CN111207740A - Method, device, equipment and computer readable medium for positioning vehicle - Google Patents
Method, device, equipment and computer readable medium for positioning vehicle Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
- G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
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Abstract
The invention discloses a method, a device, equipment and a computer readable medium for positioning a vehicle, and relates to the technical field of computers. One embodiment of the method comprises: obtaining the position and attitude angle of a vehicle in a point cloud map, and obtaining a global pose measurement value by combining the speed of the vehicle; adjusting the measurement data of an inertial measurement unit based on the geographic position of the vehicle to obtain a pose estimation value of the vehicle; and inputting the difference value of the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle, and correcting the global pose measurement value by combining the error estimation value to position the vehicle. This embodiment can improve the positioning accuracy.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a computer-readable medium for vehicle positioning.
Background
In automatic driving, the positioning method is the upstream of the whole set of system, directly influences the performance of other control parameters at the downstream, and plays an important role in the reliability of automatic driving.
The positioning method is essentially state estimation, and mainly comprises an optimization-based positioning method. The optimization-based positioning method is to obtain the maximum posterior estimation of the historical states by storing a group of historical data and using a least square method and the like, and then to correct the current state.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
based on the optimized positioning method, the difference between the corrected pose and the actual pose is large, so that the positioning accuracy is reduced.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, a device, and a computer-readable medium for vehicle positioning, which can improve positioning accuracy.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a vehicle positioning method including:
obtaining the position and attitude angle of a vehicle in a point cloud map, and obtaining a global pose measurement value by combining the speed of the vehicle;
adjusting the measurement data of an inertial measurement unit based on the geographic position of the vehicle to obtain a pose estimation value of the vehicle;
and inputting the difference value of the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle, and correcting the global pose measurement value by combining the error estimation value to position the vehicle.
The adjusting the measurement data of the inertial measurement unit based on the geographic position of the vehicle to obtain the pose estimation value of the vehicle comprises:
the geographical position of the vehicle belongs to a bumpy road section, and the measurement data of the inertial measurement unit are processed smoothly;
and obtaining the pose estimation value of the vehicle according to the measurement data after the smoothing processing.
The adjusting the measurement data of the inertial measurement unit based on the geographic position of the vehicle to obtain the pose estimation value of the vehicle comprises:
and the geographical position of the vehicle belongs to a gentle road section, and the pose estimation value of the vehicle is obtained according to the measurement data of the inertia measurement unit.
Before adjusting the measurement data of the inertial measurement unit based on the geographic position of the vehicle, the method further includes:
and judging that the geographic position of the vehicle belongs to a bumpy road section or a gentle road section based on the preset map identification.
The smoothing process of the measurement data of the inertial measurement unit includes:
and processing the measurement data of the inertial measurement unit according to the smooth acceleration and the pose estimation value of the last frame of the vehicle.
The smoothed acceleration is determined by N historical accelerations and weights of the historical accelerations, N being an integer greater than 1.
Said N is equal to 20.
According to a second aspect of the embodiments of the present invention, there is provided an apparatus for vehicle positioning, including:
the global module is used for obtaining the position and the attitude angle of the vehicle in the point cloud map and obtaining a global attitude measurement value by combining the speed of the vehicle;
the calculation module is used for adjusting the measurement data of the inertial measurement unit based on the geographic position of the vehicle to obtain a pose calculation value of the vehicle;
and the correction module is used for inputting the difference value between the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle, and correcting the global pose measurement value by combining the error estimation value so as to position the vehicle.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus for vehicle positioning, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method as described above.
According to a fourth aspect of embodiments of the present invention, there is provided a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the method as described above.
One embodiment of the above invention has the following advantages or benefits: obtaining the position and attitude angle of the vehicle in the point cloud map, and obtaining a global pose measurement value by combining the speed of the vehicle; adjusting the measurement data of the inertia measurement unit based on the geographic position of the vehicle to obtain a pose estimation value of the vehicle; and inputting the difference value of the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle, and correcting the global pose measurement value by combining the error estimation value to position the vehicle. Based on the geographic position of the vehicle, the measurement data of the inertial measurement unit can be adjusted, the influence of a possibly bumpy road section on the pose calculation value is avoided, and the positioning accuracy can be improved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of a main flow of a method of vehicle localization according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating adjusting measurement data of an inertial measurement unit according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of a flow of a method of vehicle localization according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the main structure of a vehicle positioning apparatus according to an embodiment of the invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 6 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Because the optimized positioning method needs to store a large amount of historical data, the storage capacity is increased when the locking time is passed, meanwhile, the optimized gradient is obtained by calculating the Jacobian matrix, the optimized direction is guided, the calculated amount is large, and great challenges are brought to the real-time operation of the system.
Compared with an optimized positioning method, the positioning method based on filtering has the advantages of less calculation amount, good real-time performance, less storage amount and the like, and the positioning precision is higher.
And the Kalman filter is adopted to realize the fusion of all sensors on the vehicle so as to obtain an accurate and credible positioning result. A group of global pose measurement values and a group of pose state estimation values can be obtained through each sensor, and the two values are used as the input of a Kalman filter to position the vehicle.
The pose state estimation value is generally calculated by the linear acceleration and the angular velocity measured by an Inertial Measurement Unit (IMU). The measurement model of the IMU includes formula (1) and formula (2).
wm＝wo+bw+nw(1)
am＝ao+ba+na+g (2)
Wherein, wm，amActual measurement values of angular velocity and linear acceleration respectively; w is ao，aoThe actual values of angular velocity and linear acceleration are respectively; bw，baThe zero offset of the angular velocity and the linear acceleration are respectively, and the derivatives of the angular velocity and the linear acceleration are generally considered to be in Gaussian distribution; n isw，naThe noise, which is angular velocity and linear acceleration, respectively, is generally considered to follow a gaussian distribution; g is the acceleration of gravity.
The noise and zero offset of different models of IMUs are different and generally cannot be ignored by IMUs on autonomous vehicles.
When the vehicle passes through a bumpy road section, such as a speed bump, the violent vibration of the vehicle causes the IMU to be noisy at this time much higher than when traveling on flat ground. Because the IMU abnormal data is far away from the actual pose, the output pose is still inconsistent with the actual pose even through the processing of the Kalman filter.
In particular, in the case of considering only linear accelerations, it is known that the absolute value of the acceleration of the horizontal axis perpendicular to the direction of advance of the vehicle increases when passing through the deceleration strip, the acceleration reaching 6 m/s2. If the duration of the bump is about 2 seconds, the abnormal speed increment generated by the thrust can reach 12 m/s, and the vehicle position drift can reach 6 m. Despite the correction of the kalman filter, the vehicle position drift still reaches the meter level. The vehicle positioning deviates from the actual motion track seriously, and the positioning requirement in automatic driving cannot be met completely.
In order to solve the technical problem of low positioning accuracy, the following technical scheme in the embodiment of the invention can be adopted.
Referring to fig. 1, fig. 1 is a schematic diagram of a main flow of a vehicle positioning method according to an embodiment of the present invention, which obtains a pose estimation value of a vehicle by adjusting measurement data of an inertial measurement unit based on a geographic position of the vehicle, so as to position the vehicle. As shown in fig. 1, the method specifically comprises the following steps:
s101, obtaining the position and the attitude angle of the vehicle in the point cloud map, and obtaining a global pose measurement value by combining the speed of the vehicle.
Kalman filtering can be classified into direct and indirect methods according to the difference in observed values. The direct method takes each navigation parameter as a main state, and the output value of the Kalman filter is the estimation value of each navigation state. The indirect method takes the error of each navigation parameter as a main state quantity, and the output of the Kalman filter is an error estimation value.
In the embodiment of the invention, when the vehicle is positioned by adopting the indirect method of Kalman filtering, the error of each navigation parameter needs to be input into a Kalman filter.
First, a global pose measurement value is obtained. The global pose measurement values include: three sets of measured values of position, velocity and attitude angle. Specifically, the three degrees of freedom of position are: northeast; three degrees of freedom of velocity are: northeast; the three degrees of freedom of the attitude angle are respectively: pitch (roll), yaw (yaw) and roll (pitch). Wherein, the northeast is a navigation coordinate system.
In the embodiment of the invention, an Iterative Closest Point (ICP) algorithm can be adopted to register Point cloud data obtained by a laser radar and Point clouds in a Point cloud map, and two sets of measurement values of a position and an attitude angle can be obtained through motion transformation of the current Point cloud.
The speed of the vehicle can be measured using the code wheel meter of the vehicle. In particular, the speed obtained due to the yardage meter is a speed with one degree of freedom with a direction parallel to the heading direction. The speed of one degree of freedom is decomposed into three-degree-of-freedom speed values under a navigation coordinate system, and then the three-degree-of-freedom speed in northeast is obtained.
Then, the position and attitude angle of the vehicle in the point cloud map are obtained through the point cloud data of the laser radar, and the speed of the vehicle is measured by combining with a code wheel meter of the vehicle, so that a global pose measurement value is obtained. It will be appreciated that the global pose metrics include position, attitude angle and velocity.
And S102, adjusting the measurement data of the inertia measurement unit based on the geographical position of the vehicle to obtain the pose estimation value of the vehicle.
And the pose calculation value of the vehicle is calculated to obtain a group of pose calculation values according to the linear acceleration and the angular velocity measured by the IMU on the basis of the optimal estimation of the previous frame.
v＝v0+a*Δt (4)
Wherein: p is the estimated position, including three degrees of freedom in the northeast; v is the estimated velocity, including three degrees of freedom in the northeast; q is a quaternion of the derived attitude angle, including three degrees of freedom roll, yaw, and pitch.
p0Is an optimal estimate of the position of the previous frame, v0Is an optimal estimate of the previous frame rate, q0And the quaternion corresponding to the optimal estimation of the pose of the previous frame.
a is the current acceleration measurement, w is the current angular velocity measurement, and Δ t is the time difference between the two IMU measurements.
Representing multiplication of quaternions.
Considering that the vehicle passes through different road sections: and if the road section is bumpy, the IMU abnormal data is far away from the actual pose. The measurement data of the inertial measurement unit can then be adjusted based on the geographical position of the vehicle.
Referring to fig. 2, fig. 2 is a schematic flow chart of adjusting measurement data of an inertial measurement unit according to an embodiment of the present invention, which specifically includes the following steps:
s201, judging whether the vehicle is in a bumpy road section.
And judging whether the vehicle is in the bumpy road section or not based on the geographical position of the vehicle. If the vehicle is judged to be in the bumpy road section based on the geographical position of the vehicle, S202 is executed; if it is determined that the vehicle is not on a bumpy road section, i.e., on a gentle road section, based on the geographic location of the vehicle, S204 is performed.
In the embodiment of the invention, the position of the vehicle in the point cloud map, namely the geographic position of the vehicle, can be determined through the point cloud data of the laser radar. The road sections near the geographical position can be marked in advance, so that whether the vehicle is in a bumpy road section or not can be known.
In order to accurately judge whether the vehicle is located on a bumpy road section, in the embodiment of the invention, the geographic position of the vehicle can be judged to belong to the bumpy road section or a gentle road section based on the preset map identification.
The preset map identifier is a preset identifier in the map. The road section marked with the map mark belongs to a bumpy road section; accordingly, the road segments identified by the unmarked map are non-bumpy road segments, i.e., gentle road segments.
As an example, a map is queried based on the geographic location of the vehicle, and it is known whether a preset map identifier exists in the geographic location. If the geographical position has the preset map identification, determining that the geographical position of the vehicle belongs to a bumpy road section; and if the preset map identification does not exist in the geographic position, determining that the geographic position of the vehicle belongs to the gentle road section.
S202, smoothing the measurement data of the IMU.
When the vehicle is in a bumpy road section, the output of the IMU has an abnormal value, and therefore additional processing is required.
Without changing the vehicle structure, such as: and a damping structure is added, and some methods can be adopted to realize damping through an algorithm so as to realize the smooth processing of the positioning track. One way is to directly smooth the output positioning result. The method is simple and direct, has obvious effect, but has a major defect that the dynamic performance is poor, particularly, the positioning output result lags behind the actual positioning result, although the output positioning track is smoother and closer to the true value, the real-time requirement cannot be met.
In the embodiment of the invention, the abnormal value can be generated in the output of the IMU when the vehicle is judged to be in a bumpy road section, and the measurement data of the IMU can be processed smoothly.
The smoothing of the measurement data of the IMU does not directly affect the final positioning result. The measurement data of the IMU is smoothed, so that the measurement data of the IMU is smoother and the dynamic property of the measurement data of the IMU is also deteriorated. However, the final vehicle localization result also depends on both the current speed and the global pose measurement. Therefore, smoothing the measurement data of the IMU does not directly affect the final positioning result. Also, the measured data of the IMU in the bump can be modeled to form a variance of a gaussian distribution. Therefore, after the smoothing processing, even though an abnormal value does not appear in the IMU measurement data through a bumpy road section, the vehicle pose calculation value is smoother, and the output of the Kalman filter combined with the global pose measurement value is more reliable.
In the embodiment of the invention, the pose estimation value of the vehicle can be calculated based on the angular velocity and the acceleration measured by the IMU according to the formula (3), the formula (4) and the formula (5).
When the vehicle is in a bumpy road section, the acceleration measured by the IMU has an abnormal value, and the acceleration needs to be smoothed to obtain the smoothed acceleration.
In one embodiment of the invention, the smoothed acceleration is determined by the N historical accelerations and the weights of the historical accelerations. That is, from a plurality of historical accelerations and the weights of the historical accelerations, the smoothed acceleration can be determined.
Wherein a is a smooth acceleration, aiIs the historical acceleration, ρiIs the weight corresponding to the historical acceleration, N is the number of the selected historical accelerations starting from the current, and N is an integer greater than 1.
As an example, the weight of the historical acceleration closer to the current should be no less than the weight of the historical acceleration farther from the current, i.e. data closer to the current time is more trusted. As another example, in practical application of equation (6), ρ may be madeiI.e. all weights are equal to 1.
The value of N is negatively correlated with the dynamics of the current IMU, namely: if the value N is too large, the dynamic performance of the current IMU is poor, and the acceleration of the current state of the vehicle cannot be described in real time; the N value is too small, so that the dynamic property of the IMU can be ensured, but the track still has obvious jitter when the vehicle bumps. Through a plurality of experiments, it can be known that when N is selected to be 20, the smoothness of the track can be ensured, and the dynamic property of the positioning result cannot be obviously reduced.
And S203, obtaining a pose estimation value of the vehicle according to the measurement data after the smoothing processing.
The estimated value of the position of the vehicle can be obtained based on the smoothed measurement data.
In the embodiment of the invention, the pose estimation value of the vehicle is calculated according to the smooth acceleration and by combining the pose estimation value of one frame on the vehicle.
That is, the pose estimation value of the current frame of the vehicle can be obtained in combination with the pose estimation value of the previous frame of the vehicle according to the smooth acceleration in the formula (3) and the formula (4).
And S204, obtaining a pose estimation value of the vehicle according to the measurement data of the IMU.
Under the condition that the vehicle is in a gentle road section, if the measurement data of the IMU is normal, the pose estimation value of the vehicle can be obtained according to the measurement data of the IMU.
In the embodiment of the invention, the pose estimation value of the vehicle is calculated according to the acceleration measured by the IMU and by combining the pose estimation value of a frame on the vehicle.
That is, the pose estimation value of the current frame of the vehicle can be obtained in formula (3) and formula (4) by combining the pose estimation value of the previous frame of the vehicle according to the acceleration measured by the IMU.
In the technical scheme of the embodiment of fig. 2, the measured data of the IMU is smoothed when the vehicle passes through a bumpy road section so that the dynamic performance of the vehicle is unchanged when the vehicle runs on a gentle road section. Therefore, the dynamics of vehicle positioning and the smoothness of the track can be ensured.
And S103, inputting the difference value between the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle, and correcting the global pose measurement value by combining the error estimation value to position the vehicle.
The global pose measurement value is an actual parameter measured by the laser radar and the code disc, and the pose calculation value is a calculation value based on measurement data of the IMU.
And the difference value of the global pose measurement value and the pose estimation value is the error of each navigation parameter. And inputting the difference value of the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle. And then the global pose measurement value can be corrected by combining the error estimation value to position the vehicle.
In the embodiment, the position and the attitude angle of the vehicle in the point cloud map are obtained, and the global pose measurement value is obtained by combining the speed of the vehicle; adjusting the measurement data of the inertia measurement unit based on the geographic position of the vehicle to obtain a pose estimation value of the vehicle; and inputting the difference value of the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle, and correcting the global pose measurement value by combining the error estimation value to position the vehicle. Based on the geographic position of the vehicle, the measurement data of the inertial measurement unit can be adjusted, the influence of a possibly bumpy road section on the pose calculation value is avoided, and the positioning accuracy can be improved.
Referring to fig. 3, fig. 3 is a schematic diagram of a flow of a method for vehicle positioning according to an embodiment of the present invention, which specifically includes:
s301, measurement data of the IMU.
Measurement data of the IMU on the vehicle is obtained.
And S302, scanning by the laser radar to obtain point cloud data.
And scanning by a laser radar arranged on the vehicle to obtain point cloud data. The position and attitude angle of the vehicle are known based on the point cloud data.
And S303, recording data by code disc counting.
The vehicle code wheel meter records the speed of the vehicle.
And S304, time synchronization.
Time synchronization is an important step in multi-sensor fusion, and aims to fuse data acquired by a plurality of sensors.
And S305, judging whether the vehicle is in a bumpy road section.
Whether the vehicle is on a bumpy road section can be judged according to the technical scheme in the S201. If the vehicle is in the bumpy road section, executing S306; if the vehicle is not on a bumpy road segment, S307 is executed.
And S306, smoothing the measurement data of the IMU.
According to the technical scheme in S202, smoothing the measurement data of the IMU.
S307, keeping the measurement data of the original IMU.
And keeping the measurement data of the original IMU because the vehicle is not positioned on the bumpy road section.
And S308, obtaining the pose estimation value of the previous frame.
The pose estimate for the previous frame may be obtained from the storage device.
S309, calculating the estimated values of the speed and the position.
The estimated values of the velocity and the position are calculated according to the formula (3) and the formula (4), respectively.
And S310, calculating an estimation value of the attitude angle.
And calculating the estimation value of the attitude angle according to the formula (5).
And S311, obtaining a pose estimation value of the vehicle.
The pose estimated value of the vehicle includes an estimated value of the speed, an estimated value of the position, and an estimated value of the attitude angle.
And S312, acquiring the position and attitude angle of the vehicle in the point cloud map through the point cloud data of the laser radar.
And registering the point cloud data obtained by the laser radar and the point cloud data in the point cloud map, and obtaining two sets of measurement values of the position and the attitude angle through the motion transformation of the current point cloud data.
And S313, measuring the speed of the vehicle by using the code wheel of the vehicle.
The speed of the vehicle can be measured using a code wheel meter of the vehicle.
And S314, obtaining a global pose measurement value.
The global pose measurement values include position, attitude angle, and velocity.
S315, a Kalman filter.
And inputting the difference value of the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle.
And S316, correcting the global pose measurement value.
And correcting the global pose measurement value by adopting the error estimation value to position the vehicle.
Referring to fig. 4, fig. 4 is a schematic diagram of a main structure of a vehicle positioning device according to an embodiment of the present invention, and the vehicle positioning device may implement a vehicle positioning method, as shown in fig. 4, the vehicle positioning device specifically includes:
and the global module 401 is configured to obtain a position and an attitude angle of the vehicle in the point cloud map, and obtain a global pose measurement value by combining the speed of the vehicle.
And the calculating module 402 is configured to adjust the measurement data of the inertial measurement unit based on the geographic position of the vehicle, so as to obtain a pose calculation value of the vehicle.
And a correcting module 403, configured to input a difference between the global pose measurement value and the pose estimation value into a kalman filter to obtain an error estimation value of the vehicle, and correct the global pose measurement value in combination with the error estimation value to position the vehicle.
In an embodiment of the present invention, the estimation module 402 is specifically configured to smooth the measurement data of the inertial measurement unit when the geographic location of the vehicle belongs to a bumpy road segment;
and obtaining the pose estimation value of the vehicle according to the measurement data after the smoothing processing.
In an embodiment of the present invention, the calculating module 402 is specifically configured to obtain a pose calculation value of the vehicle according to the measurement data of the inertial measurement unit when the geographic position of the vehicle belongs to a smooth section.
In an embodiment of the present invention, the calculating module 402 is further configured to determine that the geographic location of the vehicle belongs to a bumpy road section or a gentle road section based on the preset map identifier.
In an embodiment of the present invention, the estimation module 402 is specifically configured to process the measurement data of the inertial measurement unit according to the smoothed acceleration in combination with the estimated pose value of a frame on the vehicle.
In one embodiment of the invention, the smoothed acceleration is determined by N historical accelerations and weights of the historical accelerations, N being an integer greater than 1.
In one embodiment of the invention, N is equal to 20.
Fig. 5 shows an exemplary system architecture 500 of a vehicle localization method or device to which embodiments of the invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 501, 502, 503. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the method for vehicle positioning provided by the embodiment of the present invention is generally executed by the server 505, and accordingly, the device for vehicle positioning is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 6, a block diagram of a computer system 600 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a transmitting unit, an obtaining unit, a determining unit, and a first processing unit. The names of these units do not in some cases constitute a limitation to the unit itself, and for example, the sending unit may also be described as a "unit sending a picture acquisition request to a connected server".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise:
obtaining the position and attitude angle of a vehicle in a point cloud map, and obtaining a global pose measurement value by combining the speed of the vehicle;
adjusting the measurement data of an inertial measurement unit based on the geographic position of the vehicle to obtain a pose estimation value of the vehicle;
and inputting the difference value of the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle, and correcting the global pose measurement value by combining the error estimation value to position the vehicle.
According to the technical scheme of the embodiment of the invention, the position and attitude angle of the vehicle in the point cloud map are obtained, and the global pose measurement value is obtained by combining the speed of the vehicle; adjusting the measurement data of the inertia measurement unit based on the geographic position of the vehicle to obtain a pose estimation value of the vehicle; and inputting the difference value of the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle, and correcting the global pose measurement value by combining the error estimation value to position the vehicle. Based on the geographic position of the vehicle, the measurement data of the inertial measurement unit can be adjusted, the influence of a possibly bumpy road section on the pose calculation value is avoided, and the positioning accuracy can be improved.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of vehicle localization, comprising:
obtaining the position and attitude angle of a vehicle in a point cloud map, and obtaining a global pose measurement value by combining the speed of the vehicle;
adjusting the measurement data of an inertial measurement unit based on the geographic position of the vehicle to obtain a pose estimation value of the vehicle;
and inputting the difference value of the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle, and correcting the global pose measurement value by combining the error estimation value to position the vehicle.
2. The method for positioning a vehicle according to claim 1, wherein the adjusting the measurement data of the inertial measurement unit based on the geographic location of the vehicle to obtain the pose estimation value of the vehicle comprises:
the geographical position of the vehicle belongs to a bumpy road section, and the measurement data of the inertial measurement unit are processed smoothly;
and obtaining the pose estimation value of the vehicle according to the measurement data after the smoothing processing.
3. The method for positioning a vehicle according to claim 1, wherein the adjusting the measurement data of the inertial measurement unit based on the geographic location of the vehicle to obtain the pose estimation value of the vehicle comprises:
and the geographical position of the vehicle belongs to a gentle road section, and the pose estimation value of the vehicle is obtained according to the measurement data of the inertia measurement unit.
4. The method of claim 2 or 3, wherein before adjusting the measurement data of the inertial measurement unit based on the geographic location of the vehicle, further comprising:
and judging that the geographic position of the vehicle belongs to a bumpy road section or a gentle road section based on the preset map identification.
5. The vehicle localization method according to claim 2, wherein smoothing the measurement data of the inertial measurement unit comprises:
and processing the measurement data of the inertial measurement unit according to the smooth acceleration and the pose estimation value of the last frame of the vehicle.
6. The vehicle localization method according to claim 5, wherein the smoothed acceleration is determined by N number of historical accelerations and a weight of the historical accelerations, N being an integer greater than 1.
7. The vehicle localization method according to claim 6, wherein N is equal to 20.
8. An apparatus for vehicle localization, comprising:
the global module is used for obtaining the position and the attitude angle of the vehicle in the point cloud map and obtaining a global attitude measurement value by combining the speed of the vehicle;
the calculation module is used for adjusting the measurement data of the inertial measurement unit based on the geographic position of the vehicle to obtain a pose calculation value of the vehicle;
and the correction module is used for inputting the difference value between the global pose measurement value and the pose estimation value into a Kalman filter to obtain an error estimation value of the vehicle, and correcting the global pose measurement value by combining the error estimation value so as to position the vehicle.
9. An electronic device for vehicle positioning, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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| CN113324542A (en) * | 2021-06-07 | 2021-08-31 | 北京京东乾石科技有限公司 | Positioning method, device, equipment and storage medium |
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| CN114323020A (en) * | 2021-12-06 | 2022-04-12 | 纵目科技（上海）股份有限公司 | Vehicle positioning method, system, device and computer readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114323020B (en) * | 2021-12-06 | 2024-02-06 | 纵目科技（上海）股份有限公司 | Vehicle positioning method, system, equipment and computer readable storage medium |
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