CN117109594B - Autonomous orientation method based on underwater light intensity gradient - Google Patents
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Abstract
The invention relates to an autonomous orientation method based on an underwater light intensity gradient, which belongs to the field of underwater autonomous navigation, and comprises the steps of firstly, acquiring an underwater light intensity image by using an underwater image sensor, and determining imaging pixels in a Snell window by combining a horizontal attitude angle; then solving a space observation vector and a light intensity gradient vector corresponding to the pixel by using the horizontal gesture, the camera model and the refraction law; determining the normal of a plane where the sun vector and the observation vector are positioned by using the observation vector and the light intensity gradient vector, and then solving an optimization equation according to the geometric orthogonal relationship to obtain sun vector estimation; and finally, according to the relation between the solar vector and the estimated solar vector, the carrier heading information is obtained. The invention considers the space motion information contained in the underwater light intensity field, performs solar tracking through the light intensity gradient, and provides a new solution for the reliable autonomous navigation of the underwater vehicle.
Description
Technical Field
The invention belongs to the field of underwater autonomous navigation, and particularly relates to an autonomous orientation method based on an underwater light intensity gradient.
Background
The autonomous underwater vehicle has wide application prospect in the aspects of marine environment monitoring, resource exploration and the like. The navigation system is a key component for guaranteeing the underwater vehicle to execute tasks. At present, an inertial navigation system has become an indispensable navigation means for an underwater vehicle, but because errors accumulate with the passage of time, auxiliary navigation means are needed for correction. Although global navigation satellite systems are widely used for integrated navigation with inertial measurement units, the shielding of satellite signals by water makes them unusable in underwater environments. On the other hand, the underwater sound localization system relies on transponder deployment, also limiting its range of application. Geophysical fields rely on existing databases and may not be available for unknown sea areas and unstructured underwater environments. Therefore, autonomous navigation methods that do not rely on a priori knowledge are a significant challenge for underwater environments.
The living beings can perceive a natural light field containing intensity information and polarization information for navigational orientation. The underwater bionic polarization navigation has the advantages of autonomy, no error accumulation, independent priori knowledge and the like, and can be used for underwater orientation or auxiliary inertial navigation in an underwater environment. The underwater polarized light field is divided into two parts by a refractive-produced fresnel window. Paper Bioinspired polarization vision enables underwater geolocalization proposes a navigation positioning method based on out-of-window polarization information, and the carrier position is solved by rotating a polarization camera to collect out-of-window horizontal polarization azimuth data in all directions. But the illumination outside the Snell window is weaker, and the polarization perception precision is reduced, so that the sun tracking performance is reduced, and the illumination inside the Snell window is sufficient and is often used for underwater sun tracking. In the prior art, refraction is regarded as a dominant factor affecting the distribution of polarized light field under water, and the scattering effect under shallow water is negligible. However, as the water depth increases, multiple scattering increases, the ideal rayleigh scattering polarization distribution symmetry is broken, resulting in deviation of the sun position estimate.
After being refracted by the water surface, the atmospheric scattered light is mainly compressed in a conical Snell window, and the current underwater light field navigation application mainly relies on polarization information in the underwater Snell window to solve the sun position and the carrier posture. Chinese patent application No. CN202210796764.0, which is an underwater polarization autonomous course calculation method based on zenith real-time tracking, calculates the course angle of a carrier by tracking polarization information at the zenith in a Snell window; the Chinese patent application (application number: CN202210828890. X) is a three-dimensional navigation attitude determination method based on underwater downlink radiation intensity and polarized light field, and acquires polarized information of an observation direction in an underwater Snell window through an image type polarization sensor to solve a heading angle. The method is mainly applied to shallow water environment, refraction is regarded as a main factor, but as the depth increases, the scattering effect is enhanced, so that the polarization distribution mode is influenced, and the relationship with the sun position is difficult to be clear.
Underwater light intensity fields are a more intuitive optical information but are currently rarely used. The light intensity field is a scalar field that contains less spatial information than the polarization field. But the light intensity field is also formed by sunlight scattering, and can be used as an alternative information source for sun tracking. On the celestial hemisphere, the light intensity decreases with increasing scattering angle, and the direction in which the light intensity changes most is toward the sun. Therefore, how to utilize gradient distribution of the underwater light intensity field to improve the robustness of solar position estimation and provide a new solution for reliable orientation of the underwater vehicle is a key problem in the autonomous navigation field of the underwater vehicle.
Disclosure of Invention
In order to solve the technical problems, the invention provides an autonomous orientation method based on an underwater light intensity gradient, which is used for determining an imaging range of a Snell window according to a horizontal posture of a carrier, and inverting the underwater light intensity gradient and an observation pixel in an effective imaging range into an atmospheric environment through a camera model and the horizontal posture of the carrier after the underwater refraction effect compensation is considered. And calculating based on the relation between the sky light intensity distribution, the sun vector and the observation vector to obtain the sun vector. The robustness of the underwater light intensity is utilized to improve the sun tracking precision, so that the inertial navigation accumulated error is corrected, and the underwater accurate heading is provided.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an autonomous orientation method based on underwater light intensity gradient comprises the following steps:
step 1), acquiring an underwater light intensity image according to an underwater cameraAnd smoothing the underwater light intensity image +.>；
Step (a)2) Inverting an effective observation vector: solving the vector from the gravity vector from the vector line +.>To a horizontal coordinate systemDirectional cosine matrix>Determining underwater light intensity image +.>Pixel region in the region of the snell window>According to the direction cosine matrix->And camera lens model->Determining pixel +.>Corresponding horizontal coordinate System->Observation vector +.>And carrying out refraction effect compensation to obtain a corresponding observation vector +.>The method comprises the steps of carrying out a first treatment on the surface of the Coordinates of->Is +.>Represented as pixels +.>；
Step 3), inverting the light intensity gradient vector: for the underwater light intensity image in step 1)Make a refractive intensity compensation correction and calculate pixel +.in step 2)>Spatially discrete gradients ∈>According to pixelCoordinate to observation vector +.>Cartesian coordinates +.>Mapping relation between the two, calculating to obtain Jacobian matrix +.>Binding to the vector->To the horizontal coordinate system->Lower directional cosine matrix->Inversion of the observation vector in step 2)Corresponding atmospheric light intensity gradient->；
Step 4), calculating the sun vectorVector orientation was performed: according to step 2), step 3) a plurality of pixel areas are calculated +.>The corresponding observation vector of the effective pixels in the atmosphere +.>Gradient with atmospheric light intensity->Further solving the atmospheric light intensity gradient +.>And observation vector->Determining the normal vector of a plane->A set of normal vector measurement matrix is formed>The method comprises the steps of carrying out a first treatment on the surface of the According to sun vector->Normal vector->Solving the orthogonal relation of matrix->Minimum feature value +.>Corresponding feature vectors, the sun vector is obtained>Superscript T denotes the transpose of the matrix; obtaining a navigation system in combination with an astronomical calendar>Under sun vector, solving direction cosine matrix +.>Acquiring accurate underwater heading->。
Further, the step 2) includes:
determining the underwater light intensity image in step 1) according to the law of refractionPixel area belonging to the snell window in +.>Arbitrary pixel area->Pixels in +.>Its corresponding horizontal coordinate system +.>Observation vector +.>Direction cosine matrix from navigation system carrier to horizontal coordinate system>Is determined with the camera lens model according to the horizontal coordinate system +.>Observation vector +.>Solving for pixels +.>Corresponding refraction angle->；
Pixels valid for arbitrary underwater light intensity imagesWhich corresponds to the horizontal coordinate system->Observation vector +.>Compensating the refraction effect of air-water interface, and observing the corresponding vector +.>The method comprises the following steps:
，
wherein,，/>refractive index of air>Refractive index of seawater>Representing vectorsAnd vector->Dot product.
Further, the step 3) includes:
for underwater light intensity imageAfter compensation by adopting refraction law, calculating the underwater light intensity image +.>Is>；
Imaging underwater light intensityIs>Mapping back to horizontal coordinate System->In (3) obtaining the light intensity gradient corresponding to the observation vector>The method comprises the following steps:
，
wherein,respectively horizontal coordinate system->Is>Is a Jacobian matrix, modeled by a camera lens +.>Corresponding pixel->To the horizontal coordinate system->Observation vector +.>Corresponding Cartesian coordinatesThe mapping relation of (2) is calculated, and the following conditions are satisfied:
，
further, the step 4) includes:
using the atmospheric observation vector calculated in step 2)Atmospheric light intensity gradient calculated in step 3 +.>Constructing a vector perpendicular to the sun +.>Observation vector in atmosphere +.>Atmospheric light intensity gradient->Normal vector of the large circle>For a group of active pixels, the corresponding normal vector is represented as a matrix +.>：
，
Wherein,solar vector +.>For matrix->Minimum feature value +.>Corresponding feature vectors;
establishing a horizontal coordinate systemLower sun vector->Solving navigation system with astronomical calendar>Lower sun vector->Converting the relation to obtain the accurate heading of the underwater carrier>Expressed as:
，
wherein,for vector->Is>Individual components, satisfy->，Representation->Is>A component.
Compared with the prior art, the invention has the following beneficial effects:
the invention converts the light intensity scalar field into the vector field by calculating the gradient, thereby utilizing the space information contained in the light intensity field and having the advantages of autonomy, no error accumulation and the like. By the underwater light intensity gradient method, the influence of multiple scattering of water on solving the sun position by the polarization information is avoided, so that the constant deviation of sun tracking based on the polarization information is eliminated, and the accuracy and the robustness of sun dynamic tracking are improved.
Drawings
FIG. 1 is a flow chart of an autonomous orientation method based on underwater light intensity gradients.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without the inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
According to one embodiment of the invention, as shown in fig. 1, the autonomous orientation method based on the underwater light intensity gradient comprises the following specific implementation steps:
step 1, acquiring an underwater light intensity image according to an underwater cameraAnd smoothing the image using a gaussian filter.
Step 2, determining the underwater light intensity image in the step 1 according to the refraction lawPixel range belonging to the snell window>The method comprises the following steps:
，
wherein,pixels for underwater light intensity image->Corresponding to the refraction angle of the observation vector, < >>Refractive index of air>Is the refractive index of seawater;
pixels of underwater light intensity imageObservation vector in corresponding horizontal coordinate system>By a navigation system carrier->Tied to the horizontal coordinate system->Direction cosine matrix of the system>And camera lens model->Determining that:
，
wherein the method comprises the steps ofFor pixels +.>By camera lens model->Inverted observation direction, direction cosine matrix +.>The method comprises the following steps:
，
wherein the superscript T represents the transpose of the matrix;corresponding roll angle->Pitch angle->The gravity vector is obtained by solving the gravity vector, and the gravity vector meets the following conditions:
，
wherein,the gravitational vector under the system is->，/>The gravitational vector under the system is->Measured by accelerometer specific force +.>And (5) obtaining approximation.
Pixel arrangementCorresponding refraction angle->The method comprises the following steps:
，
wherein,representation->Is>A component.
Further, for any underwater light intensity image effective pixelWhich corresponds to the observation vector +.>Compensating the refraction effect of air-water interface, and observing vector +.>The method comprises the following steps:
，
wherein,，/>refractive index of air>Refractive index of seawater>Representing vectorsAnd vector->Dot product.
Step 3, for underwater light intensity imageAfter compensation by adopting refraction law, calculating the underwater light intensity image +.>Is>：
，
Wherein,is a partial derivative operator.
Further, the space discrete numerical gradient of the underwater light intensity imageMapping back to horizontal coordinate System->In the system, the light intensity gradient +.>The method comprises the following steps:
，
wherein,respectively horizontal coordinate system->Three orthonormal groups of the line, +.>Is a Jacobian matrix, modeled by a camera lens +.>Corresponding pixel->Observation vector up to horizontal coordinate system +.>Corresponding Cartesian coordinatesThe mapping relation of (2) is calculated, and the following conditions are satisfied:
，
step 4, utilizing the arbitrary underwater light intensity image effective pixel calculated in the step 2Corresponding to the observation vector after refraction compensation>Atmospheric light intensity corresponding to the observation vector calculated in step 3Gradient->It is possible to construct a vector perpendicular to the sun +.>Observation vector->Light intensity gradient->Normal vector of the large circle>For a group of active pixels, the corresponding normal vector is represented as a matrix +.>：
，
Wherein,for the number of available pixels +.>Solar vector in series->The optimal estimation can be performed by minimizing the deviation:
，
wherein,as an optimization function with respect to the sun vector and Lagrangian multiplier>Is Lagrangian multiplier +.>Is->Is (are) norms of->The method can obtain:
，
solar vectorFor matrix->Minimum feature value +.>Corresponding feature vectors.
Establishment ofUnder-tie sun vector->With astronomical calendar, solve->Under-tie sun vector->Is a conversion relation of:
，
wherein,for navigation system->Tied to the horizontal coordinate system->The directional cosine matrix of the system is expressed as follows:
，
according to the relation, the accurate heading of the underwater carrier can be finally obtainedExpressed as:
，
wherein,for vector->Is>Individual components, satisfy->，Representation->Is>A component.
While the foregoing has been described in relation to illustrative embodiments thereof, so as to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, but is to be construed as limited to the spirit and scope of the invention as defined and defined by the appended claims, as long as various changes are apparent to those skilled in the art, all within the scope of which the invention is defined by the appended claims.
Claims (4)
1. An autonomous orientation method based on underwater light intensity gradient is characterized by comprising the following steps:
step 1), acquiring an underwater light intensity image according to an underwater cameraAnd smoothing the underwater light intensity image by using a Gaussian filter；
Step 2), inverting the effective observation vector: solving the vector from the gravity vector from the vector line +.>To the horizontal coordinate system->Directional cosine matrix>Determining underwater light intensity image +.>Pixel region in the region of the snell window>According to the direction cosine matrix->And camera lens model/>Determining pixel +.>Corresponding horizontal coordinate System->Observation vector +.>And carrying out refraction effect compensation to obtain a corresponding observation vector +.>The method comprises the steps of carrying out a first treatment on the surface of the Coordinates of->Is +.>Represented as pixels +.>；
Step 3), inverting the light intensity gradient vector: for the underwater light intensity image in step 1)Make a refractive intensity compensation correction and calculate pixel +.in step 2)>Spatially discrete gradients ∈>According to pixel->Coordinate to observation vector +.>Cartesian coordinates +.>Mapping relation between the two, calculating to obtain Jacobian matrix +.>Binding to the vector->To the horizontal coordinate system->Lower directional cosine matrix->Inversion of the observation vector in step 2)>Corresponding atmospheric light intensity gradient->；
Step 4), calculating the sun vectorVector orientation was performed: according to step 2), step 3) a plurality of pixel areas are calculated +.>The corresponding observation vector of the effective pixels in the atmosphere +.>Gradient with atmospheric light intensity->Further, the methodSolving the atmospheric light intensity gradient +.>And observation vector->Determining the normal vector of a plane->A set of normal vector measurement matrix is formed>The method comprises the steps of carrying out a first treatment on the surface of the According to sun vector->Normal vector->Solving the orthogonal relation of matrix->Minimum feature value +.>Corresponding feature vectors, the sun vector is obtained>Superscript T denotes the transpose of the matrix; obtaining a navigation system in combination with an astronomical calendar>Under sun vector, solving direction cosine matrix +.>Acquiring accurate underwater heading->。
2. An autonomous orientation method based on underwater light intensity gradients as claimed in claim 1, characterized in that the step 2) comprises:
determining the underwater light intensity image in step 1) according to the law of refractionPixel area belonging to the snell window in +.>Arbitrary pixel area->Pixels in +.>Its corresponding horizontal coordinate system +.>Observation vector +.>From the carrier system of the navigation system>To the horizontal coordinate system->Directional cosine matrix>And camera lens model->Determination according to a horizontal coordinate systemObservation vector +.>Solving for pixels +.>Corresponding refraction angle->；
Pixels valid for arbitrary underwater light intensity imagesWhich corresponds to the horizontal coordinate system->Observation vector +.>Compensating the refraction effect of air-water interface, and observing the corresponding vector +.>The method comprises the following steps:
，
wherein,，/>refractive index of air>Refractive index of seawater>Representation vector->And vector->Dot product.
3. An autonomous orientation method based on underwater light intensity gradients as claimed in claim 2, characterized in that the step 3) comprises:
for underwater light intensity imageAfter compensation by adopting refraction law, calculating the underwater light intensity image +.>Is>；
Imaging underwater light intensityIs>Mapping back to horizontal coordinate System->In (3) obtaining the light intensity gradient corresponding to the observation vector>The method comprises the following steps:
，
wherein,respectively horizontal coordinate system->Is>Is a Jacobian matrix, modeled by a camera lens +.>Corresponding pixel->To the horizontal coordinate system->Observation vector +.>Corresponding Cartesian coordinatesThe mapping relation of (2) is calculated, and the following conditions are satisfied:
。
4. an autonomous orientation method based on underwater light intensity gradients as claimed in claim 3, characterized in that the step 4) comprises:
using the observation vector calculated in step 2)Atmospheric light intensity gradient calculated in step 3 +.>Constructing a vector perpendicular to the sun +.>Observation vector->Atmospheric light intensity gradient->Normal vector of the large circle>For a group of active pixels, the corresponding normal vector is represented as a matrix +.>：
，
Wherein,solar vector +.>For matrix->Minimum feature value +.>Corresponding feature vectors;
establishing a horizontal coordinate systemLower sun vector->Solving navigation system with astronomical calendar>Lower sun vector->Converting the relation to obtain the accurate heading of the underwater carrier>Expressed as:
，
wherein,for vector->Is>Individual components, satisfy->，/>Representation->Is>A component.
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