CN113916253B

CN113916253B - Covariance determining method and related device

Info

Publication number: CN113916253B
Application number: CN202010657732.3A
Authority: CN
Inventors: 王建国; 陈默
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-07-09
Filing date: 2020-07-09
Publication date: 2024-05-03
Anticipated expiration: 2040-07-09
Also published as: CN113916253A; WO2022007465A1

Abstract

The embodiment of the application provides a method and a related device for determining covariance, in particular to a sensor which can be used for assisting driving, automatic driving or unmanned driving. The method comprises the following steps: acquiring position measurement data from at least one sensor; and determining covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the statistical characteristics of the rectangular coordinate conversion measurement error and the position measurement data. The method and the device provided by the application can improve the covariance estimation precision of rectangular coordinate conversion measurement errors of the sensor position measurement data and describe the statistical characteristics of the position measurement data more accurately, so that the performance of functions such as state estimation, data association and occupied grid estimation of the position measurement data under the application scenes such as tracking, navigation and environment perception is greatly improved, and meanwhile, the ADAS capacity of an advanced driving assistance system is improved.

Description

Covariance determining method and related device

Technical Field

The application relates to the technical field of sensor data processing, in particular to a covariance determining method and a covariance determining related device.

Background

The sensor is a detecting device, which can sense the information to be measured and convert the sensed information into electric signals or other information output in the required form according to a certain rule so as to meet the requirements of information transmission, processing, storage, display, recording, control and the like. With the development of society, intelligent terminals such as intelligent transportation devices, intelligent home devices, robots and the like are gradually entering into daily life of people. The sensor plays a very important role in the intelligent terminal.

A sensor designed based on time-of-flight (TOF) principle includes: millimeter wave radar, ultrasonic radar, sonar, laser radar, or the like. The measurement data of such sensors are typically recorded in the form of a polar or spherical coordinate system, the content typically including information describing the position of the object, such as distance, azimuth angle, pitch angle, etc. However, in practical application, the above measurement data is more convenient to be converted into a rectangular coordinate system. For example, the above sensors are widely arranged in an advanced driving assistance system (ADVANCED DRIVER ASSISTANT SYSTEM, ADAS) or an automatic driving (autonomous driving, AD) system or an unmanned aerial vehicle system or an intelligent agent system such as a robot, and surrounding environment information is perceived by measuring data so as to be used for a target tracking scene. In the above scenario, the rectangular coordinate system will be more advantageous to describe the motion modeling of the object, and therefore, polar or spherical position measurement data is typically converted to rectangular coordinates for use.

Although the individual measurement errors of the polar or spherical measurement data described above are typically statistically independent, the corresponding transformed measurement errors after transformation to a rectangular coordinate system are statistically relevant. The statistical properties of the conversion measurement error can be measured by a covariance matrix, wherein the covariance between two measurement error components is typically used to measure the statistical correlation of the two error components. Current sensor related products typically provide only the variance or standard deviation or root mean square error (rootmeansquarederror, RMSE) of rectangular coordinate measurement errors, or only the measurement errors of spherical or polar coordinate measurement data corresponding to a particular angle; this will lead to a lack of covariance or too low a precision of the rectangular transformation measurement error, resulting in performance loss for functions such as state estimation, data correlation, occupancy grid (occupancy grid map, OGM) estimation, etc. in application scenarios such as tracking, navigation, environmental awareness, etc.

Disclosure of Invention

The embodiment of the application discloses a method and a related device for determining covariance, which can improve covariance estimation precision of rectangular coordinate conversion measurement errors of sensor position measurement data and describe statistical characteristics of the position measurement data more accurately, so that the position measurement data can greatly improve performance of functions such as state estimation, data association, grid occupation estimation and the like in application scenes such as tracking, navigation, environment sensing and the like.

In a first aspect, an embodiment of the present application discloses a method for determining covariance, including:

acquiring position measurement data from at least one sensor;

And determining covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the statistical characteristics of the rectangular coordinate conversion measurement error and the position measurement data.

In an embodiment of the application, first position measurement data from at least one sensor is acquired, the position measurement data comprising position information of an environment or an object, the position measurement data of most sensors being in the form of spherical coordinates or polar coordinates. Spherical coordinate position information, such as distance, azimuth angle, pitch angle and the like of an obstacle or a moving object in the environment relative to a sensor; polar position information, such as distance and azimuth angle data of an obstacle or a moving object in the environment relative to a sensor;

In actual application scenes such as tracking, navigation, environment sensing and the like, specific functions such as state estimation, data association, occupied grid estimation and the like are more convenient to use by converting the polar coordinate or spherical coordinate position measurement data into a rectangular coordinate form. At this time, it is necessary to determine the rectangular coordinate position measurement data and the corresponding statistical characteristics of the conversion measurement errors, and the statistical characteristics of the rectangular coordinate conversion position measurement errors may include variance, standard deviation, root mean square error, and the like. Although polar or spherical position measurement errors are typically statistically independent, the conversion measurement errors corresponding to rectangular position measurement data are typically statistically correlated, and thus determining the covariance of the position measurement data conversion measurement errors may more accurately describe the statistical nature of the rectangular position measurement errors. Through the embodiment, the covariance estimation precision of rectangular coordinate conversion measurement errors of the sensor position measurement data can be improved, and the statistical characteristics of the position measurement data can be described more accurately, so that the performance of functions such as state estimation, data association and occupied grid estimation of the position measurement data in application scenes such as tracking, navigation and environment sensing is greatly improved.

In a possible implementation manner of the first aspect, the determining the covariance of the rectangular coordinate transformation measurement error of the position measurement data includes:

and determining covariance of the rectangular coordinate conversion measurement error of the position measurement data according to diagonalization relation of a covariance matrix of the rectangular coordinate conversion measurement error, wherein the diagonalization relation is determined according to the position measurement data.

In the embodiment of the application, based on the statistical characteristics of the position measurement data and the rectangular coordinate transformation measurement error, the covariance of the rectangular coordinate transformation measurement error of the position measurement data is determined according to the diagonalization relation of the covariance matrix of the rectangular coordinate transformation measurement error, wherein the diagonalization relation can be a characteristic value decomposition form of the covariance matrix of the rectangular coordinate transformation measurement error, such as r=udu ^T or U ^T ru=d, or reasonable deformation of the diagonalization form, wherein R is the covariance matrix, D is the diagonal matrix, U is the orthogonal matrix determined according to the position measurement data, and U ^T is the transpose matrix of the orthogonal matrix.

From the diagonalization, the orthonormal matrix, which can be determined from the position measurement data, and the statistical characteristics of the rectangular coordinate transformation measurement error, such as the variance, standard deviation or root mean square error of the rectangular coordinate transformation measurement error, can be determined from the diagonalization of the covariance matrix.

In this embodiment, the solution of the diagonal matrix is an unnecessary step, and the covariance of the conversion measurement error of the position measurement data is not affected, and optionally, the diagonal matrix D may be further obtained based on the covariance matrix R, the orthogonal matrix U and the diagonalization relation d=u ^T RU, which may be used to determine an elliptical area of the error, and the method may be correspondingly applied to the estimation of the occupied grid to improve the accuracy of the occupied grid. The covariance of the rectangular coordinate conversion measurement error of the position measurement data is determined through the diagonalization relation of the covariance matrix, so that the covariance estimation precision of the rectangular coordinate conversion measurement error of the position measurement data of the sensor can be improved, the statistical characteristics of the position measurement data can be described more accurately, and the performance of the functions of state estimation, data association, grid occupation estimation and the like of the position measurement data in application scenes such as tracking, navigation, environment sensing and the like can be greatly improved.

In a further possible implementation manner of the first aspect, the position measurement data is two-dimensional data, and the statistical characteristic includes a first variance or standard deviation or root mean square error of a first dimension component in the position measurement data rectangular conversion measurement error and a second variance or standard deviation or root mean square error of a second dimension component in the position measurement data rectangular conversion measurement error;

the determining the covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the statistical characteristics of the rectangular coordinate conversion measurement error and the position measurement data comprises the following steps:

and determining a covariance matrix of rectangular coordinate conversion measurement errors of the position measurement data according to the position measurement data, the first variance or standard deviation or root mean square error and the second variance or standard deviation or root mean square error.

In the embodiment of the application, the position measurement data is two-dimensional position measurement data, and the covariance of the rectangular coordinate conversion measurement error of the position measurement data is determined by using the two-dimensional position measurement data, a first variance or standard deviation or root mean square error and a second variance or standard deviation or root mean square error, wherein the first variance or standard deviation or root mean square error is the statistical characteristic of the rectangular coordinate conversion measurement error, the first variance and the second variance are taken as examples, the first variance represents the variance of the first dimension component in the rectangular coordinate conversion measurement error of the position measurement data, and the second variance represents the variance of the second dimension component in the rectangular coordinate conversion measurement error of the position measurement data. The implementation mode for determining the covariance of the two-dimensional position measurement data conversion measurement error can improve the covariance estimation precision of the two-dimensional position measurement data conversion measurement error, so that the statistical characteristic description accuracy of the position measurement data is greatly improved.

In a further possible implementation manner of the first aspect, the covariance matrix of the rectangular coordinate transformation measurement error of the position measurement data includes:

wherein R is a covariance matrix of the rectangular coordinate conversion measurement error, R ₁₁ is the first variance, R ₂₂ is the second variance, R ₁₂ is a covariance between the first dimension component and the second dimension component of the rectangular coordinate conversion measurement error, and the covariance between the first dimension component and the second dimension component of the rectangular coordinate conversion measurement error is obtained according to the position measurement data, the first variance, and the second variance.

In the embodiment of the present application, the position measurement data is two-dimensional position measurement data, and the covariance between the first dimension component and the second dimension component of the rectangular coordinate transformation measurement error is obtained according to statistical characteristics (first variance and second variance) of each component of the position measurement data and the rectangular coordinate transformation measurement error, so that a covariance matrix of the position measurement data transformation measurement error can be determined based on the diagonalization relation. The implementation mode for determining the covariance of the rectangular coordinate conversion measurement error of the two-dimensional position measurement data can improve the covariance estimation precision of the rectangular coordinate conversion measurement error of the position measurement data, so that the accuracy of the statistical characteristic description of the rectangular coordinate conversion measurement error of the position measurement data is greatly improved.

In a further possible implementation manner of the first aspect, the R ₁₂ includes:

Wherein R ₁₂ is a covariance between the first dimension component and the second dimension component representing the position measurement data rectangular coordinate conversion measurement error, c _θ represents cos θ, s _θ represents sin θ, and the angle θ is an azimuth angle. The cos θ and sin θ may be calculated from measured or predicted values or filtered or smoothed values of azimuth angles, or may be determined from rectangular components x and y.

In the embodiment of the application, the position measurement data is two-dimensional position measurement data, the covariance R ₁₂ of the rectangular coordinate conversion measurement error of the position measurement data is determined by using the position measurement data, the first variance and the second variance, and the above formula implementation mode for determining R ₁₂ can improve the covariance estimation precision of the rectangular coordinate conversion measurement error of the position measurement data, so that the accuracy of the statistical characteristic description of the rectangular coordinate conversion measurement error of the position measurement data is greatly improved.

In a further possible implementation of the first aspect, the position measurement data is three-dimensional data, the statistical property comprises a first variance or standard deviation or root mean square error of a first dimension component in the position measurement data rectangular conversion measurement error, and a second variance or standard deviation or root mean square error of a second dimension component in the position measurement data rectangular conversion measurement error, and a third variance or standard deviation or root mean square error of a third dimension component in the position measurement data rectangular conversion measurement error;

And determining covariance of rectangular coordinate conversion measurement errors of the position measurement data according to the position measurement data, the first variance, the standard deviation, the second variance, the third variance and the third variance.

In the embodiment of the application, the position measurement data is three-dimensional data, and the covariance of the position measurement data conversion measurement error is determined by using three-dimensional position measurement data, a first variance or standard deviation or root mean square error, a second variance or standard deviation or root mean square error and a third variance or standard deviation or root mean square error, wherein the first variance or standard deviation or root mean square error is the statistical characteristic of the position measurement error converted at rectangular coordinates, the first variance, the second variance and the third variance are taken as examples, the first variance represents the variance of the first dimension component of the position measurement data rectangular coordinate conversion measurement error, the second variance represents the variance of the second dimension component of the position measurement data rectangular coordinate conversion measurement error, and the third variance represents the variance of the third dimension component of the position measurement data rectangular coordinate conversion measurement error. The implementation mode of determining the covariance of the position measurement data of the three-dimensional data can improve the covariance accuracy of the rectangular coordinate conversion measurement error of the position measurement data, so that the description accuracy of the statistical characteristics of the rectangular coordinate conversion measurement error of the position measurement data is greatly improved.

In a further possible implementation manner of the first aspect, the determining the covariance of the rectangular coordinate transformation measurement error of the position measurement data according to the position measurement data and the first variance or standard deviation or root mean square error and the second variance or standard deviation or root mean square error and the third variance or standard deviation or root mean square error includes:

Determining a covariance matrix of rectangular coordinate conversion measurement errors of the position measurement data according to the first variance, the second variance, the third variance, the first target covariance, the second target covariance and the third target covariance;

Wherein the first target covariance represents a covariance between the first dimension component and the second dimension component in the position measurement data rectangular coordinate conversion measurement error; the second target covariance represents covariance between the first dimension component and the third dimension component in the rectangular coordinate conversion measurement error of the position measurement data; the third target covariance represents a covariance between the second-dimensional component and the third-dimensional component in the position measurement data rectangular transformation measurement error.

In the embodiment of the application, the position measurement data are three-dimensional position measurement data, and the covariance among the components in the rectangular coordinate conversion measurement error of the position measurement data can be obtained according to the variances of the components in the rectangular coordinate conversion measurement error and the three-dimensional position measurement data. Specifically, a first target covariance is obtained according to the position measurement data, the first variance and the second variance, wherein the first target covariance represents covariance between a first-dimension component and a second-dimension component in rectangular coordinate conversion measurement errors of the position measurement data; similarly, a second target covariance is obtained according to the position measurement data, the first variance and the third variance, wherein the second target covariance represents covariance between a first-dimensional component and a third-dimensional component in rectangular coordinate conversion measurement errors of the position measurement data; and obtaining a third target covariance according to the position measurement data, the second variance and the third variance, wherein the third target covariance represents covariance between the second-dimensional component and the third-dimensional component in rectangular coordinate conversion measurement errors of the position measurement data. The covariance matrix of the rectangular coordinate conversion measurement error of the position measurement data is determined according to the first variance, the second variance, the third variance, the first target covariance, the second target covariance and the third target covariance. By the implementation mode of determining the covariance of the three-dimensional position measurement data, the covariance estimation precision of the rectangular coordinate conversion measurement error of the position measurement data can be improved, and the description accuracy of the rectangular coordinate conversion measurement error statistical characteristic of the position measurement data is greatly improved.

Wherein, R is a covariance matrix of the rectangular coordinate conversion measurement error, R ₁₁ is the first variance, R ₂₂ is the second variance, R ₃₃ is the third variance, R ₁₂ is the first target covariance, R ₁₃ is the second target covariance, and R ₂₃ is the third target covariance.

In the embodiment of the application, a specific formula implementation of a covariance matrix of the rectangular coordinate conversion measurement error of the three-dimensional position measurement data is provided, so that the covariance of the rectangular coordinate conversion measurement error of the position measurement data can be determined based on the diagonalized relation of the covariance matrix. Through the implementation mode of determining the covariance matrix of the three-dimensional position measurement data, the covariance estimation precision of the rectangular coordinate conversion measurement error of the position measurement data can be improved, and the accuracy of the statistical characteristic description of the rectangular coordinate conversion measurement error of the position measurement data is greatly improved.

And determining covariance of rectangular coordinate conversion measurement errors of the position measurement data according to the first variance, the second variance, the third variance and an angle matrix, wherein the angle matrix is determined by trigonometric function values of azimuth angles and/or pitch angles.

In the embodiment of the present application, another implementation manner of determining the covariance of the rectangular coordinate conversion measurement error of the position measurement data is provided, where the covariance of the rectangular coordinate conversion measurement error of the position measurement data is determined according to the first variance, the second variance, the third variance and the angle matrix, where the first variance, the second variance and the third variance represent statistical characteristics of the rectangular coordinate conversion position measurement error of the position measurement data, the angle matrix is determined according to the position measurement data, and in particular, may be determined according to trigonometric function values of an azimuth angle and/or a pitch angle of the position, for example, sine function values or cosine function values of the azimuth angle and/or the pitch angle, and the like. Through the implementation mode of determining the covariance of the rectangular coordinate conversion measurement error of the three-dimensional position measurement data, the covariance estimation precision of the rectangular coordinate conversion measurement error of the position measurement data can be improved, and the accuracy of the statistical characteristic description of the rectangular coordinate conversion measurement error of the position measurement data is greatly improved.

In a further possible implementation manner of the first aspect, the angle matrix includes:

wherein A is the angle matrix, the Representation/>The c _θ represents cos θ, the/>Representation ofThe s _θ represents sin θ, the/>And the angle θ is the azimuth angle.

In the embodiment of the application, the position measurement data is three-dimensional position measurement data, and another implementation way of determining covariance of rectangular coordinate conversion measurement errors of the three-dimensional position measurement data is provided, namely, the position measurement data is three-dimensional position measurement data, and according to a specific implementation way of an angle matrix, wherein the angle matrix is determined according to trigonometric function values of an azimuth angle and/or a pitch angle, the trigonometric function values of the azimuth angle and/or the pitch angle can be obtained according to the position measurement data, and in particular,For the pitch angle of the target relative to the sensor, θ is the azimuth angle of the target relative to the sensor, and the target can be the surrounding environment of the sensor, such as an obstacle in the environment, or the like, or can be a moving target; further:

Where d is the spatial distance of the object relative to the sensor, r is the planar distance of the object relative to the sensor, and is the projection component of the spatial distance d on a plane formed by the first dimension component and the second dimension component of the rectangular coordinate system, x is the first dimension component of the rectangular coordinate system of the position of the object relative to the sensor, y is the second dimension component of the rectangular coordinate system of the position of the object relative to the sensor, and z is the third dimension component of the rectangular coordinate system of the position of the object relative to the sensor. Through the implementation mode of determining the covariance of the rectangular coordinate conversion measurement error of the three-dimensional position measurement data, the covariance estimation precision of the rectangular coordinate conversion measurement error of the position measurement data can be improved, and the accuracy of the statistical characteristic description of the rectangular coordinate conversion measurement error of the position measurement data is greatly improved.

In a further possible implementation manner of the first aspect, the covariance of the rectangular coordinate transformation measurement error of the position measurement data includes:

wherein, the a is the angle matrix, the R ₁₁ is the first variance, the R ₂₂ is the second variance, the R ₃₃ is the third variance, the R ₁₂ is a first target covariance, the R ₁₃ is a second target covariance, the R ₂₃ is a third target covariance, and the first target covariance represents a covariance between the first dimension component and the second dimension component in the rectangular coordinate conversion measurement error of the position measurement data; the second target covariance represents covariance between the first dimension component and the third dimension component in the rectangular coordinate conversion measurement error of the position measurement data; the third target covariance represents a covariance between the second-dimensional component and the third-dimensional component in the position measurement data rectangular transformation measurement error.

In the embodiment of the application, the position measurement data is three-dimensional position measurement data, and another implementation manner of determining the covariance of the rectangular coordinate conversion measurement error of the position measurement data is further supplemented, namely, a formula relation among the first variance, the second variance, the third variance, the angle matrix and the covariance of the rectangular coordinate conversion measurement error of the position measurement data is provided, and according to the formula relation, the covariance of the rectangular coordinate conversion measurement error of the position measurement data can be determined according to the first variance, the second variance, the third variance and the angle matrix, wherein the determined first target covariance, second target covariance and third target covariance respectively represent the covariance between two different components in the rectangular coordinate conversion measurement error of the position measurement data, and further, the covariance matrix of the rectangular coordinate conversion measurement error of the position measurement data can be obtained according to the first target covariance, the second target covariance and the third target covariance, the first variance, the second variance and the third variance, and the diagonalization relation of the covariance matrix. By the implementation mode of determining the covariance of the three-dimensional position measurement data, the covariance estimation precision of the rectangular coordinate conversion measurement error of the position measurement data can be improved, and the accuracy of the rectangular coordinate conversion measurement error statistical characteristic description of the position measurement data is greatly improved.

In a further possible implementation manner of the first aspect, the method further includes:

And determining a diagonal matrix according to the covariance matrix and an orthogonal matrix, wherein diagonal elements of the diagonal matrix are variances of statistically independent errors, and the orthogonal matrix is determined by the position measurement data.

In the embodiment of the application, an implementation manner for obtaining a diagonal matrix is provided, and in particular, the diagonal matrix can be further determined based on a covariance matrix and an orthogonal matrix of rectangular coordinate conversion measurement errors of position measurement data and a diagonalization relation. In the diagonalization relation of the covariance matrix, the diagonal matrix is unknown, and in the covariance process of determining the rectangular coordinate conversion measurement error of the position measurement data, the solving of the diagonal matrix is an unnecessary step, the covariance of the rectangular coordinate conversion measurement error of the position measurement data is not affected, but the diagonal matrix can be used for determining an elliptical area of the error, and the diagonal matrix can be correspondingly applied to the occupied grid estimation to improve the precision of the occupied grid.

In a further possible implementation of the first aspect, the position measurement data is data in one of the following coordinate systems: rectangular coordinate system, polar coordinate system, spherical coordinate system.

In a further possible implementation of the first aspect, the position measurement data comprises a distance, an azimuth angle and a pitch angle; or, the position measurement data includes a distance and an azimuth.

In the embodiment of the application, it can be seen that when the position measurement data is two-dimensional position measurement data, the distance between the measured object and the sensor and the azimuth angle of the measured object relative to the sensor are included, and when the position measurement data is three-dimensional position measurement data, the distance between the measured object and the sensor, the azimuth angle of the measured object relative to the sensor and the pitch angle of the measured object relative to the sensor are included.

In a second aspect, an embodiment of the present application discloses an apparatus for determining covariance, including:

an acquisition unit for acquiring position measurement data from at least one sensor;

and the determining unit is used for determining covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the statistical characteristics of the rectangular coordinate conversion measurement error and the position measurement data.

In a possible implementation manner of the second aspect, in determining the covariance of the rectangular measurement error of the position measurement data, the determining unit is specifically configured to determine the covariance of the rectangular measurement error of the position measurement data according to a diagonalization relation of a covariance matrix of the rectangular measurement error, where the diagonalization relation is determined according to the position measurement data.

In a further possible implementation manner of the second aspect, the position measurement data is two-dimensional data, and the statistical characteristic includes a first variance or standard deviation or root mean square error of a first dimension component in the position measurement data rectangular conversion measurement error and a second variance or standard deviation or root mean square error of a second dimension component in the position measurement data rectangular conversion measurement error;

The determining unit is specifically further configured to determine a covariance matrix of the rectangular coordinate transformation measurement error of the position measurement data according to the position measurement data, the first variance or standard deviation or root mean square error, and the second variance or standard deviation or root mean square error.

In a further possible implementation manner of the second aspect, the covariance matrix of the rectangular coordinate transformation measurement error of the position measurement data includes:

In a further possible embodiment of the second aspect, the R ₁₂ includes:

Wherein R ₁₂ is a covariance between the first dimension component and the second dimension component representing the position measurement data rectangular coordinate conversion measurement error, c _θ represents cos θ, s _θ represents sin θ, and the angle θ is an azimuth angle. The cos θ and sin θ may be calculated from measured or predicted values or filtered or smoothed values of azimuth angles, or may be determined from rectangular coordinate components x and y.

In a further possible implementation manner of the second aspect, the position measurement data is three-dimensional data, and the statistical characteristic includes a first variance or standard deviation or root mean square error of a first dimension component in the position measurement data rectangular coordinate conversion measurement error, a second variance or standard deviation or root mean square error of a second dimension component in the position measurement data rectangular coordinate conversion measurement error, and a third variance or standard deviation or root mean square error of a third dimension component in the position measurement data rectangular coordinate conversion measurement error;

the determining unit is specifically further configured to determine a covariance of the rectangular coordinate transformation measurement error of the position measurement data according to the position measurement data, the first variance, the standard deviation, the root mean square error, the second variance, the standard deviation, the third variance, the standard deviation, the root mean square error, and the covariance of the rectangular coordinate transformation measurement error of the position measurement data according to the statistical characteristics of the rectangular coordinate transformation measurement error of the position measurement data and the rectangular coordinate transformation measurement data.

In a further possible implementation manner of the second aspect, the determining unit is specifically further configured to determine a covariance matrix of the position measurement data rectangular coordinate transformation measurement error according to the first variance, the second variance, the third variance, the first target covariance, the second target covariance, and the third target covariance, in determining a covariance of the position measurement data rectangular coordinate transformation measurement error according to the position measurement data, the first variance, the standard deviation, the root mean square error, the second variance, the standard deviation, the root mean square error, and the third variance, or the standard deviation, or the root mean square error;

In a further possible implementation manner of the second aspect, the determining unit is specifically further configured to determine the covariance of the position measurement data rectangular conversion measurement error according to the first variance, the second variance, the third variance, and an angle matrix, which is determined by trigonometric function values of azimuth and/or pitch angle, in determining the covariance of the position measurement data rectangular conversion measurement error according to the position measurement data, the first variance, the standard deviation, the root mean square error, the second variance, the standard deviation, the root mean square error, and the third variance.

In a further possible implementation manner of the second aspect, the angle matrix includes:

Where d is the spatial distance of the object relative to the sensor, r is the planar distance of the object relative to the sensor, and is the projection component of the spatial distance d on the plane formed by the first dimension component and the second dimension component of the rectangular coordinate system, x is the first dimension component of the rectangular coordinate system of the position of the object relative to the sensor, y is the second dimension component of the rectangular coordinate system of the position of the object relative to the sensor, and z is the third dimension component of the rectangular coordinate system of the object relative to the sensor. Through the implementation mode of determining the covariance of the rectangular coordinate conversion measurement error of the three-dimensional position measurement data, the covariance estimation precision of the rectangular coordinate conversion measurement error of the position measurement data can be improved, and the accuracy of the statistical characteristic description of the rectangular coordinate conversion measurement error of the position measurement data is greatly improved.

In a further possible implementation manner of the second aspect, the covariance of the rectangular coordinate transformation measurement error of the position measurement data includes:

In a further possible implementation manner of the second aspect, the determining unit is further configured to determine a diagonal matrix according to the covariance matrix and an orthogonal matrix, where diagonal elements of the diagonal matrix are variances of statistically independent errors, and the orthogonal matrix is determined by the position measurement data.

In a further possible implementation manner of the second aspect, the position measurement data is data in one of the following coordinate systems: rectangular coordinate system, polar coordinate system, spherical coordinate system.

In a further possible implementation of the second aspect, the position measurement data comprises a distance, an azimuth angle and a pitch angle; or, the position measurement data includes a distance and an azimuth.

In a third aspect, an embodiment of the present application discloses a sensor, where the sensor includes a sensing element, a conversion element, a memory, and a processor, where the sensing element is configured to obtain position measurement data from at least one sensor, the memory stores a computer program, and the processor invokes the computer program stored in the memory, and is configured to perform the following operations:

acquiring position measurement data from at least one sensor;

In a fourth aspect, an embodiment of the present application discloses an electronic device for determining covariance, the electronic device comprising a memory and a processor, the memory having stored therein a computer program for performing the method according to the first aspect or any one of the possible implementations of the first aspect, when the computer program is located to run on the processor.

In a fifth aspect, embodiments of the present application disclose a computer readable storage medium having a computer program stored therein, which when run on one or more processors performs a method as described in the first aspect or any of the possible implementations of the first aspect.

In a sixth aspect, an embodiment of the application discloses a sensor system, which may comprise at least one sensor comprising the covariance determining means of the second aspect, or the sensor of the third aspect, or the electronic device of the fourth aspect, for implementing the method as shown in the first aspect or any one of the possible embodiments of the first aspect.

In a seventh aspect, embodiments of the present application disclose a chip system comprising at least one processor and interface circuitry. Optionally, the interface circuit and the at least one processor are interconnected by a line, and the interface circuit is used for connecting an external device to the processor. The system on a chip may further comprise at least one memory storing a computer program or the interface circuit is adapted to provide the at least one processor with an external memory stored computer program; the computer program, when executed by the at least one processor, is adapted to carry out the method of the first aspect or any one of the possible implementations of the first aspect.

According to the embodiment of the application, the statistical characteristics of the position measurement data and the rectangular coordinate conversion measurement error are obtained, and the covariance of the rectangular coordinate conversion measurement error of the position measurement data is determined according to the diagonalization relation of the covariance matrix, so that the covariance estimation precision of the rectangular coordinate conversion measurement error of the sensor position measurement data is improved, the statistical characteristics of the position measurement data are described more accurately, and the performances of the position measurement data such as state estimation, data association and occupied grid estimation are greatly improved under the application scenes such as tracking, navigation and environment perception.

Drawings

In order to more clearly describe the embodiments of the present application or the technical solutions in the background art, the following will briefly describe the drawings that are required to be used in the embodiments of the present application or the background art.

FIG. 1 is a flow chart of a method for determining covariance according to an embodiment of the application;

FIG. 2 is a schematic diagram of coordinates of position measurement data according to an embodiment of the present application;

FIG. 3a is a flowchart illustrating another method for determining covariance according to an embodiment of the application;

FIG. 3b is a schematic block diagram of a method for determining covariance according to an embodiment of the application;

FIG. 4a is a flowchart illustrating another method for determining covariance according to an embodiment of the application;

FIG. 4b is a schematic block diagram of another method for determining covariance according to an embodiment of the application;

FIG. 5a is a flowchart illustrating another method for determining covariance according to an embodiment of the application;

FIG. 5b is a block diagram of another method for determining covariance according to an embodiment of the application;

fig. 6 is a schematic diagram of an application scenario of a sensor according to an embodiment of the present application;

Fig. 7 is a schematic diagram of an imaging scene of a sensor according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of a covariance determining apparatus according to an embodiment of the present application;

Fig. 9 is a schematic structural diagram of a covariance determining apparatus according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described with reference to the accompanying drawings.

The terms first and second and the like in the description, the claims and the drawings of the present application are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprising," "including," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion. Such as a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to the list of steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

In the present application, "at least one (item)" means one or more, "a plurality" means two or more, "at least two (items)" means two or three and more, "and/or" for describing an association relationship of an association object, and three kinds of relationships may exist, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of (a) or a similar expression thereof means any combination of these items. For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c".

The embodiment of the application provides a covariance determining method, and in order to describe the scheme of the application more clearly, some knowledge related to covariance is introduced first.

Covariance: covariance is used in probability theory and statistics to measure the overall error of two variables, whereas variance is a special case of covariance, i.e. when two variables are identical. Covariance represents the error of the population of two variables, as opposed to the variance representing the error of only one variable. If the trends of the two variables are identical, that is to say if one is greater than the expected value of itself and the other is greater than the expected value of itself, the covariance between the two variables is a positive value. If the trend of the two variables is opposite, i.e. one is greater than the expected value of the variable and the other is less than the expected value of the variable, the covariance between the two variables is negative. I.e. when multiple variables are independent, the variance is used to evaluate the difference in this effect; when multiple variables are correlated, covariance is used to evaluate the difference in this effect. The application range of covariance is very wide, and in agricultural science experiments, the situation that controllable quality factors and uncontrollable quantity factors influence experimental results at the same time often occurs, so that a statistical treatment method of covariance analysis is needed to be adopted, and the quality factors and the quantity factors (also called covariates) are combined to be considered. For example, the actual effect of three fertilizers on apple yield was studied, while the "base yield" of each apple tree the first year was inconsistent, but had some effect on the test results. To eliminate the influence of this factor, the covariance analysis is performed by taking the factor of the first annual output of each apple tree as a covariant to obtain the correct experimental result.

Covariance matrix: in statistics and probability theory, each element of the covariance matrix is the covariance between the individual vector elements, a natural generalization from scalar random variables to high-dimensional random vectors. Covariance matrices are everywhere visible in statistics and machine learning and generally can be considered as two parts, variance and covariance, i.e., variance forms elements on diagonals and covariance forms elements on non-diagonals. The covariance matrix can be used to represent the probability density of the multidimensional random variable so that a study of the multidimensional random variable can be achieved by the covariance matrix.

Embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

Referring to fig. 1, fig. 1 is a flowchart of a covariance determining method according to an embodiment of the application, which includes but is not limited to the following steps:

Step 101: position measurement data from at least one sensor is acquired.

Specifically, one or more position measurement data describing position information of the object to be measured is obtained from the sensor.

The sensor in this embodiment may comprise a large class of sensors based on time of flight (TOF) measurements, for example radar such as millimeter wave radar, ultrasonic radar/sonar or lidar, the position measurement data of such sensors typically being recorded in the form of polar or spherical coordinates. For example, such sensors are widely deployed in advanced driving assistance systems (ADVANCED DRIVER ASSISTANT SYSTEM, ADAS) or automated driving (autonomous driving, AD) systems or unmanned or smart systems such as robots for sensing ambient information, for example, a typical vehicle millimeter wave radar may provide the following position measurement data relative to the sensors: distance, azimuth, or azimuth and elevation, etc. However, in practical applications, the above-mentioned position measurement data often needs to be converted into a rectangular coordinate system for use more conveniently, because the rectangular coordinate system is more beneficial to describing motion modeling of the measured object, and is suitable for the tracking scene of the measured object, so that the polar coordinate or the spherical coordinate position measurement data is usually converted into rectangular coordinate for use.

The position measurement data are rectangular coordinate position data; the measurement data of the sensor can be obtained, for example, from the distance, azimuth angle or distance, azimuth angle and pitch angle measured by the sensor, or can be the result of direct output of the sensor after the sensor is converted by the data. And are not limited herein. The position measurement data may also be polar or spherical position data, such as obtained from a distance, azimuth or distance, azimuth and pitch angle measured by a sensor, without limitation.

It should be noted that the individual measurement errors of the polar or spherical measurement data are typically statistically independent, but the corresponding transformed measurement errors after transformation to a rectangular coordinate system are typically statistically correlated. Specifically, referring to fig. 2, fig. 2 is a schematic coordinate diagram of position measurement data according to an embodiment of the present application. As shown in fig. 2 (a), the distance and azimuth are measured data of the position of the measured object with respect to the sensor in a polar coordinate system, the sensor is located at the origin of the polar coordinate system, the polar axis is a ray ending with the sensor, and at this time, the position coordinates of the object are (d, θ), where d represents the distance between the object and the sensor, and θ is the angle (azimuth) of the object with respect to the sensor; in view of practical convenience, such as modeling of motion state, the above position measurement data generally needs to be converted into a rectangular coordinate system for use. As an implementation manner, the polar coordinate position may be directly converted into a rectangular coordinate by using a transformation relationship between a polar coordinate and a rectangular coordinate, as shown in (b) of fig. 2, the position measurement data of the measured object relative to the sensor in a plane rectangular coordinate system, where the sensor is located at an origin of the plane rectangular coordinate system, and the position coordinate of the object is (x ', y'), where x 'is an abscissa of the object and y' is an ordinate of the object. The conversion from the position measurement data in (a) in fig. 2 to the position measurement data in (b) in fig. 2 requires the following steps:

As can be seen from the above coordinate conversion relation, the respective measurement errors (distance d, azimuth θ) of the polar coordinate measurement data are statistically independent, while the corresponding conversion measurement errors after conversion to the rectangular coordinate system are statistically correlated, and the conversion measurement errors of x 'are correlated with the distance d and azimuth θ, and the conversion measurement errors of y' are correlated with the distance d and azimuth θ.

It should be noted that the present invention is not limited to the above-mentioned transformation relationship between polar coordinates and rectangular coordinates. For example, the coordinate conversion relation may consider the influence of the measurement error. For example, as another implementation, the position measurement data of the rectangular coordinates may also be obtained from the position data of the polar coordinates according to the following relation:

Wherein, Is the variance of the measurement error of the azimuth angle of the corresponding sensor for the target.

Similarly, as shown in fig. 2 (c), in order to measure the position of the target relative to the sensor in the spherical coordinate system, the sensor is located at the origin of the spherical coordinate system, and the position coordinates of the target areWhere d represents the distance of the target from the sensor, θ is the azimuth angle of the target from the sensor,/>Representing the pitch angle of the target relative to the sensor; the above position measurement data generally need to be converted into a rectangular coordinate system for use in view of practical convenience. As an implementation manner, the above-mentioned spherical coordinate position may be directly converted into rectangular coordinates by using a transformation relationship between the spherical coordinates and rectangular coordinates, as shown in (d) of fig. 2, which is position measurement data of the measured object with respect to the sensor in a space rectangular coordinate system, an origin of which is the sensor, and at this time, the position coordinates of the object are (x ', y', z '), where x', y ', and z' are coordinate components of the three-dimensional rectangular coordinates of the object on three coordinate axes. The conversion from the position measurement data in (c) in fig. 2 to the position measurement data in (d) in fig. 2 requires the following steps:

It should be noted that the present invention is not limited to the above-mentioned transformation relationship between the spherical coordinates and the rectangular coordinates. For example, the coordinate conversion relation may consider the influence of the measurement error. For example, as another implementation, the position measurement data of the rectangular coordinates may also be obtained from the position data of the spherical coordinates according to the following relation:

Wherein, For the variance of the azimuth measurement error of the target corresponding sensor,The variance of the pitch angle measurement error for the corresponding sensor for the target.

From the above conversion relation, it can be seen that the respective measurement errors (distance d, azimuth angle θ, pitch angle) Is statistically independent, and the corresponding conversion measurement errors after conversion to a rectangular coordinate system are statistically correlated, x' conversion measurement errors are correlated with distance d, azimuth angle θ, and pitch angle/>The conversion measurement error of y' is related to the distance d, azimuth angle θ and pitch angle/>The conversion measurement error of z' is related to distance d and pitch angle/>And (5) correlation.

The measurement data of the sensor usually has a certain measurement error due to the influence of non-ideal factors such as thermal noise and interference. Although the errors of the respective measurement data of the sensors may be statistically independent from each other, the statistics of the corresponding converted measurement errors after conversion to the rectangular coordinate system have statistical correlation, and thus, it is necessary to determine the covariance of the rectangular coordinate converted measurement errors of the position measurement data.

Step 102: and determining covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the statistical characteristics of the position measurement data and the rectangular coordinate conversion measurement error.

The statistical characteristics of the rectangular coordinate system conversion measurement error corresponding to the position measurement data may include variance, standard deviation, root mean square error, and the like. Specifically, the statistical characteristic of the rectangular coordinate transformation measurement error may include the statistical characteristic of each component of the rectangular coordinate transformation measurement error. Variance is a measure of the degree of discretization when the probability theory and statistical variance measure a random variable or set of data; standard deviation is the arithmetic square root of variance, reflecting the degree of dispersion of a dataset; root mean square error is a measure of the deviation of an observed value from a true value. The variance, standard deviation, or root mean square error are all in essence a specific representation of the statistical properties, and for ease of illustration, the variance is taken as an example in this embodiment.

And determining covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the statistical characteristics of the position measurement data and the rectangular coordinate conversion measurement error.

The position measurement data may be two-dimensional or three-dimensional rectangular coordinate position data, may be two-dimensional data in a polar coordinate system, or may be three-dimensional data in a spherical coordinate system, and is not limited herein. The statistical characteristics of the rectangular coordinate conversion measurement error of the position measurement data are the statistical characteristics of the respective components of the conversion measurement error, and are different according to the different cases of the position measurement data.

As one implementation, the position measurement data is two-dimensional data in a polar coordinate system, and the statistical characteristics of the rectangular coordinate transformation measurement errors are variances of respective components in a planar rectangular coordinate system (x, y), namely a first variance R ₁₁ on the x component and a second variance R ₂₂ on the y component. The first variance R ₁₁ and the second variance R ₂₂ can be obtained by a sensor. The invention is not limited as to how the sensor obtains rectangular coordinate conversion measurement errors by using the measured polar coordinate or spherical coordinate data.

Similarly, the position measurement data is three-dimensional data in a spherical coordinate system, and the statistical characteristics of the rectangular coordinate conversion measurement errors are variances of error components in the space rectangular coordinate system (x, y, z), namely a first variance R ₁₁ of the error components in the x coordinate axis, a second variance R ₂₂ of the error components in the y coordinate axis and a third variance R ₃₃ of the error components in the z coordinate axis. Wherein the first variance R ₁₁, the second variance R ₂₂, and the third variance R ₃₃ may be obtained by a sensor. The invention is not limited as to how the sensor obtains rectangular coordinate conversion measurement errors by using the measured polar coordinate or spherical coordinate data.

Based on the statistical characteristics of the position measurement data and the rectangular coordinate conversion measurement error, the covariance of the rectangular coordinate conversion measurement error of the position measurement data can be determined according to the diagonalization relation of the covariance matrix of the rectangular coordinate conversion measurement error. The diagonalization relation can be a eigenvalue decomposition form of a covariance matrix of the rectangular coordinate transformation measurement error, such as r=udu ^T, or U ^T ru=d, or a reasonable deformation of the diagonalization form. Wherein R is covariance matrix, D is diagonal matrix, U is orthogonal matrix determined according to position measurement data, and U ^T is transpose matrix of the orthogonal matrix. In the diagonalization relation, the orthogonal matrix is determined according to the position measurement data, so that a covariance matrix can be obtained according to the position measurement data and the diagonalization relation of the covariance matrix, and the covariance of the rectangular coordinate conversion measurement error of the position measurement data is determined.

Specifically, the position measurement data may be two-dimensional data or three-dimensional data, and the covariance matrix R of the two-dimensional data is as follows:

Or/>

The position measurement data may be two-dimensional data, R ₁₂ is a target covariance, the target covariance represents a covariance between two components of a rectangular coordinate conversion measurement error corresponding to the position measurement data, and the target covariance R ₁₂ is determined according to the position measurement data, the first variance R ₁₁ and the second variance R ₂₂;

The position measurement data may be three-dimensional data, R ₁₂ is a first target covariance, which represents covariance between a first dimension component (x) and a second dimension component (y) of a rectangular coordinate conversion measurement error corresponding to the position measurement data, the first target covariance R ₁₂ is determined according to the position measurement data, the first variance R ₁₁, and the second variance R ₂₂, R ₁₃ is a second target covariance, which represents covariance between the first dimension component (x) and a third dimension component (z) of the rectangular coordinate conversion measurement error corresponding to the position measurement data, the second target covariance R ₁₃ is determined according to the position measurement data, the first variance R ₁₁, and the third variance R ₃₃, and R ₂₃ is a third target covariance, which represents covariance between the second dimension component (y) and the third dimension component (z) of the rectangular coordinate conversion measurement error corresponding to the position measurement data, the third target covariance R ₂₃ is determined according to the position measurement data, the second variance R ₂₂, and the third variance R ₃₃.

The covariance of rectangular coordinate conversion measurement errors of the position measurement data can be obtained according to the diagonalization relation of the covariance matrix, the covariance estimation precision of the rectangular coordinate conversion measurement errors of the position measurement data of the sensor can be improved by the covariance determination method, the statistical characteristics of the position measurement data can be described more accurately, and therefore the performance of the functions of state estimation, data association, occupied grid estimation and the like of the position measurement data in application scenes such as tracking, navigation and environment perception is greatly improved.

It should be noted that the implementation step of the present invention does not need to know the elements of the diagonal matrix D in the diagonalization relation, that is, in this embodiment, the process and the result of determining the covariance of the measurement error of the position measurement data transformation do not need to take the solution of the diagonal matrix D as an essential step. Conversely, after the covariance matrix is determined by the present invention, the diagonal matrix D may be determined further by using the above-described diagonalization relation. Therefore, the diagonal matrix can be used for determining the elliptical area of the measurement error, and the diagonal matrix can be correspondingly applied to the occupied grid estimation to improve the precision of the occupied grid, so that the diagonal matrix D can be obtained optionally further based on the covariance matrix R, the orthogonal matrix U and the diagonalization relation D=U ^T RU, the elliptical area of the measurement error is determined, and the performance loss of the elliptical area in an application scene can be reduced.

Referring to fig. 3a, fig. 3a is a flowchart illustrating another covariance determining method according to an embodiment of the application, which includes, but is not limited to, the following steps:

step 301: position measurement data from at least one sensor is acquired.

Consistent with step 101 described above.

Step 302: and calculating the target covariance according to the position measurement data, the first variance and the second variance.

The position measurement data is two-dimensional data, and the embodiment provides a method for determining covariance of rectangular coordinate conversion measurement errors of the position measurement data, and according to statistical characteristics of the position measurement data and the rectangular coordinate conversion measurement errors of the position measurement data, target covariance, which is covariance between two components of planar rectangular coordinate conversion measurement errors corresponding to the position measurement data, can be determined.

Specifically, the position measurement data may be rectangular coordinate position data x and y, or polar coordinate position data (d, θ), where d represents a distance between the target and the sensor, θ represents an azimuth angle of the target and the sensor, and the statistical characteristics of rectangular coordinate conversion measurement errors of the position measurement data include a first variance R ₁₁ and a second variance R ₂₂, where the first variance R ₁₁ represents a variance of a first dimension component (x) of rectangular coordinate conversion measurement errors corresponding to the position measurement data, and the second variance R ₂₂ represents a variance of a second dimension component (y) of planar rectangular coordinate conversion measurement errors corresponding to the position measurement data. From the above-described position measurement data, the first variance R ₁₁, and the second variance R ₂₂, the target covariance thereof can be determined as follows:

Where R ₁₂ is the target covariance sought, c _θ＝cosθ,s_θ =sinθ, θ is the azimuth of the target relative to the sensor, and K is an integer. Or/>Where x and y are two components of rectangular position data, respectively. It is understood that the above-mentioned c _θ and s _θ may be calculated from angle values or right angle coordinate values.

Step 303: and determining a covariance matrix of rectangular coordinate conversion measurement errors of the position measurement data according to the first variance, the second variance and the target covariance.

Through the above steps, the first variance R ₁₁, the second variance R ₂₂, and the target covariance R ₁₂ can be known, and based on the position measurement data, a covariance matrix of the rectangular coordinate transformation measurement error of the position measurement data can be determined, where R is a covariance matrix, and the covariance matrix is as follows:

As can be seen from the covariance matrix, the data on the diagonal lines thereof are variances on the respective components, and the non-diagonal line elements represent the covariance between the conversion measurement error components. According to the embodiment of the application, the position measurement data and the statistical characteristics of the rectangular coordinate conversion measurement errors of the position measurement data are obtained, the covariance of the rectangular coordinate conversion measurement errors of the position measurement data is determined by utilizing the position measurement data, and the covariance estimation precision of the rectangular coordinate conversion measurement errors of the position measurement data is improved, so that the performance of the functions of state estimation, data association, grid occupation estimation and the like of the position measurement data in application scenes such as tracking, navigation, environment sensing and the like is greatly improved.

On the other hand, fig. 3a provides a corresponding block diagram of the covariance determining method, and in particular, referring to fig. 3b, fig. 3b is a block diagram of a covariance determining method according to an embodiment of the present application. As shown in fig. 3b, position measurement data is acquired, which comprises rectangular position data describing specific position information of the object relative to the sensor, such as (x, y); the above-mentioned rectangular coordinate position data may be obtained by converting the polar coordinate position measurement data (d, θ) or directly output after converting the sensor coordinates, although the measurement errors (distance d, azimuth θ) of the respective components of the polar coordinate position measurement data are statistically independent, the corresponding rectangular coordinate system conversion measurement errors are statistically correlated, and the statistical characteristics (first variance R ₁₁, second variance R ₂₂) of the respective components of the rectangular coordinate conversion measurement errors corresponding to the position measurement data are insufficient to sufficiently describe the statistical characteristics between the rectangular coordinate conversion measurement errors; according to the embodiment of the application, the target covariance R ₁₂ can be determined according to the statistical characteristics of the position measurement data and the rectangular coordinate conversion measurement error thereof, so that the covariance matrix R of the rectangular coordinate conversion measurement error of the position measurement data can be obtained.

Referring to fig. 4a, fig. 4a is a flowchart illustrating another method for determining covariance according to an embodiment of the application, which includes, but is not limited to, the following steps:

step 401: position measurement data from at least one sensor is acquired.

Consistent with step 101 described above.

Step 402: and calculating a first target covariance according to the position measurement data, the first variance and the second variance.

The position measurement data is three-dimensional data, and the embodiment provides a method for determining covariance of rectangular coordinate conversion measurement errors of the position measurement data, and according to the position measurement data and statistical characteristics of the rectangular coordinate conversion measurement errors of the position measurement data, a first target covariance can be determined, wherein the first target covariance represents covariance between a first dimension component (x) and a second dimension component (y) of rectangular coordinate conversion measurement errors corresponding to the position measurement data.

Specifically, the position measurement data may be three-dimensional rectangular coordinates (x, y, z), where x, y, z are rectangular coordinates of the object relative to the sensor; or the spherical coordinate positionWhere d represents the distance of the target from the sensor, θ represents the azimuth angle of the target from the sensor,/>The statistical characteristics of the rectangular coordinate conversion measurement error corresponding to the position measurement data include a first variance R ₁₁ and a second variance R ₂₂, where the first variance R ₁₁ represents the variance on the first dimension (x) of the rectangular coordinate conversion measurement error corresponding to the position measurement data and the second variance R ₂₂ represents the variance on the second dimension (y) of the rectangular coordinate conversion measurement error corresponding to the position measurement data. From the above-described position measurement data, the first variance R ₁₁, and the second variance R ₂₂, its first target covariance can be determined as follows:

Where R ₁₂ is the first target covariance, c _θ＝cosθ,s_θ＝sinθ,c_2θ =cos (2θ), θ is the azimuth of the target relative to the sensor, and K is an integer. Or/>Where x and y are two components of rectangular position data, respectively. It is understood that the above-mentioned c _θ and s _θ may be calculated from angle values or right angle coordinate values. /(I)

Step 403: and calculating a second target covariance according to the position measurement data, the first variance, the second variance and the third variance.

The statistical characteristic of the rectangular coordinate conversion measurement error of the position measurement data further includes a third variance R ₃₃, and the third variance R ₃₃ represents a variance of a third dimensional component (z) of the rectangular coordinate conversion measurement error corresponding to the position measurement data. From the above-described position measurement data, the first variance R ₁₁, the second variance R ₂₂, and the third variance R ₃₃, the second target covariance thereof can be determined as follows:

wherein R ₁₃ is the second target covariance, which second target covariance R ₁₃ represents covariance between the first dimension (x) and the third dimension (z) of the rectangular coordinate conversion measurement error corresponding to the position measurement data, c_θ＝cosθ,c_2θ＝cos(2θ)，/>Θ is the azimuth of the target relative to the sensor,/>Is the pitch angle of the target relative to the sensor, and/>K and l are integers. Or/> Where x, y and z are three components of rectangular position data, respectively. From this, it is found that c _θ、s_θ,/>, abovec_2θ、/>The angle value can be calculated by the angle value or the right angle coordinate value.

Step 404: and calculating a third target covariance according to the position measurement data, the first variance, the second variance and the third variance.

Similarly, from the above-mentioned position measurement data, the first variance R ₁₁, the second variance R ₂₂, and the third variance R ₃₃, the third target covariance thereof can be determined as follows:

Wherein R ₂₃ is the third target covariance, and the third target covariance R ₂₃ represents covariance between the second-dimensional component (y) and the third-dimensional component (z) of the rectangular coordinate conversion measurement error corresponding to the position measurement data.

Step 405: and determining a covariance matrix of rectangular coordinate conversion measurement errors of the position measurement data according to the first variance, the second variance, the third variance, the first target covariance, the second target covariance and the third target covariance.

The first variance R ₁₁, the second variance R ₂₂, the third variance R ₃₃, the first target covariance R ₁₂, the second target covariance R ₁₃, and the third target covariance R ₂₃ can be known through the above steps, and according to the above known amounts, the diagonalization relation r=udu ^T of the covariance matrix can be used to determine a covariance matrix of the rectangular coordinate conversion measurement error of the position measurement data, where R is the covariance matrix, D is the diagonal matrix, U is the orthogonal matrix obtained according to the position measurement data, and U ^T is the transpose matrix of the orthogonal matrix. The covariance matrix is as follows:

As can be seen from the covariance matrix, the data on the diagonal (R ₁₁、R₂₂、R₃₃) is the variance on each component, and the data on the off-diagonal (R ₁₂、R₁₃、R₂₃) represents the covariance between the different components of the rectangular coordinate conversion measurement error. According to the embodiment of the application, the position measurement data and the statistical characteristics of rectangular coordinate conversion measurement errors of the position measurement data are obtained, and the covariance of the rectangular coordinate conversion measurement errors of the position measurement data is determined according to the diagonalization relation of the covariance matrix, so that the covariance estimation precision of the rectangular coordinate conversion measurement errors of the position measurement data is improved, and the performances of state estimation, data association, occupied grid estimation and other functions of the position measurement data are greatly improved under the application scenes of tracking, navigation, environment sensing and the like.

On the other hand, fig. 4a provides a corresponding block diagram of the covariance determining method, and in particular, referring to fig. 4b, fig. 4b is a block diagram of another covariance determining method according to an embodiment of the application. As shown in fig. 4b, position measurement data is acquired, which includes rectangular coordinates of the position, for describing specific position information of the object, such as (x, y, z); the rectangular coordinate position data can be obtained from spherical coordinate position measurement dataConverted or directly output after sensor coordinate conversion. Although the measurement errors of the individual components of the spherical coordinate position measurement data (distance d, azimuth θ, pitch/>) Is statistically independent, the corresponding Cartesian conversion measurement errors are statistically correlated, so that the statistical properties of the Cartesian conversion measurement errors (first variance R ₁₁, second variance R ₂₂, third variance R ₃₃) are insufficient to adequately describe the statistical properties between Cartesian conversion measurement errors; according to the embodiment of the application, the target covariance (R ₁₂、R₁₃、R₂₃) is obtained according to the statistical characteristics of the position measurement data and the rectangular coordinate conversion measurement error thereof, and the covariance matrix R of the rectangular coordinate conversion measurement error of the position measurement data can be obtained.

Referring to fig. 5a, fig. 5a is a flowchart illustrating another method for determining covariance according to an embodiment of the application, which includes, but is not limited to, the following steps:

Step 501: position measurement data from at least one sensor is acquired.

Consistent with step 101 described above.

Step 502: and obtaining an angle matrix according to the position measurement data.

The position measurement data is three-dimensional data, and the embodiment provides another method for determining covariance, according to the position measurement data, an angle matrix can be obtained, the angle matrix is used for determining covariance of rectangular coordinate conversion measurement errors of the position measurement data, and the angle matrix can be as follows:

wherein A is an angle matrix, Representation/>C _θ represents cos θ,/>Representation/>S _θ represents sin θ,/>The pitch angle of the target relative to the sensor is that theta is the azimuth angle of the measured target relative to the sensor; or/> Where x, y and z are three components of rectangular position data, respectively. From this, it is found that c _θ、s_θ,/>, aboveThe angle value can be calculated by the angle value or the right angle coordinate value. The object may be an environment surrounding the sensor, such as an obstacle in the environment, or may be a moving object.

The angle matrix may also be a reasonable variation of the above equation a, and as can be seen from the above angle matrix equation a, it mainly includes trigonometric values of the azimuth and pitch angles of the target relative to the position of the sensor.

Step 503: and calculating covariance of rectangular coordinate conversion measurement errors of the position measurement data according to the angle matrix, the first variance, the second variance and the third variance.

The angle matrix a is known by the above steps, and the statistical characteristics of the rectangular coordinate conversion measurement error of the position measurement data include a first variance R ₁₁, a second variance R ₂₂, and a third variance R ₃₃, the first variance R ₁₁ representing the variance on the first dimension (x) of the rectangular coordinate conversion measurement error corresponding to the position measurement data, the second variance R ₂₂ representing the variance on the second dimension (y) of the rectangular coordinate conversion measurement error corresponding to the position measurement data, and the third variance R ₃₃ representing the variance on the third dimension (z) of the rectangular coordinate conversion measurement error corresponding to the position measurement data. According to the angle matrix a, the first variance R ₁₁, the second variance R ₂₂, and the third variance R ₃₃, the covariance of the rectangular coordinate transformation measurement error of the position measurement data can be determined as follows:

Wherein a is an angle matrix, R ₁₂ is a first target covariance, R ₁₃ is a second target covariance, R ₂₃ is a third target covariance, the first target covariance represents a covariance between a first dimension component and a second dimension component of a rectangular coordinate conversion measurement error corresponding to the position measurement data, the second target covariance represents a covariance between the first dimension component and the third dimension component of the rectangular coordinate conversion measurement error corresponding to the position measurement data, and the third target covariance represents a covariance between the second dimension component and the third dimension component of the rectangular coordinate conversion measurement error corresponding to the position measurement data.

Furthermore, the diagonalization relation of the covariance matrix of the measurement error can be converted by using rectangular coordinates of the position measurement data according to the first target covariance, the second target covariance, the third target covariance, the first variance, the second variance and the third variance to obtain the covariance matrix. The covariance estimation precision of the rectangular coordinate conversion measurement error of the position measurement data can be improved by determining the implementation mode of the covariance of the rectangular coordinate conversion measurement error of the three-dimensional position measurement data through the angle matrix, so that the accuracy of the rectangular coordinate conversion measurement error statistical characteristic description of the position measurement data is greatly improved.

On the other hand, fig. 5a provides a corresponding block diagram of the covariance determining method, and in particular, referring to fig. 5b, fig. 5b is a block diagram of another covariance determining method according to an embodiment of the application. As shown in fig. 5b, position measurement data is acquired, which includes rectangular coordinate position data for describing specific position information of the object, such as (x, y, z); the rectangular coordinate position data can be obtained from spherical coordinate position measurement dataThe sensor coordinate is converted to obtain or directly output; an angle matrix a may be obtained according to the position measurement data, and in particular, the angle matrix a is described in detail in the step 502; despite measurement errors on the various components in the spherical coordinate system (distance d, azimuth θ, pitch/>) Is statistically independent, and the corresponding Cartesian measurement errors are statistically correlated, so that the statistical properties of the respective components of the Cartesian measurement errors (first variance R ₁₁, second variance R ₂₂, third variance R ₃₃) are insufficient to adequately describe the statistical properties between Cartesian measurement errors; the application embodiment can obtain covariance [ R ₁₂,R₁₃,R₂₃ ] of the rectangular coordinate conversion measurement error of the position measurement data according to the statistical characteristics (R ₁₁、R₂₂、R₃₃) of the rectangular coordinate conversion measurement error of the position measurement data and the angle matrix A, so as to obtain covariance matrix R of the rectangular coordinate conversion measurement error of the position measurement data.

The sensors according to embodiments of the present application may include a wide variety of sensors based on time of flight (ToF) measurements, such as radar, e.g., millimeter wave radar, ultrasonic radar/sonar, or lidar. The embodiment of the application can be applied to the sensor and an advanced driving assistance system, an automatic driving system, an unmanned aerial vehicle system or other systems provided with the sensor, and particularly can be a data processing part of a sensing system, and a specific bearing platform of the sensor system can be a vehicle-mounted such as an automobile, a motorcycle or a bicycle, or an airborne such as an unmanned aerial vehicle, a helicopter or a jet plane.

Referring to fig. 6, fig. 6 is a schematic diagram of an application scenario of a sensor according to an embodiment of the present application. As shown in fig. 6, an automobile supporting an autopilot function, a sensor system carried on the automobile comprises a plurality of sensors designed based on a time-of-flight principle, specifically a forward laser radar, a forward millimeter wave radar, a lateral millimeter wave radar, a laser radar and a backward laser radar, wherein the radars (sensors) work together to obtain position measurement data of obstacles around the automobile, and the sensor system also comprises a front camera and a rear camera for acquiring image information of the surrounding environment of the automobile body and presenting the image information to a user through a display screen in the automobile.

Referring to fig. 7, fig. 7 is a schematic diagram of a possible imaging scene of a sensor according to an embodiment of the application. As shown in fig. 7, an automobile a shown in fig. 6 is automatically driven on a road, and a sensor system running on the automobile detects that an automobile B running at a certain speed is in front of the left side of the automobile body, and at this time, the automobile B is in a safety detection range of the automobile a, and the sensor system of the automobile a is regarded as a detected target possibly having a driving risk. The forward laser radar configured on the vehicle a will emit a plurality of pulse laser signals, scan a plurality of detection areas of the object field of view under a plurality of scan angles, and form an image of the object field of view, and in particular, the forward laser radar will also collect position measurement data of the object to be measured (vehicle B) under a plurality of scan angles by emitting a plurality of pulse laser signals to the object to be measured (vehicle B), which can be shown in the following table:

The measured target image of the object field is formed by the position measurement data, and the specific process can be completed by a data processing module of the sensor system. On the other hand, in order to ensure that the position measurement data in the spherical coordinate system is accurate and still can accurately locate the position of the measured object after being converted into the measurement data in the space rectangular coordinate system, the sensor needs to improve the covariance accuracy of the acquired position measurement data, and the covariance determining method provided in fig. 1, fig. 4a or fig. 5a can be adopted to improve the accuracy of the position measurement data.

The foregoing details of the method according to the embodiments of the present application and the apparatus according to the embodiments of the present application are provided below.

Referring to fig. 8, fig. 8 is a schematic structural diagram of a covariance determining apparatus according to an embodiment of the present application, where the covariance determining apparatus may include an obtaining unit 801 and a determining unit 802, where descriptions of the respective units are as follows:

an acquisition unit 801 for acquiring position measurement data from at least one sensor;

A determining unit 802, configured to determine a covariance of the rectangular coordinate transformation measurement error of the position measurement data according to the statistical characteristics of the rectangular coordinate transformation measurement error and the position measurement data.

In a possible implementation manner, in determining the covariance of the rectangular measurement error of the position measurement data, the determining unit 802 is specifically configured to determine the covariance of the rectangular measurement error of the position measurement data according to a diagonalization relation of a covariance matrix of the rectangular measurement error, where the diagonalization relation is determined according to the position measurement data.

In a possible implementation manner, the position measurement data is two-dimensional data, and the statistical characteristic includes a first variance or standard deviation or root mean square error of a first dimension component in the position measurement data rectangular conversion measurement error and a second variance or standard deviation or root mean square error of a second dimension component in the position measurement data rectangular conversion measurement error;

The determining unit 802 is specifically further configured to determine a covariance matrix of the rectangular coordinate transformation measurement error of the position measurement data according to the position measurement data, the first variance or standard deviation or root mean square error, and the second variance or standard deviation or root mean square error, in determining the covariance of the rectangular coordinate transformation measurement error of the position measurement data according to the statistical characteristics of the rectangular coordinate transformation measurement error of the position measurement data.

In one possible embodiment, the covariance matrix of the rectangular coordinate transformation measurement error of the position measurement data includes:

In one possible embodiment, the R ₁₂ includes:

In one possible embodiment, the position measurement data is three-dimensional data, and the statistical characteristic includes a first variance or standard deviation or root mean square error of a first dimension component in the position measurement data rectangular measurement error, and a second variance or standard deviation or root mean square error of a second dimension component in the position measurement data rectangular measurement error, and a third variance or standard deviation or root mean square error of a third dimension component in the position measurement data rectangular measurement error;

The determining unit 802 is specifically further configured to determine, in terms of determining the covariance of the rectangular coordinate transformation measurement error of the position measurement data according to the statistical characteristics of the rectangular coordinate transformation measurement error and the position measurement data, the covariance of the rectangular coordinate transformation measurement error of the position measurement data according to the first variance or the standard deviation or the root mean square error, the second variance or the standard deviation or the root mean square error, and the third variance or the standard deviation or the root mean square error.

In a possible implementation manner, in determining the covariance of the rectangular coordinate transformation measurement error of the position measurement data according to the position measurement data, the first variance, the standard deviation, the root mean square error, the second variance, the standard deviation, the root mean square error, and the third variance, the standard deviation, the root mean square error, the determining unit 802 is specifically further configured to determine a covariance matrix of the rectangular coordinate transformation measurement error of the position measurement data according to the first variance, the second variance, the third variance, the first target covariance, the second target covariance, and the third target covariance;

In a possible embodiment, the determining unit 802 is further specifically configured to determine the covariance of the rectangular measurement error of the position measurement data based on the first variance, the second variance, the third variance, and an angle matrix determined by a trigonometric function value of an azimuth angle and/or a pitch angle, in determining the covariance of the rectangular measurement error of the position measurement data based on the position measurement data and the first variance, the standard deviation, the root mean square error, the second variance, the standard deviation, the root mean square error, and the third variance.

In one possible implementation, the angle matrix includes:

In the embodiment of the application, the position measurement data is three-dimensional position measurement data, and another implementation way of determining covariance of rectangular coordinate conversion measurement errors of the three-dimensional position measurement data is provided, namely, the position measurement data is three-dimensional position measurement data, and according to a specific implementation way of an angle matrix, wherein the angle matrix is determined according to trigonometric function values of an azimuth angle and/or a pitch angle, the trigonometric function values of the azimuth angle and/or the pitch angle can be obtained according to the position measurement data, and in particular,For the pitch angle of the target relative to the sensor, θ is the azimuth angle of the target relative to the sensor, and the target can be the surrounding environment of the sensor, such as an obstacle in the environment, or the like, or can be a moving target; further: /(I)

In one possible embodiment, the covariance of the position measurement data rectangular transformation measurement error includes:

In a possible implementation manner, the determining unit 802 is further configured to determine a diagonal matrix according to the covariance matrix and an orthogonal matrix, where diagonal elements of the diagonal matrix are variances of statistically independent errors, and the orthogonal matrix is determined by the position measurement data.

In one possible embodiment, the position measurement data is data in one of the following coordinate systems: rectangular coordinate system, polar coordinate system, spherical coordinate system.

In one possible embodiment, the position measurement data includes distance, azimuth, and pitch; or, the position measurement data includes a distance and an azimuth.

According to the embodiment of the present application, each unit in the apparatus shown in fig. 8 may be separately or completely combined into one or several additional units, or some (some) units may be further split into a plurality of units with smaller functions to form the unit, which may achieve the same operation without affecting the implementation of the technical effects of the embodiment of the present application. The above units are divided based on logic functions, and in practical applications, the functions of one unit may be implemented by a plurality of units, or the functions of a plurality of units may be implemented by one unit. In other embodiments of the present application, the terminal-based device may also include other units, and in practical applications, these functions may also be implemented with assistance from other units, and may be implemented by cooperation of multiple units.

It should be noted that the implementation of each unit may also correspond to the corresponding description of the method embodiment shown in fig. 1, 3a, 4a and 5 a.

In the covariance determining device described in fig. 8, by acquiring the statistical characteristics of the position measurement data and the rectangular coordinate conversion measurement error, and determining the covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the diagonalization relation of the covariance matrix, the covariance estimation precision of the rectangular coordinate conversion measurement error of the sensor position measurement data is improved, and the statistical characteristics of the position measurement data are described more accurately, so that the performance of the functions of state estimation, data association, grid occupation estimation and the like of the position measurement data in application scenes such as tracking, navigation, environment perception and the like is greatly improved.

Referring to fig. 9, fig. 9 is a schematic structural diagram of a covariance determining apparatus 90 according to an embodiment of the application, where the covariance determining apparatus 90 may include a memory 901 and a processor 902. Further optionally, a bus 903 may be included, wherein the memory 901 and the processor 902 are coupled by the bus 903.

The memory 901 is used to provide a storage space, and data such as an operating system and a computer program may be stored in the storage space. Memory 901 includes, but is not limited to, random access memory (random access memory, RAM), read-only memory (ROM), erasable programmable read-only memory (erasable programmab leread only memory, EPROM), or portable read-only memory (compact disc read-only memory, CD-ROM).

The processor 902 is a module for performing arithmetic operations and logical operations, and may be one or a combination of processing modules such as a central processing unit (central processing unit, CPU), a graphics card processor (graphics processing unit, GPU), or a microprocessor (microprocessor unit, MPU).

The memory 901 stores a computer program, and the processor 902 calls the computer program stored in the memory 901 to perform the following operations:

acquiring position measurement data from at least one sensor;

In one possible implementation, the processor 902 is specifically configured to, in determining a covariance of the position measurement data rectangular transform measurement error: and determining covariance of the rectangular coordinate conversion measurement error of the position measurement data according to diagonalization relation of a covariance matrix of the rectangular coordinate conversion measurement error, wherein the diagonalization relation is determined according to the position measurement data.

In determining the covariance of the rectangular measurement error of the position measurement data according to the statistical characteristics of the rectangular measurement error and the position measurement data, the processor 902 is specifically configured to: and determining a covariance matrix of rectangular coordinate conversion measurement errors of the position measurement data according to the position measurement data, the first variance or standard deviation or root mean square error and the second variance or standard deviation or root mean square error.

In one possible embodiment, the R ₁₂ includes:

In one possible embodiment, the position measurement data is three-dimensional data, and the statistical characteristic includes a first variance or standard deviation or root mean square error of a first dimension component in the position measurement data rectangular measurement error, and a second variance or standard deviation or root mean square error of a second dimension component in the position measurement data rectangular measurement error, and a third variance or standard deviation or root mean square error of a third dimension component in the position measurement data rectangular measurement error:

in determining the covariance of the rectangular measurement error of the position measurement data according to the statistical characteristics of the rectangular measurement error and the position measurement data, the processor 902 is specifically configured to: and determining covariance of rectangular coordinate conversion measurement errors of the position measurement data according to the position measurement data, the first variance, the standard deviation, the second variance, the third variance and the third variance.

In one possible implementation, the processor 902 is specifically configured to determine a covariance of the rectangular coordinate transformation measurement error of the position measurement data based on the position measurement data and the first variance or standard deviation or root mean square error and the second variance or standard deviation or root mean square error and the third variance or standard deviation or root mean square error: determining a covariance matrix of rectangular coordinate conversion measurement errors of the position measurement data according to the first variance, the second variance, the third variance, the first target covariance, the second target covariance and the third target covariance;

In one possible implementation, the processor 902 is specifically configured to determine a covariance of the rectangular coordinate transformation measurement error of the position measurement data based on the position measurement data and the first variance or standard deviation or root mean square error and the second variance or standard deviation or root mean square error and the third variance or standard deviation or root mean square error: and determining covariance of rectangular coordinate conversion measurement errors of the position measurement data according to the first variance, the second variance, the third variance and an angle matrix, wherein the angle matrix is determined by trigonometric function values of azimuth angles and/or pitch angles.

In one possible implementation, the angle matrix includes:

wherein A is the angle matrix, the Representation/>The c _θ represents cos θ, the/>Representation/>The s _θ represents sin θ, the/>And the angle θ is the azimuth angle.

In the embodiment of the application, the position measurement data is three-dimensional position measurement data, and another implementation way of determining covariance of rectangular coordinate conversion measurement errors of the three-dimensional position measurement data is provided, namely, the position measurement data is three-dimensional position measurement data, and according to a specific implementation way of an angle matrix, wherein the angle matrix is determined according to trigonometric function values of an azimuth angle and/or a pitch angle, the trigonometric function values of the azimuth angle and/or the pitch angle can be obtained according to the position measurement data, specifically, the pitch angle of a target relative to a sensor, and θ is the azimuth angle of the target relative to the sensor, and the target can be an environment around the sensor, such as an obstacle in the environment, or the like, and can also be a moving target; further:

In one possible implementation, the processor 902 is further specifically configured to: and determining a diagonal matrix according to the covariance matrix and an orthogonal matrix, wherein diagonal elements of the diagonal matrix are variances of statistically independent errors, and the orthogonal matrix is determined by the position measurement data.

It should be noted that the specific implementation of the covariance determination device may also correspond to the corresponding description of the method embodiment shown in fig. 1, 3a, 4a and 5 a.

In the covariance determining device 90 illustrated in fig. 9, by acquiring the statistical characteristics of the position measurement data and the rectangular coordinate conversion measurement error, and determining the covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the diagonalization relation of the covariance matrix, the covariance estimation precision of the rectangular coordinate conversion measurement error of the sensor position measurement data is improved, and the statistical characteristics of the position measurement data are described more accurately, so that the performance of the functions of state estimation, data association, grid occupation estimation and the like of the position measurement data in application scenes such as tracking, navigation, environment perception and the like is greatly improved.

Embodiments of the present application also provide a computer readable storage medium having a computer program stored therein, which when run on one or more processors, can implement the method of determining covariance shown in fig. 1, 3a, 4a, and 5 a.

Embodiments of the present application also provide a computer program product that, when run on a processor, may implement the method of determining covariance shown in fig. 1, 3a, 4a, and 5 a.

Embodiments of the present application also provide a sensor system comprising at least one sensor based on time-of-flight measurements. The sensor may comprise the means for determining covariance shown in fig. 8 or the device for determining covariance shown in fig. 9. Further optionally, the sensor system may further comprise at least one of: at least one camera, at least one millimeter wave radar, at least one ultrasonic radar.

The embodiment of the application also provides a chip system, which comprises at least one processor and an interface circuit. Optionally, the interface circuit and the at least one processor are interconnected by a line, and the interface circuit is used for connecting an external device to the processor. The system on a chip may further comprise at least one memory storing a computer program or the interface circuit is adapted to provide the at least one processor with an external memory stored computer program; the computer program is configured to implement the method flows shown in fig. 1, 3a, 4a and 5a when executed by the at least one processor.

The embodiment of the application also provides a terminal which can be a transport tool or intelligent equipment and comprises an unmanned aerial vehicle, an unmanned transport vehicle, an automobile or a robot and the like, wherein the terminal comprises at least one of the covariance determining device, the sensor system, the chip system and the like.

Specifically, the terminal is a vehicle, and the vehicle further includes at least one of at least one sensor, a vehicle body, an engine, an energy source, wheels, an automobile control system, peripheral devices (such as a vehicle-mounted computer, a microphone, a speaker, etc.), and the like.

In summary, by acquiring the statistical characteristics of the position measurement data and the rectangular coordinate conversion measurement error, and determining the covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the diagonalization relation of the covariance matrix, the covariance estimation precision of the rectangular coordinate conversion measurement error of the sensor position measurement data is improved, and the statistical characteristics of the position measurement data are described more accurately, so that the performance of the functions of state estimation, data association, occupation grid estimation and the like of the position measurement data in application scenes such as tracking, navigation, environment sensing and the like is greatly improved.

Those of ordinary skill in the art will appreciate that implementing all or part of the above-described embodiment methods may be accomplished by hardware associated with a computer program that may be stored on a computer readable storage medium, which when executed, may comprise the above-described method embodiment flows. And the aforementioned storage medium includes: a read-only memory ROM or a random-access memory RAM, a magnetic or optical disk, or the like.

Claims

1. A method of determining covariance comprising:

acquiring position measurement data from at least one sensor;

Determining covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the statistical characteristics of the rectangular coordinate conversion measurement error and the position measurement data;

the determining the covariance of the rectangular coordinate conversion measurement error of the position measurement data comprises the following steps:

2. The method of claim 1, wherein the position measurement data is two-dimensional data, and the statistical characteristic comprises a first variance or standard deviation or root mean square error of a first dimension component in the position measurement data rectangular measurement error and a second variance or standard deviation or root mean square error of a second dimension component in the position measurement data rectangular measurement error;

3. The method of claim 2, wherein the covariance matrix of the position measurement data rectangular transform measurement error comprises:

Wherein R is a covariance matrix of the rectangular coordinate conversion measurement error, R ₁₁ is the first variance, R ₂₂ is the second variance, R ₁₂ represents covariance between the first dimension component and the second dimension component of the rectangular coordinate conversion measurement error of the position measurement data, and the covariance between the first dimension component and the second dimension component of the rectangular coordinate conversion measurement error is obtained according to the position measurement data, the first variance, and the second variance.

4. A method according to claim 3, wherein R ₁₂ comprises:

Wherein R ₁₂ is a covariance between the first dimension component and the second dimension component representing the rectangular coordinate transformation measurement error of the position measurement data, c _θ represents cos θ, s _θ represents sin θ, and angle θ is an azimuth angle.

5. The method of claim 1, wherein the position measurement data is three-dimensional data, the statistical characteristic comprises a first variance or standard deviation or root mean square error of a first dimension component in the position measurement data rectangular measurement error, and a second variance or standard deviation or root mean square error of a second dimension component in the position measurement data rectangular measurement error, and a third variance or standard deviation or root mean square error of a third dimension component in the position measurement data rectangular measurement error;

6. The method of claim 5, wherein said determining a covariance of said position measurement data rectangular transform measurement error from said position measurement data and said first variance or standard deviation or root mean square error and said second variance or standard deviation or root mean square error and said third variance or standard deviation or root mean square error comprises:

7. The method of claim 6, wherein the covariance matrix of the position measurement data rectangular transform measurement error comprises:

8. The method of claim 5, wherein said determining a covariance of said position measurement data rectangular transform measurement error from said position measurement data and said first variance or standard deviation or root mean square error and said second variance or standard deviation or root mean square error and said third variance or standard deviation or root mean square error comprises:

9. The method of claim 8, wherein the angle matrix comprises:

10. The method of claim 8 or 9, wherein the covariance of the position measurement data rectangular transform measurement error comprises:

11. The method according to claim 1, wherein the method further comprises:

and determining a diagonal matrix according to the covariance matrix and an orthogonal matrix, wherein diagonal elements of the diagonal matrix are variances of statistically independent errors, and the orthogonal matrix is determined according to the position measurement data.

12. The method according to any one of claims 1-9 or 11, wherein the position measurement data is data in one of the following coordinate systems: rectangular coordinate system, polar coordinate system, spherical coordinate system.

13. The method of claim 10, wherein the position measurement data is data in one of the following coordinate systems: rectangular coordinate system, polar coordinate system, spherical coordinate system.

14. The method according to any one of claims 1-9 or 11 or 13, wherein the position measurement data comprises distance, azimuth and pitch; or, the position measurement data includes a distance and an azimuth.

15. The method of claim 10, wherein the position measurement data includes distance, azimuth, and pitch; or, the position measurement data includes a distance and an azimuth.

16. The method of claim 12, wherein the position measurement data includes distance, azimuth, and pitch; or, the position measurement data includes a distance and an azimuth.

17. An apparatus for determining covariance, comprising:

The determining unit is used for determining covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the statistical characteristics of the rectangular coordinate conversion measurement error and the position measurement data;

The determining unit is specifically configured to determine a covariance of the rectangular coordinate transformation measurement error of the position measurement data according to a diagonalization relation of a covariance matrix of the rectangular coordinate transformation measurement error, where the diagonalization relation is determined according to the position measurement data.

18. The apparatus of claim 17, wherein the position measurement data is two-dimensional data, and the statistical characteristic comprises a first variance or standard deviation or root mean square error of a first dimension component in the position measurement data rectangular measurement error and a second variance or standard deviation or root mean square error of a second dimension component in the position measurement data rectangular measurement error;

The determining unit is specifically further configured to determine a covariance matrix of the rectangular coordinate transformation measurement error of the position measurement data according to the position measurement data, the first variance, the standard deviation, the root mean square error, and the second variance, the standard deviation, the root mean square error.

19. The apparatus of claim 18, wherein the covariance matrix of the position measurement data rectangular transform measurement error comprises:

20. The apparatus of claim 19, wherein the R ₁₂ comprises:

21. The apparatus of claim 17, wherein the position measurement data is three-dimensional data, the statistical characteristic comprises a first variance or standard deviation or root mean square error of a first dimension component in the position measurement data rectangular measurement error, and a second variance or standard deviation or root mean square error of a second dimension component in the position measurement data rectangular measurement error, and a third variance or standard deviation or root mean square error of a third dimension component in the position measurement data rectangular measurement error;

The determining unit is specifically further configured to determine a covariance of the rectangular coordinate transformation measurement error of the position measurement data according to the position measurement data, the first variance, the standard deviation, the second variance, the third variance, and the third variance.

22. The apparatus according to claim 21, wherein the determining unit is further configured to determine a covariance matrix of the rectangular coordinate transformation measurement error of the position measurement data based on the first variance, the second variance, the third variance, the first target covariance, the second target covariance, and the third target covariance;

23. The apparatus of claim 22, wherein the covariance matrix of the position measurement data rectangular transform measurement error comprises:

24. The apparatus according to claim 21, wherein the determining unit is further configured to determine a covariance of the rectangular coordinate conversion measurement error of the position measurement data based on the first variance, the second variance, the third variance, and an angle matrix, the angle matrix being determined by trigonometric function values of azimuth and/or pitch angles.

25. The apparatus of claim 24, wherein the angle matrix comprises:

26. The apparatus of claim 24 or 25, wherein the covariance of the position measurement data rectangular transform measurement error comprises:

27. The apparatus of claim 17, wherein the means for determining is further configured to determine a diagonal matrix based on the covariance matrix and an orthogonal matrix, diagonal elements of the diagonal matrix being variances of statistically independent errors, the orthogonal matrix being determined based on the position measurement data.

28. The apparatus of any one of claims 17-25 or 27, wherein the position measurement data is data in one of the following coordinate systems: rectangular coordinate system, polar coordinate system, spherical coordinate system.

29. The apparatus of claim 26, wherein the position measurement data is data in one of the following coordinate systems: rectangular coordinate system, polar coordinate system, spherical coordinate system.

30. The apparatus of any one of claims 17-25 or 27 or 29, wherein the position measurement data includes distance, azimuth, and pitch; or, the position measurement data includes a distance and an azimuth.

31. The apparatus of claim 26, wherein the position measurement data includes distance, azimuth, and pitch; or, the position measurement data includes a distance and an azimuth.

32. The apparatus of claim 28, wherein the position measurement data comprises distance, azimuth, and pitch; or, the position measurement data includes a distance and an azimuth.

33. A sensor comprising at least one of a sensor for acquiring position measurement data from at least one sensor, a conversion element, a memory, a processor, wherein the memory has a computer program stored therein, and wherein the processor invokes the computer program stored therein for performing the following operations:

acquiring position measurement data from at least one sensor;

And determining covariance of the rectangular coordinate conversion measurement error of the position measurement data according to the position measurement data and the statistical characteristics of the rectangular coordinate conversion measurement error and according to diagonalization relation of a covariance matrix of the rectangular coordinate conversion measurement error, wherein the diagonalization relation is determined according to the position measurement data.

34. A computer readable storage medium, in which a computer program is stored which, when run on one or more processors, performs the method of any of claims 1-16.