CN117570972B - Vehicle-mounted mobile measurement point cloud precision improving method and device based on multi-condition constraint - Google Patents

Vehicle-mounted mobile measurement point cloud precision improving method and device based on multi-condition constraint Download PDF

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CN117570972B
CN117570972B CN202311550029.2A CN202311550029A CN117570972B CN 117570972 B CN117570972 B CN 117570972B CN 202311550029 A CN202311550029 A CN 202311550029A CN 117570972 B CN117570972 B CN 117570972B
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data
pos
rtk
optimization
point cloud
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CN117570972A (en
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向泽君
袁长征
何德平
潘科
石云萍
张迎春
滕德贵
龙川
苟永刚
李创
肖志华
王琦璇
饶鸣
潘攀
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Chongqing Institute Of Surveying And Mapping Science And Technology
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    • 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
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—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
    • G01C21/16—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
    • 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
    • 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
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/08—Systems determining position data of a target for measuring distance only
    • 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
    • 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
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
    • 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

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Automation & Control Theory (AREA)
  • Length Measuring Devices With Unspecified Measuring Means (AREA)

Abstract

The invention relates to the technical field of point cloud data processing, in particular to a vehicle-mounted mobile measurement point cloud precision improving method and device based on multi-condition constraint. Acquiring RTK data, POS data and original point cloud data through a vehicle-mounted mobile measurement system, and sequentially carrying out RTK correction on the POS data by using the RTK data to acquire first POS optimization data; correcting the control point of the first POS optimization data by using an external control point to obtain second POS optimization data; and correcting the characteristic points of the second POS optimization data according to the original point cloud data to obtain final POS optimization data. Thus, through three times of precision correction, the precision of POS data is greatly improved, and the centimeter-level requirements of actual demands are met.

Description

Vehicle-mounted mobile measurement point cloud precision improving method and device based on multi-condition constraint
Technical Field
The invention relates to the technical field of point cloud data processing, in particular to a vehicle-mounted mobile measurement point cloud precision improving method and device based on multi-condition constraint.
Background
The vehicle-mounted mobile measurement system can rapidly acquire three-dimensional point cloud and image information of roads and surrounding environments, is a brand-new high-resolution earth observation technical means, and is widely applied to the aspects of novel basic mapping, digital city construction, automatic driving high-precision map production and the like. However, in practical engineering application of the vehicle-mounted mobile measurement system, the accuracy of the point cloud data is difficult to meet the requirements of centimeter level, and even reach decimeter level or meter level in a signal unlocking area, and the practical requirements are difficult to meet due to the comprehensive influence of factors such as positioning error of a global satellite navigation positioning system, attitude determination error of an inertial navigation system, distance measurement and angle measurement error of a scanner, time synchronization and calibration error of a sensor, data calculation error and the like.
In the prior art, point cloud data is generally utilized to correct POS data, for example, in the patent "laser point cloud adjustment method combining point cloud matching and sensor data" (publication No. CN115752448 a), an adjustment equation is listed in combination according to an unmanned plane LiDAR positioning principle formula and original LiDAR information of an approximate homonymy point pair, and then a sensor parameter correction value is calculated by least square solution, so as to correct the point cloud original POS data. Such a method has limited precision improvement of the point cloud data, and further methods for improving the precision are needed.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a vehicle-mounted mobile measurement point cloud precision improving method and device based on multi-condition constraint, which greatly improve the precision of point cloud data so as to meet the actual demands.
In a first aspect, the invention provides a vehicle-mounted mobile measurement point cloud precision improving method based on multi-condition constraint.
In a first implementation manner, a vehicle-mounted mobile measurement point cloud precision improving method based on multi-condition constraint comprises the following steps:
Acquiring vehicle-mounted mobile measurement data; the vehicle-mounted mobile measurement data comprise RTK data, POS data and original point cloud data;
RTK correction is carried out on the POS data by using the RTK data, so that first POS optimization data are obtained;
correcting the control point of the first POS optimization data by using an external control point to obtain second POS optimization data;
and correcting the characteristic points of the second POS optimization data according to the original point cloud data to obtain final POS optimization data.
With reference to the first implementation manner, in a second implementation manner, acquiring vehicle-mounted mobile measurement data includes:
acquiring initial acquisition data of a vehicle-mounted mobile measurement system; the initial acquisition data comprises RTK data, laser ranging data, IMU data and GNSS data;
Generating POS data according to the IMU data and the GNSS data;
And generating an original point cloud according to the POS data and the laser ranging data.
With reference to the first implementation manner, in a third implementation manner, performing RTK correction on POS data using the RTK data to obtain first POS optimization data, including:
filtering the RTK data by using the POS data to obtain RTK filtering data;
Acquiring RTK control points according to the RTK filtering data;
And carrying out RTK correction on the POS data according to the RTK control point to obtain first POS optimization data.
With reference to the third implementation manner, in a fourth implementation manner, filtering the RTK data with the POS data to obtain RTK filtered data includes:
searching POS data at the same acquisition time as each RTK data;
subtracting the POS data and the RTK data at the same acquisition time to obtain an original offset;
taking a preset time interval before and after the RTK data acquisition time as a sliding window, and performing window sliding on the RTK data to obtain a plane filtering weight and an elevation filtering weight;
carrying out weighted filtering treatment on the original offset according to the plane filtering weight and the elevation filtering weight to obtain a filtering offset;
and correcting the RTK data according to the filtering offset to obtain RTK filtering data.
With reference to the fourth implementation manner, in a fifth implementation manner, performing RTK correction on POS data according to an RTK control point to obtain first POS optimization data, including:
If the RTK control point with the same acquisition time as the POS data exists, acquiring a coordinate correction value of the POS data by adopting a first RTK correction formula; if the RTK control point with the same acquisition time as the POS data does not exist, acquiring a coordinate correction value of the POS data by adopting a second RTK correction formula;
And correcting the POS data by using the coordinate correction value to obtain first POS optimization data.
In combination with the first implementation manner, in a sixth implementation manner, performing control point correction on the first POS optimization data by using an external control point to obtain second POS optimization data, including:
determining a plurality of external control points;
determining point cloud data points and POS data corresponding to external control points;
acquiring a first forward correction value, a first transverse correction value and a first Gao Chengjiu positive value of POS data according to an external control point and a corresponding point cloud data point;
Acquiring first correction values of POS data in X, Y and Z directions according to the first forward correction value, the first transverse correction value and the first elevation correction value;
and correcting the POS data according to the first correction values of the POS data in X, Y and Z directions to obtain second POS optimization data.
With reference to the sixth implementation manner, in a seventh implementation manner, acquiring the first forward correction value of the POS data includes:
acquiring XY plane offset between an advance control point and a corresponding point cloud data point;
Acquiring the forward projection quantity of the XY plane offset in the forward direction;
and acquiring a first forward correction value according to the forward projection quantity.
With reference to the seventh implementation manner, in an eighth implementation manner, the obtaining a first forward correction value according to the forward projection amount includes:
In the above formula, TP i is the POS data POS i acquisition time; TC s、TCe is the collection time of the initial forward control point and the final forward control point respectively; Δtc ext is the forward correction epitaxy time range, typically taken 30s; ΔDC_J s、ΔDC_Je is the POS correction value at the moment corresponding to the initial heading control point and the final heading control point respectively; Δtc is is the time interval between TP i and TC s; Δtc ei is the time interval between TC e and TP i; Δdc_j 12 represents the first forward correction value when TP i is located between the acquisition instants TC 1 and TC 2 of the adjacent two forward control points.
In combination with the first implementation manner, in a ninth implementation manner, performing feature point correction on the second POS optimization data according to the original point cloud data to obtain final POS optimization data, including:
extracting characteristic points of the point cloud data to obtain homonymous characteristic points;
dividing the homonymous feature points into forward feature points, transverse feature points and elevation feature points according to the spatial features;
Obtaining the plane precision standard deviation of the forward characteristic points and the transverse characteristic points, and obtaining the elevation precision standard deviation of the elevation characteristic points;
acquiring X coordinates, Y coordinates and Z coordinates of the feature points according to the plane precision standard deviation and the elevation precision standard deviation;
And correcting the second POS optimization data according to the X coordinate, the Y coordinate and the Z coordinate of the feature point to obtain final POS optimization data.
In a first aspect, the invention provides a vehicle-mounted mobile measurement point cloud precision improving device based on multi-condition constraint.
In a tenth implementation manner, a vehicle-mounted mobile measurement point cloud precision improving device based on multi-condition constraint includes:
the vehicle-mounted mobile measurement data acquisition module is configured to acquire vehicle-mounted mobile measurement data; the vehicle-mounted mobile measurement data comprise RTK data, POS data and original point cloud data;
The first POS optimization data acquisition module is configured to carry out RTK correction on POS data by using the RTK data to acquire first POS optimization data;
The second POS optimization data acquisition module is configured to correct the control point of the first POS optimization data by utilizing an external control point to acquire second POS optimization data;
And the final POS optimization data acquisition module is configured to correct the characteristic points of the second POS optimization data according to the original point cloud data to obtain final POS optimization data.
According to the technical scheme, the beneficial technical effects of the invention are as follows:
1. Acquiring RTK data, POS data and original point cloud data through a vehicle-mounted mobile measurement system, and sequentially carrying out RTK correction on the POS data by using the RTK data to acquire first POS optimization data; correcting the control point of the first POS optimization data by using an external control point to obtain second POS optimization data; and correcting the characteristic points of the second POS optimization data according to the original point cloud data to obtain final POS optimization data. Thus, through three times of precision correction, the precision of POS data is greatly improved, and the centimeter-level requirements of actual demands are met.
2. RTK (REAL TIME KINEMATIC, carrier phase difference technology) data are synchronously acquired in the scanning process of the vehicle-mounted mobile measurement system, RTK data are filtered by utilizing the characteristic that POS data are relatively high in precision in a short time, the precision of the RTK data is improved, and then the filtered RTK data, high-precision external control points and homonymous characteristic points extracted from original point cloud data are sequentially utilized to correct the POS data, so that the point cloud data precision is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
Fig. 1 is a schematic diagram of a vehicle-mounted mobile measurement point cloud precision improving method based on multi-condition constraint provided in this embodiment;
Fig. 2 is a flowchart of a vehicle-mounted mobile measurement point cloud precision improving method based on multi-condition constraint provided in this embodiment;
Fig. 3 is a schematic structural diagram of a vehicle-mounted mobile measurement point cloud precision lifting device based on multi-condition constraint according to the present embodiment.
Detailed Description
Embodiments of the technical scheme of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and thus are merely examples, and are not intended to limit the scope of the present invention.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms first, second and the like in the description and in the claims of the embodiments of the disclosure and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to implement the embodiments of the disclosure described herein. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. The term "plurality" means two or more, unless otherwise indicated. In the embodiment of the present disclosure, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B. The term "and/or" is an associative relationship that describes an object, meaning that there may be three relationships. For example, a and/or B, represent: a or B, or, A and B. The term "corresponding" may refer to an association or binding relationship, and the correspondence between a and B refers to an association or binding relationship between a and B.
Referring to fig. 1, this embodiment provides a vehicle-mounted mobile measurement point cloud precision improving method based on multi-condition constraint, including:
Acquiring vehicle-mounted mobile measurement data; the vehicle-mounted mobile measurement data comprise RTK data, POS data and original point cloud data;
RTK correction is carried out on the POS data by using the RTK data, so that first POS optimization data are obtained;
correcting the control point of the first POS optimization data by using an external control point to obtain second POS optimization data;
and correcting the characteristic points of the second POS optimization data according to the original point cloud data to obtain final POS optimization data.
Optionally, acquiring the vehicle-mounted mobile measurement data includes: acquiring initial acquisition data of a vehicle-mounted mobile measurement system; the initial acquisition data comprises RTK data, laser ranging data, IMU data and GNSS data; generating POS data according to the IMU data and the GNSS data; and generating an original point cloud according to the POS data and the laser ranging data.
In some embodiments, POS (position and orientation system ) data is generated by resolving IMU (Inertial Measurement Unit ) data and GNSS data, the POS data content including: time T, three-dimensional coordinates (X, Y, Z), heading angle, pitch angle, roll angle. And generating an original point cloud by carrying out point cloud calculation on the POS data and the laser ranging data.
Optionally, the initial acquisition data also includes odometry data, and the like.
Optionally, the vehicle-mounted mobile measurement system is integrated with an RTK device, during operation, RTK data are synchronously collected, the collection frequency is 1Hz-10Hz, and the RTK data include: time T, three-dimensional coordinates (X, Y, Z), in-plane error HRMS, gao Chengzhong error VRMS. Because the coordinate data measured by the RTK is the antenna phase center coordinate, the RTK coordinate data is required to be converted into a system center according to the parameters of the vehicle-mounted mobile measurement system.
Optionally, performing RTK correction on the POS data using the RTK data to obtain first POS optimization data, including: filtering the RTK data by using the POS data to obtain RTK filtering data; acquiring RTK control points according to the RTK filtering data; and carrying out RTK correction on the POS data according to the RTK control point to obtain first POS optimization data.
Optionally, filtering the RTK data with the POS data to obtain RTK filtered data, including: searching POS data at the same acquisition time as each RTK data; subtracting the POS data and the RTK data at the same acquisition time to obtain an original offset; taking a preset time interval before and after the RTK data acquisition time as a sliding window, and performing window sliding on the RTK data to obtain a plane filtering weight and an elevation filtering weight; carrying out weighted filtering treatment on the original offset according to the plane filtering weight and the elevation filtering weight to obtain a filtering offset; and correcting the RTK data according to the filtering offset to obtain RTK filtering data.
In some embodiments, the specific step of filtering the RTK data with POS data includes: ① According to the acquisition time of each RTK data RTK i, corresponding POS data POS i is searched, the original offset delta R i;② is calculated according to the formula (1), delta T w time intervals are taken before and after the acquisition time of the data RTK i as sliding windows, RTK data are processed by utilizing the sliding windows, the offset is weighted and filtered according to the formula (2) and the formula (3), and the filtered offset is obtained③ By filtered offsetCorrecting the RTK data according to the formula (4) to obtain filtered RTK dataBecause the RTK data has random errors and the accuracy of the POS data in a short time is very high, the accuracy of the RTK data is improved by performing sliding window filtering processing on the RTK data through the POS data in the short time.
Optionally, subtracting the RTK data from POS data at the same acquisition time to obtain an original offset, including:
In the above equation, ΔR i is the original offset at acquisition time i, POS i is the POS data at acquisition time i, and RTK i is the RTK data at acquisition time i.
Optionally, the plane filtering weights and the elevation filtering weights are obtained by the following formula:
in the above equation, HW ij represents the planar filtering weights of Δr j to Δr i; VW ij represents the elevation filtering weight of Δr j to Δr i, n is the total number of data of the window corresponding to RTK i; ΔR j represents the original offset of the data RTK j; ΔR i represents the original offset of the data RTK i; deltaT ij is the time interval between data j and data i, deltaT w is half of the total time interval of the sliding window, HW 'ik is the plane intermediate weight, and VW' ik is the elevation intermediate weight; HRMS j、VRMSj is the in-plane error and Gao Chengzhong error of the data RTK j, respectively.
Optionally, the plane intermediate weight value HW 'ik and the elevation intermediate weight value VW' ik are calculated by the following formulas:
In the above equation, Δt ik is the time interval between data k and data i, and HRMS k、VRMSk is the in-plane error and Gao Chengzhong error of data RTK k, respectively.
Optionally, performing weighted filtering processing on the original offset according to the plane filtering weight and the elevation filtering weight, including:
In the above formula, n is the total number of data of the window corresponding to the RTK i; ΔR j represents the original offset of the data RTK j; ΔR' i is the filter offset.
Optionally, correcting the RTK data according to the filtering offset to obtain RTK filtered data, including:
in the above equation, RTK i is the RTK data at the acquisition time i, Δr 'i is the filtered offset at the acquisition time i, and RTK' i is the RTK filtered data at the acquisition time i.
Optionally, acquiring the RTK control point according to the RTK filtered data includes: segmenting RTK filtered data according to a preset distance to obtain a plurality of RTK filtered data segments; from each RTK filtering data segment, selecting an RTK data point with optimal plane precision as an RTK plane control point, and selecting an RTK data point with optimal elevation precision as an RTK elevation control point.
In some embodiments, the predetermined distance is 50m, and the RTK filtered data is segmented at 50 meters intervals.
Optionally, performing RTK correction on the POS data according to the RTK control point to obtain first POS optimization data, including: if the RTK control point with the same acquisition time as the POS data exists, acquiring a coordinate correction value of the POS data by adopting a first RTK correction formula; if the RTK control point with the same acquisition time as the POS data does not exist, acquiring a coordinate correction value of the POS data by adopting a second RTK correction formula; and correcting the POS data by using the coordinate correction value to obtain first POS optimization data.
Optionally, the first RTK correction formula includes Δpr i＝RTK′i-POSi; wherein POS i is POS data of the acquisition time i, RTK' i is RTK data of the same acquisition time of POS i, that is, an RTK control point corresponding to POS i, and Δpr i is a coordinate correction value.
Optionally, in the second RTK correction formula, the X-coordinate correction value is as follows:
In the above formula, TP i is the POS data POS i acquisition time; TR s、TRe is the acquisition time of an initial RTK control point and an end RTK control point respectively; deltaTR ext is the range of RTK correction epitaxy time, which is generally 30s; DeltaPR s.X、ΔPRe X is the correction value of the X coordinate of the starting RTK control point and the ending RTK control point at the corresponding time respectively; Δtr is is the time interval between TP i and TR s; Δtr ei is the time interval between TR e and TP i; Δtr 1i、ΔTRi2、ΔTR12 indicates that when TP i is located between the acquisition instants TR 1 and TR 2 of two adjacent RTK control points, The time interval between TR 1 and TP i、TPi and TR 2、TR1 and TR 2; Δpr 1.X、ΔPR2. X is the X coordinate correction value corresponding to TR 1 and TR 2, respectively.
Optionally, in the second RTK correction formula, the Y-coordinate correction value and the Z-coordinate correction value are calculated in the same manner as the X-coordinate correction value.
Optionally, correcting the POS data with the coordinate correction value to obtain first POS optimization data, including:
In the above equation, POS i is POS data at the acquisition time i, Δpr i is a coordinate correction value corresponding to the acquisition time i, and POSR i is first POS optimization data corresponding to the acquisition time i.
Optionally, performing control point correction on the first POS optimization data by using an external control point to obtain second POS optimization data, including: determining a plurality of external control points; determining point cloud data points and POS data corresponding to external control points; acquiring a first forward correction value, a first transverse correction value and a first Gao Chengjiu positive value of POS data according to an external control point and a corresponding point cloud data point; acquiring first correction values of POS data in X, Y and Z directions according to the first forward correction value, the first transverse correction value and the first elevation correction value; and correcting the POS data according to the first correction values of the POS data in X, Y and Z directions to obtain second POS optimization data.
Optionally, the external control points are classified into: an advance control point, a transverse control point and a height control point. The forward control point, the transverse control point and the elevation control point are respectively used for correcting errors in the forward direction, the vertical direction and the elevation direction of the POS. The same control point may be used as both an approach, lateral and elevation control point.
In some embodiments, the point cloud data points and POS data corresponding to each control point are found by manual or automatic matching.
Optionally, acquiring the first forward correction value of the POS data according to the external control point and the corresponding point cloud data point includes: determining all forward control points: determining point cloud data points and POS data corresponding to the forward control points; acquiring XY plane offset between an advance control point and a corresponding point cloud data point; acquiring the forward projection quantity of the XY plane offset in the forward direction; and acquiring a first forward correction value according to the forward projection quantity.
Optionally, acquiring the first lateral correction value of the POS data according to the external control point and the corresponding point cloud data point includes: all lateral control points are determined: determining point cloud data points and POS data corresponding to the transverse control points; acquiring XZ plane offset between a transverse control point and a corresponding point cloud data point; acquiring the transverse projection quantity of the XZ plane offset in the transverse direction; and acquiring a first transverse correction value according to the transverse projection amount.
Optionally, acquiring the first Gao Chengjiu positive values of the POS data according to the external control point and the corresponding point cloud data point includes: all elevation control points are determined: determining point cloud data points and POS data corresponding to elevation control points; acquiring YZ plane offset between an elevation control point and a corresponding point cloud data point; acquiring elevation projection quantity of YZ plane offset on an elevation; and obtaining a first Gao Chengjiu positive value according to the elevation projection quantity.
Alternatively, the XY plane offset is obtained by the following formula:
In the above formula, CP i is the forward control point plane coordinate; DP i is the point cloud data point plane coordinate corresponding to CP i; ΔDC i is the XY plane offset to the point cloud data point from the point control point.
Optionally, the forward projection amount of the XY plane offset in the forward direction is obtained by the following formula:
ΔDC_Ji＝ΔDCi.X*VJi.X+ΔDCi.Y*VJi.Y
In the above expression, VJ i is a forward unit direction vector of POS data corresponding to a forward control point, and Δdc_j i is a forward projection amount.
Optionally, the obtaining the first forward correction value Δpc_j i according to the forward projection amount includes:
In the above formula, TP i is the POS data POS i acquisition time; TC s、TCe is the collection time of the initial forward control point and the final forward control point respectively; Δtc ext is the forward correction epitaxy time range, typically taken 30s; ΔDC_J s、ΔDC_Je is the POS correction value at the moment corresponding to the initial heading control point and the final heading control point respectively; Δtc is is the time interval between TP i and TC s; Δtc ei is the time interval between TC e and TP i; Δdc_j 12 represents the first forward correction value when TP i is located between the acquisition instants TC 1 and TC 2 of the adjacent two forward control points.
Optionally, the first forward correction value Δdc_j 12 when TP i is located between the acquisition times TC 1 and TC 2 of the two adjacent forward control points is calculated as follows:
In the above formula, Δdc J1、ΔDCJ2 is the first forward correction value corresponding to TC 1、TC2, respectively;
Δtc 1i、ΔTCi2、ΔTR12 represents the time interval between TC 1 and TP i、TPi and TC 2、TC1 and TC 2, respectively; ΔTC max is the forward control point correction time frame, typically taken 30s.
Optionally, the first lateral correction value and the first elevation correction value are calculated according to a calculation mode of the first heading correction value.
Optionally, obtaining the first correction value of the POS data in X, Y and the Z-direction according to the first heading correction value, the first lateral correction value, and the first elevation correction value includes:
In the above equation, Δpc i.X、ΔPCi, Y and Δpc i, Z are first correction values in the X, Y and Z directions, Δpc_j i is a first forward correction value, Δpc_h i is a first lateral correction value, Δpc_z i is a first Gao Chengjiu positive value, θ ji is an angle between the POS data forward vector and the X axis, and θ hi is an angle between the POS data lateral vector and the X axis.
Optionally, correcting the POS data according to the first correction value of the POS data in X, Y and Z directions to obtain second POS optimization data, including:
In the above equation POSR i is the first POS optimization data, Δpc i is the direction correction value, and POSC i is the second POS optimization data.
Optionally, performing feature point correction on the second POS optimization data according to the original point cloud data to obtain final POS optimization data, including: extracting characteristic points of the point cloud data to obtain homonymous characteristic points; dividing the homonymous feature points into forward feature points, transverse feature points and elevation feature points according to the spatial features; obtaining the plane precision standard deviation of the forward characteristic points and the transverse characteristic points and obtaining the elevation precision standard deviation of the elevation characteristic points; acquiring X coordinates, Y coordinates and Z coordinates of the feature points according to the plane precision standard deviation and the elevation precision standard deviation; and correcting the second POS optimization data according to the X coordinate, the Y coordinate and the Z coordinate of the feature point to obtain final POS optimization data.
In some embodiments, the original point cloud data is used to correct the POS data, and the specific steps are as follows: ① Extracting homonymy feature points from original point cloud data in a manual or algorithm identification mode, wherein one homonymy feature point has at least two point cloud data points acquired at different moments; ② Dividing homonymous feature points into forward feature points, transverse feature points and elevation feature points according to spatial features, and correcting errors in the advancing direction, the perpendicular advancing direction and the elevation direction of the POS respectively; ③ And traversing all the point cloud data points corresponding to the same-name feature points, calculating the plane precision standard deviation Std hrms of the forward feature points and the transverse feature points and the elevation precision standard deviation Std vrms;④ of the elevation feature points, calculating the X coordinates IP i and X of the forward feature points and the transverse feature points according to the formula (5), calculating the Y coordinates IP i and Y of the forward feature points and the transverse feature points according to the formula (6), calculating the Z coordinates IP i.Z;⑤ of the elevation feature points according to the formula (7), correcting the first optimization POS data by referring to the external control points, and correcting the second optimization POS data by utilizing the feature points to obtain final optimization POS data.
Optionally, the X-coordinate of the feature point is obtained by the following formula, including:
In the above formula, m is the number of point cloud points corresponding to the homonymous feature points; CP j X is the X coordinate of the point cloud point j; HRMS j is the error in the POS data plane corresponding to point cloud point j; CP min X is the X coordinate of the minimum error point in the plane of m point cloud points; std hrms_i is the standard deviation of plane precision; std hrms_max is the maximum threshold of the plane accuracy standard deviation.
Optionally, obtaining the Y coordinate of the feature point by the following formula includes:
In the above formula, m is the number of point cloud points corresponding to the homonymous feature points; CP j Y is the Y coordinate of the point cloud point j; HRMS j is the error in the POS data plane corresponding to point cloud point j; CP min Y is the Y coordinate of the point with the smallest error in the plane of the m point cloud points; std hrms_i is the standard deviation of plane precision; std hrms_max is the maximum threshold of the plane accuracy standard deviation.
Optionally, obtaining the Z coordinate of the feature point by the following formula includes:
In the above formula, m is the number of point cloud points corresponding to the homonymous feature points; CP j Z is the Z coordinate of the point cloud point j; CP min Z is the Z coordinate of the minimum error point in the plane of the point cloud point; std hrms_i is the standard deviation of plane precision; std hrms_max is the maximum threshold of the plane accuracy standard deviation.
Optionally, the correcting step of the POS data according to the external control point also corrects the POS data by using the feature point, including: determining point cloud data points and POS data corresponding to the feature points; acquiring a second forward correction value, a second transverse correction value and a second Gao Chengjiu positive value of the POS data according to the characteristic points and the corresponding point cloud data points; acquiring a second correction value of the POS data in X, Y and Z directions according to the second forward correction value, the second transverse correction value and the second elevation correction value; and correcting the POS data according to the second correction values of the POS data in X, Y and Z directions to obtain final POS optimization data.
Optionally, performing feature point correction on the second POS optimization data according to the original point cloud data, and performing precision evaluation on the final POS optimization data after obtaining the final POS optimization data, if the precision evaluation meets the requirement, outputting the final POS optimization data, otherwise, continuing performing RTK correction, control point correction and feature point correction until the final POS optimization data meets the precision requirement.
In some embodiments, the accuracy requirement is at least in the order of centimeters, and the final POS optimization data is evaluated for accuracy to ensure that the final POS optimization data meets the actual requirement.
In some embodiments, the error propagation law is used to calculate the error of the final POS optimization data, if the error meets the accuracy requirement, the final POS optimization data is output, and if not, the RTK correction, the control point correction, and the feature point correction are continued until the error of the final POS optimization data meets the accuracy requirement.
Referring to fig. 2, the overall flow of the vehicle-mounted mobile measurement point cloud precision improving method based on the multi-condition constraint includes: 1. acquiring laser ranging data, POS data and RTK data by using a vehicle-mounted mobile measurement system; the method specifically comprises the following steps: and synchronously acquiring RTK data in the scanning process of the mobile measurement system, and generating POS data through the calculation of the IMU and the GNSS data. 2. Filtering the RTK data by using the POS data to obtain RTK filtering data; 3. screening optimal RTK data at certain intervals based on RTK precision indexes, and determining the optimal RTK data as an RTK control point; 4. RTK correction is performed on the POS data by using an RTK control point. 5. Adding high-precision external control points: 6. correcting the control point of the POS data by using an external control point; 7. performing point cloud calculation through POS data and laser ranging data to generate an original point cloud, 8, extracting feature points from the original point cloud data to obtain homonymous feature points; 9. correcting the feature points of the POS data by using the feature points with the same name; 10. and (3) carrying out precision evaluation on the corrected POS data, outputting the corrected POS data if the precision requirement is met, and otherwise, returning to the RTK correction step of the 4 th step for correction.
Referring to fig. 3, a vehicle-mounted mobile measurement point cloud precision improving device based on multi-condition constraint includes: a vehicle-mounted mobile measurement data acquisition module 101 configured to acquire vehicle-mounted mobile measurement data; the vehicle-mounted mobile measurement data comprise RTK data, POS data and original point cloud data; a first POS optimization data acquisition module 102 configured to perform RTK correction on POS data using the RTK data to obtain first POS optimization data; a second POS optimization data obtaining module 103 configured to perform control point correction on the first POS optimization data using an external control point to obtain second POS optimization data; the final POS optimization data obtaining module 104 is configured to perform feature point correction on the second POS optimization data according to the original point cloud data, so as to obtain final POS optimization data.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.

Claims (7)

1. A vehicle-mounted mobile measurement point cloud precision improving method based on multi-condition constraint is characterized by comprising the following steps:
Acquiring vehicle-mounted mobile measurement data; the vehicle-mounted mobile measurement data comprise RTK data, POS data and original point cloud data;
RTK correction is carried out on the POS data by using the RTK data, so that first POS optimization data are obtained;
Performing control point correction on the first POS optimization data by using an external control point to obtain second POS optimization data, wherein the method comprises the following steps:
determining a plurality of external control points;
determining point cloud data points and POS data corresponding to external control points;
Acquiring a first heading correction value, a first transverse correction value and a first Gao Chengjiu positive value of the POS data according to the external control point and the corresponding point cloud data point, wherein the acquiring the first heading correction value of the POS data comprises:
acquiring XY plane offset between an advance control point and a corresponding point cloud data point;
Acquiring the forward projection quantity of the XY plane offset in the forward direction;
Acquiring a first forward correction value according to the forward projection amount, including:
In the above formula, TP i is the POS data POS i acquisition time; TC s、TCe is the collection time of the initial forward control point and the final forward control point respectively; delta TC ext is an forward correction epitaxy time range, and 30s is taken; ΔDC_J s、ΔDC_Je is the POS correction value at the moment corresponding to the initial heading control point and the final heading control point respectively; Δtc is is the time interval between TP i and TC s; Δtc ei is the time interval between TC e and TP i; Δdc_j 12 represents the first forward correction value when TP i is located between the acquisition instants TC 1 and TC 2 of the two adjacent forward control points;
Acquiring first correction values of POS data in X, Y and Z directions according to the first forward correction value, the first transverse correction value and the first elevation correction value;
Correcting the POS data according to the first correction values of the POS data in X, Y and Z directions to obtain second POS optimization data;
and correcting the characteristic points of the second POS optimization data according to the original point cloud data to obtain final POS optimization data.
2. The method of claim 1, wherein obtaining vehicle-mounted movement measurement data comprises:
acquiring initial acquisition data of a vehicle-mounted mobile measurement system; the initial acquisition data comprises RTK data, laser ranging data, IMU data and GNSS data;
Generating POS data according to the IMU data and the GNSS data;
And generating an original point cloud according to the POS data and the laser ranging data.
3. The method of claim 1, wherein performing RTK correction on the POS data using the RTK data to obtain first POS optimization data comprises:
filtering the RTK data by using the POS data to obtain RTK filtering data;
Acquiring RTK control points according to the RTK filtering data;
And carrying out RTK correction on the POS data according to the RTK control point to obtain first POS optimization data.
4. The method of claim 3, wherein filtering the RTK data with the POS data to obtain RTK filtered data comprises:
searching POS data at the same acquisition time as each RTK data;
subtracting the POS data and the RTK data at the same acquisition time to obtain an original offset;
taking a preset time interval before and after the RTK data acquisition time as a sliding window, and performing window sliding on the RTK data to obtain a plane filtering weight and an elevation filtering weight;
carrying out weighted filtering treatment on the original offset according to the plane filtering weight and the elevation filtering weight to obtain a filtering offset;
and correcting the RTK data according to the filtering offset to obtain RTK filtering data.
5. The method of claim 3, wherein performing RTK correction on the POS data based on the RTK control point to obtain first POS optimization data comprises:
If the RTK control point with the same acquisition time as the POS data exists, acquiring a coordinate correction value of the POS data by adopting a first RTK correction formula; if the RTK control point with the same acquisition time as the POS data does not exist, acquiring a coordinate correction value of the POS data by adopting a second RTK correction formula;
And correcting the POS data by using the coordinate correction value to obtain first POS optimization data.
6. The method of claim 1, wherein performing feature point correction on the second POS optimization data based on the original point cloud data to obtain final POS optimization data, comprises:
extracting characteristic points of the point cloud data to obtain homonymous characteristic points;
dividing the homonymous feature points into forward feature points, transverse feature points and elevation feature points according to the spatial features;
Obtaining the plane precision standard deviation of the forward characteristic points and the transverse characteristic points, and obtaining the elevation precision standard deviation of the elevation characteristic points;
acquiring X coordinates, Y coordinates and Z coordinates of the feature points according to the plane precision standard deviation and the elevation precision standard deviation;
And correcting the second POS optimization data according to the X coordinate, the Y coordinate and the Z coordinate of the feature point to obtain final POS optimization data.
7. Vehicle-mounted mobile measurement point cloud precision lifting device based on multi-condition constraint is characterized by comprising:
the vehicle-mounted mobile measurement data acquisition module is configured to acquire vehicle-mounted mobile measurement data; the vehicle-mounted mobile measurement data comprise RTK data, POS data and original point cloud data;
The first POS optimization data acquisition module is configured to carry out RTK correction on POS data by using the RTK data to acquire first POS optimization data;
the second POS optimization data obtaining module is configured to perform control point correction on the first POS optimization data by using an external control point, and obtains second POS optimization data, and includes:
determining a plurality of external control points;
determining point cloud data points and POS data corresponding to external control points;
Acquiring a first heading correction value, a first transverse correction value and a first Gao Chengjiu positive value of the POS data according to the external control point and the corresponding point cloud data point, wherein the acquiring the first heading correction value of the POS data comprises:
acquiring XY plane offset between an advance control point and a corresponding point cloud data point;
Acquiring the forward projection quantity of the XY plane offset in the forward direction;
Acquiring a first forward correction value according to the forward projection amount, including:
In the above formula, TP i is the POS data POS i acquisition time; TC s、TCe is the collection time of the initial forward control point and the final forward control point respectively; delta TC ext is an forward correction epitaxy time range, and 30s is taken;
ΔDC_J s、ΔDC_Je is the POS correction value at the moment corresponding to the initial heading control point and the final heading control point respectively; Δtc is is the time interval between TP i and TC s; Δtc ei is the time interval between TC e and TP i;
Δdc_j 12 represents the first forward correction value when TP i is located between the acquisition instants TC 1 and TC 2 of the two adjacent forward control points;
And the final POS optimization data acquisition module is configured to correct the characteristic points of the second POS optimization data according to the original point cloud data to obtain final POS optimization data.
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