CN114216465A - Submarine topography navigation parallel matching method - Google Patents
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Abstract
A submarine topography navigation parallel matching method relates to the technical field of underwater navigation, and aims to solve the problems of low data association speed and high mismatching rate caused by lack of heading information and high submarine topography similarity of a topography matching algorithm in the prior art, and comprises the following steps: the method comprises the following steps: m hypotheses conforming to Gaussian distribution are constructed according to the pose information of the underwater robot at the current moment, wherein M is more than or equal to 2; step two: carrying out consistency detection on the M hypotheses conforming to the Gaussian distribution and the prior submarine topographic map; step three: performing point-to-surface iterative closest point algorithm registration on the assumption consistent with the prior submarine topography in the step two to obtain matched heading and displacement information; step four: and updating the pose information of the underwater robot by using the matched heading and displacement information, and then updating the submarine topography according to the updated pose information of the underwater robot. The method and the device realize the parallel matching algorithm of the submarine topography based on the parallel computing unit, and effectively solve the problems of low data association speed, high mismatching rate and the like caused by high similarity of the submarine topography.
Description
Technical Field
The invention relates to the technical field of underwater navigation, in particular to a submarine topography navigation parallel matching method.
Background
The existing inertial navigation system is an autonomous underwater navigation system commonly used by an AUV (autonomous underwater vehicle), and because errors of the inertial navigation system are accumulated along with time, the positioning accuracy of the AUV in a large-scale and long-time navigation task is greatly influenced.
In the existing terrain matching algorithm, the AUV matches the strip data obtained by the current multi-beam sonar with the prior submarine topography map to determine the real position information of the current AUV, and then calibrates the navigation system of the AUV according to the real position, thereby realizing the track optimization of the AUV.
Because the existing terrain matching algorithm lacks heading information output, the heading information of the AUV cannot be optimized; the submarine topography with similar height can interfere the existing topography matching algorithm to generate correct position information and improve the complexity of the topography matching algorithm, so that the problems of low data association speed, high mismatching rate and the like can be caused.
Disclosure of Invention
The purpose of the invention is: aiming at the problems of low data association speed and high mismatching rate caused by lack of heading information and high submarine topography similarity of a terrain matching algorithm in the prior art, the submarine topography navigation parallel matching method is provided.
The technical scheme adopted by the invention to solve the technical problems is as follows:
a submarine topography navigation parallel matching method comprises the following steps:
the method comprises the following steps: m hypotheses conforming to Gaussian distribution are constructed according to the pose information of the underwater robot at the current moment, wherein M is more than or equal to 2;
step two: carrying out consistency detection on the M hypotheses conforming to the Gaussian distribution and the prior submarine topographic map;
step three: performing point-to-surface iterative closest point algorithm registration on the assumption consistent with the prior submarine topography in the step two to obtain matched heading and displacement information;
step four: and updating the pose information of the underwater robot by using the matched heading and displacement information, and then updating the submarine topography according to the updated pose information of the underwater robot.
Further, in the step one, the acquiring step of the pose information of the underwater robot is as follows:
firstly, acquiring the acceleration and the heading information of the underwater robot, then carrying out secondary integration on the acceleration of the underwater robot to obtain the position information of the underwater robot, and finally adding the position information and the heading information to obtain the pose information of the underwater robot.
Further, the specific steps of the first step are as follows:
the method comprises the following steps: acquiring multi-beam sonar data;
the first step is: obtaining terrain elevation information in the current moment according to the multi-beam sonar data;
step one is three: and constructing M hypotheses meeting Gaussian distribution by taking the pose vertex as a center, and updating terrain elevation data corresponding to the M hypotheses according to the result of the first step and the second step.
Further, the second step comprises the following specific steps:
step two, firstly: respectively searching the updated terrain elevation data corresponding to the M hypotheses for the data of the overlapped area of the prior submarine topography and the terrain elevation data by using an NN algorithm;
step two: and (3) calculating the mean square error (MSD) of the data in the overlapping area in a parallelization manner, and selecting the hypothesis corresponding to the minimum MSD value as the hypothesis with the highest terrain similarity, namely finishing the consistency detection.
Further, the third step comprises the following specific steps:
step three, firstly: performing point-to-surface iterative nearest point algorithm registration on the hypothesis with the highest terrain similarity and the prior submarine topography, and outputting heading and displacement information;
step three: and updating the hypothesis corresponding to the minimum MSD value by using the heading and displacement information output in the step three and one to obtain the matched heading and displacement information.
Further, the MSD is represented as:
wherein N represents the number of point clouds in the adaptation area, hpAnd hMAPElevation data of the two respective adaptation zones,
and
average elevation data of two adaptation zones respectively, i represents the ith elevation data.
Further, the step of acquiring the data of the overlapping area in the step two is as follows:
and if the number of point clouds in the data with the closest point distance less than 0.1 is less than the threshold xi, the terrain reentry is not considered to exist at the moment, namely the overlapped area does not exist,
and if the number of the point clouds is larger than or equal to the threshold value xi, the terrain reentry exists at the moment, namely an overlapping area exists.
Further, the xi is one fourth of the total number of the point clouds.
The invention has the beneficial effects that:
the method and the device realize the submarine topography parallel matching algorithm based on the parallel computing unit, effectively solve the problems of low data association speed, high mismatching rate and the like caused by high submarine topography similarity, only utilize multi-hypothesis consistency detection and NN algorithm to compute a submarine topography adaptation area, screen out the hypothesis corresponding to the minimum MSD in parallel computing, and approach the AUV real pose information by the hypothesis. The point-to-surface ICP algorithm effectively solves the problems that the existing matching algorithm lacks heading information output and cannot optimize the heading of the AUV; and the sub-maps corresponding to the hypothesis are subjected to fine registration, so that the matching accuracy is improved. Simulation results show that the method and the device can effectively optimize the track of the inertial navigation system and are high in optimization speed.
Drawings
FIG. 1 is a flow chart of the present application;
FIG. 2 is a graphical illustration of a prior seafloor topography;
FIG. 3 is a graphical illustration of updating a priori seafloor topography;
FIG. 4 is a schematic diagram of a submarine topography adaptation region search;
FIG. 5 is a schematic diagram of seafloor terrain patch selection;
FIG. 6 is a multi-hypothesis distribution map;
FIG. 7 is a schematic diagram of the degree of topographic similarity of the overlapping area of hypothetical 2 and sub-map 17;
FIG. 8 is a schematic diagram of the degree of topographic similarity for the overlapping area of the hypothetical 1 and sub-map 17;
fig. 9 is a schematic diagram of an adaptation region subjected to coarse registration;
FIG. 10 is a schematic diagram of a precisely registered adaptation region;
FIG. 11 is a schematic diagram of a trajectory and a real trajectory of an inertial navigation system;
FIG. 12 is a schematic diagram of the graph optimization trajectory and the real trajectory;
fig. 13 is an error analysis chart.
Detailed Description
It should be noted that, in the present invention, the embodiments disclosed in the present application may be combined with each other without conflict.
The first embodiment is as follows: specifically describing the embodiment with reference to fig. 1, the parallel matching method for submarine topography navigation in the embodiment includes the following steps:
the method comprises the following steps: m hypotheses conforming to Gaussian distribution are constructed according to the pose information of the underwater robot at the current moment, wherein M is more than or equal to 2;
step two: carrying out consistency detection on the M hypotheses conforming to the Gaussian distribution and the prior submarine topographic map;
step three: performing point-to-surface iterative closest point algorithm registration on the assumption consistent with the prior submarine topography in the step two to obtain matched heading and displacement information;
step four: and updating the pose information of the underwater robot by using the matched heading and displacement information, and then updating the submarine topography according to the updated pose information of the underwater robot.
The invention provides a submarine topography navigation parallel matching algorithm aiming at the problems of low data association speed, high mismatching rate and the like caused by the fact that underwater topography matching based on multi-beam sonar cannot output heading information and submarine topography similarity is high, and the algorithm comprises the following steps: the method comprises the following steps that an AUV carries an inertial navigation unit, a multi-beam sonar, a parallel computing unit and a priori submarine topography; the method comprises the steps that an AUV motion trail is constructed according to an inertial navigation unit, the position and pose of each sampling moment are used as vertex information, and elevation information in the moment is obtained according to multi-beam sonar data; constructing M hypotheses meeting Gaussian distribution by taking the pose vertex as a center, and updating elevation data corresponding to the M hypotheses; putting terrain elevation data corresponding to M hypotheses into a submarine terrain data set sigma, and searching an overlapped area in a priori submarine topography and the sigma set by using NN (NN is a nearest point search method, KNN is a K nearest point, and when K is 1, the nearest point) in a parallel computing unit; s groups of submarine topography adaptation areas are scattered according to the multi-hypothesis information, and MSD of S groups of adaptation area topographies is calculated respectively; and outputting heading and displacement change information in the ICP from the multi-hypothesis terrain data input point corresponding to the MSD minimum value to the surface. The invention is based on a submarine topography navigation parallel matching algorithm, the hypothesis with the minimum MSD value is screened out by using multi-hypothesis consistency detection and NN parallel algorithm to approach AUV real pose information, so as to accelerate underwater topography matching speed and accuracy, and meanwhile, the problem that the existing topography matching lacks heading information output is solved by adopting point-to-surface ICP registration, and submarine topography fine registration is carried out.
Example (b):
the method comprises the following steps: the method comprises the following steps that an AUV carries an inertial navigation unit, a multi-beam sonar, a parallel computing unit and a priori submarine topography;
step two: the AUV establishes vertex information according to the inertial navigation unit, establishes multiple hypotheses conforming to Gaussian distribution by taking the vertexes as the center, and inputs terrain data corresponding to the hypotheses and a priori submarine terrain map in the parallel computing unit to compute a submarine terrain adaptation area;
step three: and (5) outputting heading and displacement change information by the AUV in the ICP from the input point of the adaptation area to the surface in the step two, and updating the pose and the map information of the AUV.
The method comprises the following steps: the method comprises the steps that an AUV carries a multi-beam sonar, multi-beam data of the AUV at the current sampling moment are output in real time, a terrain data scanning sub-map is constructed, and the number of point clouds in the terrain is NUM (NUM is the number of multi-beam strips multiplied by the number of each multi-beam foot point);
the first step is: carrying an inertial navigation unit by the AUV, outputting speed and heading information of the AUV under a carrier system in real time, and constructing an AUV motion track;
step one is three: and carrying the parallel computing unit and the prior submarine topography by the AUV, and outputting AUV reentry topography data in real time.
Step two, firstly: taking the pose of each sampling moment as vertex information;
step two: adopting an NN algorithm for sub-map topographic data and the prior submarine topographic map at the moment, and if the point cloud number with the distance of the nearest point less than 0.1m is greater than NUM/4, determining that an overlapping area exists at the moment;
step two and step three: if no overlapping area exists in the second step, updating the prior submarine topography map by using the sub-map topography data at the moment, otherwise, constructing M multiple hypotheses meeting Gaussian distribution (mean value is 0 and variance is sigma) by using the vertex at the moment as a center, and updating the topography data corresponding to the M hypotheses by using the mapping relation between the vertex and the sub-map;
step two, four: placing the terrain data corresponding to M multiple hypotheses into a submarine terrain data set sigma, inputting the sigma and a prior submarine terrain map into a parallel computing unit, and outputting the terrain data of an overlapping area by adopting an NN algorithm;
step three, firstly: dispersing the topographic data of the overlapping area output in the second step four into S groups of submarine topographic adaptation areas, calculating MSD of the S groups of adaptation areas, and selecting a hypothesis corresponding to the minimum MSD value;
step three: performing point-to-surface ICP (inductively coupled plasma) fine registration on the hypothesis and adaptation area data corresponding to the prior submarine topography to output heading and displacement information;
step three: and updating the assumed pose information screened out in the step three by using the heading and displacement information output in the step three, wherein the assumed pose information is a matching result.
The flow chart of the present application is shown in fig. 1. One application situation of the invention can be that the AUV carries on the inertia navigation unit to output AUV movement track, the multibeam sonar outputs the submarine topography data sub map, the parallel computing unit and the prior submarine topography map calculate the overlap area, the prior submarine topography map is shown in figure 2; taking the position and pose at each sampling moment as vertex information, adopting an NN algorithm for sub-map topographic data and a prior submarine topographic map at the moment, and if the number of point clouds of which the distance between nearest points is less than 0.1m is less than a threshold xi (the threshold is obtained empirically, and is changed along with AUV tracks and multi-beam sonar scanning ranges, and is taken as one fourth of the total number of sub-map point clouds), determining that no terrain reentry exists at the moment, as shown in fig. 4 and 5, and as a result, no overlapping area exists between a sub-map 2 and the sub-map 1, and no terrain reentry exists between the sub-map 2 and the sub-map 1, placing a terrain sub-map corresponding to the sub-map 2 at the moment into the prior submarine topographic map, and updating the prior submarine topographic map as shown in fig. 3; if the terrain reentry exists, constructing M multiple hypotheses which satisfy Gaussian distribution (mean value is 0 and variance is sigma) by taking a vertex at the moment as a center, wherein the multiple hypotheses are distributed as shown in the following figure 6, and updating terrain data corresponding to the M multiple hypotheses according to the mapping relation between the vertex information and the sub map; inputting a submarine topography data set sigma and a prior submarine topography into a parallel computing unit, outputting reentrant topography data by adopting an NN algorithm, and deleting the hypothesis that no topography reentrants exist, as shown in fig. 4 and 5, and deleting hypothesis 3 because no topography reentrants exist between hypothesis 3 and a sub-map 17; the output topographic data of the overlapping area is scattered into S (S < ═ M) groups of submarine topographic adaptation areas, MSD of the S groups of adaptation areas is calculated, and an assumption corresponding to the minimum MSD value is selected, as shown in FIGS. 7 and 8, although the overlapping area between assumption 1 and sub-map 17 is large, the topographic similarity is low, so assumption 1 is deleted, and the topographic similarity between assumption 2 and sub-map 17 is highest, so assumption 2 is taken as the pose information which is most approximate to the current time of AUV; as shown in fig. 9, due to the limitation of the number of multiple hypotheses and the sampling randomness, only coarse registration is performed on the seafloor terrain corresponding to the hypothesis 1 and the sub-map 17, so that point-to-surface ICP fine registration is performed on the hypothesis 1 and the sub-map 17 to output heading and displacement information, and the fine registration effect is shown in fig. 10; and updating the pose and sub-map information of the hypothesis 2 according to the output heading and displacement information. The final optimized trajectory is shown in fig. 11 and 12, and the error is shown in fig. 13.
It should be noted that the detailed description is only for explaining and explaining the technical solution of the present invention, and the scope of protection of the claims is not limited thereby. It is intended that all such modifications and variations be included within the scope of the invention as defined in the following claims and the description.
Claims (8)
1. A submarine topography navigation parallel matching method is characterized by comprising the following steps:
the method comprises the following steps: m hypotheses conforming to Gaussian distribution are constructed according to the pose information of the underwater robot at the current moment, wherein M is more than or equal to 2;
step two: carrying out consistency detection on the M hypotheses conforming to the Gaussian distribution and the prior submarine topographic map;
step three: performing point-to-surface iterative closest point algorithm registration on the assumption consistent with the prior submarine topography in the step two to obtain matched heading and displacement information;
step four: and updating the pose information of the underwater robot by using the matched heading and displacement information, and then updating the submarine topography according to the updated pose information of the underwater robot.
2. The submarine topography navigation parallel matching method according to claim 1, wherein in the first step, the underwater robot pose information acquisition step is as follows:
firstly, acquiring the acceleration and the heading information of the underwater robot, then carrying out secondary integration on the acceleration of the underwater robot to obtain the position information of the underwater robot, and finally adding the position information and the heading information to obtain the pose information of the underwater robot.
3. The submarine topography navigation parallel matching method according to claim 2, characterized in that the concrete steps of the first step are:
the method comprises the following steps: acquiring multi-beam sonar data;
the first step is: obtaining terrain elevation information in the current moment according to the multi-beam sonar data;
step one is three: and constructing M hypotheses meeting Gaussian distribution by taking the pose vertex as a center, and updating terrain elevation data corresponding to the M hypotheses according to the result of the first step and the second step.
4. The submarine topography navigation parallel matching method according to claim 3, characterized in that the second step comprises the following specific steps:
step two, firstly: respectively searching the updated terrain elevation data corresponding to the M hypotheses for the data of the overlapped area of the prior submarine topography and the terrain elevation data by using an NN algorithm;
step two: and (3) calculating the mean square error (MSD) of the data in the overlapping area in a parallelization manner, and selecting the hypothesis corresponding to the minimum MSD value as the hypothesis with the highest terrain similarity, namely finishing the consistency detection.
5. The submarine topography navigation parallel matching method according to claim 4, characterized in that the concrete steps of the third step are:
step three, firstly: performing point-to-surface iterative nearest point algorithm registration on the hypothesis with the highest terrain similarity and the prior submarine topography, and outputting heading and displacement information;
step three: and updating the hypothesis corresponding to the minimum MSD value by using the heading and displacement information output in the step three and one to obtain the matched heading and displacement information.
6. The seafloor terrain navigation parallel matching method of claim 5, wherein the MSD representation is:
wherein N represents the number of point clouds in the adaptation area, hpAnd hMAPElevation data of the two respective adaptation zones,
and
average elevation data of two adaptation zones respectively, i represents the ith elevation data.
7. The submarine topography navigation parallel matching method according to claim 6, wherein the step of acquiring data of the overlapping area in the first step is as follows:
and if the number of point clouds in the data with the closest point distance less than 0.1 is less than the threshold xi, the terrain reentry is not considered to exist at the moment, namely the overlapped area does not exist,
and if the number of the point clouds is larger than or equal to the threshold value xi, the terrain reentry exists at the moment, namely an overlapping area exists.
8. The seafloor terrain navigation parallel matching method of claim 7, wherein the xi is one fourth of the total number of the point clouds.
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