Mobile equipment control method and device, electronic equipment and storage medium

Abstract

The invention relates to a mobile equipment control method and device, electronic equipment and a storage medium. The method comprises: obtaining current first measurement data of a target mobile device and a target movement position corresponding to the target mobile device, the first measurement data comprising mileage data obtained by measurement of an odometer and inertial measurement data obtained by measurement of an inertial measurement unit; performing pose estimation on the target mobile device based on the first measurement data to obtain estimated pose data of the target mobile device; predicting a next moving state of the target mobile device based on the estimated pose data to obtain predicted pose data; filtering the predicted pose data to obtain updated pose data; and performing movement control on the target mobile device according to the updated pose data and the target movement position. By adopting the method, the control accuracy of the mobile equipment can be improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent electronic technology, and in particular to a mobile device control method, device, electronic device and storage medium. Background Art

[0002] At present, when controlling a mobile device, the data measured by a measuring component is usually used as a control basis. This measuring component inevitably has component errors, and during the movement of the mobile device, it is affected by the mobile environment factors and human factors, so that the measuring component may also have environmental errors. The measurement errors of the measuring component caused by environmental errors and component errors will accumulate as the moving distance of the mobile device increases, and the above-mentioned errors will make it impossible to accurately control the mobile device, resulting in low control accuracy of the mobile device. Summary of the invention

[0003] Based on this, it is necessary to provide a mobile device control method, device, electronic device, computer-readable storage medium and computer program product that can improve the control accuracy of the mobile device in order to address the above technical problems.

[0004] In a first aspect, the present application provides a method for controlling a mobile device. The method comprises:

[0005] Acquire current first measurement data of the target mobile device and a target mobile position corresponding to the target mobile device, wherein the first measurement data includes: mileage data obtained by odometer measurement and inertial measurement data obtained by inertial measurement unit measurement;

[0006] Based on the first measurement data, performing position and posture estimation on the target mobile device to obtain estimated position and posture data of the target mobile device;

[0007] Predicting a next movement state of the target mobile device based on the estimated posture data to obtain predicted posture data;

[0008] Filtering the predicted posture data to obtain updated posture data;

[0009] The target mobile device is controlled to move according to the updated posture data and the target moving position.

[0010] In one embodiment, the method of estimating the pose of the target mobile device based on the first measurement data to obtain the estimated pose data of the target mobile device includes: determining a method for estimating the pose of the first measurement data based on the type of environment in which the target mobile device is located, and estimating the pose of the target mobile device based on the determined method for estimating the pose and the first measurement data to obtain the estimated pose data of the target mobile device.

[0011] In one of the embodiments, based on the type of environment in which the target mobile device is located, a method for performing pose estimation on the first measurement data is determined, and based on the determined pose estimation method and the first measurement data, the pose of the target mobile device is estimated to obtain estimated pose data of the target mobile device, including: if the type of environment in which the target mobile device is located is the first type, complementary filtering is performed on the first measurement data to obtain processed data; based on the processed data, the pose of the target mobile device is estimated to obtain estimated pose data of the target mobile device.

[0012] In one of the embodiments, based on the type of environment in which the target mobile device is located, a method for performing pose estimation on the first measurement data is determined, and based on the determined pose estimation method and the first measurement data, the pose of the target mobile device is estimated to obtain estimated pose data of the target mobile device, including: if the type of environment in which the target mobile device is located is the second type, second measurement data is obtained, and the second measurement data includes positioning data obtained by a positioning sensor; the second measurement data is fused with the first measurement data to obtain processed data; based on the processed data, the pose of the target mobile device is estimated to obtain estimated pose data of the target mobile device.

[0013] In one of the embodiments, the method further includes: obtaining environmental information of the mobile environment in which the target mobile device is located, and determining a positioning accuracy category of the positioning sensor for the target mobile device based on the environmental information; if the positioning accuracy category of the positioning sensor for the target mobile device indicates accurate positioning, determining that the type of environment in which the target mobile device is located is the second type; otherwise, determining that the type of environment in which the target mobile device is located is the first type.

[0014] In one of the embodiments, the environmental information includes at least one of environmental signal information, environmental magnetic field information and environmental movement range information; the determination of the positioning accuracy category of the target mobile device based on the environmental information includes: extracting environmental characteristics of the mobile environment where the target mobile device is located according to the environmental information; obtaining an influence coefficient based on the environmental characteristics, of the degree of influence of the mobile environment where the target mobile device is located on the measurement accuracy of the positioning sensor; and identifying the type of the mobile environment where the target mobile device is located according to the influence coefficient to obtain the positioning accuracy category.

[0015] In one embodiment, before estimating the pose of the target mobile device based on the first measurement data and obtaining the estimated pose data of the target mobile device, the method further includes: performing complementary noise suppression on the first measurement data to obtain the first measurement data after noise suppression; and performing bias correction on the first measurement data after noise suppression.

[0016] In one embodiment, filtering the predicted posture data to obtain updated posture data includes: constructing covariance information based on the first measurement data and the estimated posture data; generating filtering gain information based on the covariance information; and filtering the predicted posture data using the filtering gain information to obtain updated posture data.

[0017] In one of the embodiments, the predicted posture data is filtered by filtering gain information to obtain updated posture data, including: filtering the predicted posture data by filtering gain information to obtain filtered processed data; smoothing the filtered processed data to obtain smoothed processed data; performing closed-loop detection and correction on the smoothed data to obtain updated posture data.

[0018] In a second aspect, the present application also provides a mobile device control device. The device includes:

[0019] An acquisition module, configured to acquire current first measurement data of a target mobile device and a target mobile position corresponding to the target mobile device, wherein the first measurement data includes: mileage data obtained by an odometer and inertial measurement data obtained by an inertial measurement unit;

[0020] An estimation module, configured to estimate the position and posture of the target mobile device based on the first measurement data, and obtain estimated position and posture data of the target mobile device;

[0021] A prediction module, configured to predict a next movement state of the target mobile device based on the estimated posture data to obtain predicted posture data;

[0022] A processing module, used for filtering the predicted posture data to obtain updated posture data;

[0023] A control module is used to control the movement of the target mobile device according to the updated posture data and the target moving position.

[0024] In a third aspect, the present application further provides an electronic device. The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0025] Acquire current first measurement data of the target mobile device and a target mobile position corresponding to the target mobile device, wherein the first measurement data includes: mileage data obtained by odometer measurement and inertial measurement data obtained by inertial measurement unit measurement;

[0026] Based on the first measurement data, performing position and posture estimation on the target mobile device to obtain estimated position and posture data of the target mobile device;

[0027] Predicting a next movement state of the target mobile device based on the estimated posture data to obtain predicted posture data;

[0028] Filtering the predicted posture data to obtain updated posture data;

[0029] The target mobile device is controlled to move according to the updated posture data and the target moving position.

[0030] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0031] Acquire current first measurement data of the target mobile device and a target mobile position corresponding to the target mobile device, wherein the first measurement data includes: mileage data obtained by odometer measurement and inertial measurement data obtained by inertial measurement unit measurement;

[0032] Based on the first measurement data, performing position and posture estimation on the target mobile device to obtain estimated position and posture data of the target mobile device;

[0033] Predicting a next movement state of the target mobile device based on the estimated posture data to obtain predicted posture data;

[0034] Filtering the predicted posture data to obtain updated posture data;

[0035] The target mobile device is controlled to move according to the updated posture data and the target moving position.

[0036] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0037] Acquire current first measurement data of the target mobile device and a target mobile position corresponding to the target mobile device, wherein the first measurement data includes: mileage data obtained by odometer measurement and inertial measurement data obtained by inertial measurement unit measurement;

[0038] Based on the first measurement data, performing position and posture estimation on the target mobile device to obtain estimated position and posture data of the target mobile device;

[0039] Predicting a next movement state of the target mobile device based on the estimated posture data to obtain predicted posture data;

[0040] Filtering the predicted posture data to obtain updated posture data;

[0041] The target mobile device is controlled to move according to the updated posture data and the target moving position.

[0042] The mobile device control method, device, electronic device and storage medium obtain the current first measurement data of the target mobile device and the target mobile position corresponding to the target mobile device, wherein the first measurement data includes: mileage data obtained by odometer measurement and inertial measurement data obtained by inertial measurement unit measurement; based on the first measurement data, the target mobile device is estimated to obtain estimated posture data of the target mobile device; based on the estimated posture data, the next movement state of the target mobile device is predicted to obtain predicted posture data; the predicted posture data is filtered to obtain updated posture data; and the target mobile device is moved according to the updated posture data and the target mobile position. Dynamic control, taking into account the current first measurement data, that is, the current data obtained by the measuring component, there is a certain measurement error, therefore, by performing posture estimation on the first measurement data, the estimated posture data is obtained, which reduces the difference between the estimated posture data and the actual posture data of the mobile device, and controls the mobile device based on the estimated posture data and the target moving position. This nonlinear processing process converts the nonlinear processing process into a linear processing process through a series of methods such as state prediction and filtering processing, which reduces the difference between the updated posture data and the posture state data generated in the actual next moving state of the mobile device. The updated posture data is the basis for the movement decision of the mobile device, so the control accuracy of the mobile device is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of an application scenario of a method for controlling a mobile device in an embodiment;

[0044] Figure 2 A schematic diagram of a flow chart of a method for controlling a mobile device in one embodiment;

[0045] Figure 3 A schematic diagram of a flow chart of a method for controlling movement of a target mobile device in one embodiment;

[0046] Figure 4 A schematic diagram of a step flow chart of a method for estimating posture data in one embodiment;

[0047] Figure 5 A schematic diagram of the steps of a method for filtering and processing posture data in one embodiment;

[0048] Figure 6 is a structural block diagram of a mobile device control device in one embodiment;

[0049] Figure 7 FIG. 4 is a diagram showing the internal structure of an electronic device in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] The mobile device control method provided in the embodiment of the present application can be applied to Figure 1 In the application scenario shown. As shown in the figure, the target mobile device 102 communicates with the server 104 through the network. The communication between the target mobile device 102 and the server 104 can also be carried out through a connecting line. There is no limitation here. The data storage system can store the data that the server 104 needs to process (first measurement data, target mobile position, second measurement data, environmental information, etc.), and obtain the current first measurement data of the target mobile device and the target mobile position corresponding to the target mobile device 102 through the server 104. The first measurement data includes: mileage data obtained by odometer measurement, and inertial measurement data obtained by inertial measurement unit measurement; based on the first measurement data, the target mobile device 102 is estimated to obtain the estimated posture data of the target mobile device 102; based on the estimated posture data, the next moving state of the target mobile device 102 is predicted to obtain the predicted posture data; the predicted posture data is filtered to obtain the updated posture data; according to the updated posture data and the target mobile position, the target mobile device 102 is controlled to move. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0052] In another application scenario, the target mobile device 102 is configured with a storage unit, a controller and a data processing unit. The storage unit is used to store data that needs to be processed by the data processing unit (first measurement data, target mobile position, second measurement data, environmental information, etc.). The data processing unit is used to obtain the current first measurement data of the target mobile device and the target mobile position corresponding to the target mobile device 102. The first measurement data includes: mileage data obtained by odometer measurement, and inertial measurement data obtained by inertial measurement unit measurement; based on the first measurement data, the target mobile device 102 is estimated to obtain estimated posture data of the target mobile device 102; based on the estimated posture data, the next moving state of the target mobile device 102 is predicted to obtain predicted posture data; the predicted posture data is filtered to obtain updated posture data, and the controller is used to control the movement of the target mobile device 102 according to the updated posture data and the target mobile position.

[0053] In one embodiment, Figure 2 As shown, a mobile device control method is provided, and the method is applied to Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0054] Step 202, obtaining current first measurement data of the target mobile device and a target mobile position corresponding to the target mobile device, wherein the first measurement data includes: mileage data obtained by odometer measurement and inertial measurement data obtained by inertial measurement unit measurement.

[0055] The target mobile device in step 202 may be a robot or other mobile devices equipped with an odometer and an inertial measurement unit. The current measurement data (including but not limited to the first measurement data and the second measurement data) may be measurement data at the current time point or measurement data for the current time period. The target mobile position is the position to which the target mobile device is expected to be controlled to move. The target mobile position may be expressed in the form of coordinate values ​​in a coordinate system established in the environment where the target mobile device is located or in the form of longitude and latitude, which are not limited here.

[0056] As an embodiment, obtaining current first measurement data of the target mobile device and the target mobile position corresponding to the target mobile device includes: obtaining current measurement data sent by the target mobile device and the target mobile position corresponding to the target mobile device.

[0057] It is understandable that each measuring component (including but not limited to the above-mentioned odometer, inertial measurement unit and measurement sensor, etc.) may have different measurement time settings, resulting in the situation that the current measurement data obtained are not synchronized in time. For example, the odometer measures the data of the target mobile device at 13:45, the inertial measurement unit measures the data of the target mobile device at 13:15, and the positioning sensor measures the data of the target mobile device at 13:15. At this time, the mileage data is not synchronized with the inertial measurement data and the positioning data respectively. If the positioning data, inertial measurement data and mileage data are directly used for pose estimation, it is easy for the estimated pose data to not match the current pose of the target mobile device, resulting in inaccurate estimated pose data.

[0058] Optionally, after step 202, the method further includes: obtaining the measurement time of each current measurement data, and when the measurement time of each current measurement data does not belong to the same time, updating the measurement data that do not belong to the same time by interpolation method, wherein the interpolation method includes but is not limited to linear interpolation method and spline interpolation method, etc.

[0059] In this way, it can be ensured that each current measurement data belongs to the same measurement time, thereby improving the accuracy of the estimated pose data.

[0060] Optionally, after step 202, the method further includes: performing complementary noise suppression on the current measurement data to obtain the current measurement data after noise suppression; and performing bias correction on the current measurement data after noise suppression.

[0061] Furthermore, noise complementary suppression is performed on the current measurement data to obtain the current measurement data after noise suppression, including: for each current measurement data, according to the measurement frequency band characteristics corresponding to the measurement data, noise complementary suppression is performed on the measurement data to obtain the current measurement data after noise suppression, wherein the measurement frequency band characteristics characterize the interference degree of the measurement components corresponding to the current measurement data in different measurement frequency bands.

[0062] As an embodiment, according to the measurement frequency band characteristics corresponding to the measurement data, noise complementary suppression is performed on the measurement data to obtain the current measurement data after noise suppression, including: according to the measurement frequency band characteristics corresponding to the measurement data, a target frequency band whose interference degree corresponding to the measurement data meets a preset interference condition is selected from each measurement frequency band, and noise suppression is performed on the measurement data through frequency band filtering complementary to the target frequency band to obtain the current measurement data after noise suppression, for example, when the target frequency band is a high frequency band, the frequency band filtering is a low-pass filtering.

[0063] Among them, the preset interference condition can be the highest value among the interference levels of all measured frequency bands, or it can be higher than the preset level threshold. The preset level threshold is a critical interference level value set as needed to determine a higher interference level. The preset level threshold can also be determined by the interference level distribution of historical data measured by the measurement component corresponding to the current measurement data.

[0064] In this way, by performing complementary filtering noise suppression on the noise in the target frequency band, the influence of the noise with a high interference degree on the current measurement data is reduced, and the accuracy of the current measurement data is improved.

[0065] As an embodiment, bias correction is performed on the current measurement data after noise suppression, including: performing static bias correction on the current measurement data after noise suppression to obtain static bias corrected measurement data, and performing dynamic bias correction on the static bias corrected measurement data.

[0066] Furthermore, static bias correction is performed on the current measurement data after noise suppression to obtain measurement data after static bias correction, including: obtaining historical static measurement data of the measurement component corresponding to the measurement data when the target mobile device is stationary, and determining the average value of the historical static measurement data as the static bias value; and according to the static bias value, static bias correction is performed on the current measurement data after noise suppression to obtain measurement data after static bias correction.

[0067] As an embodiment, dynamic bias correction is performed on the measurement data after static bias correction, including: obtaining historical dynamic measurement data of the measurement component corresponding to the measurement data after static bias correction when the target mobile device moves; and performing dynamic bias correction on the measurement data after static bias correction according to the historical dynamic measurement data through an adaptive filter.

[0068] It is understandable that there may be a situation where a measurement component corresponding to the measurement data has a measurement error, resulting in inaccurate measurement data.

[0069] As an embodiment, after step 202, the method further includes: if the measurement component corresponding to the measurement data is a calibration measurement component, then calibrating and correcting the measurement data; if the measurement component corresponding to the measurement data is a temperature-sensitive component, then obtaining the ambient temperature of the environment where the target mobile device is located, and performing temperature compensation correction on the measurement data according to the ambient temperature.

[0070] In this way, for the calibration measurement components, the measurement data is calibrated and corrected, thereby improving the accuracy of the measurement data obtained by the calibration measurement components; for temperature-sensitive components, the measurement data is temperature compensated and corrected based on the ambient temperature, so that the measurement data of the target mobile device in the same state at different temperatures are the same; in summary, the accuracy of the measurement data is guaranteed.

[0071] It is understandable that when there are multiple measurement data obtained by the measuring component, there may be abnormal measurement data. If all measurement data are used as the basis for pose estimation, inaccurate estimated pose data may easily occur.

[0072] As an embodiment, after step 202, the method further includes: obtaining data statistical parameters of the multiple measurement data obtained for each measurement data, wherein the data statistical parameters include at least one of the standard deviation, the median deviation and the variance; determining the measurement data range according to the data statistical parameters, and eliminating the data that does not fall within the measurement data range from the multiple measurement data.

[0073] In this way, the measurement data after elimination is guaranteed to be normal, thus improving the accuracy of the estimated pose data.

[0074] Step 204: estimate the position and posture of the target mobile device based on the first measurement data to obtain estimated position and posture data of the target mobile device.

[0075] Exemplarily, step 204 includes: extracting first state data from the first measurement data, wherein the first state data includes at least one of angle data, speed data and position data; based on the first state data, performing posture estimation on the target mobile device to obtain estimated posture data of the target mobile device.

[0076] Step 206: predict the next movement state of the target mobile device based on the estimated posture data to obtain predicted posture data.

[0077] Exemplarily, step 206 includes: extracting second state data from the estimated posture data, wherein the second state data includes at least one of angle data, speed data, and position data; predicting the next moving state of the target mobile device according to the second state data through a dynamic model to obtain predicted posture data.

[0078] As one embodiment, a next moving state of the target mobile device is predicted based on the second state data through a dynamic model to obtain predicted posture data, including: if the second state data includes angle data, and the predicted posture data includes predicted angle data, then through the dynamic model, according to the time interval between the occurrence time of the next moving state of the target mobile device and the current time, the angle and angular velocity in the second state data are integrated to obtain predicted angle data.

[0079] Optionally, through the dynamic model, according to the time interval between the occurrence time of the next moving state of the target mobile device and the current time, the angle and angular velocity in the second state data are integrated to obtain the predicted angle data, which can be expressed by the formula:

[0080]

[0081] in, To predict the angle data, is the angle in the second state data, For the second state data The acceleration at For the time interval.

[0082] As another embodiment, the next moving state of the target mobile device is predicted according to the second state data through a dynamic model to obtain predicted posture data, including: if the second state data includes speed data, and the predicted posture data includes predicted speed data, then through the dynamic model, according to the time interval between the occurrence time of the next moving state of the target mobile device and the current time, the speed and acceleration in the second state data are integrated to obtain predicted speed data.

[0083] Optionally, through the dynamic model, according to the time interval between the occurrence time of the next moving state of the target mobile device and the current time, the speed and acceleration in the second state data are integrated to obtain the predicted speed data, which can be expressed by the formula:

[0084]

[0085] in, To estimate the speed data, is the speed in the second state data, For speed data The acceleration at .

[0086] As another embodiment, the next moving state of the target mobile device is predicted according to the second state data through a dynamic model to obtain predicted posture data, including: if the second state data includes position data, and the predicted posture data includes predicted position data, then through the dynamic model, according to the time interval between the occurrence time of the next moving state of the target mobile device and the current time, the position data and speed in the second state data are integrated to obtain the predicted position data.

[0087] Optionally, through the dynamic model, according to the time interval between the occurrence time of the next moving state of the target mobile device and the current time, the position data and speed in the second state data are integrated to obtain the predicted position data, which can be expressed by the formula:

[0088]

[0089] in, To predict location data, is the position data in the second state data, For the second state data The speed of time.

[0090] Step 208, filtering the predicted posture data to obtain updated posture data.

[0091] Exemplarily, step 208 includes: filtering the predicted pose data through a preset filtering algorithm to obtain updated pose data, wherein the preset filtering algorithm includes but is not limited to an EKF (Extended Kalman Filter) algorithm, a KF (Kalman Filter) algorithm, a UKF (Unscented Kalman Filter) algorithm, and an interactive multi-model Kalman filter algorithm, etc.

[0092] Step 210 , controlling the movement of the target mobile device according to the updated posture data and the target moving position.

[0093] For example, refer to Figure 3Step 210 includes: acquiring radar data, constructing map information of the environment where the target mobile device is located through a SLAM (Simultaneous Localization and Mapping) algorithm based on the radar data and the updated posture data; and controlling the movement of the target mobile device based on the relative position of the target mobile position in the map information.

[0094] In this embodiment, by obtaining the current first measurement data of the target mobile device and the target mobile position corresponding to the target mobile device, the first measurement data includes: mileage data obtained by odometer measurement and inertial measurement data obtained by inertial measurement unit measurement; based on the first measurement data, the target mobile device is estimated to obtain estimated posture data of the target mobile device; based on the estimated posture data, the next movement state of the target mobile device is predicted to obtain predicted posture data; the predicted posture data is filtered to obtain updated posture data; according to the updated posture data and the target mobile position, the target mobile device is controlled to move, taking into account the current first measurement data, that is, The current data measured by the measuring component has a certain measurement error. Therefore, by performing posture estimation on the first measurement data to obtain estimated posture data, the difference between the estimated posture data and the actual posture data of the mobile device is reduced, and the mobile device is controlled based on the estimated posture data and the target moving position. This nonlinear processing process converts the nonlinear processing process into a linear processing process through a series of methods such as state prediction and filtering processing, thereby reducing the difference between the updated posture data and the posture state data generated in the actual next moving state of the mobile device. The updated posture data is the basis for the movement decision of the mobile device, so the control accuracy of the mobile device is improved.

[0095] In one embodiment, Figure 4 As shown, a method for estimating pose data is provided, in which: Figure 2 The step 204 of estimating the position and posture of the target mobile device based on the first measurement data to obtain estimated position and posture data of the target mobile device includes:

[0096] Step 402, based on the type of environment in which the target mobile device is located, determine a method for performing pose estimation on the first measurement data, and perform pose estimation on the target mobile device based on the determined pose estimation method and the first measurement data to obtain estimated pose data of the target mobile device.

[0097] Exemplarily, step 402 includes: determining a method for performing pose estimation on first measurement data based on the type of environment in which the target mobile device is located; extracting first state data from the first measurement data, and processing the first state data based on the determined pose estimation method to obtain processed data; and performing pose estimation on the target mobile device based on the processed data to obtain estimated pose data of the target mobile device.

[0098] As an embodiment, the first state data is processed based on a determined posture estimation method to obtain processed data, including: if the environment type where the target mobile device is located is the first type, the first state data is subjected to complementary filtering processing to obtain processed data.

[0099] Further, performing complementary filtering processing on the first state data to obtain processed data includes: performing complementary filtering processing on the first state data according to the measurement characteristics of the measurement component corresponding to the first state data to obtain processed data.

[0100] As another embodiment, the first state data is processed based on a determined posture estimation method to obtain processed data, including: if the environment type in which the target mobile device is located is the second type, second measurement data is obtained, and the second measurement data includes positioning data obtained by the positioning sensor; the second measurement data is fused with the first measurement data to obtain processed data.

[0101] Furthermore, the second measurement data is fused with the first measurement data to obtain processed data, including: weighting the first measurement data by a first preset weight to obtain first weighted data, and weighting the second measurement data by a second preset weight to obtain second weighted data; fusing the first weighted data and the second weighted data to obtain processed data.

[0102] Optionally, the method also includes: obtaining environmental information of the mobile environment where the target mobile device is located, and determining the positioning accuracy category of the positioning sensor for the target mobile device based on the environmental information; if the positioning accuracy category of the positioning sensor for the target mobile device indicates accurate positioning, then determining that the environment type where the target mobile device is located is the second type; otherwise, determining that the environment type where the target mobile device is located is the first type.

[0103] As one embodiment, environmental information includes at least one of environmental signal information, environmental magnetic field information and environmental movement range information; determining the positioning accuracy category of the positioning sensor for the target mobile device based on the environmental information includes: extracting environmental characteristics of the mobile environment where the target mobile device is located according to the environmental information; obtaining an influence coefficient based on the environmental characteristics, of the degree of influence of the mobile environment where the target mobile device is located on the measurement accuracy of the positioning sensor; and identifying the type of the mobile environment where the target mobile device is located according to the influence coefficient to obtain the positioning accuracy category.

[0104] Among them, if the environmental information includes environmental signal information, the generated influence coefficient is higher when the environmental signal information represents a weaker environmental signal; if the environmental information includes environmental magnetic field information, the generated influence coefficient is higher when the environmental magnetic field information represents a more complex environmental magnetic field; if the environmental information includes the environmental movement range, the smaller the environmental movement range, the higher the generated influence coefficient; the lower the influence coefficient, the closer the positioning accuracy category is to the positioning accuracy.

[0105] In this way, the influence of environmental signals, environmental magnetic fields and environmental movement range on the measurement accuracy of the positioning sensor is taken into account, and the second measurement data obtained by the positioning sensor is used together with the first measurement data as the basis for posture estimation when the environment where the target mobile device is located does not affect the measurement accuracy of the positioning sensor, thereby improving the accuracy of the estimated posture data.

[0106] In this embodiment, a method for performing pose estimation on the first measurement data is determined based on the environment type in which the target mobile device is located, and pose estimation is performed on the target mobile device based on the determined pose estimation method and the first measurement data to obtain estimated pose data of the target mobile device. Taking into account the impact of different environment types on pose estimation, the pose estimation method determined by the environment type and the first measurement data are used together to provide a decision-making basis for the pose estimation process of the target mobile device, thereby improving the accuracy of the estimated pose data.

[0107] In one embodiment, Figure 5 As shown, a method for filtering and processing posture data is provided, in which: Figure 2 The step 208 of filtering the predicted posture data to obtain updated posture data includes:

[0108] Step 502: construct covariance information based on the first measurement data and the estimated posture data.

[0109] As an embodiment, step 502 includes: the covariance information includes a residual covariance matrix; constructing an observation residual according to current measurement data and estimated pose data; and constructing a residual covariance matrix according to the Jacobian matrix and the noise covariance matrix corresponding to the observation residual.

[0110] Furthermore, based on the current measurement data and the estimated posture data, an observation residual is constructed, including: based on the current measurement data, a priori posture estimation is performed on the target mobile device to obtain an estimated covariance matrix, and based on the estimated posture data, an actual covariance matrix is ​​constructed; and based on the difference between the estimated covariance matrix and the actual covariance matrix, an observation residual is constructed.

[0111] Step 504: Generate filtering gain information according to the covariance information.

[0112] As an embodiment, step 504 includes: constructing filtering gain information according to the actual covariance matrix, the estimated covariance matrix and the residual covariance matrix.

[0113] Furthermore, filtering gain information is constructed according to the actual covariance matrix, the estimated covariance matrix and the residual covariance matrix, including: the filtering gain information includes the filtering gain matrix; and the product of the actual covariance matrix, the estimated covariance matrix and the residual covariance matrix is ​​determined as the filtering gain matrix.

[0114] Step 506, filtering the predicted posture data by filtering the gain information to obtain updated posture data.

[0115] As an embodiment, step 506 includes: filtering the predicted posture data by filtering gain information to obtain filtered processed data; smoothing the filtered processed data to obtain smoothed processed data; performing closed-loop detection correction on the smoothed processed data to obtain updated posture data.

[0116] Furthermore, the predicted posture data is filtered by filtering gain information to obtain filtered processing data, including: multiplying the product of the filtering gain matrix and the predicted covariance matrix corresponding to the predicted posture data as the filtered processing data.

[0117] It can be understood that, since the filtered data is time-series related data, by smoothing the filtered data, the temporal fluctuations in the filtered data can be removed, making the obtained smoothed data smoother.

[0118] As an embodiment, closed-loop detection and correction are performed on the smoothed data to obtain updated posture data, including: obtaining historical posture data corresponding to the target mobile device (including but not limited to estimated posture data and posture data before the estimated posture data), and performing closed-loop detection and correction on the smoothed data based on the historical posture data to obtain updated posture data.

[0119] In this way, it is ensured that the accumulated error due to the longer moving distance of the mobile device can be reduced, and the accuracy of the updated posture data is improved.

[0120] In this embodiment, covariance information is constructed based on the first measurement data and the estimated posture data; filter gain information is generated based on the covariance information; the predicted posture data is filtered through the filtering gain information to obtain updated posture data. Through covariance construction and filtering, the nonlinear processing process is converted into a linear processing process, thereby improving the accuracy of the updated posture data.

[0121] As a more detailed embodiment, the current first measurement data of the target mobile device and the target mobile position corresponding to the target mobile device are obtained, the first measurement data including: mileage data obtained by odometer measurement and inertial measurement data obtained by inertial measurement unit measurement; environmental information of the mobile environment where the target mobile device is located is obtained, and environmental characteristics of the mobile environment where the target mobile device is located are extracted according to the environmental information; according to the environmental characteristics, the influence of the mobile environment where the target mobile device is located on the measurement accuracy of the positioning sensor is obtained to obtain an influence coefficient; according to the influence coefficient, the type of the mobile environment where the target mobile device is located is identified to obtain a positioning accuracy category; if the type of the environment where the target mobile device is located is of the first type, complementary filtering is performed on the first measurement data to obtain processed data; based on the processed data, the pose of the target mobile device is estimated to obtain estimated pose data of the target mobile device; if the type of the environment where the target mobile device is located is of the second type, second measurement data is obtained, and the second measurement data includes positioning data obtained by the positioning sensor; the second measurement data is fused with the first measurement data to obtain processed data; based on the processed data, the pose of the target mobile device is estimated to obtain estimated pose data of the target mobile device.

[0122] Furthermore, noise complementary suppression is performed on the first measurement data to obtain the first measurement data after noise suppression; bias correction is performed on the first measurement data after noise suppression; covariance information is constructed based on the first measurement data and the estimated posture data; filter gain information is generated based on the covariance information; the predicted posture data is filtered using the filter gain information to obtain filtered processed data; the filtered processed data is smoothed to obtain smoothed processed data; closed-loop detection correction is performed on the smoothed data to obtain updated posture data.

[0123] In this embodiment, by obtaining the current first measurement data of the target mobile device and the target mobile position corresponding to the target mobile device, the first measurement data includes: mileage data obtained by odometer measurement and inertial measurement data obtained by inertial measurement unit measurement; based on the first measurement data, the target mobile device is estimated to obtain estimated posture data of the target mobile device; based on the estimated posture data, the next movement state of the target mobile device is predicted to obtain predicted posture data; the predicted posture data is filtered to obtain updated posture data; according to the updated posture data and the target mobile position, the target mobile device is controlled to move, taking into account the current first measurement data, that is, The current data measured by the measuring component has a certain measurement error. Therefore, by performing posture estimation on the first measurement data to obtain estimated posture data, the difference between the estimated posture data and the actual posture data of the mobile device is reduced, and the mobile device is controlled based on the estimated posture data and the target moving position. This nonlinear processing process converts the nonlinear processing process into a linear processing process through a series of methods such as state prediction and filtering processing, thereby reducing the difference between the updated posture data and the posture state data generated in the actual next moving state of the mobile device. The updated posture data is the basis for the movement decision of the mobile device, so the control accuracy of the mobile device is improved.

[0124] Furthermore, taking into account the impact of different environment types on posture estimation, the posture estimation method determined by the environment type and the first measurement data are used together to provide a decision-making basis for the posture estimation process of the target mobile device, thereby improving the accuracy of the estimated posture data; through covariance construction and filtering processing, the nonlinear processing process is converted into a linear processing process, thereby improving the accuracy of the updated posture data.

[0125] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0126] Based on the same inventive concept, the embodiment of the present application also provides a mobile device control device for implementing the mobile device control method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more mobile device control device embodiments provided below can refer to the limitations of the mobile device control method above, and will not be repeated here.

[0127] In one embodiment, Figure 6 As shown, a mobile device control device 600 is provided, comprising: an acquisition module 602, an estimation module 604, a prediction module 606, a processing module 608 and a control module 610, wherein:

[0128] The acquisition module 602 is used to acquire current first measurement data of the target mobile device and a target mobile position corresponding to the target mobile device, wherein the first measurement data includes: mileage data obtained by odometer measurement and inertial measurement data obtained by inertial measurement unit measurement;

[0129] An estimation module 604 is used to estimate the position and posture of the target mobile device based on the first measurement data to obtain estimated position and posture data of the target mobile device;

[0130] A prediction module 606 is used to predict the next movement state of the target mobile device based on the estimated posture data to obtain predicted posture data;

[0131] A processing module 608 is used to filter the predicted posture data to obtain updated posture data;

[0132] The control module 610 is used to control the movement of the target mobile device according to the updated posture data and the target moving position.

[0133] In one embodiment, the estimation module 604 is also used to determine a method for performing pose estimation on the first measurement data based on the type of environment in which the target mobile device is located, and to perform pose estimation on the target mobile device based on the determined pose estimation method and the first measurement data to obtain estimated pose data of the target mobile device.

[0134] In one embodiment, the estimation module 604 is also used to perform complementary filtering on the first measurement data to obtain processed data if the environment type in which the target mobile device is located is the first type; based on the processed data, perform posture estimation on the target mobile device to obtain estimated posture data of the target mobile device.

[0135] In one embodiment, the estimation module 604 is also used to obtain second measurement data if the environment type in which the target mobile device is located is of the second type, the second measurement data including positioning data obtained by the positioning sensor; fuse the second measurement data with the first measurement data to obtain processed data; and estimate the position and posture of the target mobile device based on the processed data to obtain estimated position and posture data of the target mobile device.

[0136] In one embodiment, the device also includes: a determination module, which is used to obtain environmental information of the mobile environment where the target mobile device is located, and determine the positioning accuracy category of the positioning sensor for the target mobile device based on the environmental information; if the positioning accuracy category of the positioning sensor for the target mobile device indicates accurate positioning, then the environment type where the target mobile device is located is determined to be the second type; otherwise, the environment type where the target mobile device is located is determined to be the first type.

[0137] In one embodiment, the environmental information includes at least one of environmental signal information, environmental magnetic field information and environmental movement range information; the determination module is also used to extract environmental characteristics of the mobile environment where the target mobile device is located based on the environmental information; based on the environmental characteristics, the degree of influence of the mobile environment where the target mobile device is located on the measurement accuracy of the positioning sensor is obtained to obtain an influence coefficient; based on the influence coefficient, the type of the mobile environment where the target mobile device is located is identified to obtain a positioning accuracy category.

[0138] In one embodiment, based on the first measurement data, the pose of the target mobile device is estimated. Before obtaining the estimated pose data of the target mobile device, the processing module 608 is also used to perform noise complementary suppression on the first measurement data to obtain the first measurement data after noise suppression; and perform bias correction on the first measurement data after noise suppression.

[0139] In one embodiment, the processing module 608 is also used to construct covariance information based on the first measurement data and the estimated posture data; generate filtering gain information based on the covariance information; and filter the predicted posture data through the filtering gain information to obtain updated posture data.

[0140] In one embodiment, the processing module 608 is also used to filter the predicted posture data through filtering gain information to obtain filtered processing data; smooth the filtered processing data to obtain smoothed processing data; perform closed-loop detection correction on the smoothed processing data to obtain updated posture data.

[0141] Each module in the above mobile device control device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in an electronic device in the form of hardware, or can be stored in a memory in an electronic device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0142] In one embodiment, an electronic device is provided. The electronic device may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown. The electronic device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the electronic device is used to exchange information between the processor and the external device. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a mobile device control method is implemented. The display unit of the electronic device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the electronic device housing, or an external keyboard, touchpad or mouse.

[0143] Those skilled in the art will understand that Figure 7 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0144] In one embodiment, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0145] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0146] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0147] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0148] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0149] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for controlling a mobile device, characterized in that: The method comprises: Acquire current first measurement data of the target mobile device and a target mobile position corresponding to the target mobile device, wherein the first measurement data includes: mileage data obtained by odometer measurement and inertial measurement data obtained by inertial measurement unit measurement; Based on the first measurement data, performing position and posture estimation on the target mobile device to obtain estimated position and posture data of the target mobile device; Predicting a next movement state of the target mobile device based on the estimated posture data to obtain predicted posture data; Filtering the predicted posture data to obtain updated posture data; The target mobile device is controlled to move according to the updated posture data and the target moving position.

2. The method according to claim 1, characterized in that The step of estimating the position and posture of the target mobile device based on the first measurement data to obtain estimated position and posture data of the target mobile device includes: Based on the type of environment in which the target mobile device is located, a method for performing pose estimation on the first measurement data is determined, and based on the determined pose estimation method and the first measurement data, the pose of the target mobile device is estimated to obtain estimated pose data of the target mobile device.

3. The method according to claim 2, characterized in that The method of determining a pose estimation method for the first measurement data based on the type of environment in which the target mobile device is located, and performing pose estimation on the target mobile device based on the determined pose estimation method and the first measurement data to obtain estimated pose data of the target mobile device includes: If the environment type where the target mobile device is located is the first type, performing complementary filtering processing on the first measurement data to obtain processed data; Based on the processed data, the position and posture of the target mobile device are estimated to obtain estimated position and posture data of the target mobile device.

4. The method according to claim 2, characterized in that: The method of determining a pose estimation method for the first measurement data based on the type of environment in which the target mobile device is located, and performing pose estimation on the target mobile device based on the determined pose estimation method and the first measurement data to obtain estimated pose data of the target mobile device includes: If the environment type where the target mobile device is located is the second type, obtaining second measurement data, where the second measurement data includes positioning data obtained by a positioning sensor; fusing the second measurement data with the first measurement data to obtain processed data; Based on the processed data, the position and posture of the target mobile device are estimated to obtain estimated position and posture data of the target mobile device.

5. The method according to any one of claims 2 to 4, characterized in that: The method further comprises: Acquire environmental information of a mobile environment where the target mobile device is located, and determine a positioning accuracy category of a positioning sensor for the target mobile device based on the environmental information; If the positioning sensor indicates that the positioning accuracy category of the target mobile device is accurate, it is determined that the environment type where the target mobile device is located is the second type; otherwise, it is determined that the environment type where the target mobile device is located is the first type.

6. The method according to claim 5, characterized in that The environmental information includes at least one of environmental signal information, environmental magnetic field information, and environmental movement range information; and determining the positioning accuracy category of the target mobile device based on the environmental information includes: Extracting environmental characteristics of the mobile environment where the target mobile device is located according to the environmental information; Obtaining an influence coefficient based on the degree of influence of the mobile environment where the target mobile device is located on the measurement accuracy of the positioning sensor; According to the influence coefficient, the type of the mobile environment where the target mobile device is located is identified to obtain a positioning accuracy category.

7. The method according to claim 1, characterized in that Before estimating the position and posture of the target mobile device based on the first measurement data and obtaining the estimated position and posture data of the target mobile device, the method further includes: Performing complementary noise suppression on the first measurement data to obtain noise-suppressed first measurement data; The first measurement data after noise suppression is subjected to offset correction.

8. The method according to any one of claims 1 to 4, characterized in that: The filtering process of the predicted posture data to obtain updated posture data includes: Constructing covariance information according to the first measurement data and the estimated pose data; Generate filtering gain information according to the covariance information; The predicted posture data is filtered by filtering the gain information to obtain updated posture data.

9. The method according to claim 8, characterized in that The filtering gain information is used to filter the predicted posture data to obtain updated posture data, including: The predicted posture data is filtered by filtering the gain information to obtain filtered data; Smoothing the filtered data to obtain smoothed data; Perform closed-loop detection and correction on the smoothed data to obtain updated posture data.

10. A mobile device control device, characterized in that: The device comprises: An acquisition module, configured to acquire current first measurement data of a target mobile device and a target mobile position corresponding to the target mobile device, wherein the first measurement data includes: mileage data obtained by an odometer and inertial measurement data obtained by an inertial measurement unit; An estimation module, configured to estimate the position and posture of the target mobile device based on the first measurement data, and obtain estimated position and posture data of the target mobile device; A prediction module, configured to predict a next movement state of the target mobile device based on the estimated posture data to obtain predicted posture data; A processing module, used for filtering the predicted posture data to obtain updated posture data; A control module is used to control the movement of the target mobile device according to the updated posture data and the target moving position.

11. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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