CN116184468A - Fusion positioning method and device for automatic driving vehicle and electronic equipment - Google Patents
Fusion positioning method and device for automatic driving vehicle and electronic equipment Download PDFInfo
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
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- 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
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
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Abstract
The application discloses a fusion positioning method, a fusion positioning device and electronic equipment of an automatic driving vehicle, wherein the method comprises the following steps: acquiring preset cache queue data and original laser radar positioning data of an automatic driving vehicle; determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on preset cache queue data and original laser radar positioning data; determining corrected laser radar positioning data according to the accumulated positioning delay data corresponding to the laser radar in a preset time period; and carrying out fusion positioning according to the corrected laser radar positioning data to obtain a first fusion positioning result of the automatic driving vehicle. The method and the device utilize the preset iteration strategy to estimate the accumulated positioning delay error of the laser SLAM in a period of time so as to correct the positioning result of the laser SLAM, thereby providing more accurate observation information for fusion positioning of the automatic driving vehicle, improving the positioning stability and the positioning precision, and being suitable for more complex road scenes.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to a fusion positioning method and device for an automatic driving vehicle and electronic equipment.
Background
In an automatic driving scene, high-precision positioning of an automatic driving vehicle is required to be realized, and a multi-sensor fusion positioning mode is generally adopted at present, namely positioning information acquired by a plurality of sensors is fused through a Kalman filter, so that high-precision positioning of the vehicle is realized. For example, one fused positioning scheme in the prior art is one implemented based on IMU (Inertial Measurement Unit ) and RTK (Real-time differential positioning).
However, when the automatic driving vehicle encounters a scene such as an city, a canyon, a tunnel and the like, the RTK is interfered or no signal is generated, so that the RTK cannot work, and particularly, in a long tunnel working condition, high-precision positioning information cannot be obtained.
The common solution is to add a positioning result of the laser SLAM (Simultaneous Localization and Mapping) for fusion positioning, but the positioning result output by the laser SLAM is delayed to a certain extent, so that the direct fusion can possibly cause abnormal positioning, system alarm and further increase the manual takeover rate.
Disclosure of Invention
The embodiment of the application provides a fusion positioning method and device for an automatic driving vehicle and electronic equipment, so as to improve the positioning stability and positioning accuracy of the automatic driving vehicle.
The embodiment of the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a fusion positioning method for an autopilot vehicle, where the method includes:
acquiring preset cache queue data and original laser radar positioning data of an automatic driving vehicle;
determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the original laser radar positioning data;
determining corrected laser radar positioning data according to accumulated positioning delay data corresponding to the laser radar in a preset time period;
and carrying out fusion positioning according to the corrected laser radar positioning data to obtain a first fusion positioning result of the automatic driving vehicle.
Optionally, the preset buffer queue data includes a plurality of first timestamps, the original laser radar positioning data includes a second timestamp, and determining, based on the preset buffer queue data and the original laser radar positioning data, cumulative positioning delay data corresponding to the laser radar in a preset time period by using a preset iteration strategy includes:
Determining whether a target first timestamp with a difference value smaller than a preset time difference threshold value from the second timestamp exists in the preset cache queue data;
if so, determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data, the target first timestamp and the original laser radar positioning data;
if not, discarding the original laser radar positioning data.
Optionally, the preset buffer queue data further includes a wheel speed, and determining, based on the preset buffer queue data, the target first timestamp, and the original laser radar positioning data, the cumulative positioning delay data corresponding to the laser radar in the preset time period by using a preset iteration strategy includes:
determining a first timestamp of a previous moment corresponding to the current moment according to the preset cache queue data;
determining the preset time period according to the first time stamp of the previous moment and the target first time stamp;
correcting the wheel speeds corresponding to the first time stamps in the preset time period by utilizing a preset wheel speed correction strategy to obtain corrected wheel speeds in the preset time period;
And determining accumulated positioning delay data corresponding to the laser radar in the preset time period by utilizing a preset iteration strategy according to the corrected wheel speed in the preset time period and the original laser radar positioning data.
Optionally, the preset cache queue data further includes a heading angle, and determining, according to the corrected wheel speed in the preset time period and the original laser radar positioning data, cumulative positioning delay data corresponding to the laser radar in the preset time period by using a preset iteration strategy includes:
determining the time differences between all adjacent two first time stamps in the preset time period;
and carrying out iterative computation by utilizing the corrected wheel speed and course angle corresponding to each first timestamp in the preset time period and the time difference between all adjacent two first timestamps in the preset time period based on the original laser radar positioning data to obtain the accumulated positioning delay data corresponding to the laser radar in the preset time period.
Optionally, the original laser radar positioning data includes a transverse positioning position and a longitudinal positioning position of the laser radar, and determining, based on the preset cache queue data and the original laser radar positioning data, cumulative positioning delay data corresponding to the laser radar in a preset time period by using a preset iteration strategy includes:
Determining a transverse accumulated positioning delay position corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the transverse positioning position of the laser radar;
and determining a longitudinal accumulated positioning delay position of the laser radar corresponding to a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the longitudinal positioning position of the laser radar.
Optionally, the corrected laser radar positioning data is laser radar positioning data of a previous time corresponding to the current time, and the method further includes:
determining the positioning prediction time of the laser radar;
determining predicted laser radar positioning data at the current moment according to the corrected laser radar positioning data, the positioning prediction time of the laser radar and the preset cache queue data;
and carrying out fusion positioning according to the predicted laser radar positioning data at the current moment to obtain a second fusion positioning result of the automatic driving vehicle.
Optionally, the determining the predicted laser radar positioning data at the current moment according to the corrected laser radar positioning data, the positioning prediction time of the laser radar and the preset cache queue data includes:
Determining corrected wheel speeds and course angles corresponding to the positioning prediction time according to the preset cache queue data;
determining a positioning prediction distance at the current moment according to the positioning prediction time of the laser radar and the corresponding corrected wheel speed and course angle;
and determining predicted laser radar positioning data at the current moment according to the corrected laser radar positioning data and the positioning prediction distance at the current moment.
In a second aspect, embodiments of the present application further provide a fusion positioning device for an autonomous vehicle, where the device includes:
the acquisition unit is used for acquiring preset cache queue data and original laser radar positioning data of the automatic driving vehicle;
the first determining unit is used for determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the original laser radar positioning data;
the correction unit is used for determining corrected laser radar positioning data according to the accumulated positioning delay data corresponding to the laser radar in the preset time period;
and the first fusion positioning unit is used for carrying out fusion positioning according to the corrected laser radar positioning data to obtain a first fusion positioning result of the automatic driving vehicle.
In a third aspect, embodiments of the present application further provide an electronic device, including:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform any of the methods described hereinbefore.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform any of the methods described above.
The above-mentioned at least one technical scheme that this application embodiment adopted can reach following beneficial effect: according to the fusion positioning method of the automatic driving vehicle, preset cache queue data and original laser radar positioning data of the automatic driving vehicle are firstly obtained; then determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on preset cache queue data and original laser radar positioning data; then, correcting the original laser radar positioning data according to the accumulated positioning delay error generated by the laser radar in a preset time period to obtain corrected laser radar positioning data; and finally, carrying out fusion positioning according to the corrected laser radar positioning data to obtain a first fusion positioning result of the automatic driving vehicle. According to the fusion positioning method for the automatic driving vehicle, the accumulated positioning delay error of the laser SLAM in a period of time is estimated by using the preset iteration strategy, so that the positioning result of the laser SLAM is corrected, more accurate observation information is provided for fusion positioning of the automatic driving vehicle, positioning stability and positioning accuracy are improved, and the fusion positioning method is suitable for more complex road scenes.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a flow chart of a fusion positioning method of an automatic driving vehicle in an embodiment of the application;
FIG. 2 is a schematic structural diagram of a fusion positioning device for an autonomous vehicle according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
The embodiment of the application provides a fusion positioning method of an automatic driving vehicle, as shown in fig. 1, and provides a flow chart of the fusion positioning method of the automatic driving vehicle in the embodiment of the application, where the method at least includes the following steps S110 to S140:
step S110, obtaining preset cache queue data and original laser radar positioning data of the automatic driving vehicle.
When the fusion positioning of the automatic driving vehicle is realized, a preset cache queue needs to be constructed first, and the preset cache queue is used for caching fusion positioning data of the automatic driving vehicle within a period of time such as 1s, for example, the data such as wheel speed and course angle provided by an RTK/IMU can be included as the basis for the subsequent compensation laser SLAM positioning delay.
In addition, it is also necessary to acquire the original laser radar positioning data output by the current laser SLAM, where the original laser radar positioning data may be understood as positioning data having a certain positioning error due to the processing delay of the laser SLAM as a whole.
Step S120, determining cumulative positioning delay data corresponding to the lidar in a preset time period by using a preset iteration strategy based on the preset cache queue data and the original lidar positioning data.
In an actual road scene, an automatic driving vehicle may be in uniform motion or acceleration or deceleration motion in a positioning delay time period, so that the embodiment of the application can perform iterative calculation on positioning delay errors generated by the laser radar in the preset time period by adopting a certain iterative strategy based on the preset cache queue data and original laser radar positioning data, and thus the accumulated positioning delay data of the laser radar in the time period is calculated, and the iterative strategy is mainly designed based on a uniform speed change model, so that the method is applicable to uniform motion scenes of the automatic driving vehicle and complex motion scenes such as acceleration and deceleration, and the accuracy of laser positioning results of the automatic driving vehicle in the complex scenes is further improved.
Step S130, determining corrected laser radar positioning data according to the accumulated positioning delay data corresponding to the laser radar in the preset time period.
After the accumulated positioning delay data corresponding to the laser radar in the preset time period is predicted, the accumulated positioning delay data can be directly used as corrected laser radar positioning data, namely the accumulated positioning delay data output after iterative calculation is the laser radar positioning data with the accumulated positioning delay error compensated.
And step S140, fusion positioning is carried out according to the corrected laser radar positioning data, and a first fusion positioning result of the automatic driving vehicle is obtained.
Compared with the original laser radar positioning data, the corrected laser radar positioning data is more accurate and reliable, so that the corrected laser radar positioning data can be used as new observation information to be input into an extended Kalman filter for fusion positioning with the observation information of other sensors, and a fusion positioning result of an automatic driving vehicle is obtained.
According to the fusion positioning method for the automatic driving vehicle, the accumulated positioning delay error of the laser SLAM in a period of time is estimated by using the preset iteration strategy, so that the positioning result of the laser SLAM is corrected, more accurate observation information is provided for fusion positioning of the automatic driving vehicle, positioning stability and positioning accuracy are improved, and the fusion positioning method is suitable for more complex road scenes.
In some embodiments of the present application, the preset buffer queue data includes a plurality of first timestamps, the original lidar positioning data includes a second timestamp, and determining, based on the preset buffer queue data and the original lidar positioning data, cumulative positioning delay data corresponding to the lidar in a preset time period by using a preset iteration policy includes: determining whether a target first timestamp with a difference value smaller than a preset time difference threshold value from the second timestamp exists in the preset cache queue data; if so, determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data, the target first timestamp and the original laser radar positioning data; if not, discarding the original laser radar positioning data.
The preset cache queue can be particularly used for caching data such as wheel speed and course angle provided by the RTK/IMU in a period of time and corresponding first time stamp, wherein the first time stamp represents output time of the data such as the wheel speed and the course angle. Assuming that the time length is 1s and the data output frequency is 100Hz, 100 pieces of data can be cached in the preset cache queue at most. When the original laser radar positioning data is acquired, the original laser radar positioning data carries a second time stamp for representing the output time of the original laser radar positioning data.
Based on the above, the first timestamp in the preset buffer queue can be used for measuring the positioning delay condition of the original laser radar positioning data acquired currently, and determining whether the positioning delay degree of the original laser radar positioning data can be tolerated or not and whether the positioning delay degree can be used for subsequent fusion positioning or not. Specifically, the second time stamp corresponding to the original laser radar positioning data may be compared with the plurality of first time stamps in the preset buffer queue, and whether the target first time stamp time_x with the difference value smaller than the preset time difference threshold value between the plurality of first time stamps and the second time stamp exists or not is determined, for example, may be expressed as follows:
Time-time0 < preset time difference threshold
Where time is the first time stamp and time0 is the second time stamp, the magnitude of the predetermined time difference threshold is related to the data output frequency of the RTK/IMU, for example, the data output frequency of the RTK/IMU is 100Hz, that is, one data is output every 0.01s, and the predetermined time difference threshold may be set to a value less than 0.01s, for example, 0.005s.
If the difference value between the first time stamps and the second time stamps is smaller than the target first time stamp time_x of the preset time difference threshold value, the time difference between the currently acquired original laser radar positioning data and the data in the cache queue is in a tolerable range, so that the original laser radar positioning data can be further corrected based on the data in the cache queue, and if the first time stamp meeting the condition is not present, the time difference between the currently acquired original laser radar positioning data and all the data in the cache queue is beyond the tolerable range, and the positioning delay of the original laser radar positioning data is too large to be used for subsequent fusion positioning.
In some embodiments of the present application, the preset buffer queue data further includes a wheel speed, and determining, based on the preset buffer queue data, the target first timestamp, and the original laser radar positioning data, the cumulative positioning delay data corresponding to the laser radar in the preset time period by using a preset iteration policy includes: determining a first timestamp of a previous moment corresponding to the current moment according to the preset cache queue data; determining the preset time period according to the first time stamp of the previous moment and the target first time stamp; correcting the wheel speeds corresponding to the first time stamps in the preset time period by utilizing a preset wheel speed correction strategy to obtain corrected wheel speeds in the preset time period; and determining accumulated positioning delay data corresponding to the laser radar in the preset time period by utilizing a preset iteration strategy according to the corrected wheel speed in the preset time period and the original laser radar positioning data.
When determining the accumulated positioning delay data corresponding to the preset time period by using the preset iteration strategy, the embodiment of the application can determine the preset time period, wherein the preset time period can be understood as the positioning delay time of the laser SLAM, and can be determined according to the time difference between the first time stamp of the previous time corresponding to the current time cached in the preset cache queue and the first time stamp of the target.
It should be noted that, the reason why the positioning delay error of the laser SLAM is compensated to the previous time but not the current time is that if the positioning delay error of the laser SLAM is compensated to the current time, the corrected laser radar positioning data at the current time is obtained, and then the prediction is performed again according to the corrected laser radar positioning data at the current time when the original laser radar positioning data is not obtained at present, that is, the prediction is equivalent to the prediction of the next time at the current time, and a certain error may be introduced in the advance prediction.
The preset time period corresponds to a plurality of first time stamps in a preset cache queue, each first time stamp corresponds to data such as wheel speed and angle, and in order to improve accuracy of wheel speed calculation, a certain wheel speed correction strategy can be adopted to correct the wheel speed corresponding to each first time stamp in the preset time period, so that the corrected wheel speed corresponding to each first time stamp in the preset time period is obtained, and the wheel speed correction strategy mainly comprises strategies such as online calibration and gradient compensation, and can be expressed as follows:
vel_re＝vel*vel_k*cos(fabs(ori_pitch)
the wheel speed after correction is suitable for fusion positioning under complex road scenes such as climbing and the like under the condition that an RTK positioning signal is good and consideration is given to influence factors such as tire pressure.
In some embodiments of the present application, the preset buffer queue data further includes a heading angle, and determining, according to the corrected wheel speed in the preset time period and the original positioning data of the laser radar, the cumulative positioning delay data corresponding to the laser radar in the preset time period by using a preset iteration policy includes: determining the time differences between all adjacent two first time stamps in the preset time period; and carrying out iterative computation by utilizing the corrected wheel speed and course angle corresponding to each first timestamp in the preset time period and the time difference between all adjacent two first timestamps in the preset time period based on the original laser radar positioning data to obtain the accumulated positioning delay data corresponding to the laser radar in the preset time period.
The preset time period includes a plurality of first time stamps in the preset cache queue, so that the accumulated positioning delay data corresponding to the whole preset time period by the laser radar can be calculated in a plurality of stages according to the plurality of first time stamps included in the preset time period, for example, t0, t1, t2, … … and tn first time stamps are sequentially included in the preset time period, and then the corresponding time period can be divided into a plurality of iterative stages of t0-t1, t1-t2, … … and t (n-1) -tn.
Specifically, the time difference between two first time stamps corresponding to each iteration stage in the preset time period can be calculated first, the target first time stamp t0 is taken as a starting point, the positioning delay distance between the target first time stamp t0 and the first time stamp t1 at the next moment corresponding to the target first time stamp is calculated, the positioning position of the laser radar corresponding to the first time stamp t1 at the next moment can be calculated by combining the positioning position of the laser radar corresponding to the target first time stamp, namely the original positioning position of the laser radar, then the positioning position of the laser radar corresponding to the first time stamp t1 at the next moment is calculated by taking the first time stamp t1 at the next moment as a starting point, the positioning position of the laser radar corresponding to t2 is calculated, and so on until the previous moment tn corresponding to the current moment is calculated, and the positioning position of the laser radar corresponding to the previous moment tn is the accumulated positioning delay position of the laser radar corresponding to the whole preset time period.
In some embodiments of the present application, the original lidar positioning data includes a lateral positioning position and a longitudinal positioning position of the lidar, and determining, based on the preset cache queue data and the original lidar positioning data, cumulative positioning delay data corresponding to the lidar in a preset time period by using a preset iteration policy includes: determining a transverse accumulated positioning delay position corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the transverse positioning position of the laser radar; and determining a longitudinal accumulated positioning delay position of the laser radar corresponding to a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the longitudinal positioning position of the laser radar.
Because the positioning positions originally output by the laser SLAM include the east-to-north transverse positioning position lidar_pos_x and the north-to-north longitudinal positioning position lidar_pos_y under the northeast-to-north coordinate system, the embodiment of the application can further respectively and iteratively calculate the transverse accumulated positioning delay position and the longitudinal accumulated positioning delay position of the laser radar corresponding to the preset time period according to the heading angle yaw actually corresponding to each first timestamp.
For ease of understanding, the iterative process described above may be expressed in the form:
lidar_delay_pos_x’＝lidar_delay_pos_x+vel_re*cos(yaw)*
(time_pos_last-time_pos)
lidar_delay_pos_y’＝lidar_delay_pos_y+vel_re*sin(yaw)*
(time_pos_last-time_pos)
the time_pos_last is a start first time stamp corresponding to the current iteration stage, the time_pos is a stop first time stamp corresponding to the current iteration stage, the lidar_delay_pos_x and the lidar_delay_pos_y are respectively a transverse positioning delay position and a longitudinal positioning delay position of the laser radar calculated in each iteration stage in the whole iteration process, and the lidar_delay_pos_x 'and the lidar_delay_pos_y' are a transverse accumulated positioning delay position and a longitudinal accumulated positioning delay position of the laser radar obtained after the whole iteration process is completed, namely, a laser radar positioning position at a previous moment corresponding to the current moment is predicted.
Because the iterative calculation process is divided into a plurality of stages, the wheel speed and the course angle used in each stage are the wheel speed and the course angle data actually corresponding to the time stamp of each stage, and the finally obtained iterative calculation result is more in line with the actual running condition of the automatic driving vehicle and can be suitable for more complex road scenes.
In some embodiments of the present application, the corrected lidar positioning data is lidar positioning data of a previous time corresponding to the current time, and the method further includes: determining the positioning prediction time of the laser radar; determining predicted laser radar positioning data at the current moment according to the corrected laser radar positioning data, the positioning prediction time of the laser radar and the preset cache queue data; and carrying out fusion positioning according to the predicted laser radar positioning data at the current moment to obtain a second fusion positioning result of the automatic driving vehicle.
In the foregoing embodiment, when the original laser radar positioning data can be currently acquired, the positioning delay existing in the acquired original laser radar positioning data is compensated to the previous time corresponding to the current time, however, since the output frequency of the laser SLAM is generally lower than the output frequency of the RTK/IMU, for example, the output frequency of the laser SLAM is generally 5Hz, and the output frequency of the RTK/IMU is 100Hz, the situation that no laser SLAM is output in the process of outputting the RTK/IMU may occur, or the laser SLAM may not output a positioning result due to shielding, interference, etc., and at this time, the positioning track may be jumped when the laser SLAM is directly withdrawn from positioning or the fusion positioning is directly performed.
Based on this, the embodiment of the application may further predict the laser radar positioning data under the condition that the original laser radar positioning data is not obtained, specifically may determine the positioning prediction time of the laser radar first, for example, a time period from the last time of outputting the corrected laser radar positioning data to the current time may be taken as the positioning prediction time, and the current time is the time corresponding to the latest positioning data output by the RTK/IMU.
Since the fusion positioning may also have a data loss, that is, the time difference between the current time and the time of outputting the corrected laser radar positioning data last time is not necessarily a fixed time interval calculated according to the fusion positioning output frequency, but may be greater than the fixed time interval, if the span of the positioning prediction time is too large, the direct long-time prediction may result in a larger error of the prediction result, so the embodiment of the present application may first constrain the positioning prediction time, for example, may be expressed as follows:
if(abs(dt_lidar_pre_time)>1.0)
dt_lidar_pre_time＝0.01
the lidar_pre_time is a positioning prediction time, and if the positioning prediction time calculated according to the current time in the preset cache queue and the last time of outputting the corrected laser radar positioning data is greater than 1s, the positioning prediction time can be directly set to 0.01s. It should be noted that 1s is an adjustable threshold set according to the requirement, and 0.01s is mainly a fixed time interval calculated according to the fused positioning output frequency, which is of course, how to set the above values specifically to perform constraint, and those skilled in the art can flexibly adjust according to the actual requirement, which is not limited specifically herein.
After the positioning prediction time is determined, the laser radar positioning data corrected by the embodiment and the preset cache queue data are combined, so that the predicted laser radar positioning data at the current moment can be determined, the predicted information of the laser SLAM can be provided in the process that the laser SLAM is not output, and the predicted information is used as additional observation information at the current moment to be input into an extended Kalman filter for fusion positioning, and further the fusion positioning result of the automatic driving vehicle at the current moment is obtained.
In some embodiments of the present application, the determining the predicted lidar positioning data at the current time according to the corrected lidar positioning data, the positioning prediction time of the lidar, and the preset cache queue data includes: determining corrected wheel speeds and course angles corresponding to the positioning prediction time according to the preset cache queue data; determining a positioning prediction distance at the current moment according to the positioning prediction time of the laser radar and the corresponding corrected wheel speed and course angle; and determining predicted laser radar positioning data at the current moment according to the corrected laser radar positioning data and the positioning prediction distance at the current moment.
When determining the predicted lidar positioning data at the current time, the corrected wheel speed vel_pre 'and the heading angle ori_yaw' corresponding to the positioning prediction time can be determined according to the preset cache queue data, and the calculation mode of the corrected wheel speed is the same as that in the previous embodiment, and is not repeated herein. The heading angle ori_yw 'corresponding to the positioning prediction time can be calculated according to the average value of the heading angles in the positioning prediction time, or the average value of the heading angle at the moment corresponding to the corrected laser radar positioning data and the heading angle at the current moment can be used as the heading angle ori_yw' corresponding to the positioning prediction time, the positioning prediction distance at the current moment is calculated according to the positioning prediction time and the corresponding corrected wheel speed vel_pre ', and finally the predicted laser radar positioning data at the current moment is calculated according to the corrected laser radar positioning data, the positioning prediction distance at the current moment and the heading angle ori_yw' corresponding to the positioning prediction time.
Since the corrected laser radar positioning data includes the corrected lateral positioning position lidar_delay_pos_x ' and the corrected longitudinal positioning position lidar_delay_pos_y ', the positioning prediction distance can be decomposed laterally and longitudinally according to the heading angle ori_yw ', so as to obtain the lateral prediction distance and the longitudinal prediction distance. And finally, calculating the transverse positioning position of the laser SLAM predicted at the current moment by using the corrected transverse positioning position lidar_delay_pos_x 'and the transverse prediction distance, and calculating the transverse positioning position of the laser SLAM predicted at the current moment by using the corrected longitudinal positioning position lidar_delay_pos_y' and the longitudinal prediction distance.
The predicted lateral positioning position lidar_pre_pos_x and the longitudinal positioning position lidar_pre_pos_y of the laser SLAM may be expressed as follows:
lidar_pre_pos_x＝lidar_delay_pos_x’+vel_pre’*cos(ori_yaw’)*lidar_pre_timelidar_pre_pos_y＝lidar_delay_pos_y’+vel_pre’*sin(ori_yaw’)*lidar_pre_time
the embodiment of the application further provides a fusion positioning device 200 of an autopilot vehicle, as shown in fig. 2, and a schematic structural diagram of the fusion positioning device of the autopilot vehicle in the embodiment of the application is provided, where the device 200 includes: an acquisition unit 210, a first determination unit 220, a correction unit 230, and a first fusion positioning unit 240, wherein:
an acquiring unit 210, configured to acquire preset cache queue data and original laser radar positioning data of an autonomous vehicle;
a first determining unit 220, configured to determine, based on the preset cache queue data and the original positioning data of the laser radar, cumulative positioning delay data corresponding to the laser radar in a preset time period by using a preset iteration strategy;
a correction unit 230, configured to determine corrected positioning data of the lidar according to the cumulative positioning delay data of the lidar corresponding to the preset time period;
and the first fusion positioning unit 240 is configured to perform fusion positioning according to the corrected laser radar positioning data, so as to obtain a first fusion positioning result of the automatic driving vehicle.
In some embodiments of the present application, the preset buffer queue data includes a plurality of first time stamps, the original lidar positioning data includes a second time stamp, and the first determining unit 220 is specifically configured to: determining whether a target first timestamp with a difference value smaller than a preset time difference threshold value from the second timestamp exists in the preset cache queue data; if so, determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data, the target first timestamp and the original laser radar positioning data; if not, discarding the original laser radar positioning data.
In some embodiments of the present application, the preset buffer queue data further includes a wheel speed, and the first determining unit 220 is specifically configured to: determining a first timestamp of a previous moment corresponding to the current moment according to the preset cache queue data; determining the preset time period according to the first time stamp of the previous moment and the target first time stamp; correcting the wheel speeds corresponding to the first time stamps in the preset time period by utilizing a preset wheel speed correction strategy to obtain corrected wheel speeds in the preset time period; and determining accumulated positioning delay data corresponding to the laser radar in the preset time period by utilizing a preset iteration strategy according to the corrected wheel speed in the preset time period and the original laser radar positioning data.
In some embodiments of the present application, the preset cache queue data further includes a heading angle, and the first determining unit 220 is specifically configured to: determining the time differences between all adjacent two first time stamps in the preset time period; and carrying out iterative computation by utilizing the corrected wheel speed and course angle corresponding to each first timestamp in the preset time period and the time difference between all adjacent two first timestamps in the preset time period based on the original laser radar positioning data to obtain the accumulated positioning delay data corresponding to the laser radar in the preset time period.
In some embodiments of the present application, the raw lidar positioning data includes a lateral positioning position and a longitudinal positioning position of the lidar, and the first determination unit 220 is specifically configured to: determining a transverse accumulated positioning delay position corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the transverse positioning position of the laser radar; and determining a longitudinal accumulated positioning delay position of the laser radar corresponding to a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the longitudinal positioning position of the laser radar.
In some embodiments of the present application, the corrected lidar positioning data is lidar positioning data of a previous time corresponding to the current time, and the apparatus further includes: the second determining unit is used for determining the positioning prediction time of the laser radar; the third determining unit is used for determining predicted laser radar positioning data at the current moment according to the corrected laser radar positioning data, the positioning prediction time of the laser radar and the preset cache queue data; and the second fusion positioning unit is used for carrying out fusion positioning according to the predicted laser radar positioning data at the current moment to obtain a second fusion positioning result of the automatic driving vehicle.
In some embodiments of the present application, the third determining unit is specifically configured to: determining corrected wheel speeds and course angles corresponding to the positioning prediction time according to the preset cache queue data; determining a positioning prediction distance at the current moment according to the positioning prediction time of the laser radar and the corresponding corrected wheel speed and course angle; and determining predicted laser radar positioning data at the current moment according to the corrected laser radar positioning data and the positioning prediction distance at the current moment.
It can be understood that the above-mentioned fusion positioning device for an automatic driving vehicle can implement each step of the fusion positioning method for an automatic driving vehicle provided in the foregoing embodiment, and the relevant explanation about the fusion positioning method for an automatic driving vehicle is applicable to the fusion positioning device for an automatic driving vehicle, which is not described herein again.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 3, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 3, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the fusion positioning device of the automatic driving vehicle on a logic level. The processor is used for executing the programs stored in the memory and is specifically used for executing the following operations:
acquiring preset cache queue data and original laser radar positioning data of an automatic driving vehicle;
determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the original laser radar positioning data;
determining corrected laser radar positioning data according to accumulated positioning delay data corresponding to the laser radar in a preset time period;
and carrying out fusion positioning according to the corrected laser radar positioning data to obtain a first fusion positioning result of the automatic driving vehicle.
The method performed by the fusion positioning device of the autonomous vehicle disclosed in the embodiment shown in fig. 1 of the present application may be applied to a processor or implemented by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The electronic device may further execute the method executed by the fusion positioning device of the autopilot vehicle in fig. 1, and implement the function of the fusion positioning device of the autopilot vehicle in the embodiment shown in fig. 1, which is not described herein.
The embodiments of the present application also provide a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by an electronic device that includes a plurality of application programs, enable the electronic device to perform a method performed by a fusion positioning device for an autonomous vehicle in the embodiment shown in fig. 1, and specifically are configured to perform:
acquiring preset cache queue data and original laser radar positioning data of an automatic driving vehicle;
determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the original laser radar positioning data;
determining corrected laser radar positioning data according to accumulated positioning delay data corresponding to the laser radar in a preset time period;
and carrying out fusion positioning according to the corrected laser radar positioning data to obtain a first fusion positioning result of the automatic driving vehicle.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A fusion positioning method of an autonomous vehicle, wherein the method comprises:
acquiring preset cache queue data and original laser radar positioning data of an automatic driving vehicle;
determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the original laser radar positioning data;
Determining corrected laser radar positioning data according to accumulated positioning delay data corresponding to the laser radar in a preset time period;
and carrying out fusion positioning according to the corrected laser radar positioning data to obtain a first fusion positioning result of the automatic driving vehicle.
2. The method of claim 1, wherein the pre-set cache queue data comprises a plurality of first time stamps, the original lidar positioning data comprises a second time stamp, and determining cumulative positioning delay data for the lidar over a pre-set time period using a pre-set iteration strategy based on the pre-set cache queue data and the original lidar positioning data comprises:
determining whether a target first timestamp with a difference value smaller than a preset time difference threshold value from the second timestamp exists in the preset cache queue data;
if so, determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data, the target first timestamp and the original laser radar positioning data;
if not, discarding the original laser radar positioning data.
3. The method of claim 2, wherein the predetermined cache queue data further comprises wheel speed, and wherein determining cumulative positioning delay data for the lidar over a predetermined period of time using a predetermined iteration strategy based on the predetermined cache queue data and the target first timestamp and the raw lidar positioning data comprises:
determining a first timestamp of a previous moment corresponding to the current moment according to the preset cache queue data;
determining the preset time period according to the first time stamp of the previous moment and the target first time stamp;
correcting the wheel speeds corresponding to the first time stamps in the preset time period by utilizing a preset wheel speed correction strategy to obtain corrected wheel speeds in the preset time period;
and determining accumulated positioning delay data corresponding to the laser radar in the preset time period by utilizing a preset iteration strategy according to the corrected wheel speed in the preset time period and the original laser radar positioning data.
4. The method of claim 3, wherein the predetermined cache queue data further includes a heading angle, and the determining, according to the corrected wheel speed in the predetermined time period and the original laser radar positioning data, cumulative positioning delay data corresponding to the laser radar in the predetermined time period using a predetermined iteration strategy includes:
Determining the time differences between all adjacent two first time stamps in the preset time period;
and carrying out iterative computation by utilizing the corrected wheel speed and course angle corresponding to each first timestamp in the preset time period and the time difference between all adjacent two first timestamps in the preset time period based on the original laser radar positioning data to obtain the accumulated positioning delay data corresponding to the laser radar in the preset time period.
5. The method of claim 1, wherein the original lidar positioning data includes a lateral positioning location and a longitudinal positioning location of the lidar, and wherein determining cumulative positioning delay data for the lidar over a predetermined period of time using a predetermined iteration strategy based on the predetermined cache queue data and the original lidar positioning data comprises:
determining a transverse accumulated positioning delay position corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the transverse positioning position of the laser radar;
and determining a longitudinal accumulated positioning delay position of the laser radar corresponding to a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the longitudinal positioning position of the laser radar.
6. The method of claim 1, wherein the modified lidar location data is lidar location data for a previous time corresponding to a current time, the method further comprising:
determining the positioning prediction time of the laser radar;
determining predicted laser radar positioning data at the current moment according to the corrected laser radar positioning data, the positioning prediction time of the laser radar and the preset cache queue data;
and carrying out fusion positioning according to the predicted laser radar positioning data at the current moment to obtain a second fusion positioning result of the automatic driving vehicle.
7. The method of claim 6, wherein the determining the predicted lidar positioning data for the current time based on the modified lidar positioning data and the predicted time of positioning of the lidar and the pre-set cache queue data comprises:
determining corrected wheel speeds and course angles corresponding to the positioning prediction time according to the preset cache queue data;
determining a positioning prediction distance at the current moment according to the positioning prediction time of the laser radar and the corresponding corrected wheel speed and course angle;
and determining predicted laser radar positioning data at the current moment according to the corrected laser radar positioning data and the positioning prediction distance at the current moment.
8. A fusion positioning device for an autonomous vehicle, wherein the device comprises:
the acquisition unit is used for acquiring preset cache queue data and original laser radar positioning data of the automatic driving vehicle;
the first determining unit is used for determining accumulated positioning delay data corresponding to the laser radar in a preset time period by utilizing a preset iteration strategy based on the preset cache queue data and the original laser radar positioning data;
the correction unit is used for determining corrected laser radar positioning data according to the accumulated positioning delay data corresponding to the laser radar in the preset time period;
and the first fusion positioning unit is used for carrying out fusion positioning according to the corrected laser radar positioning data to obtain a first fusion positioning result of the automatic driving vehicle.
9. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 7.
10. A computer readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-7.
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