CN111751852A - Unmanned vehicle GNSS positioning reliability evaluation method based on point cloud registration - Google Patents
Unmanned vehicle GNSS positioning reliability evaluation method based on point cloud registration Download PDFInfo
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- 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
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- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- 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
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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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- G—PHYSICS
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- 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
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/21—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service
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Abstract
The invention provides a point cloud registration-based unmanned vehicle GNSS positioning reliability evaluation method, which is used for solving the problem of reliability judgment of a GPS navigation signal when an unmanned vehicle runs in a park environment. In the invention, firstly, the map acquisition is carried out on the driving route. And collecting and storing the laser radar point cloud pictures of the corresponding points while collecting the navigation map. When the unmanned vehicle runs, searching the nearest point in front of the vehicle in a navigation map by taking the current point as a reference and calculating a distance value between the two points; and matching the laser radar point cloud picture of the current point with the laser radar point cloud picture corresponding to the nearest navigation point by utilizing an ICP (inductively coupled plasma) algorithm, and calculating a distance value between the two groups of point clouds. If the difference between the two distance values is smaller than the set threshold, it indicates that the current GNSS positioning signal is reliable. The method can analyze and evaluate the positioning reliability of the unmanned vehicle in real time by means of RTK-GPS navigation, provides judgment basis for the decision control module, and guarantees the driving safety of the unmanned vehicle.
Description
Technical Field
The invention belongs to the field of unmanned driving, and particularly relates to an unmanned vehicle GNSS positioning reliability evaluation method based on point cloud registration.
Background
The unmanned vehicle is an intelligent vehicle which realizes unmanned driving through a computer, senses the surrounding environment of the vehicle by using a vehicle-mounted sensor, and controls the steering and the speed of the vehicle according to the road, vehicle position and obstacle information obtained by sensing, thereby enabling the vehicle to safely and reliably run on the road. The key technologies of the unmanned automobile comprise: environment perception, navigation positioning, path planning, decision control and the like. The precise positioning and navigation are the most basic requirements of the unmanned vehicle for normal driving in unknown or known environment, and are core elements for guiding the unmanned vehicle on a macroscopic level and reaching a destination according to a set route or an autonomous selected route. Currently, vehicle positioning technologies can be divided into: satellite positioning, magnetic induction, inertial navigation positioning, vision, SLAM for lidar, and the like. The satellite positioning system is a Global Navigation Satellite System (GNSS), and includes a united states Global Positioning System (GPS), a chinese beidou satellite positioning system (BDS), a russian GLONASS satellite positioning system (GLONASS), and a european GALILEO satellite positioning system (GALILEO).
At present, the differential GPS is still the main positioning mode of the unmanned vehicle, and by using rtk (real time kinematic), i.e., carrier phase differential technology, the reference station transmits its observed value and the station coordinates to the mobile station through a data chain, and the mobile station performs real-time carrier phase differential processing on the acquired satellite data and the data from the reference station to obtain a centimeter-level positioning result. The carrier phase differential positioning module generally goes through four positioning modes in sequence, generally obtains standard Single Point Positioning (SPP), obtains pseudo-code differential positioning (RTD) quickly after obtaining pseudo-range differential correction information, obtains a real value as floating point RTK positioning (the positioning precision of floating point solution is low) by carrier phase integer ambiguity, and obtains an integer solution as fixed RTK positioning (the positioning precision of fixed solution is highest) by carrier phase integer ambiguity. The four positioning mode accuracies are respectively 10 meter level, decimeter level and centimeter level.
Although the traditional RTK positioning technology can eliminate the influence of error sources such as receiver and satellite clock error, troposphere delay, ionosphere delay and the like on positioning errors, the positioning accuracy is improved, but the problems of shielding and multipath are not solved. Limited by the satellite state, the satellite signals are shielded or the shielding time is long in the building dense area in the city, so that the satellite is unlocked, the initialization of the ambiguity of the whole circle is influenced, and the positioning accuracy is low or even fails; under the influence of sky environment, if the interference condition of an ionosphere is serious, the number of public satellites of a mobile station and a reference station is small, and positioning cannot be performed; due to interference and limitation of obstacles on a data transmission link, such as high mountains and high buildings, or interference of a high-frequency signal source in the range of the data transmission link, signals between the reference station and the mobile station are seriously attenuated in the transmission process, and positioning accuracy is influenced. Therefore, when the unmanned vehicle is guided by the RTK-GPS, the positioning reliability of the unmanned vehicle needs to be analyzed and evaluated in real time, and if the current positioning signal is reliable, the unmanned vehicle can normally run according to a planned route; if the current positioning signal is unreliable, the decision control module is reported to adopt other positioning modes or parking strategies and the like so as to ensure the safe operation of the unmanned vehicle.
Technical contrast analysis
1) The invention patent 1:
the patent names are as follows: automatic evaluation method and system for relative precision of automatic driving positioning system
Application No.: application date of CN 201810750858.8: 2018-07-10
Applicant (patentee): shanghai Fizeau data communication technology Limited
The basic contents are as follows: the invention discloses an automatic evaluation method and system for relative precision of an automatic driving positioning system, wherein the method comprises the following steps: taking the current moment as a center, acquiring a positioning track and a curve of a preset time interval before and after a corresponding GPS track; the acquired curve of the preset time interval of the positioning track is subjected to similar change to the acquired curve of the preset time interval of the GPS track, so that two ends of the two curves are overlapped; taking the current moment as a center, intercepting a curve of a preset time interval of the positioning track with two overlapped ends and a curve of a preset time interval before and after the curve of the preset time interval of the GPS track; and calculating the distance from the positioning curve at each moment to the GPS curve in the curve of the intercepted preset time interval to obtain a maximum value M, wherein if M is smaller than a preset distance value, the positioning quality at the current moment is qualified. The method can be used for completely and automatically evaluating the quality of the positioning result, so that the labor is saved, and the evaluation result is more reliable.
The difference from the invention is that:
the difference between the above patent and the present invention is mainly embodied in the following two aspects:
(1) purpose(s) to
The above patent uses the comparison between the positioning track and the GPS track to evaluate whether the positioning quality at the current time is qualified, and aims to provide an automatic and highly accurate method and system for evaluating the relative accuracy of an automatic driving positioning system. The invention aims to provide a reliability evaluation method for unmanned vehicle GNSS absolute positioning, which is used for evaluating the reliability of GPS positioning at the current moment by comparing the matching result of a laser radar point cloud picture with a GNSS navigation positioning error.
(2) Technical route
The above patent takes the current time as the center, and obtains the positioning track and the corresponding curve of the GPS track in the pre-and post-preset time interval to make similar changes, so that the two ends of the two curves are overlapped; and then intercepting a curve of a preset time interval of the positioning track with two coincident ends and a curve of a preset time interval of the curve of the preset time interval of the GPS track, calculating the distance from the positioning curve to the GPS curve at each moment and obtaining a maximum value M, and when the M is smaller than a preset distance value, evaluating the positioning quality at the current moment as qualified. The method needs to acquire a GPS navigation map of a driving route in advance, simultaneously stores laser radar point cloud maps corresponding to all longitude and latitude points, then searches the nearest point in front of a vehicle in the navigation map by taking the current point as a reference and calculates the distance value between the two points when the vehicle runs, and simultaneously matches the laser radar point cloud map of the current point with the laser radar point cloud map corresponding to the nearest navigation point by utilizing an ICP algorithm and calculates the distance value between the two groups of point clouds. Comparing the difference between the two distance values with a preset threshold value, and if the difference is smaller than the preset threshold value, indicating that the current GNSS positioning is reliable; otherwise, it is unreliable.
2) The invention patent 2:
the patent names are as follows: unmanned vehicle positioning safety test system and method during GPS deception
Application No.: application date of CN 201810726268.1: 2018-07-04
Applicant (patentee): automation research institute of Shandong provincial academy of sciences
The basic contents are as follows: the invention discloses a system and a method for testing positioning safety of an unmanned vehicle during GPS deception, which comprises the following steps: the system comprises an unmanned test vehicle, a grating sensor, a GPS signal generator, an IMU sensor and a test server. The grating sensor is installed at a set reference point position on a driving path of the unmanned test vehicle, the GPS signal generator and the IMU sensor are both arranged on the unmanned test vehicle, and the test server is respectively communicated with the GPS signal generator, the IMU sensor and the grating sensor. The method can effectively evaluate the reliability of the unmanned vehicle on the GPS deception data, and has important guiding significance for guiding the safety design of the unmanned vehicle.
The difference from the invention is that:
the difference between the above patent and the present invention is mainly embodied in the following two aspects:
(1) kind of sensor
The sensors used in the above patents mainly include: grating sensor, high accuracy GPS signal positioning equipment, GPS signal generator, IMU sensor. The invention only needs to use GNSS navigation equipment and laser radar, and IMU has accumulated error, and increases with time, and is only suitable for local positioning or auxiliary positioning in short time.
(2) Technical route
In the patent, key reference points are arranged in a test field, grating sensors are installed, and the precise positions of the reference points of the grating sensors are calibrated by using high-precision GPS signal positioning equipment. And in the driving process of the unmanned vehicle, the real positioning data of the vehicle is obtained through the IMU and the grating calibration data. When the unmanned vehicle runs to the GPS deception information road section, the unmanned vehicle is positioned through the GPS deception position information synthesized by the GPS signal generator, the actual position deviation of the detected vehicle is calculated in real time through the IMU, and the safety of positioning of the unmanned vehicle during GPS deception is judged according to the difference value between the position deviation and the set threshold value. The invention does not need to synthesize GPS deception information and utilizes the real driving state of the unmanned vehicle in the park to test. Pre-collecting a GPS navigation map of a driving route and laser radar point cloud charts corresponding to all longitude and latitude points; when the vehicle runs, the nearest point in front of the vehicle is searched in the navigation map by taking the current point as a reference, the distance value between the two points is calculated, meanwhile, the laser radar point cloud picture of the current point is matched with the laser radar point cloud picture corresponding to the nearest navigation point by utilizing an ICP algorithm, and the distance value between the two groups of point clouds is calculated. Comparing the difference between the two distance values with a preset threshold value, and if the difference is smaller than the preset threshold value, indicating that the current GNSS positioning is reliable; otherwise, it is unreliable.
3) Invention patent 3:
the patent names are as follows: navigation positioning accuracy evaluation method
Application No.: application date of CN 201810270226.1: 2018-03-29
Applicant (patentee): china non-ferrous metals Changsha reconnaissance design research institute Co., Ltd
The basic contents are as follows: the invention provides a navigation positioning accuracy evaluation method, which comprises the following steps: the first step is as follows: installing a combined navigation device utilizing a single-axis gyroscope, an inclinometer and a speedometer on a robot carrier, and marking a reference point; erecting an RTK base station, and installing an RTK rover on a robot carrier, wherein the reference point of the RTK rover is ensured to be coincident with the reference point of the robot carrier; the second step is that: starting the robot carrier, and recording the position coordinate calculated by the combined navigation device and the position coordinate output by the RTK rover station while walking; the third step: and calculating the two sets of coordinate information by adopting a relative positioning precision algorithm and an absolute positioning precision algorithm to obtain a relative positioning precision value and an absolute positioning precision. The method has the advantages of simple steps, capability of obtaining accurate relative positioning accuracy values and absolute positioning accuracy values, and capability of effectively evaluating the positioning accuracy of the integrated navigation device.
The difference from the invention is that:
the above patents are different from the present invention mainly in the following three aspects:
(1) purpose(s) to
The main purpose of the above patent is to obtain a relative positioning accuracy value and an absolute positioning accuracy value for estimating the navigation positioning accuracy by calculating the coordinate position output by the integrated navigation device and the coordinate position output by the RTK rover station. The invention aims to evaluate the reliability of the GPS positioning at the current moment by comparing the matching result of the laser radar point cloud picture with the GNSS navigation positioning error.
(2) Kind of sensor
The sensors used in the above patents are mainly: the system comprises a combined navigation device utilizing a single-axis gyroscope, an inclinometer and a mileometer and RTK-GPS navigation equipment. The sensors used in the present invention are GNSS navigation devices and lidar.
(3) Technical route
The method adopts a relative positioning precision algorithm and an absolute positioning precision algorithm to calculate and process the coordinates calculated by the combined navigation device and the position coordinate information output by the RTK rover station in the walking process of the robot, and obtains a relative positioning precision value and an absolute positioning precision value. The method comprises the steps of collecting a GPS navigation map of a driving route and laser radar point cloud maps corresponding to all longitude and latitude points in advance, searching the nearest point in front of a vehicle in the navigation map by taking the current point as a reference and calculating the distance value between the two points when the vehicle runs, matching the laser radar point cloud map of the current point with the laser radar point cloud map corresponding to the nearest navigation point by utilizing an ICP algorithm, and calculating the distance value between two groups of point clouds. And comparing the difference between the two distance values with a preset threshold value, thereby evaluating the positioning reliability of the current GNSS.
Disclosure of Invention
According to the characteristics of application scenes of low-speed unmanned vehicles, the invention is mostly in semi-open environments such as industrial parks, university campuses, scenic spots and the like, structured and unstructured roads are mixed, forest shade roads are very common, building shielding is serious and the like, and provides a GNSS positioning reliability assessment method based on laser radar point cloud registration, so that the problem of judging the reliability of GPS navigation signals when the unmanned vehicles run on any road section of the environments such as parks and the like is solved.
The GNSS positioning reliability evaluation method provided by the invention utilizes the point cloud registration of the laser radar to assist in judging the effectiveness of the current navigation state. The overall system framework is shown in fig. 1. First, a map of the driving route needs to be collected. And collecting and storing the laser radar point cloud pictures of the corresponding points while collecting the navigation map. Then, loading a navigation map file when the vehicle actually runs, searching a point with the nearest distance in front of the vehicle in the navigation map by taking the current point as a reference, respectively carrying out UTM projection transformation on the points, and calculating the distance values of the two points under a plane rectangular coordinate system; and matching the laser radar Point cloud picture of the current Point with the laser radar Point cloud picture corresponding to the nearest navigation Point by utilizing an ICP (iterative closest Point) algorithm, and calculating a distance value between the two groups of Point clouds. The two distance values are subjected to difference and compared with a preset error threshold value parameter, and if the difference between the two distance values is smaller than a set threshold value, the current GNSS positioning signal is reliable; otherwise, it is unreliable. The method is experimentally verified in the low-speed unmanned vehicle in the park, and provides a basis for the decision control module to judge.
The GPS positioning is used for solving the distance value between the current point and the nearest point in front of the vehicle searched in the navigation map in the driving process of the unmanned vehicle. Since the coordinate system referred by the positioning data output by the GNSS navigation receiver is the world geodetic coordinate system, the coordinate system is converted into a corresponding planar rectangular coordinate system by using the UTM projection algorithm. And calculating the distance between the two longitude and latitude points and the components of the distance between the two longitude and latitude points on each coordinate axis under the plane rectangular coordinate system, and calculating the azimuth angle between the two longitude and latitude points by utilizing a geodetic subject inverse calculation algorithm. After the longitude and latitude and the azimuth angle of the current point and the nearest navigation point are known, the distance value of the two longitude and latitude points under the rectangular plane coordinate system and the distance component of each coordinate axis can be obtained by utilizing UTM projection.
And the laser radar point cloud registration distance calculation is used for solving the distance value after the matching of the environment point cloud picture corresponding to the current point and the environment point cloud picture corresponding to the nearest navigation point. The ICP algorithm is a scanning matching algorithm between point sets, and the essence of the algorithm is that the corresponding relation between a reference point set and a target point set is found, so that the rotation and translation relations between two frames of scanning data are obtained, and the two frames of scanning data meet the optimal matching under a certain measurement criterion. After the laser radar point cloud corresponding to the current point and the laser radar point cloud corresponding to the nearest navigation point are known, the rotating matrix R of the two point sets under the constraint of the minimum Euclidean distance square sum of all the nearest point pairs can be solved by utilizing an ICP algorithmαAnd the translation vector T ═ Txtytz]TWherein t isx、ty、tzRespectively representing the components of the translation in the x, y and z axes.
And the positioning reliability evaluation is used for evaluating the current GNSS positioning data effectiveness of the unmanned vehicle in real time. The calculated GPS positioning distance value and the laser radar point cloud registration distance value are subjected to difference, the relation between the difference and a set threshold value is compared, if the distance difference is smaller than the set threshold value, the current GNSS positioning data is reliable, and unmanned vehicles can normally run according to a planned route; if the distance difference is larger than the set threshold value, the current GNSS positioning data is unreliable, and the current GNSS positioning data is reported to the decision control module to adopt other positioning modes or parking strategies and the like, so that the driving safety of the unmanned vehicle is ensured.
Advantageous effects
The invention provides a GNSS positioning reliability evaluation method based on laser radar point cloud registration, which aims at the problems that when a low-speed unmanned vehicle runs in scenes such as a park, a scenic spot and the like, the GNSS positioning signal is easily interfered by different degrees and even fails due to the environment influence such as high-rise trees, tunnel overpasses and the like, and the multipath effect, so that the validity of the current positioning state can be judged in real time, the problem of evaluating the reliability of a GPS navigation signal when the unmanned vehicle runs in any road section is solved, and necessary guarantee is provided for the running safety of the vehicle.
Drawings
FIG. 1 is a block diagram of the system
FIG. 2 is a schematic view of a nearest navigation point
FIG. 3 is a schematic diagram of the known longitude and latitude coordinates of two points for obtaining the azimuth angle
FIG. 4UTM projection schematic
FIG. 5ICP Algorithm flow chart
FIG. 6(a) open road section 6(b) high building sheltered road section 6(c) overpass sheltered road section
FIG. 7 GNSS positioning data for open road segments
FIG. 8 laser radar point cloud matching localization effect for open road segment
FIG. 9 GNSS positioning effect of high-rise sheltered road section
FIG. 10 laser radar point cloud matching positioning effect of high-rise sheltered road section
FIG. 11 GNSS positioning effect of overpass sheltered road section
FIG. 12 laser radar point cloud matching positioning effect of overpass shielding road section
Detailed Description
The method realizes the evaluation of the reliability of the GNSS positioning signal based on the point cloud registration of the laser radar. First, a map of the driving route needs to be collected. And collecting and storing the laser radar point cloud pictures of the corresponding points while collecting the navigation map. Then, loading a navigation map file when the vehicle actually runs, searching a point with the nearest distance in front of the vehicle in the navigation map by taking the current point as a reference, respectively carrying out UTM projection transformation on the points, and calculating the distance values of the two points under a plane rectangular coordinate system; and matching the laser radar Point cloud picture of the current Point with the laser radar Point cloud picture corresponding to the nearest navigation Point by utilizing an ICP (Iterative Closest Point) algorithm, and calculating a distance value between the two groups of Point clouds. The two distance values are subjected to difference and compared with a preset error threshold value parameter, and if the difference between the two distance values is smaller than a set threshold value, the current GNSS positioning signal is reliable; otherwise, it is unreliable.
Nearest navigation point search and distance calculation
Before the experiment begins, a longitude and latitude map acquired by a GNSS receiver and a laser radar point cloud map corresponding to each longitude and latitude point are acquired, and then a GPS point closest to the front of a vehicle is searched in a navigation map by taking the current point as a reference and the distance is calculated.
The schematic diagram of the nearest navigation point is shown in fig. 2, wherein: p is a radical of0Representing a current location of the vehicle in real time; p is a radical of1、p2、p3.., points in the navigation map α1α being the heading angle of the vehicle body, i.e. the angle between the direction of motion of the vehicle and the true north direction2Angle between the line drawn by the point on the map and the body and the direction of movement of the vehicle α3The angle of the straight line drawn by the point on the map and the vehicle body to true north, and α3Is solved at p0、p1And calculating on the premise of latitude and longitude coordinates, and solving the azimuth angle between the two points according to inverse solution of the geodetic theme. w represents the component of the distance between the current point and the nearest navigation point in the searched map in the x-axis direction under the rectangular plane coordinate system, and h represents the component of the distance between the current point and the nearest navigation point in the searched map in the y-axis direction under the rectangular plane coordinate system. From FIG. 2, equation (1) can be derived:
at this time, the azimuth angle, i.e., the included angle with due north, needs to be solved according to the longitude and latitude coordinates of the known two points, as shown in fig. 3, and the solution can be performed according to the inverse calculation problem in the geodetic theme solution. Suppose that: the longitude and latitude coordinates of the point A are (lat)A,lonA) The longitude and latitude coordinates of the point B are (lat)B,lonB). A. The projection of the two points B in the Cartesian coordinate system is A' (X)a,Ya,Za)、B'(Xb,Yb,Zb). Wherein:
by the same token, X can be obtainedb,Yb,Zb。
this gives:
The invention solves the azimuth angle by adopting the following conclusions, namely: if point B is northeast of point A, then the tangent of angle β is: tan β [111 × longitude difference × cos (latitude of B) ]/[110 × latitude difference ], and arctan β is the angle sought. If B is in the south-east of A, the required angle is determined by calculating beta according to the above formula and subtracting beta from 180 degrees. If B is in the southwest of A, the required angle is obtained by adding 180 degrees to the calculated beta. If B is in the northwest of A, the required angle is determined by subtracting the calculated beta from 360 deg.
After the two longitude and latitude points and the azimuth angles thereof are known, the two longitude and latitude points are respectively converted by using a UTM (Universal Transverse Mercator) projection algorithm, and then the distance values of the two longitude and latitude points under a plane rectangular coordinate system and the distance components on each coordinate axis can be obtained.
The UTM projections are collectively referred to as "Universal Transverse Mercator projection". In a geometrical sense, the UTM projection belongs to a secant elliptic cylinder projection with a transverse axis. Imagine that a cylinder is used to transversely cut two equal altitude circles of 80 degrees of earth and south latitude and 84 degrees of north latitude, and is close to being tangent to the central meridian, according to the condition that the central meridian of the projection belt is a straight line and the equator projection is a straight line, the spherical orthomorphism projection in a certain range of the longitude difference at both sides of the central meridian is projected on the cylinder surface, then the cylinder surface is cut and flattened along the generatrix passing through the south and north poles, and the UTM projection plane can be obtained, as shown in fig. 4. After the UTM projection, the two mutually-cut meridian lines are not deformed, and the length ratio of the central meridian line is 0.9996. The purpose of the UTM projection forward-backward solution is to find the correlation between the UTM planar coordinates and the geodetic coordinates, and the problem of finding the UTM planar coordinates X, Y is called UTM projection forward solution given the longitude L, latitude B of the geodetic coordinates.
The UTM projection forward solution formula is as follows:
in the formula:
T＝tan2B；C＝e'2cos2B；A＝(L-L0) cos B formula (12)
The above conversion equations are all based on ellipsoids, where: l is the calculation point longitude; b is the calculation point latitude; a is an ellipsoid body major semiaxis; b is an ellipsoid short semi-axis; e is the first eccentricity of the ellipsoid:
e' is the second eccentricity:
L0is the central meridian longitude; FE is east latitude offset, and is generally specified as 500000 m; FN is north latitude offset, north hemisphere is typically 0, south hemisphere typically provides FN of 10000000 m; k is a radical of0For projection scale factor, UTM projection k0＝0.9996。
Laser radar point cloud registration and distance calculation
When calculating the plane distance between the current positioning point and the nearest navigation point, the point cloud data of the laser radar at the current moment is required to be matched with the pre-stored point cloud image corresponding to the nearest navigation point, the distance value between the two groups of point clouds is calculated, and finally the two distance values are compared.
The invention adopts ICP (Iterative Closest Point) algorithm to carry out Point cloud registration, and under the constraint of the minimum square sum of Euclidean distances of all Closest Point pairs, a rotation matrix R between two Point sets is obtainedαAnd the translation vector T ═ Txtytz]TWherein t isx、ty、tzRespectively representing the components of the translation amount in the x, y and z axes, thereby obtaining the matching distance between the two groups of point clouds.
The ICP is a scanning matching algorithm between point sets, and the essence of the algorithm is to find the corresponding relationship between a reference point set and a target point set, thereby obtaining a rotation matrix and a translation matrix between two frames of scanning data, and making the two meet the optimal matching under a certain measurement criterion. The ICP algorithm can be divided into two parts: searching corresponding points and solving the pose. Let P, M be two point sets, P denotes the point set currently awaiting registration, containing n data points; m denotes a reference point set. The basic principle of the ICP algorithm is as follows:
(1) determining the corresponding relation: taking a point P in the point set PiFind and p in MiM nearest tojA 1 is to piAnd mjRegarding as a group of corresponding point pairs, sequentially finding out the corresponding points of all the points in the point set P in M according to the method, and finally obtaining n pairs (P pairs)i,mj)；
(2) Solving a transformation relation: p is a radical ofiAnd mjThere is a certain rotation and translation relation between them, let RαT is a rotation matrix and T is a translation matrix. n pairs (p)i,mj) Corresponding to n equation sets, and finally calculating to obtain RαAnd T;
(3) applying a transformation: based on RαAnd T transforming the set of points P into a set of points P2Defining a function E (equation (15)), and setting a condition for terminating the iteration, i.e. P after transformation2When the sum of the distance between each point and the corresponding point in M is less than a given threshold or more than a preset maximum iteration number, the iteration is terminated, and the output is optimal (R)αT), otherwise, the iteration continues.
The specific flow of the ICP algorithm is shown in fig. 5. If the corresponding points and the initial parameters are estimated accurately, the ICP algorithm can obtain very high matching precision, and is very suitable for the fields of vehicle positioning, navigation and the like.
Evaluation of positioning reliability
Respectively calculating the distance value between the current point and the nearest navigation point and the matching distance value between the laser radar point cloud at the current moment and the laser radar point cloud corresponding to the nearest navigation point through the processes, comparing the error between the two distance values with a preset threshold parameter, and if the error between the two distance values is smaller than a set threshold, indicating that the current GNSS positioning signal is reliable; on the contrary, if the error between the two signals is larger than or equal to the set threshold, the current GNSS positioning signal is unreliable, so that a judgment basis is provided for the decision control of the unmanned vehicle.
Considering the allowable deviation of the unmanned vehicle during actual running and analyzing the results of a plurality of experiments, the distance error reference threshold value in the invention is set to be 0.2 m. In the actual test process, a road with a complex environment is selected for experiment, and the experimental scene is shown in fig. 6(a), 6(b) and 6 (c). The route simultaneously comprises an open road section, a high-rise sheltering road section and an overpass sheltering road section, and represents the influence of different degrees on positioning signals when an unmanned vehicle runs in complex environments such as a garden and the like, so that the reliability of GNSS positioning when the state of a GPS signal is disturbed by different degrees is evaluated.
(1) Open road section
When the unmanned vehicle travels on an open road section, the latitude and longitude data output by the GNSS receiver is as shown in fig. 7. Because the high-rise trees do not shield, the RTK-GPS signal state is better, the positioning precision is higher, the output positioning data is smooth and continuous, and a jump point or a failure point cannot appear. The positioning effect of the lidar point cloud matching is shown in fig. 8, and the track map is a part of a global map established by taking a vehicle movement starting point as an origin. As can be seen from the figure, the point cloud matching error of the laser radar on the road section is very small, and the positioning effect is good. The calculated GPS positioning distance value and the laser radar point cloud matching distance value are shown in table 5-1.
TABLE 5-1 distance error for two positioning modes for open road section
| GPS positioning distance value | Laser radar point cloud matching distance value | The difference between the two distance values is related to the set threshold value | Evaluation of positioning reliability |
| 0.05 | 0.10 | Is less than | Reliability |
| 0.06 | 0.12 | Is less than | Reliability |
| 0.05 | 0.11 | Is less than | Reliability |
| 0.10 | 0.13 | Is less than | Reliability |
| 0.08 | 0.11 | Is less than | Reliability |
| 0.08 | 0.12 | Is less than | Reliability |
| 0.03 | 0.15 | Is less than | Reliability |
| 0.09 | 0.11 | Is less than | Reliability |
| 0.12 | 0.12 | Is less than | Reliability |
| 0.13 | 0.15 | Is less than | Reliability |
| 0.10 | 0.11 | Is less than | Reliability |
The table shows that the GPS signal state of the open road section is stable, the positioning precision is high, the calculated GPS positioning distance value and the distance value after the point cloud registration of the laser radar are both small and approximately equal, and the difference between the two distance values is far smaller than the set threshold value, so that the GNSS positioning signal of the road section is judged to be reliable.
(2) High-rise sheltered road section
The longitude and latitude data output by the GNSS receiver is shown in fig. 9 before and after the unmanned vehicle enters a road section blocked by a high building. Under the influence of problems such as multipath effect caused by high-rise shielding and building surface reflection and refraction, the RTK-GPS signal state is unstable, the positioning accuracy of most branch points is within an error allowable range, but large deviation can occur due to jump of individual points. The positioning effect of the lidar point cloud matching is shown in fig. 10, and the track map is a part of a global map established by taking a vehicle movement starting point as an origin. As can be seen from the figure, in the road section, the laser radar is positioned by means of point cloud matching, so that the laser radar is hardly influenced and the positioning effect is good. The calculated GPS positioning distance value and laser radar point cloud matching distance value are shown in table 5-2.
TABLE 5-2 distance error of two positioning modes of front and back of high-rise sheltered road section
| GPS positioning distance value | Laser radar point cloud matching distance value | The difference between the two distance values is related to the set threshold value | Evaluation of positioning reliability |
| 0.10 | 0.12 | Is less than | Reliability |
| 0.12 | 0.15 | Is less than | Reliability |
| 0.35 | 0.12 | Is greater than | Is unreliable |
| 0.45 | 0.20 | Is greater than | Is unreliable |
| 0.55 | 0.25 | Is greater than | Is unreliable |
| 0.92 | 0.51 | Is greater than | Is unreliable |
| 0.42 | 0.31 | Is less than | Reliability |
| 0.51 | 0.43 | Is less than | Reliability |
| 0.66 | 0.30 | Is greater than | Is unreliable |
| 0.47 | 0.23 | Is greater than | Is unreliable |
| 0.89 | 0.69 | Is equal to | Is unreliable |
| 1.52 | 0.89 | Is greater than | Is unreliable |
| 1.21 | 1.31 | Is less than | Reliability |
It can be known from the table that, in a road section with tall-building trees, the state of the GPS signal is unstable and even the positioning error in a certain specific time period is large due to the influence of shielding or building mirror reflection or refraction, the calculated GPS positioning distance value fluctuates greatly, the difference value compared with the distance value after the point cloud registration of the laser radar is large and small, when the difference value is smaller than a set threshold value, the GNSS positioning signal at the moment is judged to be reliable, and when the difference value is larger than the set threshold value, the GNSS positioning signal at the moment is judged to be unreliable.
(3) Overpass shelters from highway section
The longitude and latitude data output by the GNSS receiver before and after the unmanned vehicle enters a road section blocked by the overpass are shown in fig. 11. Due to the shielding of the overpass, the RTK-GPS signal state is seriously influenced, the deviation of the positioning data of the vehicle running under the bridge is very large, and the positioning failure problem occurs because the GPS data can not be received. The slow recovery can be realized only after the vehicle leaves the bridge for a period of time. The positioning effect of the lidar point cloud matching is shown in fig. 12, and the track map is a part of a global map established by taking a vehicle movement starting point as an origin. As can be seen from the figure, the laser radar carries out positioning by means of point cloud matching, so that the error is small, and the positioning effect is good. The calculated GPS positioning distance value and laser radar point cloud matching distance value are shown in table 5-3.
TABLE 5-3 distance error of two positioning modes of front and back of covered road section of overpass
The table shows that before the unmanned vehicle enters the overpass, the GPS signal state is good, the positioning accuracy can meet the requirement, the calculated GPS positioning distance value and the distance value after the laser radar point cloud registration are both small and approximately equal, the difference between the two distance values is far smaller than a set threshold value, and the GNSS positioning signal is judged to be reliable; when the vehicle runs under the overpass, the GPS signals are completely lost under the influence of shielding, the calculated GPS positioning distance value is large, the difference value compared with the distance value after the point cloud registration of the laser radar is far larger than a set threshold value, and the GNSS positioning signals at the moment are judged to be unreliable; after the vehicle runs out of the overpass, the GPS signals are slowly recovered, the calculated GPS positioning distance value is gradually reduced, the difference between the calculated GPS positioning distance value and the distance value after the laser radar point cloud registration is also gradually smaller than a set threshold value, and the GNSS positioning signals are gradually converted into reliable signals.
Claims (1)
1. The unmanned vehicle GNSS positioning reliability evaluation method based on point cloud registration adopts GNSS navigation equipment and a laser radar, wherein the GNSS navigation equipment is used for acquiring real-time GPS positioning data of a vehicle, and the laser radar is used for acquiring real-time environmental point cloud data corresponding to the current position of the vehicle, and is characterized by comprising the following steps: firstly, navigation map collection is carried out on a driving route. Collecting and storing a laser radar point cloud picture corresponding to a navigation point while collecting the navigation map; then, loading a navigation map file when the vehicle actually runs, searching a navigation point with the nearest distance in front of the vehicle in the navigation map by taking the current point of the vehicle as a reference, respectively carrying out UTM projection transformation on the position of the current point of the vehicle and the position of the navigation point with the nearest distance in front, and calculating the distance value of the two points under a plane rectangular coordinate system; matching the laser radar Point cloud picture of the current Point with the laser radar Point cloud picture corresponding to the nearest navigation Point by utilizing an ICP (Iterative Closest Point) algorithm, and calculating a distance value between the two groups of Point clouds; finally, the two distance values are subjected to difference and compared with a preset error threshold value parameter, if the difference between the two distance values is smaller than a set threshold value, the current GNSS positioning signal is reliable, and the unmanned vehicle can normally run according to a planned route; otherwise, it is unreliable.
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