CN111854756A - Single line laser-based unmanned aerial vehicle positioning method in diversion culvert - Google Patents

Single line laser-based unmanned aerial vehicle positioning method in diversion culvert Download PDF

Info

Publication number
CN111854756A
CN111854756A CN202010607882.3A CN202010607882A CN111854756A CN 111854756 A CN111854756 A CN 111854756A CN 202010607882 A CN202010607882 A CN 202010607882A CN 111854756 A CN111854756 A CN 111854756A
Authority
CN
China
Prior art keywords
aerial vehicle
unmanned aerial
data
line laser
diversion culvert
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.)
Granted
Application number
CN202010607882.3A
Other languages
Chinese (zh)
Other versions
CN111854756B (en
Inventor
董延超
王浩天
宁少淳
冀玲玲
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tongji University
Original Assignee
Tongji University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Tongji University filed Critical Tongji University
Priority to CN202010607882.3A priority Critical patent/CN111854756B/en
Publication of CN111854756A publication Critical patent/CN111854756A/en
Application granted granted Critical
Publication of CN111854756B publication Critical patent/CN111854756B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Navigation (AREA)

Abstract

The invention relates to a single-line laser-based unmanned aerial vehicle positioning method in a diversion culvert, which comprises the following steps of 1: constructing a point cloud model of the water diversion culvert; step 2: acquiring height information and original attitude data acquired by an unmanned aerial vehicle; and step 3: acquiring unmanned aerial vehicle position data through a position acquisition sub-method, and acquiring optimized unmanned aerial vehicle attitude data through an attitude acquisition sub-method; and 4, step 4: and obtaining the accurate positioning of the unmanned aerial vehicle in the diversion culvert according to the position data and the optimized attitude data of the unmanned aerial vehicle. Compared with the prior art, the invention has the advantages of high positioning precision, high processing speed and the like.

Description

Single line laser-based unmanned aerial vehicle positioning method in diversion culvert
Technical Field
The invention relates to the technical field of accurate positioning of an unmanned aerial vehicle in a diversion culvert, in particular to a method for positioning the unmanned aerial vehicle in the diversion culvert based on single line laser.
Background
The pumped storage power station pumps water to an upper reservoir by using electric energy in the low ebb of the electric load and discharges water to a lower reservoir to generate power in the peak period of the electric load, and the pumped storage power station is also called as an energy storage type hydropower station. The utility model has the advantages of it can be with the unnecessary electric energy when the electric wire netting load is low, the high value electric energy of electric wire netting peak period is changed into, still is suitable for frequency modulation, phase modulation, stabilizes electric power system's cycle and voltage, and the maintenance problem of the diversion culvert that is used for leading water has also obtained the general attention of industry gradually. In the past, people usually enter a diversion culvert to observe the wall condition, and once a safety accident occurs, rescue workers outside the culvert are difficult to accurately position and rescue people trapped in the culvert.
In order to solve the problems, an energy storage type hydropower station adopts an unmanned aerial vehicle technology to maintain and overhaul a diversion culvert, for example, a culvert or bridge unmanned aerial vehicle and an unmanned aerial vehicle inspection method are disclosed in the Chinese patent CN108681337A, an unmanned aerial vehicle power device in the patent is provided with an ultrasonic obstacle avoidance system and a visual positioning system, and autonomous tracking in the culvert or bridge of the unmanned aerial vehicle is realized through the two systems. However, the visual image acquired by the visual positioning system in the patent contains higher noise, and has larger deviation on the positioning of the unmanned aerial vehicle, so that the unmanned aerial vehicle is possibly deviated from the air route and the risk of crash is caused, and therefore an unmanned aerial vehicle positioning method capable of realizing the accurate positioning of the unmanned aerial vehicle in the culvert is urgently needed in the energy storage type hydropower station.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide the method for positioning the unmanned aerial vehicle in the water diversion culvert based on the single-line laser, which has high positioning precision and high processing speed.
The purpose of the invention can be realized by the following technical scheme:
a method for positioning an unmanned aerial vehicle in a diversion culvert based on single line laser comprises the following steps:
step 1: constructing a point cloud model of the water diversion culvert;
Step 2: acquiring height information and original attitude data acquired by an unmanned aerial vehicle;
and step 3: acquiring unmanned aerial vehicle position data through a position acquisition sub-method, and acquiring optimized unmanned aerial vehicle attitude data through an attitude acquisition sub-method;
and 4, step 4: and obtaining the accurate positioning of the unmanned aerial vehicle in the diversion culvert according to the position data and the optimized attitude data of the unmanned aerial vehicle.
Preferably, the step 1 specifically comprises:
acquiring parameter information of the diversion culvert according to the CAD image of the diversion culvert, constructing a 3D model according to the parameter information of the culvert, and then generating a point cloud model of the diversion culvert through the 3D model.
Preferably, the step 2 specifically comprises:
the height data of the unmanned aerial vehicle are obtained through a barometer installed on the unmanned aerial vehicle, and meanwhile, the original attitude data of the unmanned aerial vehicle is obtained through inertial navigation equipment installed on the unmanned aerial vehicle.
Preferably, the position acquiring method in step 3 specifically includes:
step 3-1-1: obtaining an analytical equation of a central axis of the culvert in a point cloud model of the water diversion culvert;
step 3-1-2: and substituting the altitude information of the unmanned aerial vehicle into the axis analytic equation to obtain the position data of the unmanned aerial vehicle.
Preferably, the posture acquiring method in step 3 specifically includes:
Step 3-2-1: acquiring single-line laser data;
step 3-2-2: preprocessing single-line laser data;
step 3-2-3: transferring the single-line laser data to a corresponding position in a water diversion culvert point cloud model coordinate system, and then matching the single-line laser data with the water diversion culvert point cloud model to obtain calculation attitude data;
step 3-2-4: and fusing the original attitude data and the calculated attitude data to obtain optimized attitude data of the unmanned aerial vehicle.
More preferably, the step 3-2-2 is specifically:
and carrying out filtering and denoising processing on the single-line laser data.
More preferably, the single-line laser data in the step 3-2-3 is matched with the water diversion culvert point cloud model through an iterative closest point algorithm.
More preferably, the concrete steps of matching the single-line laser data with the water diversion culvert point cloud model are as follows:
step 3-2-3-1: searching the closest point:
taking a point P in a single line laser data point cloud PiThen finding the distance p in the model point cloud MiPoint m with the closest euclidean distancei，(pi,mi) A set of corresponding point pairs is formed;
step 3-2-3-2: solving for p by singular value decompositioniAnd miA transformation relation (R, t) between, wherein R is piAnd miT is p iAnd miThe displacement relationship of (a);
step 3-2-3-3: for each point P in the laser point cloud PiUsing the transformation relation (R, t), i.e. P '═ RP + t, we get a set of points P', defining the objective function:
Figure BDA0002561449740000031
wherein n is the number of the point clouds in the point cloud P;
step 3-2-3-4: and (4) judging whether the target function f (R, t) is smaller than a preset threshold or reaches a preset maximum iteration number, if so, ending the loop of the current round and outputting (R, t), otherwise, returning to the step 3-2-3-1.
More preferably, the KDTree method is used to search the closest point in step 3-2-3-1.
More preferably, the original attitude data in step 3-2-4 is fused with the calculated attitude data by a kalman filter fusion algorithm, specifically:
the attitude data output by the inertial navigation equipment and the laser radar equipment are respectively R1 and R2And the relation between the pose and the real pose meets the following conditions:
Ri＝R+ni,i＝1,2
wherein ,n1 and n2Respectively representing noise respectively superposed on the real pose R by the inertial navigation equipment and the laser radar equipment;
the derivative of the state error of the inertial navigation device is:
Figure BDA0002561449740000032
wherein ,
Figure BDA0002561449740000033
and
Figure BDA0002561449740000034
respectively representing the position, the speed, the attitude angle, the accelerometer bias and the derivative of the gyroscope bias error of the inertial navigation equipment at the time t;
Figure BDA0002561449740000035
representing the rotation relation of the inertial navigation system and the machine system; ∧Representing a transformation from lie algebra to lie groups; n isa、nω、
Figure BDA0002561449740000036
And
Figure BDA0002561449740000037
respectively representing accelerometer white noise, gyroscope white noise, accelerometer bias white noise and gyroscope bias white noise;
the covariance prediction formula of the incremental error is as follows:
Pt+t＝(1+Ftt)Pt(1+Ftt)T+(Gtt)Q(Gtt)T
the initial value of P is set to 0, Q represents a noise item diagonal covariance matrix, and specifically comprises the following steps:
Figure BDA0002561449740000041
the error of the laser radar equipment during laser scanning matching is described by adopting a first-order Markov process, and specifically comprises the following steps:
Figure BDA0002561449740000042
wherein ,
Figure BDA0002561449740000043
and
Figure BDA0002561449740000044
representing an attitude angle error of the lidar device; t is1γ、T1θ and T1ψRespectively corresponding correlation time of the three attitude angle errors; the xi is1γ、ξ1θ and ξ1ψWhite noise corresponding to the three attitude angle errors;
the covariance prediction formula is specifically:
Pt+t＝(1+Ftt)Pt(1+Ftt)T+(Gtt)Q(Gtt)T
the initial value of P is set to 0, Q represents a noise item diagonal covariance matrix, and specifically comprises the following steps:
Figure BDA0002561449740000045
finally, the fused true posture R is specifically:
Figure BDA0002561449740000046
wherein ,WiIs an information matrix representing the proportion of the sensor output in the final result, W1 and W2Respectively, the inverse of the covariance matrix of the inertial navigation device and the lidar device.
Compared with the prior art, the invention has the following advantages:
the utility model provides an unmanned aerial vehicle's high accuracy location in realizing diversion culvert: the unmanned aerial vehicle positioning method obtains the position data of the unmanned aerial vehicle in the diversion culvert through the height data of the unmanned aerial vehicle; the optimized unmanned aerial vehicle attitude data is obtained by the original attitude data acquired by the inertial navigation equipment and the calculated attitude data obtained by using the iterative closest point algorithm, and finally the calculated attitude data and the original attitude data are fused to obtain accurate unmanned aerial vehicle positioning data, so that the positioning precision is high.
Secondly, the processing speed is fast: the unmanned aerial vehicle positioning method solves the calculation pose data by using an iterative closest point algorithm, and searches the nearest point of the space by using a KDTree structure, thereby accelerating the processing speed of the algorithm and improving the processing efficiency.
Drawings
FIG. 1 is a schematic flow chart of a positioning method of an unmanned aerial vehicle in a diversion culvert according to the present invention;
FIG. 2 is a schematic flow chart of the matching of single-line laser data and a diversion culvert point cloud model in the invention;
FIG. 3 is a schematic structural diagram of a 3Dmax model of a diversion culvert in the embodiment of the invention;
FIG. 4 is a schematic structural diagram of a diversion culvert point cloud model in the embodiment of the invention;
fig. 5 is a schematic structural diagram of a KDTree search space closest point in an embodiment of the present invention;
fig. 6 is a schematic registration diagram of an actual point cloud in the embodiment of the present invention.
The reference numbers in the figures indicate:
A. projection of single line laser data in a point cloud model, and B, tracking tracks of the unmanned aerial vehicle.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
A method for positioning an unmanned aerial vehicle in a diversion culvert based on single line laser is disclosed, the flow of which is shown in figure 1, and comprises the following steps:
step 1: constructing a point cloud model of the water diversion culvert;
the method specifically comprises the following steps: acquiring parameter information of the diversion culvert according to the CAD image of the diversion culvert, constructing a 3D model according to the parameter information of the culvert, and generating a point cloud model of the diversion culvert through the 3D model;
step 2: acquiring height information and original attitude data acquired by an unmanned aerial vehicle;
the method specifically comprises the following steps: acquiring height data of the unmanned aerial vehicle through a barometer installed on the unmanned aerial vehicle, and acquiring original attitude data of the unmanned aerial vehicle through inertial navigation equipment installed on the unmanned aerial vehicle;
and step 3: acquiring unmanned aerial vehicle position data through a position acquisition sub-method, and acquiring optimized unmanned aerial vehicle attitude data through an attitude acquisition sub-method;
the position acquisition sub-method specifically comprises the following steps:
step 3-1-1: obtaining an analytical equation of a central axis of the culvert in a point cloud model of the water diversion culvert;
step 3-1-2: substituting the height information of the unmanned aerial vehicle into a central axis analytical equation to obtain position data of the unmanned aerial vehicle;
the posture acquisition sub-method specifically comprises the following steps:
step 3-2-1: acquiring single-line laser data;
step 3-2-2: preprocessing the single-line laser data, specifically, filtering and denoising the single-line laser data;
Step 3-2-3: transferring the single-line laser data to a corresponding position in a water diversion culvert point cloud model coordinate system, and then matching the single-line laser data with the water diversion culvert point cloud model to obtain calculation attitude data;
step 3-2-4: fusing the original attitude data and the calculated attitude data to obtain optimized attitude data of the unmanned aerial vehicle;
the single-line laser data in the step 3-2-3 are matched with the point cloud model of the water diversion culvert through an iterative closest point algorithm, and the flow is shown as the figure 2, and specifically comprises the following steps:
step 3-2-3-1: searching the nearest point by adopting a KDTree method:
taking a point P in a single line laser data point cloud PiThen finding the distance p in the model point cloud MiPoint m with the closest euclidean distancei，(pi,mi) A set of corresponding point pairs is formed;
step 3-2-3-2: solving for p by singular value decompositioniAnd miA transformation relation (R, t) between, wherein R is piAnd miT is piAnd miThe displacement relationship of (a);
step 3-2-3-3: for each point P in the laser point cloud PiUsing the transformation relation (R, t), i.e. P '═ RP + t, we get a set of points P', defining the objective function:
Figure BDA0002561449740000061
wherein n is the number of the point clouds in the point cloud P;
step 3-2-3-4: judging whether the target function f (R, t) is smaller than a preset threshold or reaches a preset maximum iteration number, if so, ending the cycle of the current round and outputting (R, t), otherwise, returning to the step 3-2-3-1;
And 3-2-4, fusing the original attitude data with the calculated attitude data through a Kalman filtering fusion algorithm, specifically:
the attitude data output by the inertial navigation equipment and the laser radar equipment are respectively R1 and R2And the relation between the pose and the real pose meets the following conditions:
Ri＝R+ni,i＝1,2
wherein ,n1 and n2Respectively representing noise respectively superposed on the real pose R by the inertial navigation equipment and the laser radar equipment;
the derivative of the state error of the inertial navigation device is:
Figure BDA0002561449740000071
wherein ,
Figure BDA0002561449740000072
and
Figure BDA0002561449740000073
respectively representing the position, the speed, the attitude angle, the accelerometer bias and the derivative of the gyroscope bias error of the inertial navigation equipment at the time t;
Figure BDA0002561449740000074
representing the rotation relation of the inertial navigation system and the machine system;∧representing a transformation from lie algebra to lie groups; n isa、nω、
Figure BDA0002561449740000075
And
Figure BDA0002561449740000076
respectively representing accelerometer white noise, gyroscope white noise, accelerometer bias white noise and gyroscope bias white noise;
the covariance prediction formula of the incremental error is as follows:
Pt+t＝(1+Ftt)Pt(1+Ftt)T+(Gtt)Q(Gtt)T
the initial value of P is set to 0, Q represents a noise item diagonal covariance matrix, and specifically comprises the following steps:
Figure BDA0002561449740000077
the error of the laser radar equipment during laser scanning matching is described by adopting a first-order Markov process, and specifically comprises the following steps:
Figure BDA0002561449740000078
wherein ,
Figure BDA0002561449740000079
and
Figure BDA00025614497400000710
representing an attitude angle error of the lidar device; t is1γ、T1θ and T1ψRespectively corresponding correlation time of the three attitude angle errors; the xi is 1γ、ξ1θ and ξ1ψWhite noise corresponding to the three attitude angle errors;
the covariance prediction formula is specifically:
Pt+t＝(1+Ftt)Pt(1+Ftt)T+(Gtt)Q(Gtt)T
the initial value of P is set to 0, Q represents a noise item diagonal covariance matrix, and specifically comprises the following steps:
Figure BDA0002561449740000081
finally, the fused true posture R is specifically:
Figure BDA0002561449740000082
wherein ,WiIs an information matrix representing the proportion of the sensor output in the final result, W1 and W2Respectively, the inverse of the covariance matrix of the inertial navigation device and the lidar device.
And 4, step 4: and obtaining the accurate positioning of the unmanned aerial vehicle in the diversion culvert according to the position data and the optimized attitude data of the unmanned aerial vehicle.
One specific example is provided below:
acquiring parameter information such as the inner diameter, the attitude angle, the length and the like of a diversion culvert according to a project construction CAD drawing during construction of the diversion culvert, constructing a simplified 3Dmax model according to the parameter information, wherein the established 3Dmax model is shown in FIG. 3, and then generating a point cloud model of the diversion culvert through the 3Dmax model, which is shown in FIG. 4.
The axis analytic equation of the diversion culvert is determined in the point cloud model of the diversion culvert, and the altitude data returned by the barometer of the unmanned aerial vehicle and the attitude data containing large noise returned by the inertial navigation equipment are substituted into the axis analytic equation of the diversion culvert to obtain the position data of the unmanned aerial vehicle, wherein the axis analytic equation in the embodiment specifically comprises the following steps:
Figure BDA0002561449740000083
The axis equation can be divided into two parts, and partly be the circular arc section, and another part is the diagonal segment, simultaneously because the repeatability of diagonal segment, when highly being higher than 30 meters after, can be approximate think that unmanned aerial vehicle is in 30 meters height always.
Then matching with a drainage culvert point cloud model through an iterative closest point ICP algorithm, searching a space closest point by using a KDTree structure during matching, and continuously refining a three-dimensional space through tree nodes to accelerate the index speed of the space point, wherein the structure of the KDTree when searching the space closest point is shown in figure 5; and then, obtaining calculated attitude data, and finally, fusing the calculated attitude data with the original attitude data to obtain optimized attitude data of the unmanned aerial vehicle, so as to correct the attitude and obtain a final positioning result, as shown in fig. 6, black point cloud in fig. 6 is a diversion culvert model, A is the attitude after ICP registration is carried out on single line laser data point cloud and model point cloud, and the attitude B of the unmanned aerial vehicle can be obtained from the attitude data, and after the unmanned aerial vehicle attitude is subjected to fusion correction, the tracking track of the unmanned aerial vehicle according to the corrected positioning data can be found out that the deviation between the track of the unmanned aerial vehicle and the central axis of the diversion culvert is very small, so that the unmanned aerial vehicle positioning method achieves the expected effect and can provide accurate positioning data for automatic tracking of the unmanned aerial vehicle.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A method for positioning an unmanned aerial vehicle in a diversion culvert based on single line laser is characterized by comprising the following steps:
step 1: constructing a point cloud model of the water diversion culvert;
step 2: acquiring height information and original attitude data acquired by an unmanned aerial vehicle;
and step 3: acquiring unmanned aerial vehicle position data through a position acquisition sub-method, and acquiring optimized unmanned aerial vehicle attitude data through an attitude acquisition sub-method;
and 4, step 4: and obtaining the accurate positioning of the unmanned aerial vehicle in the diversion culvert according to the position data and the optimized attitude data of the unmanned aerial vehicle.
2. The method for positioning the unmanned aerial vehicle in the diversion culvert based on the single-line laser according to claim 1, wherein the step 1 specifically comprises the following steps:
acquiring parameter information of the diversion culvert according to the CAD image of the diversion culvert, constructing a 3D model according to the parameter information of the culvert, and then generating a point cloud model of the diversion culvert through the 3D model.
3. The method for positioning the unmanned aerial vehicle in the diversion culvert based on the single-line laser according to claim 1, wherein the step 2 specifically comprises the following steps:
the height data of the unmanned aerial vehicle are obtained through a barometer installed on the unmanned aerial vehicle, and meanwhile, the original attitude data of the unmanned aerial vehicle is obtained through inertial navigation equipment installed on the unmanned aerial vehicle.
4. The method for positioning the unmanned aerial vehicle in the diversion culvert based on the single line laser as claimed in claim 1, wherein the position obtaining sub-method in the step 3 is specifically:
step 3-1-1: obtaining an analytical equation of a central axis of the culvert in a point cloud model of the water diversion culvert;
step 3-1-2: and substituting the altitude information of the unmanned aerial vehicle into the axis analytic equation to obtain the position data of the unmanned aerial vehicle.
5. The method for positioning the unmanned aerial vehicle in the diversion culvert based on the single-line laser as claimed in claim 1, wherein the attitude acquisition sub-method in the step 3 specifically comprises:
step 3-2-1: acquiring single-line laser data;
step 3-2-2: preprocessing single-line laser data;
step 3-2-3: transferring the single-line laser data to a corresponding position in a water diversion culvert point cloud model coordinate system, and then matching the single-line laser data with the water diversion culvert point cloud model to obtain calculation attitude data;
Step 3-2-4: and fusing the original attitude data and the calculated attitude data to obtain optimized attitude data of the unmanned aerial vehicle.
6. The method for positioning the unmanned aerial vehicle in the diversion culvert based on the single-line laser according to claim 5, wherein the step 3-2-2 is specifically as follows:
and carrying out filtering and denoising processing on the single-line laser data.
7. The method for positioning the unmanned aerial vehicle in the diversion culvert based on the single line laser as claimed in claim 5, wherein the single line laser data in the step 3-2-3 is matched with the diversion culvert point cloud model through an iterative closest point algorithm.
8. The single-line laser-based unmanned aerial vehicle positioning method in the diversion culvert according to claim 7, wherein the specific steps of matching the single-line laser data with the diversion culvert point cloud model are as follows:
step 3-2-3-1: searching the closest point:
taking a point P in a single line laser data point cloud PiThen finding the distance p in the model point cloud MiPoint m with the closest euclidean distancei，(pi,mi) A set of corresponding point pairs is formed;
step 3-2-3-2: solving for p by singular value decompositioniAnd miA transformation relation (R, t) between, wherein R is piAnd miT is p iAnd miThe displacement relationship of (a);
step 3-2-3-3: for each point P in the laser point cloud PiUsing the transformation relation (R, t), i.e. P '═ RP + t, we get a set of points P', defining the objective function:
Figure FDA0002561449730000021
wherein n is the number of the point clouds in the point cloud P;
step 3-2-3-4: and (4) judging whether the target function f (R, t) is smaller than a preset threshold or reaches a preset maximum iteration number, if so, ending the loop of the current round and outputting (R, t), otherwise, returning to the step 3-2-3-1.
9. The method for positioning the unmanned aerial vehicle in the diversion culvert based on the single line laser as claimed in claim 8, wherein a KDTree method is adopted to search the closest point in the step 3-2-3-1.
10. The method for positioning the unmanned aerial vehicle in the diversion culvert based on the single line laser as claimed in claim 5, wherein the original attitude data in the step 3-2-4 is fused with the calculated attitude data by a Kalman filtering fusion algorithm, specifically:
the attitude data output by the inertial navigation equipment and the laser radar equipment are respectively R1 and R2And the relation between the pose and the real pose meets the following conditions:
Ri＝R+ni,i＝1,2
wherein ,n1 and n2Respectively representing noise respectively superposed on the real pose R by the inertial navigation equipment and the laser radar equipment;
the derivative of the state error of the inertial navigation device is:
Figure FDA0002561449730000031
wherein ,
Figure FDA0002561449730000032
and
Figure FDA0002561449730000033
respectively representing the position, the speed, the attitude angle, the accelerometer bias and the derivative of the gyroscope bias error of the inertial navigation equipment at the time t;
Figure FDA0002561449730000034
representing the rotation relation of the inertial navigation system and the machine system; ^ represents the transformation from lie algebra to lie group; n isa、nω、
Figure FDA0002561449730000035
And
Figure FDA0002561449730000036
respectively representing accelerometer white noise, gyroscope white noise, accelerometer bias white noise and gyroscope bias white noise;
the covariance prediction formula of the incremental error is as follows:
Pt+t＝(1+Ftt)Pt(1+Ftt)T+(Gtt)Q(Gtt)T
the initial value of P is set to 0, Q represents a noise item diagonal covariance matrix, and specifically comprises the following steps:
Figure FDA0002561449730000037
the error of the laser radar equipment during laser scanning matching is described by adopting a first-order Markov process, and specifically comprises the following steps:
Figure FDA0002561449730000038
wherein ,
Figure FDA0002561449730000039
and
Figure FDA00025614497300000310
representing an attitude angle error of the lidar device; t is1γ、T1θ and T1ψRespectively corresponding correlation time of the three attitude angle errors; the xi is1γ、ξ1θ and ξ1ψAs three attitude angle error pairsWhite noise is required;
the covariance prediction formula is specifically:
Pt+t＝(1+Ftt)Pt(1+Ftt)T+(Gtt)Q(Gtt)T
the initial value of P is set to 0, Q represents a noise item diagonal covariance matrix, and specifically comprises the following steps:
Figure FDA0002561449730000041
finally, the fused true posture R is specifically:
Figure FDA0002561449730000042
wherein ,WiIs an information matrix representing the proportion of the sensor output in the final result, W1 and W2Respectively, the inverse of the covariance matrix of the inertial navigation device and the lidar device.
CN202010607882.3A 2020-06-30 2020-06-30 Single-line laser-based method for positioning unmanned aerial vehicle in diversion culvert Active CN111854756B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010607882.3A CN111854756B (en) 2020-06-30 2020-06-30 Single-line laser-based method for positioning unmanned aerial vehicle in diversion culvert

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010607882.3A CN111854756B (en) 2020-06-30 2020-06-30 Single-line laser-based method for positioning unmanned aerial vehicle in diversion culvert

Publications (2)

Publication Number Publication Date
CN111854756A true CN111854756A (en) 2020-10-30
CN111854756B CN111854756B (en) 2023-08-29

Family

ID=72988698

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010607882.3A Active CN111854756B (en) 2020-06-30 2020-06-30 Single-line laser-based method for positioning unmanned aerial vehicle in diversion culvert

Country Status (1)

Country Link
CN (1) CN111854756B (en)

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150377405A1 (en) * 2014-06-25 2015-12-31 Pearson Engineering Ltd Inspection systems
CN106441286A (en) * 2016-06-27 2017-02-22 上海大学 Unmanned aerial vehicle tunnel inspection system based on BIM technology
CN106683137A (en) * 2017-01-11 2017-05-17 中国矿业大学 Monocular multi-target identification and positioning method based on artificial mark
KR101839599B1 (en) * 2017-05-31 2018-03-16 (주)지트 Road facility surveying system using drone
CN108681337A (en) * 2018-05-21 2018-10-19 河北艺海机器人科技有限公司 A kind of culvert or the special inspection unmanned plane of bridge and unmanned plane visiting method
CN109709801A (en) * 2018-12-11 2019-05-03 智灵飞（北京）科技有限公司 A kind of indoor unmanned plane positioning system and method based on laser radar
CN109945856A (en) * 2019-02-18 2019-06-28 天津大学 Based on inertia/radar unmanned plane autonomous positioning and build drawing method
CN110262546A (en) * 2019-06-18 2019-09-20 武汉大学 A kind of tunnel intelligent unmanned plane cruising inspection system and method
US20200026310A1 (en) * 2017-12-27 2020-01-23 Topcon Corporation Three-Dimensional Information Processing Unit, Apparatus Having Three-Dimensional Information Processing Unit, Unmanned Aerial Vehicle, Informing Device, Method and Program for Controlling Mobile Body Using Three-Dimensional Information Processing Unit

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150377405A1 (en) * 2014-06-25 2015-12-31 Pearson Engineering Ltd Inspection systems
GB2529302A (en) * 2014-06-25 2016-02-17 Pearson Eng Ltd Improvements in or relating to inspection systems
CN106441286A (en) * 2016-06-27 2017-02-22 上海大学 Unmanned aerial vehicle tunnel inspection system based on BIM technology
CN106683137A (en) * 2017-01-11 2017-05-17 中国矿业大学 Monocular multi-target identification and positioning method based on artificial mark
KR101839599B1 (en) * 2017-05-31 2018-03-16 (주)지트 Road facility surveying system using drone
US20200026310A1 (en) * 2017-12-27 2020-01-23 Topcon Corporation Three-Dimensional Information Processing Unit, Apparatus Having Three-Dimensional Information Processing Unit, Unmanned Aerial Vehicle, Informing Device, Method and Program for Controlling Mobile Body Using Three-Dimensional Information Processing Unit
CN108681337A (en) * 2018-05-21 2018-10-19 河北艺海机器人科技有限公司 A kind of culvert or the special inspection unmanned plane of bridge and unmanned plane visiting method
CN109709801A (en) * 2018-12-11 2019-05-03 智灵飞（北京）科技有限公司 A kind of indoor unmanned plane positioning system and method based on laser radar
CN109945856A (en) * 2019-02-18 2019-06-28 天津大学 Based on inertia/radar unmanned plane autonomous positioning and build drawing method
CN110262546A (en) * 2019-06-18 2019-09-20 武汉大学 A kind of tunnel intelligent unmanned plane cruising inspection system and method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
NATHAN E.SERRANO: "Autonomous quadrotor unmanned aerial vehicle for culvert inspection", MIT LIBRARIES *
陈显龙;王小康;刘盼盼;: "基于UTM-30LX的旋翼无人机定位检测系统设计", 地理信息世界, no. 03 *
陈玉清等: "无人机辅助地铁设施与管线巡检的应用研究", 工程技术研究, vol. 6, no. 5 *

Also Published As

Publication number Publication date
CN111854756B (en) 2023-08-29

Similar Documents

Publication Publication Date Title
CN109341706B (en) Method for manufacturing multi-feature fusion map for unmanned vehicle
US11802769B2 (en) Lane line positioning method and apparatus, and storage medium thereof
Schäfer et al. Multicopter unmanned aerial vehicle for automated inspection of wind turbines
CN111693050B (en) Indoor medium and large robot navigation method based on building information model
CN107167139A (en) A kind of Intelligent Mobile Robot vision positioning air navigation aid and system
CN110687919B (en) Unmanned aerial vehicle autonomous flight control method for hydropower station water pipeline inspection
CN105910604A (en) Multi-sensor-based autonomous obstacle avoidance navigation system
Otsu et al. Where to look? Predictive perception with applications to planetary exploration
CN112200863B (en) Unmanned aerial vehicle monitoring telegraph pole inclination system based on synchronous positioning and mapping
CN112378397B (en) Unmanned aerial vehicle target tracking method and device and unmanned aerial vehicle
CN108426582B (en) Indoor three-dimensional map matching method for pedestrians
CN113920198B (en) Coarse-to-fine multi-sensor fusion positioning method based on semantic edge alignment
CN112068152A (en) Method and system for simultaneous 2D localization and 2D map creation using a 3D scanner
CN113359808A (en) Multi-stage path planning method for power inspection of unmanned aerial vehicle and related device
Xiong et al. Road-Model-Based road boundary extraction for high definition map via LIDAR
CN117406771B (en) Efficient autonomous exploration method, system and equipment based on four-rotor unmanned aerial vehicle
CN112380933B (en) Unmanned aerial vehicle target recognition method and device and unmanned aerial vehicle
CN111854756B (en) Single-line laser-based method for positioning unmanned aerial vehicle in diversion culvert
CN117234203A (en) Multi-source mileage fusion SLAM downhole navigation method
CN111857167B (en) Single-line laser data ellipse fitting-based method for positioning unmanned aerial vehicle in diversion culvert
CN116698014A (en) Map fusion and splicing method based on multi-robot laser SLAM and visual SLAM
Nomatsu et al. Development of an autonomous mobile robot with self-localization and searching target in a real environment
Chen et al. Real-time active detection of targets and path planning using UAVs
Qiu et al. Design and implementation of an autonomous landing control system of unmanned aerial vehicle for power line inspection
Kovalev et al. UAV's autonomous navigation principe based on Earth remote sensing data

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
GR01 Patent grant
GR01 Patent grant