CN113393519A

CN113393519A - Laser point cloud data processing method, device and equipment

Info

Publication number: CN113393519A
Application number: CN202010170000.1A
Authority: CN
Inventors: 吕枘蓬; 刘晓泉; 曹亮; 岳顺强; 金豆
Original assignee: Wuhan Navinfo Technology Co ltd
Current assignee: Wuhan Navinfo Technology Co ltd
Priority date: 2020-03-12
Filing date: 2020-03-12
Publication date: 2021-09-14
Anticipated expiration: 2040-03-12
Also published as: CN113393519B

Abstract

The embodiment of the application provides a laser point cloud data processing method, a device and equipment, wherein the method comprises the following steps: acquiring laser point cloud data and a POS track of a target ground object, wherein the laser point cloud data and the POS track have an association relation; acquiring a point cloud error model according to the laser point cloud data, the POS track and the association relation; determining a target energy function according to the laser point cloud data, the POS track and the point cloud error model; obtaining a plurality of solving coefficients according to the target energy function; and correcting each track point in the POS track according to the plurality of solving coefficients and the point cloud error model to obtain a corrected target POS track and further obtain corrected target laser point cloud data. The method provided by the embodiment of the application can solve the problem that the data accuracy of the automatic driving map acquisition is low due to the fact that errors exist in the laser point cloud data acquired in the prior art.

Description

Laser point cloud data processing method, device and equipment

Technical Field

The embodiment of the application relates to the technical field of data processing, in particular to a method, a device and equipment for processing laser point cloud data.

Background

With the continuous development of automatic driving technology, more and more automatic driving services are beginning to enter people's life circle. Accurate positioning is crucial to tasks such as environment awareness and path planning for autonomous driving.

In the prior art, an automatic driving map is adopted for positioning, and although the automatic driving map is a high-precision map, when automatic driving map data are collected, due to the influence of a collecting environment and the limitation of the precision of existing equipment, when the same ground object is collected for multiple times, the laser point cloud data of the same ground object can be misaligned.

Therefore, the laser point cloud data acquired in the prior art has errors, and the data accuracy acquired by the automatic driving map is low.

Disclosure of Invention

The embodiment of the application provides a laser point cloud data processing method, device and equipment, and aims to solve the problem that data accuracy of automatic driving map acquisition is low due to errors of laser point cloud data acquired in the prior art.

In a first aspect, an embodiment of the present application provides a laser point cloud data processing method, including:

acquiring laser point cloud data of a target ground object and a POS track corresponding to the target ground object, wherein the laser point cloud data and the POS track have an association relation;

acquiring a point cloud error model according to the laser point cloud data, the POS track and the incidence relation, wherein the point cloud error model comprises a plurality of solving coefficients, and the solving coefficients are used for representing parameters in the point cloud error model;

determining a target energy function according to the laser point cloud data, the POS track and the point cloud error model;

obtaining the plurality of solving coefficients according to the target energy function;

correcting each track point in the POS track according to the plurality of solving coefficients and the point cloud error model to obtain a corrected target POS track;

and obtaining corrected target laser point cloud data according to the laser point cloud data, the target POS track and the incidence relation.

In one possible design, the laser point cloud data includes coordinates of the laser point cloud in a world coordinate system, and the association relationship includes a POS track, coordinates of the laser point cloud in an inertial navigation INS coordinate system, and a first coordinate conversion relationship between the coordinates of the laser point cloud in the world coordinate system;

the acquiring of the point cloud error model according to the laser point cloud data, the POS track and the incidence relation between the laser point cloud data and the POS track comprises the following steps:

taking the coordinates of the laser point cloud in a world coordinate system as approximate values of POS positions and positions;

according to the POS position and pose approximate value and the first coordinate conversion relation, a first error model of the laser point cloud is obtained through Taylor formula expansion, and the first error model is a model containing differential correction of POS tracks;

the POS track is subjected to interval division, and a spline function error model of the POS track is obtained through quadratic spline function interpolation according to each segmented interval track after division;

and replacing the differential correction quantity of the POS track with the spline function error model of the POS track according to the first error model to obtain the point cloud error model.

In one possible design, the performing interval division on the POS trajectory, and obtaining a spline function error model of the POS trajectory through quadratic spline function interpolation according to each segmented interval trajectory after division, includes:

calculating the track mileage of the POS track;

segmenting the POS track according to equal distances to obtain segmented interval tracks;

and obtaining a spline function error model of the POS track through secondary spline function interpolation according to each divided subsection interval track and the corresponding track mileage. In one possible design, the POS trace further includes a trace point quality factor;

determining a target energy function according to the laser point cloud data, the POS track and the point cloud error model, including:

determining a first energy function according to the laser point cloud data through the point cloud error model;

determining a second energy function through a spline function error model of the POS track according to the adjacent subsection interval track;

determining a third energy function through a spline function error model of the POS track according to the track point quality factor;

taking the sum of the first energy function, the second energy function and the third energy function as a target energy function;

wherein the objective energy function includes the plurality of solution coefficients.

In one possible design, the determining, from the laser point cloud data, a first energy function via the point cloud error model includes:

acquiring all homonymous point pairs according to the laser point cloud data;

according to all the homonymous points, performing error correction on all the homonymous point pairs through the point cloud error model to obtain all target homonymous point pairs after error correction;

taking the sum of the squares of the distances of all the target homonym pairs as a first energy function.

In one possible design, the determining the second energy function by a spline error model of the POS trajectory according to the adjacent piecewise interval trajectory includes:

obtaining a spline function error of each subsection interval track in the adjacent subsection intervals through a spline function error model of the POS track according to the adjacent subsection interval tracks;

and calculating the square sum of the difference between the spline function errors of the adjacent segmental interval tracks and the square of the difference between the first derivatives of the spline function errors of the adjacent segmental interval tracks according to the spline function errors of each segmental interval track in the adjacent segmental intervals, and determining a second energy function by obtaining the weight of the continuity of the adjacent segmental interval tracks through the continuity of the adjacent segmental interval tracks.

In one possible design, the determining, according to the quality factor of the trace point, a third energy function through a spline function error model of the POS trace includes:

obtaining an absolute deviation weight of the obtained track points through the quality factors of the track points, and obtaining the absolute deviation of each track point in the POS track according to the absolute deviation weight of the track points and a spline function error model of the POS track;

and taking the sum of the absolute deviations of all track points in the POS track as a third energy function. In a possible design, the correcting, according to the plurality of solution coefficients and the point cloud error model, each trajectory point in the POS trajectory to obtain a corrected target POS trajectory includes:

inputting the solving coefficients into the point cloud error model to obtain a target point cloud error model;

and inputting each track point in the POS track into the target point cloud error model to obtain a corrected target POS track.

In one possible design, the POS track includes coordinates of each track point on the POS track in an inertial navigation INS coordinate system and a posture of each track point, and the coordinates in the inertial navigation INS coordinate system are geodetic coordinates with the inertial navigation INS as a coordinate origin; the obtaining of the corrected target laser point cloud data according to the laser point cloud data, the target POS track and the incidence relation comprises:

the obtaining of the corrected target laser point cloud data according to the laser point cloud data, the target POS track and the incidence relation comprises:

according to the coordinates of the laser point cloud in a world coordinate system, performing inverse transformation on the first coordinate transformation relation to obtain the coordinates of the laser point cloud in an inertial navigation INS coordinate system;

obtaining a target coordinate of the laser point cloud after being corrected in a world coordinate system through the first coordinate conversion relation according to the target POS track and the coordinate of the laser point cloud in an inertial navigation INS coordinate system;

and taking the corrected target coordinate of the laser point cloud in the world coordinate system as the corrected target laser point cloud data.

In a second aspect, an embodiment of the present application provides a laser point cloud data processing apparatus, including:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring laser point cloud data of a target ground object and acquiring a POS track corresponding to the target ground object, and the laser point cloud data and the POS track have an incidence relation;

the point cloud error model establishing module is used for acquiring a point cloud error model according to the laser point cloud data, the POS track and the incidence relation, wherein the point cloud error model comprises a plurality of solving coefficients, and the solving coefficients are used for representing parameters in the point cloud error model;

the target energy function determining module is used for determining a target energy function according to the laser point cloud data, the POS track and the point cloud error model;

the coefficient solving module is used for acquiring the solving coefficients according to the target energy function;

the POS track correction module is used for correcting each track point in the POS track according to the solving coefficients and the point cloud error model to obtain a corrected target POS track;

and the laser point cloud data correction module is used for obtaining corrected target laser point cloud data according to the laser point cloud data, the target POS track and the incidence relation.

the point cloud error model building module comprises: the system comprises a POS position and pose approximate value determining unit, a first error model determining unit, a spline function error model determining unit and a point cloud error model determining unit;

the POS position and pose approximate value determining unit is used for taking the coordinates of the laser point cloud in a world coordinate system as POS position and pose approximate values;

the first error model determining unit is used for obtaining a first error model of the laser point cloud by expanding through a Taylor formula according to the POS position and pose approximate value and a first coordinate conversion relation, wherein the first error model is a model containing a differential correction quantity of a POS track;

the spline function error model determining unit is used for carrying out interval division on the POS track, and obtaining a spline function error model of the POS track through secondary spline function interpolation according to each segmented interval track after division;

and the point cloud error model determining unit is used for replacing the differential correction quantity of the POS track with the spline function error model of the POS track according to the first error model to obtain the point cloud error model.

In a third aspect, an embodiment of the present application provides a laser point cloud data processing apparatus, including: at least one processor and memory;

the memory stores computer-executable instructions;

the at least one processor executes computer-executable instructions stored by the memory to cause the at least one processor to perform the laser point cloud data processing method as set forth above in the first aspect and various possible designs of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the laser point cloud data processing method according to the first aspect and various possible designs of the first aspect are implemented.

The laser point cloud data processing method, the laser point cloud data processing device and the laser point cloud data processing equipment provided by the embodiment firstly acquire laser point cloud data of a target ground object and a POS track corresponding to the target ground object, wherein the laser point cloud data and the POS track have an association relation, the association relation is obtained by carrying out coordinate conversion on the laser point cloud data and converting the laser point cloud data into a coordinate associated with the POS track, then a point cloud error model represented by a POS position and orientation error is acquired according to the determined association relation between the laser point cloud data and the POS track, a target energy function is constructed through the laser point cloud data, the POS track and the point cloud error model, as the point cloud error model contains a plurality of solving coefficients, the target energy function also contains the plurality of solving coefficients, a plurality of solving coefficients are obtained through analysis according to the target energy function, and then the POS position and orientation error is obtained for correcting the POS track, through correction of the POS track and based on the incidence relation, correction of laser point cloud data can be achieved, the problem of double images among the laser point clouds is further eliminated, and accuracy of data acquisition is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.

Fig. 1 is a schematic flow chart of a laser point cloud data processing method according to an embodiment of the present disclosure;

fig. 2 is a schematic flow chart of a laser point cloud data processing method according to still another embodiment of the present disclosure;

fig. 3 is a schematic flowchart of a laser point cloud data processing method according to another embodiment of the present disclosure;

fig. 4 is a schematic flowchart of a laser point cloud data processing method according to yet another embodiment of the present disclosure;

fig. 5 is a schematic flowchart of a laser point cloud data processing method according to another embodiment of the present disclosure;

fig. 6 is a schematic flowchart of a laser point cloud data processing method according to yet another embodiment of the present disclosure;

fig. 7 is a schematic flowchart of a laser point cloud data processing method according to another embodiment of the present disclosure;

fig. 8 is a schematic structural diagram of a laser point cloud data processing apparatus according to an embodiment of the present disclosure;

fig. 9 is a schematic structural diagram of a laser point cloud data processing apparatus according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the above-described drawings (if any) are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In the prior art, in order to optimize the accuracy of collected multi-view point cloud data (i.e., laser point cloud data), a common method is to regard 2-view point cloud as a rigid body, obtain homonymous points of the 2-view point cloud, then minimize distance deviations of all homonymous points to obtain a transformation matrix between the point clouds, then perform coordinate transformation on the point clouds, perform multi-view point cloud registration, and eliminate deviations between the multi-view point clouds, so as to achieve the purpose of optimizing the accuracy of the multi-view point cloud. Therefore, according to the point cloud data, the energy function is constructed by comprehensively considering homonymy point correspondence, track continuity and POS track absolute position precision, the POS track is corrected through integral optimization solution, the point cloud is corrected, the laser point cloud data ghost problem is eliminated, and the point cloud data are processed more accurately and the precision is higher.

Therefore, in order to eliminate the ghost problem of the laser point cloud data and improve the data accuracy, the embodiment of the application provides a laser point cloud data processing method, a device, equipment and a storage medium. Fig. 1 is a schematic flow diagram of a laser point cloud data processing method provided in an embodiment of the present application, and referring to fig. 1, the laser point cloud data processing method includes:

s101, laser point cloud data of a target ground object and a POS track of the target ground object are obtained, and the laser point cloud data and the POS track have an association relation.

In this embodiment, when the laser radar in the high-precision map acquisition system transmits a laser beam to acquire the target feature for multiple times, the coordinates of the laser point cloud corresponding to the target feature in the scanner coordinate system and the corresponding scanning time are measured, where the coordinates of the laser point cloud in the scanner coordinate system may be a coordinate system established by the laser radar lidar itself. And then, a POS track is measured by a navigation positioning and Orientation System (POS System) when the laser radar emits a laser beam to collect the target ground object for multiple times, wherein the POS track comprises geodetic coordinates (B, L, H) of the origin of coordinates of the inertial navigation INS, postures (r, p, y) measured by the inertial navigation INS, a track quality factor and POS track time. And matching the scanning time with the POS track time, and obtaining the laser point cloud data of the target ground object, namely the coordinates of the laser point cloud in a world coordinate system, through the incidence relation between the laser point cloud data and the POS track. Each track point of the POS track can form a track point set T. In practical application, when laser point cloud data and a POS track are obtained, data preprocessing can be performed firstly, track point mileage of the POS track is calculated, and the POS track is segmented at equal intervals; and according to the time of the homonymous point, finding the corresponding position of the homonymous point on the track, interpolating mileage, uniformly thinning the homonymous point according to the distance and the like.

Specifically, the association relationship between the coordinates of the laser point cloud in the world coordinate system and the POS track may be determined through coordinate conversion according to the coordinates of the laser point cloud in the scanner coordinate system and the POS track.

In this embodiment, since the data obtained by scanning with the laser radar is the coordinates in the scanner coordinate system, the POS track is the coordinates in the inertial navigation INS coordinate system, and the corresponding position of the laser point cloud data needs to be found from the POS track, the laser point cloud data can be converted into the coordinates that can be expressed by the POS track through coordinate conversion, that is, the association relationship between the laser point cloud data and the POS track is determined. The coordinate transformation may be a transformation in a World coordinate System (i.e., World geographic System1984, which is a coordinate System established for use by the GPS global positioning System, WGS84 coordinate System), a transformation in a gaussian projection coordinate System, or the like.

In practical application, due to the fact that the POS tracks have deviations, laser point cloud data have deviations, and then data accuracy on a map is not high, and therefore the purpose of correcting the laser point cloud data can be achieved by correcting the POS tracks.

As shown in fig. 2, fig. 2 is a schematic flow chart of a laser point cloud data processing method according to still another embodiment of the present application. The POS track comprises coordinates of each track point on the POS track in an inertial navigation INS coordinate system and postures of the track points, and coordinates in the inertial navigation INS coordinate system are geodetic coordinates taking the inertial navigation INS as a coordinate origin.

Determining the association relationship between the coordinates of the laser point cloud in a world coordinate system and the POS track through coordinate conversion according to the coordinates of the laser point cloud in a scanner coordinate system and the POS track, wherein the association relationship comprises the following steps:

s201, obtaining the coordinates of the laser point cloud in the inertial navigation INS coordinate system according to the coordinates of the laser point cloud in the scanner coordinate system, a preset first rotation matrix from the scanner coordinate system to the inertial navigation INS coordinate system and the eccentricity from the preset scanner coordinate system to the inertial navigation INS coordinate system;

s202, according to the coordinates of each track point on the POS track in an inertial navigation INS coordinate system and the posture of each track point, obtaining a second rotation matrix from the inertial navigation INS coordinate system to a local horizontal coordinate system where the target ground object is located, a third rotation matrix from the local horizontal coordinate system to a world coordinate system, and the translation amount from the local horizontal coordinate system to the world coordinate system;

s203, obtaining the coordinates of the laser point cloud in a world coordinate system according to the coordinates of the laser point cloud in an inertial navigation INS coordinate system, the second rotation matrix, the third rotation matrix and the translation amount, wherein the coordinates of the laser point cloud in the world coordinate system are represented by the POS track;

and S204, taking the relation of the coordinates of the laser point cloud in the world coordinate system represented by the POS track as the incidence relation.

In this embodiment, for example, the conversion into the world coordinate system is performed, and the conversion relationship of the laser point cloud from the scanner coordinate system to the WGS84 coordinate system is:

in the above formula (1)The coordinate of the laser point cloud in the scanner coordinate system is X_lidarThe coordinates of each track point on the POS track in the inertial navigation INS coordinate system are (B, L, H), the posture of each track point is (r, p, y), and a first rotation matrix from a preset scanner coordinate system to the inertial navigation INS coordinate system is (r, p, y)

The preset eccentricity from the coordinate system of the scanner to the coordinate system of the inertial navigation INS is the

The coordinate of the laser point cloud in the inertial navigation INS coordinate system is X_insThe second rotation matrix from the inertial navigation INS coordinate system to the local horizontal coordinate system of the target ground object is the

The third rotation matrix from the local horizontal coordinate system to the world coordinate system is

The translation amount from the local horizontal coordinate system to the world coordinate system is

The coordinates of the laser point cloud in the inertial navigation INS coordinate system are as follows:

therefore, the coordinate relationship between the coordinates of the laser point cloud in the inertial navigation INS coordinate system and the coordinates of the laser point cloud in the scanner coordinate system, that is, the above equation (2), is obtained by performing equivalent exchange on the POS track, the coordinates of the laser point cloud in the scanner coordinate system, and the second coordinate conversion relationship between the coordinates of the laser point cloud in the world coordinate system, so as to obtain the first coordinate conversion relationship between the POS track, the coordinates of the laser point cloud in the inertial navigation INS coordinate system, and the coordinates of the laser point cloud in the world coordinate system. And taking the conversion relation of the laser point cloud from the scanner coordinate system to the WGS84 coordinate system as the association relation of the laser point cloud data and the POS track, namely the relation of the POS track representing the coordinate of the laser point cloud in the world coordinate system. Where the second coordinate conversion relationship is that in the above formula (1)

S102, obtaining a point cloud error model according to the laser point cloud data, the POS tracks and the incidence relation, wherein the point cloud error model comprises a plurality of solving coefficients, and the solving coefficients are used for representing parameters in the point cloud error model.

In this embodiment, the point cloud error model is a model expressed by a POS pose error, a taylor formula is developed for the association relationship between the laser point cloud data obtained in the coordinate conversion process and the POS track, so that an error model containing POS track differential correction amount established based on the laser point cloud can be obtained, then, by considering the spatial continuity of the POS track, and taking the track mileage as an independent variable, secondary spline function interpolation is performed on each interval segmented track divided by the POS track, so that a POS spline function error model is obtained, and then, the POS error model is fused into the error model containing POS track differential correction amount, so that a point cloud error model is obtained. During the interpolation of the quadratic spline function, the POS spline function error model comprises unknown interval spline coefficients, so the point cloud error model is represented by POS position and pose errors (namely the POS spline function error model), and the point cloud error model comprises the unknown interval spline coefficients, namely a plurality of solving coefficients.

S103, determining a target energy function according to the laser point cloud data, the POS track and the point cloud error model.

In the embodiment, the actual homonymous point pair with the error corrected by the homonymous point pair is calculated through the laser point cloud data and the point cloud error model, and then the distance deviation of the homonymous point is determined based on the actual homonymous point pair; and then based on the continuity of the POS track, determining the continuity of the adjacent segmented interval track, namely the boundary condition of the spline function: the quadratic spline function is continuous at the nodes and the first derivative is continuous; the absolute deviation of each track point in the POS track can be calculated through the POS track and the POS track pose error; and determining a target energy function by comprehensively considering the distance deviation of the homonymous points, the continuity of the adjacent segmented interval tracks and the absolute deviation of each track point in the POS track. And solving a plurality of solving coefficients contained in the POS track spline function error model and the point cloud error model by constructing an energy function, thereby realizing the correction of the POS track.

S104, obtaining the plurality of solving coefficients according to the target energy function;

and S105, correcting each track point in the POS track according to the plurality of solving coefficients and the point cloud error model to obtain a corrected target POS track.

In this embodiment, the POS track spline function error model and the plurality of solving coefficients included in the point cloud error model are solved by constructing the energy function, so as to obtain the POS track spline function error model and the point cloud error model that do not include the unknown coefficient, and then the POS track is corrected based on the POS track spline function error model that does not include the unknown coefficient.

And S106, obtaining corrected target laser point cloud data according to the laser point cloud data, the target POS track and the incidence relation.

In this embodiment, target laser point cloud data is inversely calculated according to the target POS track according to the association relationship in the coordinate conversion process, thereby realizing correction of the laser point cloud data. The method realizes the correction of the laser point cloud data by correcting the POS track, and comprehensively considers the homonymy point corresponding relation, the track continuity and the track absolute position precision to improve the precision of the laser point cloud data.

Therefore, in this embodiment, the laser point cloud data of the target feature and the POS track of the target feature are obtained, then the laser point cloud data are subjected to coordinate conversion to convert into coordinates associated with the POS track, then the taylor formula expansion is performed on the coordinates after the laser point cloud data conversion according to the determined association relationship between the laser point cloud data and the POS track, and then the POS track is subjected to the quadratic spline interpolation calculation to obtain a point cloud error model represented by the POS pose error, a target energy function is constructed by the laser point cloud data, the POS track and the point cloud error model, since the point cloud error model includes a plurality of solving coefficients, the target energy function also includes the plurality of solving coefficients, and by minimizing the target energy function, a plurality of solving coefficients are obtained by analysis, the POS pose error is obtained to correct the POS track, through correction of the POS track and based on the incidence relation, correction of laser point cloud data can be achieved, the problem of double images among the laser point clouds is further eliminated, and accuracy of data acquisition is improved.

In practical application, firstly, a laser radar emits a laser beam to acquire coordinates of a laser point cloud corresponding to a target ground object in a scanner coordinate System for multiple times, a POS track is acquired through a navigation positioning and Orientation System (POS System), the POS track comprises coordinates of all track points on the POS track in an inertial navigation INS coordinate System, postures of all the track points and track point quality factors, and then the coordinates of the laser point cloud in a world coordinate System are acquired through coordinate conversion according to the coordinates of the laser point cloud in the scanner coordinate System, the coordinates of all the track points on the POS track in the inertial navigation INS coordinate System and the postures of all the track points, wherein the coordinates of the laser point cloud in the world coordinate System are coordinates expressed by the POS track; according to the coordinates expressed by the POS track, a first error model of the laser point cloud is obtained through Taylor formula expansion, wherein the first error model is a model containing differential correction of the POS track; the POS track is subjected to interval division, and a spline function error model of the POS track is obtained through a quadratic spline function according to each segmented interval track after division; obtaining a point cloud error model according to the first error model and the spline function error model of the POS track, wherein the differential correction model is equal to the spline function error model of the POS track; according to all the obtained homonymous points, carrying out error correction on all the homonymous point pairs through the laser point cloud error model to obtain all actual homonymous point pairs after error correction; taking the sum of the squares of all the actual homonym point-to-distance pairs as a first energy function; obtaining a spline function error of each subsection interval track in the adjacent subsection intervals through a spline function error model of the POS track according to the adjacent subsection interval tracks; determining a second energy function according to the spline function error of each subsection interval track in the adjacent subsection intervals; obtaining the absolute deviation of the track points according to the quality factors of the track points and a spline function error model of the POS track; determining a third energy function according to the absolute deviation; taking the sum of the first energy function, the second energy function and the third energy function as a target energy function; minimizing the target energy function to obtain a coefficient of each segmented interval in a spline function error model of the POS track; inputting the coefficient of each segmented interval into a spline function error model of the POS track to obtain the correction quantity of each track point in the POS track; obtaining the coordinates of each target track point in the inertial navigation INS coordinate system and the postures of each target track point after correction according to the correction quantity of each track point in the POS track, the coordinates of each track point in the inertial navigation INS coordinate system and the postures of each track point; and obtaining the coordinates of the laser point cloud after correction in the world coordinate system through coordinate conversion according to the coordinates of each target track point in the inertial navigation INS coordinate system and the postures of each target track point. According to the method, according to the coordinate conversion of the laser point cloud data, the homonymy point correspondence, the track continuity and the track absolute position precision are comprehensively considered to construct an energy function, the POS track is integrally optimized and solved, the point cloud is corrected, the ghost problem of the laser point cloud data is eliminated, and the point cloud data is processed more accurately and more precisely.

How to construct a point cloud error model based on the association relationship between the laser point cloud data and the POS track and the obtained POS track is shown in fig. 3, where fig. 3 is a schematic flow diagram of a laser point cloud data processing method provided in another embodiment of the present application, and this embodiment describes S103 in detail on the basis of the above embodiment, for example, on the basis of the embodiment described in fig. 2. The laser point cloud data comprises coordinates of a laser point cloud in a world coordinate system, and the association relationship comprises a POS track, coordinates of the laser point cloud in an inertial navigation INS coordinate system and a first coordinate conversion relationship among the coordinates of the laser point cloud in the world coordinate system;

s301, taking the coordinates of the laser point cloud in a world coordinate system as approximate POS (point of sale) pose values;

s302, according to the POS position and pose approximate value and a first coordinate conversion relation, a first error model of the laser point cloud is obtained through Taylor formula expansion, and the first error model is a model containing a differential correction quantity of a POS track;

s303, performing interval division on the POS track, and obtaining a spline function error model of the POS track through secondary spline function interpolation according to each divided interval track;

s304, replacing the differential correction quantity of the POS track with the spline function error model of the POS track according to the first error model to obtain the point cloud error model.

In this embodiment, the first coordinate transformation relation is in the above formula (1)

For example, the coordinates of the laser point cloud in the world coordinate system are selected and recorded as

Will be provided with

As approximate value of POS pose: (I.e. approximate value of the track point coordinates in the POS track), the above formula (1) is put at the point

And (4) expanding according to a Taylor expansion formula, and taking a first term to obtain:

wherein, let dPOS ═ the differential correction amount (dB, dL, dH, dr, dp, dy), and K is the point

The linear or nonlinear relationship with the differential correction amount can be derived from the above equation (3), X^*KdPOS is the first error model of the laser point cloud. Since dPOS is an unknown quantity, error calculations are mainly performed on the POS trajectory. The POS track is divided into sections, a spline function error model of the POS track is obtained through quadratic spline function interpolation according to each divided section track, the spline function error model of the POS track comprises section spline coefficients which are a plurality of solving coefficients, then the spline function error model of the POS track is used for replacing dPOS to obtain a point cloud error model, the point cloud error model is represented by POS track pose errors, and the point cloud error model comprises a plurality of solving coefficients.

Specifically, how to calculate the POS track pose error is shown in fig. 4, where fig. 4 is a schematic flow chart of a laser point cloud data processing method according to yet another embodiment of the present application, and this embodiment describes S303 in detail on the basis of the above embodiment, for example, on the basis of the embodiment described in fig. 3. The interval division is carried out on the POS track, and a spline function error model of the POS track is obtained through quadratic spline function interpolation according to each segmented interval track after division, and the method comprises the following steps:

s401, calculating the track mileage of the POS track;

s402, segmenting the POS track at equal intervals to obtain segmented interval tracks;

and S403, obtaining a spline function error model of the POS track through secondary spline function interpolation according to each divided segmental interval track and the corresponding track mileage.

Wherein, the spline function error model of the POS track is as follows: delta POS_i(l)＝a_i+b_i(l-l_i)+c_i(l-l_i)²(ii) a Wherein l_iThe accumulated mileage of the trace point between the ith segment of the quadratic spline function is calculated, wherein l is the current trace point mileage, a_i,b_i,c_iAll are ith segment of inter-spline coefficients, and the plurality of solution coefficients comprise the ith segment of inter-spline coefficients.

In this embodiment, due to the spatial continuity of the trajectory, the trajectory mileage is used as an independent variable, the POS trajectory is segmented at equal intervals to obtain each segmented interval trajectory after division, and a secondary spline function is used to construct a POS error (that is, a POS pose error) for each interval segment of the POS trajectory, that is, a spline function error model of the POS trajectory is:

ΔPOS_i(l)＝a_i+b_i(l-l_i)+c_i(l-l_i)² (4)

in the above formula (4), l_iThe accumulated mileage of the trace point between the ith segment of the quadratic spline function is calculated, wherein l is the current trace point mileage, a_i,b_i,c_iAll are ith segment of inter-spline coefficients, and the plurality of solution coefficients comprise the ith segment of inter-spline coefficients. Therefore, the POS pose error can be calculated through the formula (4), and due to the fact that the spline coefficient of each segmented interval is an unknown quantity, other models need to be constructed to solve the problem, and then the point cloud error model is obtained.

Specifically, because the differential correction amount of the POS trace, which is dPOS, is the POS pose error, the above equation (4) can be substituted into the above equation (3), so that:

X^*＝KdPOS＝KΔPOS

for solving the spline coefficients of each segment interval, an energy function may be constructed, as shown in fig. 5, fig. 5 is a schematic flow chart of a laser point cloud data processing method according to another embodiment of the present application, and this embodiment details S104 on the basis of the above embodiment, for example, on the basis of the embodiment described in fig. 4. The POS track also comprises track point quality factors and time; determining a target energy function according to the laser point cloud data, the POS track and the point cloud error model, including:

s501, according to the laser point cloud data, determining a first energy function through the point cloud error model.

Specifically, all homonymous point pairs are obtained according to the laser point cloud data; according to all the homonymous points, performing error correction on all the homonymous point pairs through the point cloud error model to obtain all target homonymous point pairs after error correction; taking the sum of the squares of the distances of all the target homonym pairs as a first energy function

And S502, determining a second energy function through a spline function error model of the POS track according to the adjacent segmented interval track.

Specifically, a spline function error of each subsection interval track in the adjacent subsection interval is obtained through a spline function error model of the POS track according to the adjacent subsection interval track; according to the spline function error of each subsection interval track in the adjacent subsection interval, calculating the square sum of the difference between the spline function errors of the adjacent subsection interval tracks and the square of the difference between the first derivatives of the spline function errors of the adjacent subsection interval tracks, obtaining the weight of the continuity of the adjacent subsection interval tracks through the continuity of the adjacent subsection interval tracks, and determining a second energy function

And S503, determining a third energy function through a spline function error model of the POS track according to the track point quality factor.

Specifically, obtaining an absolute deviation weight of each track point through a track point quality factor, and obtaining the absolute deviation of each track point in the POS track according to the absolute deviation weight of each track point and a spline function error model of the POS track; and taking the sum of the absolute deviations of all track points in the POS track as a third energy function.

And S504, taking the sum of the first energy function, the second energy function and the third energy function as a target energy function.

In this embodiment, a first energy function based on a homonymy point pair error is constructed based on coordinates of laser point cloud data in a world coordinate system and a point cloud error model including a plurality of solution coefficients, that is, the first energy function is:

wherein E is₁S in_j,t_jRepresenting a pair of homonymous points, P representing a set of homonymous points, then s_j、t_jIs from an element in the set P of homonymous point pairs, j represents the number of homonymous point pairs in the set P of homonymous point pairs, i.e. s_j,t_jThe j-th pair of the same-name points is shown, and a, b and c show a plurality of solving coefficients.

Based on the continuity of the adjacent segmented interval tracks, through the boundary conditions of the spline function, the secondary spline function is continuous at the node and the first derivative is continuous, and the second energy function is constructed as follows:

by determination of E₁S in_j,t_jThe node of the segment interval can correspondingly obtain E₂I in (1) is the interval node of the section I, and then l is determined_iAnd

wherein, Δ POS_i(l_i) And Delta POS_i+1(l_i) Calculating the sum of squares sigma of the differences between the spline errors of the trajectories of adjacent piecewise intervals for the spline errors of the trajectories of the adjacent piecewise intervals_i‖ΔPOS_i(l_i)-ΔPOS_i+1(l_i)‖²And | Δ POS 'of the difference between the first derivatives of spline function errors of the adjacent segment interval trajectories'_i(l_i)-ΔPOS′_i+1(l_i)‖²The weight w of the continuity of the adjacent segment interval trajectory obtained by the continuity of the adjacent segment interval trajectory¹(ii) a Then, the weight w of the continuity of the adjacent segment interval tracks is used¹Fused to sigma_i‖ΔPOS_i(l_i)-ΔPOS_i+1(l_i)‖²And |. Δ POS'_i(l_i)-ΔPOS′_i+1(l_i)‖²To obtain a second energy function.

According to the absolute deviation weight difference of the track points and a spline function error model of the POS track, a third energy function based on the absolute deviation of the track points is constructed, namely the third energy function is as follows:

wherein E is₃The middle k represents that elements in a track point set T are formed by track points of the POS track, namely the kth track point, and then l is obtained through a segmentation interval where the kth track point is located_k。

Will E₁、E₂、E₃The sum of which is taken as the target energy function, namely:

E(a,b,c)＝E₁+E₂+E₃

minimizing the energy function min according to the constructed target energy function_a,b,cE (a, b, c), a plurality of solution coefficients may be obtained.

How to correct the deviation of each track point in the POS track according to the obtained multiple solution coefficients is further achieved, referring to fig. 6, fig. 6 is a schematic flow diagram of a laser point cloud data processing method according to another embodiment of the present application, and this embodiment describes S106 in detail on the basis of the above embodiment. And according to the plurality of solving coefficients and the point cloud error model, correcting each track point in the POS track to obtain a corrected target POS track, comprising:

s601, inputting the solving coefficients into the point cloud error model to obtain a target point cloud error model;

and S602, inputting each track point in the POS track into the target point cloud error model to obtain a corrected target POS track.

In this embodiment, the plurality of solution coefficients are input into the point cloud error model to obtain a target point cloud error model, and then a correction amount of each track point is interpolated based on the target point cloud error model, that is, a deviation amount (or correction amount) Δ POS_i(l) Then through the POS^*Get POS trace POS after correcting^*The target POS track is obtained.

Specifically, POS according to the corrected POS track^*How to correct the coordinates of the laser point cloud data in the world coordinate system is shown in fig. 7, where fig. 7 is a schematic flow chart of a laser point cloud data processing method according to another embodiment of the present application, and this embodiment describes in detail S107 based on the above-mentioned embodiment, for example, based on the embodiment described in fig. 6. The obtaining of the corrected target laser point cloud data according to the laser point cloud data, the target POS track and the incidence relation comprises:

s701, according to the coordinates of the laser point cloud in a world coordinate system, performing inverse transformation on the first coordinate transformation relation to obtain the coordinates of the laser point cloud in an inertial navigation INS coordinate system;

s702, obtaining a corrected target coordinate of the laser point cloud in a world coordinate system through the first coordinate conversion relation according to the target POS track and the coordinate of the laser point cloud in an inertial navigation INS coordinate system;

and S703, taking the corrected target coordinate of the laser point cloud in the world coordinate system as the corrected target laser point cloud data.

In this embodiment, the target POS track POS is recorded^*＝(B^*,L^*,H^*,r^*,p^*,y^*) Firstly, the relationship is converted according to the first coordinate

And (3) performing inverse transformation on the first coordinate conversion relation, namely inversely calculating the coordinates of the laser point cloud data in the INS coordinate system, namely the point cloud coordinates in the INS coordinate system:

then coordinates of the laser point cloud data in the INS coordinate system and a target POS track POS^*Substituting the coordinate into a conversion relation (a first coordinate conversion relation) of the laser point cloud from a scanner coordinate system to a WGS84 coordinate system to obtain a target coordinate of the laser point cloud corrected in a world coordinate system, namely a point cloud coordinate in a corrected WGS84 coordinate system:

in the embodiment, according to the coordinate conversion of the laser point cloud data, the homonymy point correspondence, the track continuity and the track absolute position precision are comprehensively considered to construct the energy function, the POS track is integrally optimized and solved, the point cloud is corrected, the ghost problem of the laser point cloud data is eliminated, and the point cloud data is processed more accurately and the precision is higher.

In order to implement the laser point cloud data processing method, the embodiment provides a laser point cloud data processing device. Referring to fig. 8, fig. 8 is a schematic structural diagram of a laser point cloud data processing apparatus according to an embodiment of the present disclosure; the laser point cloud data processing apparatus 80 includes: a first acquisition module 801, a point cloud error model establishment module 802, a target energy function determination module 803, a coefficient solving module 804, a POS track correction module 805 and a laser point cloud data correction module 806; the laser point cloud data acquisition module 801 is used for acquiring laser point cloud data of a target ground object and a POS track corresponding to the target ground object, wherein the laser point cloud data and the POS track have an association relationship; a point cloud error model establishing module 802, configured to obtain a point cloud error model according to the laser point cloud data, the POS track, and the association relationship, where the point cloud error model includes a plurality of solving coefficients, and the solving coefficients are used to represent parameters in the point cloud error model; a target energy function determining module 803, configured to determine a target energy function according to the laser point cloud data, the POS track, and the point cloud error model; a coefficient solving module 804, configured to obtain the plurality of solving coefficients according to the target energy function in the past year; a POS track correction module 805, configured to correct each track point in the POS track according to the multiple solution coefficients and the point cloud error model, to obtain a corrected target POS track; and a laser point cloud data correction module 806, configured to obtain corrected target laser point cloud data according to the laser point cloud data, the target POS track, and the association relationship.

In this embodiment, a first obtaining module 801, a point cloud error model establishing module 802, a target energy function determining module 803, a coefficient solving module 804, a POS track correcting module 805, and a laser point cloud data correcting module 806 are provided, and are configured to obtain laser point cloud data of a target ground object and a POS track corresponding to the target ground object, where the laser point cloud data and the POS track have an association relationship, the association relationship is obtained by performing coordinate conversion on the laser point cloud data, and converting the laser point cloud data into coordinates associated with the POS track, and then according to the association relationship between the determined laser point cloud data and the POS track, a point cloud error model represented by a POS pose error is obtained, and a target energy function is established by the laser point cloud data, the POS track, and the point cloud error model, and because the point cloud error model contains a plurality of solving coefficients, the target energy function also contains the plurality of solving coefficients, according to the target energy function, a plurality of solving coefficients are obtained through analysis, POS position and pose errors are further obtained and used for correcting POS tracks, correction of laser point cloud data can be achieved through correction of the POS tracks and based on the incidence relation, the ghost image problem among the laser point clouds is further eliminated, and the accuracy of data collection is improved.

The apparatus provided in this embodiment may be used to implement the technical solutions of the above method embodiments, and the implementation principles and technical effects are similar, which are not described herein again.

In one possible design, the laser point cloud data comprises coordinates of a laser point cloud in a scanner coordinate system, the POS track comprises coordinates of each track point on the POS track in an inertial navigation INS coordinate system and a posture of each track point, and the coordinates in the inertial navigation INS coordinate system are geodetic coordinates with the inertial navigation INS as a coordinate origin; a coordinate conversion module comprising: the device comprises a first coordinate determining unit, a parameter acquiring unit, a second coordinate determining unit and an incidence relation determining unit; the first coordinate determination unit is used for obtaining the coordinates of the laser point cloud in the inertial navigation INS coordinate system according to the coordinates of the laser point cloud in the scanner coordinate system, a preset first rotation matrix from the scanner coordinate system to the inertial navigation INS coordinate system and a preset eccentricity from the scanner coordinate system to the inertial navigation INS coordinate system; the parameter acquisition unit is used for acquiring a second rotation matrix from the inertial navigation INS coordinate system to a local horizontal coordinate system of the target ground object, a third rotation matrix from the local horizontal coordinate system to a world coordinate system and translation from the local horizontal coordinate system to the world coordinate system according to the coordinates of each track point on the POS track in the inertial navigation INS coordinate system and the posture of each track point; the second coordinate determination unit is used for obtaining the coordinates of the laser point cloud in a world coordinate system according to the coordinates of the laser point cloud in an inertial navigation INS coordinate system, the second rotation matrix, the third rotation matrix and the translation amount, wherein the coordinates of the laser point cloud in the world coordinate system are represented by the POS track; and the association relation determining unit is used for taking the relation of the coordinates of the laser point cloud in the world coordinate system represented by the POS track as the association relation.

In one possible design, the laser point cloud data comprises coordinates of the laser point cloud in a world coordinate system, and the association relationship comprises a first coordinate conversion relationship among the POS track, the coordinates of the laser point cloud in an inertial navigation INS coordinate system, and the coordinates of the laser point cloud in the world coordinate system; the point cloud error model building module comprises: the system comprises a POS position and pose approximate value determining unit, a first error model determining unit, a spline function error model determining unit and a point cloud error model determining unit; the POS position and pose approximate value determining unit is used for taking the coordinates of the laser point cloud in a world coordinate system as POS position and pose approximate values; the first error model determining unit is used for obtaining a first error model of the laser point cloud by expanding through a Taylor formula according to the POS position and pose approximate value and a first coordinate conversion relation, wherein the first error model is a model containing a differential correction quantity of a POS track; the spline function error model determining unit is used for carrying out interval division on the POS track, and obtaining a spline function error model of the POS track through secondary spline function interpolation according to each segmented interval track after division; and the point cloud error model determining unit is used for replacing the differential correction quantity of the POS track with the spline function error model of the POS track according to the first error model to obtain the point cloud error model.

In one possible design, the spline function error model determining unit is specifically configured to:

calculating the track mileage of the POS track;

and obtaining a spline function error model of the POS track through secondary spline function interpolation according to each divided subsection interval track and the corresponding track mileage.

Wherein, the spline function error model of the POS track is as follows: delta POS_i(l)＝a_i+b_i(l-l_i)+c_i(l-l_i)²。

Wherein l_iThe accumulated mileage of the trace point between the ith segment of the quadratic spline function is calculated, wherein l is the current trace point mileage, a_i,b_i,c_iAll are ith segment of inter-spline coefficients, and the plurality of solution coefficients comprise the ith segment of inter-spline coefficients.

In one possible design, the POS trace further includes a trace point quality factor; the target energy function determination module is specifically configured to: determining a first energy function according to the laser point cloud data through the point cloud error model; determining a second energy function through a spline function error model of the POS track according to the adjacent subsection interval track; determining a third energy function through a spline function error model of the POS track according to the track point quality factor; taking the sum of the first energy function, the second energy function and the third energy function as a target energy function; wherein the objective energy function includes the plurality of solution coefficients.

The target energy function determining module is further specifically configured to: acquiring all homonymous point pairs according to the laser point cloud data; according to all the homonymous points, performing error correction on all the homonymous point pairs through the point cloud error model to obtain all target homonymous point pairs after error correction; taking the sum of the squares of the distances of all the target homonym pairs as a first energy function.

The target energy function determining module is further specifically configured to: obtaining a spline function error of each subsection interval track in the adjacent subsection intervals through a spline function error model of the POS track according to the adjacent subsection interval tracks; and calculating the square sum of the difference between the spline function errors of the adjacent segmental interval tracks and the square of the difference between the first derivatives of the spline function errors of the adjacent segmental interval tracks according to the spline function errors of each segmental interval track in the adjacent segmental intervals, and determining a second energy function by obtaining the weight of the continuity of the adjacent segmental interval tracks through the continuity of the adjacent segmental interval tracks.

The target energy function determining module is further specifically configured to: obtaining an absolute deviation weight of the obtained track points through the quality factors of the track points, and obtaining the absolute deviation of each track point in the POS track according to the absolute deviation weight of the track points and a spline function error model of the POS track; and taking the sum of the absolute deviations of all track points in the POS track as a third energy function.

In one possible design, the POS track modification module is specifically configured to:

inputting the solving coefficients into the point cloud error model to obtain a target point cloud error model; and inputting each track point in the POS track into the target point cloud error model to obtain a corrected target POS track.

In one possible design, the POS track includes coordinates of each track point on the POS track in an inertial navigation INS coordinate system and a posture of each track point, and the coordinates in the inertial navigation INS coordinate system are geodetic coordinates with the inertial navigation INS as a coordinate origin; the laser point cloud data correction module is specifically used for:

according to the coordinates of the laser point cloud in a world coordinate system, performing inverse transformation on the first coordinate transformation relation to obtain the coordinates of the laser point cloud in an inertial navigation INS coordinate system; obtaining a target coordinate of the laser point cloud after being corrected in a world coordinate system through the first coordinate conversion relation according to the target POS track and the coordinate of the laser point cloud in an inertial navigation INS coordinate system; and taking the corrected target coordinate of the laser point cloud in the world coordinate system as the corrected target laser point cloud data.

According to the embodiment of the application, the homonymy point correspondence, the track continuity and the track absolute position precision are comprehensively considered to construct the energy function according to the coordinate conversion of the laser point cloud data, the POS track is corrected through integral optimization, the point cloud is further corrected, the ghost problem of the laser point cloud data is eliminated, and the point cloud data is processed more accurately and is higher in precision.

In order to implement the laser point cloud data processing method, the embodiment provides a laser point cloud data processing device. Fig. 9 is a schematic structural diagram of a laser point cloud data processing apparatus according to an embodiment of the present application. As shown in fig. 9, the laser point cloud data processing apparatus 90 of the present embodiment includes: a processor 901 and a memory 902; a memory 902 for storing computer-executable instructions; a processor 901 for executing computer executable instructions stored in the memory to implement the steps performed in the above embodiments. Reference may be made in particular to the description relating to the method embodiments described above.

The embodiment of the application also provides a computer-readable storage medium, wherein a computer executing instruction is stored in the computer-readable storage medium, and when a processor executes the computer executing instruction, the laser point cloud data processing method is realized.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules is only one logical division, and other divisions may be realized in practice, for example, a plurality of modules may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form. In addition, functional modules in the embodiments of the present application may be integrated into one processing unit, or each module may exist alone physically, or two or more modules are integrated into one unit. The unit formed by the modules can be realized in a hardware form, and can also be realized in a form of hardware and a software functional unit.

The integrated module implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present application. It should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile storage NVM, such as at least one disk memory, and may also be a usb disk, a removable hard disk, a read-only memory, a magnetic or optical disk, etc. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus. The storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the storage medium may reside as discrete components in an electronic device or host device.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A laser point cloud data processing method is characterized by comprising the following steps:

2. The method of claim 1, wherein the laser point cloud data comprises coordinates of the laser point cloud in a world coordinate system, and the correlation is a first coordinate transformation relationship between the POS track, the coordinates of the laser point cloud in an inertial navigation INS coordinate system, and the coordinates of the laser point cloud in the world coordinate system;

the acquiring of the point cloud error model according to the laser point cloud data, the POS track and the association relation comprises the following steps:

taking the coordinates of the laser point cloud in a world coordinate system as approximate values of POS positions and positions; according to the POS position and pose approximate value and the first coordinate conversion relation, a first error model of the laser point cloud is obtained through Taylor formula expansion, and the first error model is a model containing differential correction of POS tracks;

3. The method of claim 2, wherein the step of performing interval division on the POS trajectory and obtaining a spline function error model of the POS trajectory by quadratic spline function interpolation according to each segmented interval trajectory after division comprises:

calculating the track mileage of the POS track;

4. The method of claim 3, wherein the POS trace further includes a trace point quality factor;

5. The method of claim 4, wherein determining a first energy function from the laser point cloud data via the point cloud error model comprises:

acquiring all homonymous point pairs according to the laser point cloud data;

6. The method of claim 4, wherein determining the second energy function from the adjacent piecewise-interval trajectory through a spline function error model of the POS trajectory comprises:

7. The method of claim 4, wherein determining the third energy function by a spline error model of the POS trajectory based on the trajectory point quality factor comprises:

and taking the sum of the absolute deviations of all track points in the POS track as a third energy function.

8. The method according to any one of claims 2 to 7, wherein the step of correcting each trajectory point in the POS trajectory according to the plurality of solution coefficients and the point cloud error model to obtain a corrected target POS trajectory comprises:

9. A laser point cloud data processing apparatus, comprising:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring laser point cloud data of a target ground object and a POS track corresponding to the target ground object, and the laser point cloud data and the POS track have an incidence relation;

10. A laser point cloud data processing apparatus, comprising: at least one processor and memory;

the memory stores computer-executable instructions;

the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the laser point cloud data processing method of any of claims 1 to 8.