CN111077549A

CN111077549A - Position data correction method, apparatus and computer readable storage medium

Info

Publication number: CN111077549A
Application number: CN201911419761.XA
Authority: CN
Inventors: 刘宏基; 刘明; 王鲁佳
Original assignee: Shenzhen Yiqing Innovation Technology Co ltd
Current assignee: Shenzhen Yiqing Innovation Technology Co ltd
Priority date: 2019-12-31
Filing date: 2019-12-31
Publication date: 2020-04-28
Anticipated expiration: 2039-12-31
Also published as: CN111077549B

Abstract

The present application relates to a position data correction method, apparatus, and computer-readable storage medium. The method comprises the following steps: acquiring first position data of a predicted first time; when first positioning data is acquired at a first moment, acquiring a positioning error value according to the first positioning data and the first position data, acquiring a position error value corresponding to a first time period, and correcting the first position data according to the positioning error value and the position error value, wherein the first time period is a time period before the first moment; when a first distance value between the first distance value and a first reference object is acquired at a first moment, a second distance value between the first distance value and the first reference object is determined in the map according to the first position data, and the first position data is corrected according to the first distance value and the second distance value. The method can improve the accuracy of the predicted position data.

Description

Position data correction method, apparatus and computer readable storage medium

Technical Field

The present application relates to the field of automatic driving technologies, and in particular, to a method, an apparatus, and a computer-readable storage medium for correcting position data.

Background

In the field of autopilot, autopilot usually requires a prediction of the vehicle's driving route, driving location by sensors in the vehicle, and then relies on a computer-based intelligent pilot to achieve unmanned driving. However, the position data predicted by the sensor in the conventional method is not accurate.

Disclosure of Invention

In view of the above, it is desirable to provide a method, an apparatus, a computer device, and a storage medium for correcting position data, which can improve the accuracy of predicted position data.

A method of position data correction, the method comprising:

acquiring first position data of a predicted first time;

when first positioning data is acquired at the first moment, acquiring a positioning error value according to the first positioning data and the first position data, acquiring a position error value corresponding to a first time period, and correcting the first position data according to the positioning error value and the position error value, wherein the first time period is a time period before the first moment;

when a first distance value between the first position data and a first reference object is acquired at the first moment, a second distance value between the first position data and the first reference object is determined in a map according to the first position data, and the first position data is corrected according to the first distance value and the second distance value.

In one embodiment, the obtaining the positioning difference variances corresponding to at least two time instants before the first time instant includes: acquiring second positioning data corresponding to each moment in at least two moments before the first moment; obtaining a positioning differential value corresponding to two adjacent moments according to the second positioning data corresponding to each moment; and determining a positioning difference variance according to the positioning difference value.

In one embodiment, the method further comprises: determining at least two candidate reference objects from the map according to the first position data, and determining a candidate reference object with the closest distance from the at least two candidate reference objects as the second reference object; when the at least two candidate references are both the second reference, re-performing the step of determining at least two candidate references from the map.

In one embodiment, the first reference comprises a road course; the first position data comprises first heading data; the method further comprises the following steps:

when a first included angle value between the first forward direction data and the road route during running of the vehicle is obtained, a second included angle value between the first forward direction data and the road route in the map is obtained; and correcting the first advancing direction data according to the first included angle value and the second included angle value.

A position data correcting device, the device comprising:

an obtaining module, configured to obtain first position data of a predicted first time;

the first correction module is used for obtaining a positioning error value according to the first positioning data and the first position data when the first positioning data is obtained at the first moment, obtaining a position error value corresponding to a first time period, and correcting the first position data according to the positioning error value and the position error value, wherein the first time period is a time period before the first moment;

and the second correction module is used for determining a second distance value between the first distance value and a first reference object in a map according to the first position data when the first distance value between the first distance value and the first reference object is acquired at the first moment, and correcting the first position data according to the first distance value and the second distance value.

A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:

acquiring first position data of a predicted first time;

A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:

acquiring first position data of a predicted first time;

According to the position data correction method, the position data correction device, the computer equipment and the storage medium, when the first positioning data at the first moment is obtained, the positioning error value is obtained according to the first positioning data and the first position data, and the error between the positioning data and the first position data can be obtained through more accurate positioning data; the method comprises the steps of obtaining a position prediction error value corresponding to a first time period, correcting first position data according to the positioning error value and the position prediction error, and correcting the predicted first position data according to the positioning error value and the position prediction error value, so that the position prediction error value accumulated in the first time period can be eliminated, namely the error accumulated in a period of time is eliminated, and subsequently obtained second position data can be more accurate; when the first distance value between the unmanned vehicle and the first reference object is acquired, the second distance value between the unmanned vehicle and the first reference object is determined in the map according to the first position data, the first position data is corrected according to the first distance value and the second distance value, the subsequent predicted position data can be more accurate, the unmanned vehicle can be prevented from deviating from the air route, the unmanned vehicle can walk according to the preset path, and the driving safety of the unmanned vehicle is improved.

Drawings

FIG. 1 is a diagram of an exemplary location data correction method;

FIG. 2 is a schematic flow chart illustrating a method for correcting location data according to one embodiment;

FIG. 3 is a schematic flow chart illustrating a method for correcting position data according to another embodiment;

FIG. 4 is a flow diagram illustrating a process for modifying first position data according to one embodiment;

FIG. 5 is a flow diagram illustrating a process for determining a first variance value in one embodiment;

FIG. 6 is a schematic illustration of the determination of candidate references in one embodiment;

FIG. 7 is a flowchart illustrating a location data correction method according to yet another embodiment;

fig. 8 is a block diagram showing the structure of a position data correcting device in one embodiment;

FIG. 9 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one datum from another. For example, a first variance value may be referred to as a second variance value, and similarly, a second variance value may be referred to as a first variance value, without departing from the scope of the present application. The first variance value and the second variance value are both variance values, but they are not the same variance value.

The position data correction method provided by the application can be applied to the application environment shown in fig. 1. Among other things, the vehicle 110 can include a computer device therein that can be used to acquire data, process data, output data, and the like. The computer device may communicate with other servers and the like over a network. The vehicle may be a vehicle, an airplane, etc. without limitation. The computer device may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices.

In one embodiment, as shown in fig. 2, a flow chart of a location data correction method is provided. The IMU (Inertial measurement unit) can be used for measuring at least one data of three-axis attitude angle, angular velocity, acceleration and motion direction of an object. The angular velocity, acceleration, direction of motion, etc. of the vehicle may also be measured by a gyroscope. An encoder (encoder) may be used to obtain the speed of motion of the unmanned vehicle. Or the movement speed of the unmanned vehicle is obtained through the accelerometer. The position of the unmanned vehicle at the next moment can be predicted through the inertial measurement unit and the encoder. The integrator may be considered as a software module for calculating the position of the unmanned vehicle according to the kinematic model based on the data obtained by the IMU and the encoder. The predicted frequency of the position data depends on the IMU and the data frequency of the encoder. The transmission frequency of the encoder data in the test tool used was 20HZ (hertz) and the data frequency of the IMU was 100HZ (hertz), so the transmission frequency of the position data could reach 20HZ (hertz). When the first positioning data is acquired, the integrator B predicts the position of the current positioning data corresponding to the first moment again from the moment corresponding to the last positioning data by a real-time prediction method of the integrator A, compares the predicted position with the positioning data to calculate an Error, and then determines a target Error value at the moment by fusing the Error as an observed value and an integral Error predicted by an Error State Kalman Filter method (Error State Kalman Filter). Then, the target error value is injected into the predicted position value of the integrator a at the corresponding time to be corrected. The positioning data is time stamped, but because of the data transmission delay, the time at which the positioning data is received is different from the time of the time stamp. Therefore, the location of the current positioning data at the time corresponds to the location of the unmanned vehicle at the time corresponding to the timestamp of the positioning data currently being processed. And visually detecting lane line deviation or the distance of the signboards, namely acquiring a first distance value between the unmanned vehicle and the first reference object by means of radar and the like. And (4) obtaining a second distance value between the unmanned vehicle and the first reference object in the map by lane line deviation, signboard distance and the like in the high-precision map. Taking the current time as time t4 as an example, the first time is time t 4. The location data at time t5 may be predicted from time t4 and the IMU and encoder data. The high-precision map may be a map which is established in advance and conforms to the OpenDRIVE format. The high-precision map can be accurate to the position data of the first reference object and the second reference object.

In one embodiment, the unmanned vehicle can perform unmanned vehicle state estimation by fusing sensors such as an IMU (inertial measurement unit), an encoder, and the like, but both the IMU and the encoder are erroneous sensors, and thus if state estimation is performed by relying on them only, the estimated errors are gradually increased cumulatively. To remove this gradual accumulated error, other sensors need to be relied upon for correction. The prediction result may be calibrated using a Positioning signal such as a GNSS (Global Navigation Satellite System), a GPS (Global Positioning System), or the like. But positioning signal accuracy and stability are extremely challenging, especially in environments with high obstacles such as high buildings, trees, etc., the positioning signal is easily blocked. In addition, the positioning signal generates a multipath phenomenon caused by reflection in a propagation path, and a certain error is generated when the position is calculated due to problems of transmission delay, satellite clock error and the like caused by refraction in the atmosphere. Under good reception conditions, the positioning accuracy is typically several meters, and if a differential positioning technique is used, the accuracy can be improved to a meter level or even a centimeter level. However, such good receiving conditions are very harsh in urban application scenarios, and it is difficult to ensure a good positioning environment at all times. Once the positioning data with large potential errors cannot be identified and shielded, the positioning result is biased by the wrong positioning data, and even the positioning result vibrates and fluctuates along with the wrong positioning data. Once such a situation occurs, subsequent path planning and control of the unmanned vehicle will be greatly affected. While the error is gradually accumulated by only depending on the sensor to predict the position, the accuracy of the predicted position can be ensured only by eliminating the accumulated error.

As shown in fig. 3, taking the vehicle as an unmanned vehicle as an example, the method is applied to the unmanned vehicle in fig. 1 as an example for description, and is a schematic flow chart of a position data correction method in another embodiment, including:

step 302, first position data of a predicted first time is obtained.

The first time may refer to any one time, and specifically may be a current time. For example, the first time may be time t1, time t2, etc., but is not limited thereto. The first position data refers to position data predicted by a sensor. The position data may include longitude and latitude data, and may also include altitude data.

Specifically, the unmanned vehicle may acquire first position data of a first time predicted by the sensor.

Step 304, when the positioning data is obtained at the first time, obtaining a positioning error value according to the positioning data and the first position data, obtaining a position error value corresponding to the first time period, and correcting the first position data according to the positioning error value and the position error value, wherein the first time period is a time period before the first time.

The positioning data is data for positioning the unmanned vehicle. For example, the Positioning data may be GPS (Global Positioning System) data, GNSS (Global Navigation satellite System) data, or the like, but is not limited thereto. The positioning data may include longitude and latitude data, and may also include altitude data. For example, the sensor predicts the position data of the unmanned vehicle at the time t4 according to the direction, the speed and the like of the unmanned vehicle at the time t3, and the time t4 is the first time in the embodiment. The first positioning data and the first position data may have a certain error.

The unmanned vehicle has errors in position prediction through the sensor every time, and the errors are accumulated along with the increase of prediction time, so that the errors are larger and larger. The position prediction error value corresponding to the first time period is a position prediction error value accumulated in a time period before the first time. The first time period may specifically be a time period preceding and adjacent to the first time instant. For example, the first time is time t4, and the first time period may be the time period t1 to t 4.

The position prediction error value, which may also be referred to as an integration error, is the integration of the error of the IMU itself over an integration period. The integral error may refer to an integral value accumulated over a time period spaced between two integrator positions. The position prediction error value at a certain time is a coefficient a × a speed error + a coefficient B × a position error of the last prediction. The formula is integrated in time, that is, the value of the position prediction error in a certain time period is equal to the coefficient a × t × the speed error + the coefficient B × the position error of the last prediction × t. The unmanned vehicle obtains a position prediction error value corresponding to a first time period before a first time.

Specifically, when the unmanned vehicle acquires the positioning data at the first time, a first error value corresponding to the first positioning data and a second error value corresponding to the first positioning data may be acquired, and the positioning error value is obtained according to the first error value and the second error value. The unmanned vehicle may add the first error value and the second error value to obtain a positioning error value. The first error value may be subtracted to obtain a positioning error value, and the like. The first error value is an error value of a self-band of the positioning data corresponding to the first moment, and the positioning signal corresponding to each positioning data has a corresponding error value. The second error value is an error value of the first position data itself. Each position data has a corresponding error value.

The unmanned vehicle fuses the positioning error value and the position prediction error value to obtain a target error value; the first position data is corrected based on the target error value. The unmanned vehicle may fuse the positioning error value and the position prediction error value via an error kalman filter algorithm. And the Kalman filtering algorithm automatically calculates the variance of the first position data and the second position data to obtain a corresponding proportion, and processes the proportion so as to correct the first position data. For example, assuming that the target error value e, the first position data of the integrator a at the corresponding time is P1, and the target position data is P2, P2 is P1+ e.

Step 306, when the first distance value between the first reference object and the first position data is obtained at the first moment, determining a second distance value between the first reference object and the second position data in the map, and correcting the first position data according to the first distance value and the second distance value.

The first reference object can be used for referring whether the unmanned vehicle walks along the vehicle track or not. For example, the first reference object lane route, the lane line may be a lane line, a road edge, or the like, but is not limited thereto. The first distance value is a distance value acquired by the unmanned vehicle in the actual driving process. The map may be a high-precision map. The high-precision map can be accurate to the positioning data of the reference object.

Specifically, the unmanned vehicle may acquire a first distance value between the unmanned vehicle and a first reference object through a depth camera, a laser point cloud, a radar, or the like during driving. Due to the first position data, i.e. the position of the unmanned vehicle, the unmanned vehicle determines a second distance value from the first reference object in the map according to the first position data. For example, if the first reference object is a lane line, the first distance value and the second distance value are both the vertical distance between the unmanned vehicle and the lane line. The unmanned vehicle fuses the first distance value and the second distance value to obtain a target distance deviation value, and the first position data is corrected according to the target distance deviation value. For example, the unmanned vehicle may correct the first position data based on a target distance deviation value obtained by subtracting the first distance value and the second distance value. Alternatively, the unmanned vehicle may correct the first position data based on the target distance deviation value by using, as the target distance deviation value, the larger one of the first distance value and the second distance value.

In this embodiment, correcting the first position data according to the first distance value and the second distance value includes: acquiring a first distance weight corresponding to the first distance value and a second distance weight corresponding to the second distance value, and respectively processing the first distance value and the second distance value according to the first distance weight and the second distance weight to obtain a target distance deviation value; and correcting the first position data according to the target distance deviation value. The first distance weight and the second distance weight may both be preset weights. The data with high accuracy has high weight.

According to the position data correction method, because the positioning data and the first distance value are not necessarily acquired in real time, when the positioning data is acquired, error correction is performed according to the positioning data; when the first distance value is obtained, error correction is carried out according to the first distance value, namely corresponding processing is carried out on the obtained data, so that the error correction frequency can be improved; when the first positioning data at the first moment is obtained, a positioning error value is obtained according to the first positioning data and the first position data, and an error between the positioning data and the first position data can be obtained through more accurate positioning data; the method comprises the steps of obtaining a position prediction error value corresponding to a first time period, correcting first position data according to the positioning error value and the position prediction error, and correcting the predicted first position data according to the positioning error value and the position prediction error value, so that the position prediction error value accumulated in the first time period can be eliminated, namely the error accumulated in a period of time is eliminated, and subsequently obtained second position data can be more accurate; when a first distance value between the unmanned vehicle and a first reference object is acquired, a second distance value between the unmanned vehicle and the first reference object is determined in a map according to the first position data, the first position data is corrected according to the first distance value and the second distance value, the unmanned vehicle can be prevented from deviating from a route, the unmanned vehicle can walk according to a preset path, the driving safety of the unmanned vehicle is also improved, and the reference object exists on a driving road for a long time, so that the frequency of acquiring the distance value is higher, the correction frequency can be improved, the predicted position data is more accurate, the situation that positioning, course angle and the like cannot be corrected by using data for a long time does not exist, and the problem that errors cannot be corrected when the positioning signals are poor is solved.

In one embodiment, as shown in fig. 4, a flow chart of the first position data correction in one embodiment is shown. Modifying the first position data based on the positioning error value and the position prediction error value, comprising:

step 402, a first weight corresponding to the positioning error value and a second weight corresponding to the position prediction error value are obtained.

Step 404, performing corresponding weighting processing on the positioning error value and the position prediction error value according to the first weight and the second weight to obtain a target error value.

The positioning error value corresponds to the first weight, and the position prediction error value corresponds to the second weight. The first weight and the second weight may or may not have equal values. The first weight and the second weight may be preset weights for the unmanned vehicle, or weights calculated according to the positioning data. When the accuracy of the positioning data is high, the first weight corresponding to the positioning error value is larger; when the accuracy of the first position data is high, the second weight corresponding to the position prediction error value is larger.

Specifically, the unmanned vehicle obtains a first weight corresponding to the positioning error value and a second weight corresponding to the position prediction error value, and performs corresponding weighting processing on the positioning error value and the position prediction error value according to the first weight and the second weight to obtain a target error value. For example, if the first weight is 0.3 and the second weight is 0.7, the target error value is 0.3 × the positioning error value +0.7 × the position prediction error value.

Step 406, the first position data is corrected according to the target error value.

Specifically, for example, assuming that the target error value e, the first position data of the integrator a at the time corresponding to P1, and the target position data is P2, P2 is P1+ e.

In this embodiment, fusing the positioning error value and the position prediction error value to obtain a target error value includes: obtaining a first weight corresponding to the positioning error value and a second weight corresponding to the position prediction error value; and carrying out corresponding weighting processing on the positioning error value and the position prediction error value according to the first weight and the second weight to obtain a target error value.

According to the position data correction method, a first weight corresponding to a positioning error value and a second weight corresponding to a position prediction error value are obtained, corresponding weighting processing is carried out on the positioning error value and the position prediction error value according to the first weight and the second weight to obtain a target error value, position data at a first moment are corrected according to the target error value, and the correction amplitude of the first position data can be obtained through the weights, so that the position prediction error value accumulated in a first time period can be eliminated, namely, errors accumulated in a period of time are eliminated, and subsequently obtained second position data can be more accurate.

In one embodiment, the obtaining of the first weight includes: acquiring a first variance value corresponding to the positioning data; acquiring a second variance value corresponding to the position prediction error value; and determining a first weight according to the first variance value and the second variance value, wherein the first variance value and the first weight are in negative correlation.

In particular, the first variance value is used to characterize the accuracy of the positioning data. The second variance value is used to characterize the accuracy of the first location data. The greater the variance value, the smaller the accuracy; the accuracy is greater when the variance value is smaller. Thus, when the first variance value is less than the second variance value, the first weight is greater than the second weight. The first variance value is negatively correlated with the first weight, and the second variance value is negatively correlated with the second weight. When the positioning system calculates the positioning data, the variance value corresponding to each positioning data can be calculated. When the sensor predicts the first position data, the first position data also has a corresponding variance value. And the second variance value corresponding to the position prediction error value corresponding to the first time period is the variance value accumulated by the position data in a certain time period. The unmanned vehicle may determine the first weight and the second weight according to the first variance value and the second variance value.

In this embodiment, when the positioning data reliability is high (the final variance value is small enough), the influence of the positioning data on the predicted first position data is large, and the positioning data can calibrate the predicted first position data, so that the accumulated error caused by the IMU and the encoder is intermittently eliminated.

Under the condition that the reliability of the positioning data is low (the final variance value is large), the correction effect of the positioning data on the predicted first position data is suppressed to be very small, and even no correction effect is generated, so that the influence of inaccurate and unstable positioning data on the first position data is reduced.

In this embodiment, when the variance ratio of the positioning data is larger, a small proportion is given to the positioning data when the error state kalman filter calculates the scale factor, which is equivalent to a small correction effect of the positioning data on the final result. For example, when the positioning data has no variance but the variance of the position prediction error value is infinite, it is apparent that the positioning data is more accurate. The weights of the two calculated by the error state kalman filter at this time are approximately 100% for the positioning data and 0 for the position prediction error value. This means that the correction of the positioning to the final result is very large. Since if the positioning display error is 1, 1 is finally corrected. However, if the variances of the two are equal, the calculated ratio is 50% each, the positioning error value is 1, and the position prediction error value is 0.8, and at this time, only 1 × 0.5+0.8 × 0.5 is corrected to 0.9 according to the ratio. The correction of the positioning in this case amounts to only 50%.

According to the position data correction method, the first variance value corresponding to the positioning data is obtained, the second variance value corresponding to the position prediction error value is obtained, the first weight is determined according to the first variance value and the second variance value, wherein the first variance value and the first weight are in negative correlation, the first weight can be determined according to the accuracy of each kind of data, the first position data can be corrected more accurately, and the position data obtained through subsequent prediction is more accurate.

In one embodiment, obtaining the first variance value corresponding to the first positioning data includes: acquiring a first positioning variance value corresponding to first positioning data; acquiring positioning difference variances corresponding to at least two moments before the first moment; and determining a first variance value corresponding to the first positioning data according to the first positioning variance value and the positioning difference variance.

Specifically, each positioning data has a corresponding positioning variance value. For example, the variance values of the GNSS signals themselves at the time t1, t2, t3, t4 and t5 are D1, D2, D3, D4 and D5, respectively. The positioning difference variances corresponding to at least two time instants before the first time instant mean that the at least two time instants correspond to one positioning difference variance. Taking the first time as the time t5 as an example, and the previous times are the times t1, t2, t3, and t4, the times t1, t2, t3, and t4 correspond to a positioning difference variance D Δ. The unmanned vehicle can calculate an accumulated final variance value according to the first positioning variance value and the positioning difference variance, and the final variance value is used as a first variance value corresponding to the first positioning data.

In this embodiment, the positioning data itself will have an estimated positioning accuracy value, i.e. a first positioning variance value. For example, in the positioning data, there are precision estimation fields such as a three-dimensional position precision factor, a horizontal component precision factor, a clock error precision factor, and the like. When the GNSS data is calculated, the variance value of one GNSS data is calculated to represent the accuracy of the signal through the values of the fields. But this value is not enough to help us judge the reliability of GNSS data. The reason is that these accuracy estimates are determined by taking into account the GNSS signal receiver and the number of satellites that can be observed and their geometrical distribution. For example, if two satellites are in close distance in the satellite signals received by the receiver, the signals of the two satellites will have an overlapping region at a smaller angle, and the closer the satellite signals are, the larger the overlapping region is, the larger the potential error of positioning is, the larger the value of the accuracy factor is, and the lower the accuracy is.

According to the position data correction method, the first positioning variance value corresponding to the first positioning data is obtained, the positioning difference variance value corresponding to at least two moments before the first moment is obtained, the first variance value corresponding to the first positioning data is determined according to the first positioning variance value and the positioning difference variance, the accumulated variance value related to the positioning data before the first moment can be obtained, the first weight is determined, the accuracy of the weight is improved, and the correction accuracy is improved.

In one embodiment, obtaining the positioning difference variances corresponding to at least two time instants before the first time instant comprises: acquiring second positioning data corresponding to each moment in at least two moments before the first moment; obtaining a positioning differential value corresponding to two adjacent moments according to the second positioning data corresponding to each moment; and determining a positioning difference variance according to the positioning difference value.

Taking the first time as time t5 as an example, time t1, time t2, time t3 and time t4 are before time t 5. Then the positioning data corresponding to t1, t2, t3 and t4, respectively, are referred to as second positioning data. And then the unmanned vehicle obtains the positioning differential value corresponding to two adjacent moments according to the second positioning data corresponding to each moment. That is, the positioning difference values corresponding to t2 and t1, and the positioning difference value … … corresponding to t3 and t2 are not limited thereto.

The variance values of the positioning data defining the time points t1, t2, t3, t4 and t5 are respectively D1, D2, D3, D4 and D5. The corresponding positioning data respectively comprises B1 (latitude), L1 (longitude), H1 (altitude), B2, L2, H2 … … B5, L5 and H5. The differential data defining the longitude and latitude heights are respectively delta 1^ B, delta 1^ L, delta 1^ H, delta 2^ B, delta 2^ L, delta 2^ H … … delta 4^ B, delta 4^ L and delta 4^ H. The variance of the positioning signal in the ideal state is defined as D. When we receive the positioning data at time t2, we first calculate the positioning difference values corresponding to two adjacent times as follows:

Δ1^B＝B2-B1，Δ1^L＝L2-L1，Δ1^H＝H2-H1

and calculating the positioning difference value at other moments by analogy. When the positioning data at the time t5 is received, we have four sets of differential data, and then we calculate the positioning differential variance of the four sets of differential data to be D Δ.

The position data correction method acquires second positioning data corresponding to each of at least two moments before the first moment; obtaining a positioning differential value corresponding to two adjacent moments according to the second positioning data corresponding to each moment; the positioning difference variance is determined according to the positioning difference value, and the accuracy of determining the positioning data according to the positioning data can be obtained, so that the weight of the positioning data is determined, and the influence of inaccurate data on predicted position data is reduced.

In one embodiment, fig. 5 is a flowchart illustrating a process of determining the first variance value in one embodiment. Determining a first variance value corresponding to the positioning data according to the first positioning variance value and the positioning difference variance, including:

step 502, obtaining a reference positioning variance value, wherein the reference positioning variance value is obtained according to a reference positioning signal.

And the reference positioning variance value is obtained according to the reference positioning data. The reference positioning variance value is a fixed value and does not vary with any factor. In order to ensure the stability of the signal, the reference positioning data refers to a reference positioning signal when the signal operates in a good weather condition and an unobstructed road condition. The variance of the reference positioning signal is used as a measure of the magnitude of the variance of the signal. The unmanned vehicle acquires a reference positioning variance value stored in advance.

And step 504, obtaining variance multiplying power according to the positioning difference variance and the reference positioning variance value.

Specifically, variance values of the positioning data themselves at the time points t1, t2, t3, t4 and t5 are defined as D1, D2, D3, D4 and D5 respectively. The corresponding positioning data respectively comprises B1 (latitude), L1 (longitude), H1 (altitude), B2, L2, H2 … … B5, L5 and H5. The differential data defining the longitude and latitude heights are respectively delta 1^ B, delta 1^ L, delta 1^ H, delta 2^ B, delta 2^ L, delta 2^ H … … delta 4^ B, delta 4^ L and delta 4^ H. The variance of the positioning signal in the ideal state is defined as D. When we receive the positioning data at time t2, first calculate the positioning difference value as follows:

Δ1^B＝B2-B1，Δ1^L＝L2-L1，Δ1^H＝H2-H1

and calculating the positioning difference value at other moments by analogy. When the positioning data at the time t5 is received, we have four sets of differential data, and then we calculate the positioning differential variance of the four sets of differential data to be D Δ. The D Δ is not a single value, but is a matrix containing the latitude difference variance, longitude difference variance, and altitude difference variance. In particular, this positioning difference variance matrix may be a diagonal matrix. Wherein the latitude difference variance can be calculated based on Δ 1^ B, Δ 2^ B, Δ 3^ B, Δ 4^ B. The longitude differential variance can be computed from Δ 1^ L, Δ 2^ L, Δ 3^ L, Δ 4^ L. The height difference variance can be calculated from Δ 1^ H, Δ 2^ H, Δ 3^ H, Δ 4^ H. When the variance is used for calculation, the matrix is directly used for operation, and the variance of each quantity is independently calculated. The variance multiplier may be used to measure how unreliable the signal corresponding to the first positioning data is compared to the ideal case. Specifically, the unmanned vehicle divides the positioning difference variance and the reference positioning difference value to obtain the variance multiplying power. Or the unmanned vehicle subtracts the positioning difference variance from the reference positioning difference value to obtain the variance magnification.

Step 506, determining a first variance value corresponding to the first positioning data according to the variance multiplying factor, the positioning difference variance and the first positioning variance value, wherein the first variance value is positively correlated with the positioning difference variance.

Wherein the first variance value is positively correlated with the positioning difference variance. I.e., the larger the value in the positioning difference variance, the larger the first variance value; the smaller the value in the positioning difference variance, the smaller the first variance value.

Specifically, the unmanned vehicle may multiply the variance multiplying power by the positioning difference variance, and then calculate the first positioning difference variance value to obtain a first variance value corresponding to the first positioning data. For example, taking t5 as the first time as an example, the first variance value

And D delta is corresponding to the square calculation, so that the influence of D delta on the first variance value is larger than the influence of the first positioning variance value on the first variance value. In such a calculation mode, the accuracy factor data carried by the positioning data is utilized, namely the influence of the geometric position of the satellite is referred to. The influence of the actual operating environment conditions on the positioning data is also considered. And the influence of the change degree of the actually received positioning data is enlarged by calculating the variance multiplying power, and once the change range of the received positioning data is too large, the variance of the positioning data is rapidly increased, so that the influence of the variance on the predicted positioning data is reduced, and the stability of the positioning data is ensured.

The position data correction method obtains a reference positioning variance value, wherein the reference positioning variance value is obtained according to a reference positioning signal, a variance multiplying power is obtained according to a positioning difference variance and the reference positioning variance value, and a first variance value corresponding to first positioning data is determined according to the variance multiplying power, the positioning difference variance and the first positioning variance value, wherein the first variance value is in positive correlation with the positioning difference variance, so that a more accurate first variance value can be obtained, and the weight is determined.

In one embodiment, determining a second distance value from the map to the first reference object based on the first location data comprises: determining a second reference object from the map based on the first location data; acquiring second reference object position data corresponding to a second reference object; and determining a second distance value between the first reference object and the map according to the second reference object position data and the first position data.

Wherein the second reference may be a reference that appears on the road. For example, the second reference object may be, but is not limited to, a guideboard, a building, a tree, a traffic light, a stop line, a zebra crossing, etc. The second reference position data may include a second reference latitude and a second reference longitude, may further include a second reference height, and the like, without being limited thereto.

Specifically, the unmanned vehicle may acquire at least one candidate reference object from the map according to the first position data, and use the candidate reference object closest to the first position data as the second reference object. The unmanned vehicle acquires second reference object position data corresponding to the second reference object. The unmanned vehicle determines a second distance value between the unmanned vehicle and the first reference object in the map according to the second reference object data and the first position data. For example, if the unmanned vehicle obtains the second reference object position data, the linear distance value between the second reference object position data and the unmanned vehicle can be calculated, or the distance between the second reference object and the first reference object can be calculated, the distance between the first reference object and the unmanned vehicle can be calculated according to the pythagorean theorem or the like.

According to the position data correction method, the second reference object is determined from the map according to the first position data, the second reference object position data corresponding to the second reference object is obtained, the second distance value between the map and the first reference object is determined according to the second reference object position data and the first position data, the distance between the map and the first reference object can be determined according to the two reference objects, the first position data can be corrected, and the accuracy of position data correction is improved.

In one embodiment, the first reference object comprises a road course and the second reference object comprises a road sign; the first position data includes first vehicle position data and first vehicle heading data.

Determining a second distance value from the map to the first reference object based on the second reference object position data and the first position data, comprising: determining a target straight line according to the first vehicle position data and the first vehicle advancing direction data, wherein the target straight line passes through the position of the vehicle and is the same as the first direction; a second distance value to the first reference object is determined from the map based on the road course and the target straight line. Modifying the first position data based on the positioning error value and the position error value, comprising: and correcting the data corresponding to the first direction in the first position data according to the positioning error value and the position error value.

The vehicle advancing direction data can be obtained by measuring through a gyroscope, an IMU or other sensors. The first direction may be a lateral direction and may be perpendicular to the lane line. After the target straight line passes through the position of the vehicle, i.e., the position of the vehicle is a point on the target straight line, and the position of the road sign is determined, a second distance between the vehicle and the lane line in the map may be determined based on the road sign and the target straight line (the point including the position of the vehicle). And the unmanned vehicle corrects the data corresponding to the first direction in the first position data according to the positioning error value and the position error value. For example, the latitude-corresponding data in the first position data may be corrected according to the positioning error value and the position error value. The method for eliminating the error is not limited by the positioning signal, is independent of the positioning signal and does not interfere with the positioning signal.

According to the position data correction method, the road route, the road sign and the like exist in the driving process of the vehicle, so that the calibration and positioning results and the like through the reference objects have strong universality and are basically not limited by the environment; and the road line and the road sign lamp can exist on the driving road for a long time, so that the frequency of obtaining the distance value is higher, the correction frequency can be improved, the predicted position data is more accurate, the situation that no data is available for correcting positioning, course angle and the like for a long time does not exist, the problem that errors cannot be corrected when the positioning signals are poor is solved, the position data of the unmanned vehicle can be transversely corrected by comparing the deviation of the road line and the deviation of the road line in the map, the position accumulated error in the direction perpendicular to the road is periodically eliminated, and the accuracy of the predicted position data is improved.

In one embodiment, the position data correction method further includes: determining at least two candidate reference objects from the map according to the first position data, and determining a candidate reference object with the closest distance from the at least two candidate reference objects as a second reference object; and when the at least two candidate reference objects are both taken as the second reference objects, the step of determining the at least two candidate reference objects from the map is executed again.

Specifically, the unmanned vehicle determines at least two candidate reference objects from the map according to the first position data, and determines a candidate reference object closest to the unmanned vehicle from the at least two candidate reference objects as a second reference object. For example, the unmanned vehicle may determine a preset number of candidate references from the map based on the first location data. Alternatively, the unmanned vehicle determines the candidate reference object within the preset distance from the map based on the first position data, and the like are not limited thereto. The step of determining at least two candidate references from the map based on the first location data is not re-performed until both of the at least two candidate references serve as second references.

For example, as shown in FIG. 6, a schematic diagram of determining candidate references in one embodiment is shown. For example, the candidate reference objects within 50 meters preset by the unmanned vehicle are road point 1, road point 2, and road point 3. If the road point 2 at the present time is the second reference, the road point 1 is cleared. And not reacquire at least two candidate references from the map until the road point 3 is also cleared.

The position data correction method determines at least two candidate reference objects from the map according to the first position data, determines the candidate reference object closest to the first position data from the at least two candidate reference objects as the second reference object, and re-executes the step of determining the at least two candidate reference objects from the map when both the at least two candidate reference objects are used as the second reference object, thereby acquiring a plurality of candidate reference objects at one time, reducing the number of times of acquiring data from the map, and improving the position data correction efficiency.

In one embodiment, the first reference object comprises a lane line and the first position data comprises first heading data. The position data correction method further includes: when a first included angle value between first advancing direction data and a road line in the running process of a vehicle is obtained, a second included angle value between the first advancing direction data and the road line in a map is obtained; and correcting the first advancing direction data according to the first included angle value and the second included angle value.

When the unmanned vehicle obtains a first included angle value between a first advancing direction and a road line in the driving process of the vehicle, and simultaneously obtains a second included angle value between the first advancing direction and the road line in the map, the included angle difference value can be obtained through calculation. And correcting the first advancing direction data according to the included angle difference.

In this embodiment, the unmanned vehicle may find the pixel points representing the lane lines from the acquired image, acquire two pixel points closest to the unmanned vehicle, and acquire coordinates of the two points in the actual scene after projecting the two pixel points to the three-dimensional scene. The inclination angle of the straight line is determined from the coordinates of the two points, and the inclination angle is taken as the inclination angle of the lane line. And calculating to obtain a first included angle difference value according to the inclination angle of the vehicle body and the inclination angle of the lane line.

According to the position data correction method, when a first included angle value between first advancing direction data and a road line in the running process of a vehicle is obtained, a second included angle value between the first advancing direction data and the road line in a map is obtained; and correcting the first advancing direction data according to the first included angle value and the second included angle value, namely calculating the deviation angle of the vehicle body course angle, so as to correct the course error of the vehicle body, and further eliminate the error accumulated by the gyroscope regularly.

In one embodiment, the position data correction method further includes: acquiring movement data of a sensor at a first moment; and predicting second position data at a second time according to the movement data and the target position data, wherein the second time is a backward time of the first time.

The backward time refers to a time after the first time. In particular the second moment in time may be the next moment in time adjacent to the first moment in time. The movement data may include, but is not limited to, direction of movement, speed of movement, and the like.

Specifically, the unmanned vehicle can acquire the moving direction of the unmanned vehicle through the IMU, and the moving speed of the unmanned vehicle can be acquired through the encoder. And the unmanned vehicle predicts second position data at a second moment according to the movement direction, the movement speed and the target position data. That is, the position data of the next time is predicted according to the current position data, the current movement direction and the current movement speed.

In this embodiment, after the correction, all the time points after the corrected time point need to be predicted again because the previous prediction is already wrong. This process is repeated cyclically, and the first position data at the corresponding time is corrected each time positioning data arrives. In this way, the positioning data is guaranteed to be as accurate as possible.

The position data correction method obtains the movement data of the sensor at the first moment, and predicts and obtains the second position data of the second moment according to the movement data and the target position data, wherein the second moment is the backward moment of the first moment, so that the position prediction of the next moment can be carried out according to the target position data with the positioning error and the sensor error eliminated, the prediction accuracy is improved, and the unmanned vehicle is prevented from deviating from the route.

In one embodiment, an error data correction method includes:

step a1 acquires first positioning data at a first time and first position data predicted at the first time.

In step a2, a positioning error value is obtained according to the first positioning data and the first position data.

Step a3, a position prediction error value corresponding to a first time period is obtained, wherein the first time period is a time period before the first time.

Step a4, a first positioning variance value corresponding to the first positioning data is obtained.

Step a5, acquiring second positioning data corresponding to each of at least two time points before the first time point.

Step a6, obtaining a positioning difference value corresponding to two adjacent time points according to the second positioning data corresponding to each time point.

Step a7, determining a positioning difference variance based on the positioning difference value.

Step a8, obtaining a reference positioning variance value, wherein the reference positioning variance value is obtained according to the reference positioning data.

And a step a9, obtaining variance multiplying power according to the positioning difference variance and the reference positioning variance value.

Step a10, determining a first variance value corresponding to the positioning data according to the variance multiplying factor, the positioning difference variance and the positioning variance value, wherein the first variance value is positively correlated with the positioning difference variance.

In step a11, a second weight corresponding to the position prediction error value is obtained.

Step a12, performing corresponding weighting processing on the positioning error value and the position prediction error value according to the first weight and the second weight to obtain a target error value.

Step a13, correcting the first position data according to the target error value to obtain target position data;

step a14, when the first distance value between the first reference object and the second reference object is acquired at the first moment, the second reference object is determined from the map according to the first position data.

Step a15, second reference object position data corresponding to the second reference object is acquired.

Step a16, determining a second distance value between the first reference object and the map according to the second reference object position data and the first position data.

Step a17, the first position data is corrected based on the first distance value and the second distance value.

Step a18, determining at least two candidate reference objects from the map according to the first position data, and determining the candidate reference object with the closest distance from the at least two candidate reference objects as the second reference object.

Step a19, when at least two candidate references are both used as the second reference, the step of determining at least two candidate references from the map is executed again.

Step a20, movement data at a first time is acquired.

Step a21 predicts second position data at a second time, which is a backward time from the first time, based on the movement data and the target position data.

Step a22, when a first included angle value between the first forward direction data and the road line is acquired while the vehicle is running, a second included angle value between the first forward direction data and the road line in the map is acquired.

Step a23, correcting the first heading data according to the first included angle value and the second included angle value.

In one embodiment, as shown in fig. 7, a schematic flow chart of a position data correction method in another embodiment is shown. A first distance value between the vehicle and a lane line on the road is detected by the visual detection module 702 and sent to the location module 704. When the positioning module 704 receives the first distance value, step 708 is executed to obtain first position data and send the first position data to the map parsing module 706. After receiving the first position data of the vehicle, the map analysis module 706 obtains a road sign from the map according to the first position data of the vehicle, and sends the road sign to the positioning module 704. After the positioning module 704 receives the road sign sent by the map parsing module 706, step 710 is executed to determine a target straight line according to vehicle position data and vehicle forward direction data included in the first position data of the vehicle; the target line passes through the position of the vehicle and is the same as the first direction, i.e., perpendicular to the orientation of the vehicle. Step 712 is performed to determine the road sign closest to the target straight line from the received road signs. Step 714 is executed, and a second distance value between the vehicle and the lane line in the map is determined according to the selected road sign closest to the target straight line and the target straight line. Step 716 is performed to obtain a target distance deviation value according to the first distance value and the second distance value. The positioning module can also correct the first position data according to the target distance deviation value. Step 718 is executed to output the target distance deviation value.

In one embodiment, to calibrate the positioning error in the direction of the road, i.e. the longitudinal error, we can do this with road markings in a similar way. The content issued by the visual inspection node is a first distance value between the unmanned vehicle and the sign board closest to the front. This first distance value may be obtained by a depth camera or by a variety of methods such as comparison calculation with a real-time laser point cloud. And after a first distance value between the current unmanned vehicle and the signboard sent by the visual detection node is obtained, high-precision map response is requested by sending information such as the position, the direction, the type and the direction of the detected signboard and the like of the current unmanned vehicle. The content responded by the high-precision map analysis node is the position data of the signboards in the map detected by the visual detection node. Then, a second distance value between the unmanned vehicle and the signboard in the map can be calculated, and the distance value is compared with the first distance value issued by the visual detection node, so that an error value of the predicted unmanned vehicle position is calculated.

It should be understood that, although the steps in the flowcharts of fig. 3 to 5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 3 to 5 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the other steps or stages.

In one embodiment, as shown in fig. 8, a position data correcting apparatus is provided, which includes an obtaining module 802, a first correcting module 804, and a second correcting module 806, wherein:

the first correction module is used for obtaining a positioning error value according to the first positioning data and the first position data when the first positioning data is obtained at a first moment, obtaining a position error value corresponding to a first time period, and correcting the first position data according to the positioning error value and the position error value, wherein the first time period is a time period before the first moment;

and the second correction module is used for determining a second distance value between the first position data and the first reference object in the map according to the first position data when the first distance value between the first position data and the first reference object is acquired at the first moment, and correcting the first position data according to the first distance value and the second distance value.

According to the position data correction device, since the positioning data and the first distance value are not necessarily acquired in real time, when the positioning data is acquired, error correction is performed according to the positioning data; when the first distance value is obtained, error correction is carried out according to the first distance value, namely corresponding processing is carried out on the obtained data, so that the error correction frequency can be improved; when the first positioning data at the first moment is obtained, a positioning error value is obtained according to the first positioning data and the first position data, and an error between the positioning data and the first position data can be obtained through more accurate positioning data; the method comprises the steps of obtaining a position prediction error value corresponding to a first time period, correcting first position data according to the positioning error value and the position prediction error, and correcting the predicted first position data according to the positioning error value and the position prediction error value, so that the position prediction error value accumulated in the first time period can be eliminated, namely the error accumulated in a period of time is eliminated, and subsequently obtained second position data can be more accurate; when a first distance value between the unmanned vehicle and a first reference object is acquired, a second distance value between the unmanned vehicle and the first reference object is determined in a map according to the first position data, the first position data is corrected according to the first distance value and the second distance value, the unmanned vehicle can be prevented from deviating from a route, the unmanned vehicle can walk according to a preset path, the driving safety of the unmanned vehicle is also improved, and the reference object exists on a driving road for a long time, so that the frequency of acquiring the distance value is higher, the correction frequency can be improved, the predicted position data is more accurate, the situation that positioning, course angle and the like cannot be corrected by using data for a long time does not exist, and the problem that errors cannot be corrected when the positioning signals are poor is solved.

In one embodiment, the first modification module 804 is configured to obtain a first weight corresponding to a positioning error value and a second weight corresponding to a position prediction error value; performing corresponding weighting processing on the positioning error value and the position prediction error value according to the first weight and the second weight to obtain a target error value; the first position data is corrected based on the target error value.

The position data correction device obtains a first weight corresponding to the positioning error value and a second weight corresponding to the position prediction error value, performs corresponding weighting processing on the positioning error value and the position prediction error value according to the first weight and the second weight to obtain a target error value, corrects the position data at the first moment according to the target error value, and obtains the correction amplitude of the first position data through the weights, so that the position prediction error value accumulated in the first time period can be eliminated, namely, the error accumulated in a period of time is eliminated, and the subsequently obtained second position data can be more accurate.

In one embodiment, the first modification module 804 is configured to obtain a first variance value corresponding to the positioning data; acquiring a second variance value corresponding to the position prediction error value; and determining a first weight according to the first variance value and the second variance value, wherein the first variance value and the first weight are in negative correlation.

The position data correction device acquires a first variance value corresponding to the positioning data, acquires a second variance value corresponding to the position prediction error value, and determines a first weight according to the first variance value and the second variance value, wherein the first variance value is negatively correlated with the first weight, and the first weight can be determined according to the accuracy of each kind of data, so that the first position data can be corrected more accurately, and the position data obtained by subsequent prediction can be more accurate.

In one embodiment, the first modification module 804 is configured to obtain a first positioning variance value corresponding to the first positioning data; acquiring positioning difference variances corresponding to at least two moments before the first moment; and determining a first variance value corresponding to the first positioning data according to the first positioning variance value and the positioning difference variance.

According to the position data correcting device, the first positioning variance value corresponding to the first positioning data is obtained, the positioning difference variance value corresponding to at least two moments before the first moment is obtained, the first variance value corresponding to the first positioning data is determined according to the first positioning variance value and the positioning difference variance value, and the accumulated variance value related to the positioning data before the first moment can be obtained, so that the first weight is determined, the accuracy of the weight is improved, and the correcting accuracy is improved.

In one embodiment, the first modification module 804 is configured to obtain second positioning data corresponding to each of at least two time instants before the first time instant; obtaining a positioning differential value corresponding to two adjacent moments according to the second positioning data corresponding to each moment; and determining a positioning difference variance according to the positioning difference value.

The position data correction device acquires second positioning data corresponding to each of at least two moments before the first moment; obtaining a positioning differential value corresponding to two adjacent moments according to the second positioning data corresponding to each moment; the positioning difference variance is determined according to the positioning difference value, and the accuracy of determining the positioning data according to the positioning data can be obtained, so that the weight of the positioning data is determined, and the influence of inaccurate data on predicted position data is reduced.

In one embodiment, the first modification module 804 is configured to obtain a reference positioning variance value, where the reference positioning variance value is obtained according to a reference positioning signal; obtaining variance multiplying power according to the positioning difference variance and the reference positioning variance value; and determining a first variance value corresponding to the first positioning data according to the variance multiplying power, the positioning difference variance and the first positioning variance value, wherein the first variance value is positively correlated with the positioning difference variance.

The position data correcting device obtains a reference positioning variance value, wherein the reference positioning variance value is obtained according to a reference positioning signal, a variance multiplying power is obtained according to a positioning difference variance and the reference positioning variance value, and a first variance value corresponding to first positioning data is determined according to the variance multiplying power, the positioning difference variance and the first positioning variance value, wherein the first variance value is in positive correlation with the positioning difference variance, so that a more accurate first variance value can be obtained, and the weight is determined.

In one embodiment, the second modification module 806 is configured to determine a second reference object from the map based on the first position data; acquiring second reference object position data corresponding to a second reference object; and determining a second distance value between the first reference object and the map according to the second reference object position data and the first position data.

The position data correction device determines the second reference object from the map according to the first position data, acquires the second reference object position data corresponding to the second reference object, determines the second distance value between the map and the first reference object according to the second reference object position data and the first position data, and can determine the distance between the map and the first reference object according to the two reference objects, thereby correcting the first position data and improving the accuracy of position data correction.

In one embodiment, the first reference object comprises a road course and the second reference object comprises a road sign; the first position data includes first vehicle position data and first vehicle heading data. The second correction module 806 is configured to determine a target straight line according to the first vehicle position data and the first vehicle advancing direction data, where the target straight line passes through a position where the vehicle is located and is the same as the first direction; determining a second distance value between the first reference object and the map according to the road route and the target straight line; and correcting the data corresponding to the first direction in the first position data according to the positioning error value and the position error value.

The position data correction device has the advantages that the road route, the road signs and the like are always generated in the driving process of the vehicle, so that the calibration and positioning results and the like through the reference objects have strong universality and are basically not limited by the environment; and the road line and the road sign lamp can exist on the driving road for a long time, so that the frequency of obtaining the distance value is higher, the correction frequency can be improved, the predicted position data is more accurate, the situation that no data is available for correcting positioning, course angle and the like for a long time does not exist, the problem that errors cannot be corrected when the positioning signals are poor is solved, the position data of the unmanned vehicle can be transversely corrected by comparing the deviation of the road line and the deviation of the road line in the map, the position accumulated error in the direction perpendicular to the road is periodically eliminated, and the accuracy of the predicted position data is improved.

In one embodiment, the second modification module 806 is configured to determine at least two candidate references from the map according to the first position data, and determine a candidate reference closest to the first position data from the at least two candidate references as the second reference; and when the at least two candidate reference objects are both taken as the second reference object, re-determining the at least two candidate reference objects from the map according to the first position data.

The position data correction device determines at least two candidate reference objects from the map according to the first position data, determines the candidate reference object closest to the first position data from the at least two candidate reference objects as the second reference object, and re-executes the step of determining the at least two candidate reference objects from the map when both the at least two candidate reference objects are used as the second reference object, thereby acquiring a plurality of candidate reference objects at one time, reducing the number of times of acquiring data from the map, and improving the position data correction efficiency.

In one embodiment, the first reference object comprises a lane line and the first position data comprises first heading data. The position data correction apparatus further includes a third correction module. The third correction module is used for acquiring a second included angle value between the first advancing direction data and the road line in the map when the first included angle value between the first advancing direction data and the road line in the running process of the vehicle is acquired; and correcting the first advancing direction data according to the first included angle value and the second included angle value.

The position data correction device acquires a second included angle value between the first advancing direction data and the road line in the map when acquiring a first included angle value between the first advancing direction data and the road line when the vehicle is in the driving process; and correcting the first advancing direction data according to the first included angle value and the second included angle value, namely calculating the deviation angle of the vehicle body course angle, so as to correct the course error of the vehicle body, and further eliminate the error accumulated by the gyroscope regularly.

In one embodiment, the first modification module 804 is further configured to obtain movement data of the sensor at a first time; and predicting second position data at a second time according to the movement data and the target position data, wherein the second time is a backward time of the first time.

The position data correction device acquires the movement data of the sensor at the first moment, and predicts and obtains the second position data of the second moment according to the movement data and the target position data, wherein the second moment is the backward moment of the first moment, so that the position prediction of the next moment can be carried out according to the target position data with the positioning error and the sensor error eliminated, the prediction accuracy is improved, and the unmanned vehicle is prevented from deviating from the route.

For specific limitations of the position data correction device, reference may be made to the above limitations of the position data correction method, which are not described herein again. The respective modules in the position data correcting apparatus described above may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules. For specific limitations of the position data correction device, reference may be made to the above limitations of the position data correction method, which are not described herein again. The respective modules in the position data correcting apparatus described above may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a position data correction method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In an embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above-described method embodiments when executing the computer program.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the respective method embodiment as described above.

In one embodiment, an unmanned vehicle is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above method embodiments.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), for example.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. A method of position data correction, the method comprising:

acquiring first position data of a predicted first time;

2. The method of claim 1, wherein modifying the first position data based on the positioning error value and the position prediction error value comprises:

obtaining a first weight corresponding to the positioning error value and a second weight corresponding to the position prediction error value;

performing corresponding weighting processing on the positioning error value and the position prediction error value according to the first weight and the second weight to obtain a target error value;

the first position data is corrected based on the target error value.

3. The method of claim 2, wherein the obtaining of the first weight comprises:

acquiring a first variance value corresponding to the first positioning data;

obtaining a second variance value corresponding to the position prediction error value;

determining the first weight according to the first variance value and the second variance value, wherein the first variance value is negatively correlated with the first weight.

4. The method of claim 3, wherein the obtaining the first variance value corresponding to the first positioning data comprises:

acquiring a first positioning variance value corresponding to the first positioning data;

acquiring positioning difference variances corresponding to at least two moments before the first moment;

and determining a first variance value corresponding to the first positioning data according to the first positioning variance value and the positioning difference variance.

5. The method of claim 4, wherein the determining a first variance value corresponding to the positioning data according to the first positioning variance value and the positioning difference variance comprises:

acquiring a reference positioning variance value, wherein the reference positioning variance value is obtained according to a reference positioning signal;

obtaining variance multiplying power according to the positioning difference variance and the reference positioning variance value;

and determining a first variance value corresponding to the first positioning data according to the variance multiplying factor, the positioning difference variance and the first positioning variance value, wherein the first variance value is positively correlated with the positioning difference variance.

6. The method of claim 1, wherein determining a second distance value from the map to the first reference object based on the first location data comprises:

determining a second reference object from the map according to the first position data;

acquiring second reference object position data corresponding to the second reference object;

and determining a second distance value between the first reference object and the map according to the second reference object position data and the first position data.

7. The method of claim 6, wherein the first reference comprises a road course and the second reference comprises a road sign; the first position data comprises first vehicle position data and first vehicle heading data;

the determining a second distance value from the map to the first reference object based on the second reference object position data and the first position data comprises:

determining a target straight line according to the first vehicle position data and the first vehicle advancing direction data, wherein the target straight line passes through the position of the vehicle and is the same as the first direction;

determining a second distance value between the first reference object and the map according to the road route and the target straight line;

said modifying said first position data based on said positioning error value and said position error value comprises:

and correcting the data corresponding to the first direction in the first position data according to the positioning error value and the position error value.

8. The method according to any one of claims 1 to 7, further comprising:

acquiring movement data at the first moment;

and predicting second position data of a second moment according to the movement data and the target position data, wherein the second moment is a backward moment of the first moment.

9. A position data correcting apparatus, characterized in that the apparatus comprises:

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 8.