for 6D SLAM) algorithm, obtains each frame point The accumulated error T of cloud pose2, finally use T2Optimize the pose from radar odometer, the pose after note optimization is T3；
T3=T2*T1
7) final global three-dimensional map is obtained
The fused pose T obtained using step 6)4Under the point cloud data to world coordinate system of switched laser radar, obtain Take final global map；
Some coordinate of point under radar fix system is P in note point cloud1, the coordinate being transformed under world coordinate system is P2；
P2=T4*P1。

Claims (1)

1. the SLAM method of a kind of combination GPS and radar odometer, which is characterized in that the SLAM method includes the following steps:
1) data acquire
High, the angle RPY, time stamp data using differential GPS acquisition longitude and latitude, the angle RPY includes Roll- roll angle, Pitch- pitch angle With Yaw- yaw angle；Point cloud data and timestamp are acquired using laser radar LiDAR；
2) processing GPS data is displaced (X, Y, Z) and the angle posture RPY
(X, Y, Z) is the displacement of the initial position LiDAR to the current location LiDAR, and what the angle RPY indicated is the current location LiDAR Posture, wherein Z, that is, difference in height obtains the altitude information of the current location LiDAR and initial position simultaneously directly from DGPS data Difference is asked to obtain, the value of X, Y are by being transformed into UTM plane coordinate system for the longitude and latitude data of the current location LiDAR and initial position Poor acquisition is descended and asks, the angle RPY is directly obtained from DGPS data；The calculating of (X, Y, Z) is as follows:
(X, Y, Z)=(XEnd,YEnd,ZEnd)-(XJust,YJust,ZJust)
Wherein, (XEnd,YEnd,ZEnd) that represent is the position of current radar, (XJust,YJust,ZJust) what is represented is the position of initial radar；
3) point cloud data of GPS data and LiDAR is matched
Data Matching is realized in such a way that timestamp is aligned；
The time stamp T ime of the data of GPS gathersGPSIt is all seconds, the time stamp T ime of the data of laser radar acquisitionLiDARIt is distance The number of seconds of nearest integral point；In addition, TimeGPSAnd TimeLiDARThere are 18 seconds leap second is poor；In order to realize timestamp format Unification, to TimeGPSIt pre-processes, remembers that the timestamp of pretreated GPS is TimeGPSL:
TimeGPSL=TimeGPS%3600-18
4) point cloud data of step 2) treated GPS data and LiDAR is combined to examine the reliability of GPS data
Continuous two frame data F collected for LiDAR1, F2, first with treated, GPS data converts it to world coordinates Under system, remember the point cloud after converting into FW1, FW2, then, F is extracted using the Feature Points Extraction of LOAM algorithmW1, FW2Angle point And millet cake, remember FW1The quantity of angle point and millet cake is C2, S2；Using the corresponding relationship matching process in LOAM algorithm in FW2Feature F is looked in point1Characteristic point corresponding relationship, note find corresponding relationship characteristic point quantity be C1, S1；Corresponding relationship is found in calculating Angle point and millet cake quantity accounting R1, R2:
If R1, R2, C2, S2Both greater than given threshold value, it is considered that GPS data is reliable；
5) method of radar odometer obtains the angle RPY and displacement (X, Y, Z)
Using laser radar obtain point cloud data, using high-precision radar odometer LOAM algorithm obtain the angle RPY and (X, Y, Z)；
6) pose merges
It is reliably local in GPS data, the pose for using GPS the to obtain pose final as system；It is insecure in GPS data The pose data of the section starting point obtained by GPS are first passed to LOAM algorithm as initial value, are obtained using LOAM algorithm by section Obtain the pose T of each frame point cloud in the section1, the transformation matrix being calculated by the angle RPY and displacement (X, Y, Z) information then will The pose data of above-mentioned pose and the road segment end obtained by GPS are input in ELCH algorithm as input, obtain each frame The accumulated error T of point cloud pose2, finally use T2Optimize the pose from radar odometer, the pose after note optimization is T3；
T3=T2*T1
7) final global three-dimensional map is obtained
Use the fused pose T 6) obtained4Under the point cloud data to world coordinate system of switched laser radar, obtain final Global map；
Some coordinate of point under radar fix system is P in note point cloud1, the coordinate being transformed under world coordinate system is P2；
P2=T4*P1。
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