Fusion positioning method, device and electronic equipment of autonomous vehicle

Abstract

The application discloses a fusion positioning method and device of an automatic driving vehicle and electronic equipment, and the method comprises the following steps: obtaining preset cache queue data and original laser radar positioning data of the automatic driving vehicle; determining positioning delay data corresponding to the original laser radar positioning data according to the preset cache queue data and the original laser radar positioning data; correcting the original laser radar positioning data according to the positioning delay data corresponding to the original laser radar positioning data, to obtain corrected laser radar positioning data; and performing fusion positioning according to the corrected laser radar positioning data, to obtain a first fusion positioning result of the automatic driving vehicle. The application corrects the positioning error introduced by the positioning delay of the laser SLAM using the laser radar positioning data, provides more accurate observation information for the fusion positioning of the automatic driving vehicle, and further improves the fusion stability and positioning accuracy, and is suitable for more complex road scenes.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a fusion positioning method, device and electronic device for autonomous vehicles. Background Technology

[0002] In autonomous driving scenarios, high-precision positioning of autonomous vehicles is required. Currently, multi-sensor fusion positioning is commonly used, which involves fusing positioning information collected by multiple sensors using a Kalman filter to achieve high-precision vehicle positioning. For example, one existing fusion positioning scheme is based on IMU (Inertial Measurement Unit) and RTK (Real-time kinematic) fusion positioning.

[0003] However, this solution may fail to work when autonomous vehicles encounter scenarios such as cities, canyons, and tunnels, as RTK may be interfered with or lose signal. This is especially true in long tunnel conditions where high-precision positioning information cannot be obtained.

[0004] A common solution is to incorporate the localization results from laser SLAM (Simultaneous Localization and Mapping) for fusion localization. However, the localization results output by laser SLAM itself usually have a certain degree of delay. Directly fusing these results may lead to localization anomalies, system alarms, and thus increase the rate of manual intervention. Summary of the Invention

[0005] This application provides a fusion positioning method, device, and electronic device for autonomous vehicles to improve the positioning stability and accuracy of autonomous vehicles.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] In a first aspect, embodiments of this application provide a fusion positioning method for autonomous vehicles, wherein the method includes:

[0008] Acquire the preset cache queue data and raw LiDAR positioning data of autonomous vehicles;

[0009] Based on the preset cache queue data and the original lidar positioning data, determine the positioning delay data corresponding to the original lidar positioning data;

[0010] The original lidar positioning data is corrected based on the positioning delay data corresponding to the original lidar positioning data to obtain the corrected lidar positioning data.

[0011] Fusing positioning according to the modified laser radar positioning data to obtain a first fusion positioning result of the autonomous vehicle.

[0012] Optionally, the preset cache queue data comprises a plurality of first time stamps, the original laser radar positioning data comprises a second time stamp, and the determining of the positioning delay data corresponding to the original laser radar positioning data according to the preset cache queue data and the original laser radar positioning data comprises:

[0013] determining whether there is a target first time stamp in the preset cache queue data, which has a difference from the second time stamp less than a preset time difference threshold;

[0014] if there is, determining the positioning delay data corresponding to the original laser radar positioning data according to the preset cache queue data, the target first time stamp and the original laser radar positioning data;

[0015] if there is not, discarding the original laser radar positioning data.

[0016] Optionally, the preset cache queue data further comprises a wheel speed and a heading angle, and the determining of the positioning delay data corresponding to the original laser radar positioning data according to the preset cache queue data, the target first time stamp and the original laser radar positioning data comprises:

[0017] determining a positioning delay time corresponding to the original laser radar positioning data according to the target first time stamp and a time stamp of a current moment;

[0018] determining a predicted speed corresponding to the positioning delay time according to a wheel speed corresponding to the target first time stamp and a wheel speed of the current moment;

[0019] determining a positioning delay distance corresponding to the original laser radar positioning data according to the positioning delay time corresponding to the original laser radar positioning data, the predicted speed and the heading angle.

[0020] Optionally, the determining of the predicted speed corresponding to the positioning delay time according to the wheel speed corresponding to the target first time stamp and the wheel speed of the current moment comprises:

[0021] determining the wheel speed of the current moment according to the preset cache queue data;

[0022] determining an average value of the wheel speed corresponding to the target first time stamp and the wheel speed of the current moment, and taking the average value of the wheel speed corresponding to the target first time stamp and the wheel speed of the current moment as the predicted speed corresponding to the positioning delay time.

[0023] Optionally, the original laser radar positioning data comprises a lateral positioning position and a longitudinal positioning position of the laser radar, the positioning delay data comprises a lateral positioning delay distance and a longitudinal positioning delay distance, and the correcting the original laser radar positioning data according to corresponding positioning delay data of the original laser radar positioning data to obtain modified laser radar positioning data comprises:

[0024] determining a modified lateral positioning position of the laser radar according to the lateral positioning position of the laser radar and the lateral positioning delay distance;

[0025] determining a modified longitudinal positioning position of the laser radar according to the longitudinal positioning position of the laser radar and the longitudinal positioning delay distance.

[0026] Optionally, the method further comprises:

[0027] in a case where the original laser radar positioning data is not acquired at the current time, determining a positioning prediction time of the laser radar;

[0028] determining predicted laser radar positioning data at the current time according to the modified laser radar positioning data and the positioning prediction time of the laser radar and the preset cache queue data;

[0029] performing fusion positioning according to the predicted laser radar positioning data at the current time to obtain a second fusion positioning result of the autonomous vehicle.

[0030] Optionally, the determining predicted laser radar positioning data at the current time according to the modified laser radar positioning data and the positioning prediction time of the laser radar and the preset cache queue data comprises:

[0031] determining a predicted speed and a heading angle corresponding to the positioning prediction time according to the preset cache queue data;

[0032] determining a positioning prediction distance at the current time according to the positioning prediction time of the laser radar and the corresponding predicted speed and heading angle;

[0033] determining predicted laser radar positioning data at the current time according to the modified laser radar positioning data and the positioning prediction distance at the current time.

[0034] In a second aspect, the embodiments of the present application further provide a fusion positioning device of an autonomous vehicle, wherein the device comprises:

[0035] an acquisition unit configured to acquire preset cache queue data of the autonomous vehicle and original laser radar positioning data;

[0036] The first determining unit is configured to determine positioning delay data corresponding to the original laser radar positioning data according to the preset cache queue data and the original laser radar positioning data.

[0037] The correcting unit is configured to correct the original laser radar positioning data according to the positioning delay data corresponding to the original laser radar positioning data, to obtain corrected laser radar positioning data.

[0038] The first fusion positioning unit is configured to perform fusion positioning according to the corrected laser radar positioning data, to obtain a first fusion positioning result of the autonomous vehicle.

[0039] In a third aspect, an electronic device is provided, including:

[0040] a processor; and

[0041] a memory arranged to store computer-executable instructions that, when executed, cause the processor to perform any of the methods described above.

[0042] In a fourth aspect, a computer-readable storage medium is provided, which stores one or more programs, and when the electronic device including a plurality of application programs executes the one or more programs, the electronic device performs any of the methods described above.

[0043] The above at least one technical solution adopted by the embodiments of the present application can achieve the following beneficial effects: the fusion positioning method of the autonomous vehicle according to the embodiments of the present application first acquires preset cache queue data and original laser radar positioning data of the autonomous vehicle; then determines positioning delay data corresponding to the original laser radar positioning data according to the preset cache queue data and the original laser radar positioning data; then corrects the original laser radar positioning data according to the positioning delay data corresponding to the original laser radar positioning data, to obtain corrected laser radar positioning data; and finally performs fusion positioning according to the corrected laser radar positioning data, to obtain a first fusion positioning result of the autonomous vehicle. The fusion positioning method of the autonomous vehicle according to the embodiments of the present application corrects the positioning error introduced by the positioning delay of the laser SLAM using the laser radar positioning data, provides more accurate observation information for the fusion positioning of the autonomous vehicle, and thus improves the fusion stability and positioning accuracy, and is suitable for more complex road scenes. BRIEF DESCRIPTION OF DRAWINGS

[0044] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of the present application, illustrate the illustrative embodiments of the present application and their description serve to explain the present application, and do not limit the present application in any way. In the drawings:

[0045] Figure 1 FIG. 1 shows a flowchart of a fusion positioning method of an autonomous vehicle according to an embodiment of the present application.

[0046] Figure 2 FIG. 2 shows a structural diagram of a fusion positioning device of an autonomous vehicle according to an embodiment of the present application.

[0047] Figure 3 FIG. 3 shows a structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described below in detail with reference to the embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0049] The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the drawings.

[0050] The embodiments of the present application provide a fusion positioning method of an autonomous vehicle, as shown in FIG. 1, a flowchart of a fusion positioning method of an autonomous vehicle according to an embodiment of the present application is provided, and the method at least includes the following steps S110 to S140: Figure 1

[0051] Step S110, obtaining preset cache queue data and original laser radar positioning data of an autonomous vehicle.

[0052] In the fusion positioning of the autonomous vehicle, a preset cache queue needs to be constructed first, which is used to cache the fusion positioning data of the autonomous vehicle in a period of time, such as 1s, for example, can include the wheel speed, heading angle and other data provided by RTK / IMU, as the basis for subsequent compensation of laser SLAM positioning delay.

[0053] In addition, the original laser radar positioning data output by the current laser SLAM also needs to be obtained, which can be understood as the positioning data with a certain positioning error caused by the overall processing delay of the laser SLAM.

[0054] Step S120, determining the positioning delay data corresponding to the original laser radar positioning data according to the preset cache queue data and the original laser radar positioning data.

[0055] ​Based on the wheel speed, heading angle and other data stored in the preset cache queue and the currently obtained original laser radar positioning data, the positioning delay data corresponding to the original laser radar positioning data can be further predicted, and the positioning delay data specifically can include the position error of the positioning position output by the original laser SLAM.

[0056] In step S130, the original laser radar positioning data is corrected according to the positioning delay data corresponding to the original laser radar positioning data, and the corrected laser radar positioning data is obtained.

[0057] After predicting the positioning error caused by the positioning delay of the laser SLAM, the positioning position output by the original laser SLAM can be corrected using the positioning error, so as to obtain the corrected laser radar positioning data, that is, the laser radar positioning data that compensates for the positioning delay error.

[0058] In step S140, fusion positioning is performed according to the corrected laser radar positioning data, and a first fusion positioning result of the autonomous vehicle is obtained.

[0059] The corrected laser radar positioning data is more accurate and reliable than the original laser radar positioning data, so the above corrected laser radar positioning data can be input as new observation information into the extended Kalman filter to perform fusion positioning with the observation information of other sensors, so as to obtain the fusion positioning result of the autonomous vehicle.

[0060] The fusion positioning method of the autonomous vehicle in the embodiment of the application corrects the positioning error introduced by the positioning delay of the laser SLAM using the laser radar positioning data, provides more accurate observation information for the fusion positioning of the autonomous vehicle, and further improves the fusion stability and positioning accuracy, and is suitable for more complex road scenes.

[0061] In some embodiments of the application, the preset cache queue data includes a plurality of first time stamps, the original laser radar positioning data includes a second time stamp, and the determination of the positioning delay data corresponding to the original laser radar positioning data based on the preset cache queue data and the original laser radar positioning data includes: determining whether there is a target first time stamp in the preset cache queue data whose difference with the second time stamp is less than a preset time difference threshold; if there is, determining the positioning delay data corresponding to the original laser radar positioning data according to the preset cache queue data, the target first time stamp and the original laser radar positioning data; and if there is not, discarding the original laser radar positioning data.

[0062] The preset cache queue is specifically used to cache data such as wheel speed and heading angle provided by the RTK / IMU within a certain period, along with the corresponding first timestamp. The first timestamp indicates the output time of the wheel speed and heading angle data. Assuming a time length of 1 second and a data output frequency of 100Hz, the preset cache queue can cache up to 100 data entries. When the original LiDAR positioning data is acquired, it carries a second timestamp to represent the output time of the original LiDAR positioning data.

[0063] Based on this, the first timestamp in the preset cache queue can be used to measure the positioning delay of the currently acquired raw LiDAR positioning data, determining whether the positioning delay of the raw LiDAR positioning data is tolerable and whether it can be used for subsequent fusion positioning. Specifically, the second timestamp corresponding to the raw LiDAR positioning data can be compared with multiple first timestamps in the preset cache queue to determine whether there is a target first timestamp time_x among the multiple first timestamps whose difference from the second timestamp is less than a preset time difference threshold. For example, it can be expressed in the following form:

[0064] |time-time0| < preset time difference threshold

[0065] Where time is the first timestamp and time0 is the second timestamp, the preset time difference threshold is related to the data output frequency of the RTK / IMU. For example, if the data output frequency of the RTK / IMU is 100Hz, that is, one data is output every 0.01s, then the preset time difference threshold can be set to a value less than 0.01s, such as 0.005s.

[0066] If there is a target first timestamp time_x among multiple first timestamps whose difference with the second timestamp is less than the preset time difference threshold, it means that the time difference between the currently acquired original LiDAR positioning data and the data in the cache queue is within the tolerable range. Therefore, the original LiDAR positioning data can be further corrected based on the data in the cache queue. However, if there is no first timestamp that meets the above conditions, it means that the time difference between the currently acquired original LiDAR positioning data and all the data in the cache queue exceeds the tolerable range. The positioning delay of the original LiDAR positioning data is too large and cannot be used for subsequent fusion positioning.

[0067] In some embodiments of the present application, the preset cache queue data further comprises wheel speed and heading angle, and the determining of the positioning delay data corresponding to the original laser radar positioning data according to the preset cache queue data and the target first timestamp and the original laser radar positioning data comprises: determining the positioning delay time corresponding to the original laser radar positioning data according to the target first timestamp and the timestamp of the current moment; determining the predicted speed corresponding to the positioning delay time according to the wheel speed corresponding to the target first timestamp and the wheel speed of the current moment; and determining the positioning delay distance corresponding to the original laser radar positioning data according to the positioning delay time corresponding to the original laser radar positioning data and the corresponding predicted speed and heading angle.

[0068] The positioning data corresponding to the target first timestamp is data relatively close in time to the original laser radar positioning data, because the target first timestamp and the wheel speed and heading angle in the preset cache queue corresponding to the target first timestamp can be used as the correction basis of the original laser radar positioning data.

[0069] Specifically, the positioning delay time corresponding to the original laser radar positioning data can be determined according to the target first timestamp and the timestamp of the current moment, and the timestamp of the current moment can be determined based on the timestamp time_new of the latest frame in the preset cache queue, so the positioning delay time t delay which can be expressed as follows:

[0070] t delay = |time_x-time_new|

[0071] Then the predicted speed vel_pre of the laser SLAM in the positioning delay time can be calculated according to the wheel speed corresponding to the target first timestamp and the wheel speed of the current moment. By default, the vehicle travels at a constant speed, so the positioning delay distance corresponding to the original laser radar positioning data can be calculated based on the positioning delay time and the speed of the laser SLAM in the positioning delay time.

[0072] In some embodiments of the present application, the determining of the predicted speed corresponding to the positioning delay time according to the wheel speed corresponding to the target first timestamp and the wheel speed of the current moment comprises: determining the wheel speed of the current moment according to the preset cache queue data; determining the average value of the wheel speed corresponding to the target first timestamp and the wheel speed of the current moment, and taking the average value of the wheel speed corresponding to the target first timestamp and the wheel speed of the current moment as the predicted speed corresponding to the positioning delay time.

[0073] The wheel speed at the current time can also be obtained according to the latest frame of data in the preset cache queue. In order to improve the accuracy and reliability of the predicted speed, the wheel speed vel timex corresponding to the target first timestamp and the wheel speed vel new at the current time can be averaged as the predicted speed vel pre of the laser SLAM corresponding to the positioning delay time. Of course, in addition to calculating the average value in the above manner, the wheel speed corresponding to all time stamps between the target first timestamp and the current time stamp can also be averaged based on the preset cache queue as the predicted speed. How to determine it is flexible for those skilled in the art to set according to actual needs, which is not listed here.

[0074] In some embodiments of the present application, the original laser radar positioning data includes a lateral positioning position and a longitudinal positioning position of the laser radar, the positioning delay data includes a lateral positioning delay distance and a longitudinal positioning delay distance, and the original laser radar positioning data is corrected according to the corresponding positioning delay data of the original laser radar positioning data to obtain the corrected laser radar positioning data. It includes: determining the corrected lateral positioning position of the laser radar according to the lateral positioning position of the laser radar and the lateral positioning delay distance; determining the corrected longitudinal positioning position of the laser radar according to the longitudinal positioning position of the laser radar and the longitudinal positioning delay distance.

[0075] Since the positioning position output by the laser SLAM originally includes the lateral positioning position lidar_pos_x in the east direction and the longitudinal positioning position lidar_pos_y in the north direction in the east-north-sky coordinate system, the lateral and longitudinal decomposition of the positioning delay distance can be further performed according to the heading angle ori_yaw, so as to obtain the lateral positioning delay distance and the longitudinal positioning delay distance. Finally, the lateral positioning delay distance is used to correct the lateral positioning position of the laser SLAM, and the longitudinal positioning delay distance is used to correct the longitudinal positioning position of the laser SLAM. The corrected lateral positioning position lidar_new_pos_x and the longitudinal positioning position lidar_new_pos_y can be represented as follows:

[0076] lidar_new_pos_x = lidar_pos_x + vel_pre * cos(ori_yaw) * t delay

[0077] lidar_new_pos_y = lidar_pos_y + vel_pre * sin(ori_yaw) * t delay

[0078] In some embodiments of the present application, the method further comprises: determining a positioning prediction time of the laser radar in a case where the original laser radar positioning data is not acquired at the current time; determining predicted laser radar positioning data at the current time according to the modified laser radar positioning data and the positioning prediction time of the laser radar and the preset cache queue data; and performing fusion positioning according to the predicted laser radar positioning data at the current time to obtain a second fusion positioning result of the autonomous vehicle.

[0079] The foregoing embodiments are in a case where the original laser radar positioning data acquired is compensated for positioning delay to the current time. However, since the output frequency of laser SLAM is generally lower than that of RTK / IMU, for example, the output frequency of laser SLAM is usually 5 Hz, and the output frequency of RTK / IMU is 100 Hz, there may be a case where no laser SLAM output occurs in the process of RTK / IMU output, or laser SLAM may not output positioning results due to occlusion, interference, etc. At this time, directly exiting the laser SLAM positioning may cause the positioning trajectory to jump.

[0080] Based on this, the embodiments of the present application can further predict the laser radar positioning data in a case where the original laser radar positioning data is not acquired. Specifically, the positioning prediction time of the laser radar can be determined first, for example, the time period from the time of the last output of the modified laser radar positioning data to the current time can be taken as the positioning prediction time, and the current time is the time corresponding to the latest positioning data output by RTK / IMU.

[0081] Since data loss may also occur in fusion positioning, that is, the time difference between the current time and the time of the last output of the modified laser radar positioning data is not necessarily a fixed time interval calculated according to the fusion positioning output frequency, but may be greater than the fixed time interval. Therefore, if the span of the positioning prediction time is too large, directly predicting for a long time may result in a large error of the prediction result. Therefore, the embodiments of the present application can first constrain the positioning prediction time, for example, which can be expressed in the following form:

[0082] if(abs(dt_lidar_pre_time)>1.0)

[0083] dt_lidar_pre_time=0.01

[0084] lidar_pre_time is the positioning prediction time. If the positioning prediction time calculated according to the current time and the previous time in the preset cache queue 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 requirements, and 0.01s is a fixed time interval calculated according to the fusion positioning output frequency. Of course, how to set the above values for constraint can be flexibly adjusted by a person skilled in the art according to actual requirements, which is not limited here.

[0085] After the positioning prediction time is determined, the predicted lidar positioning data at the current time can be determined by combining the corrected lidar positioning data and the preset cache queue data in the foregoing embodiments, so that the predicted information of the laser SLAM can be provided in the process of no output of the laser SLAM, which is input into the extended Kalman filter as additional observation information at the current time for fusion positioning, and then the fusion positioning result of the autonomous vehicle at the current time is obtained.

[0086] In some embodiments of the present application, 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 comprises: determining the predicted speed and heading angle corresponding to the positioning prediction time according to the preset cache queue data; determining the positioning prediction distance at the current time according to the positioning prediction time of the lidar and the corresponding predicted speed and heading angle; and determining the predicted lidar positioning data at the current time according to the corrected lidar positioning data and the positioning prediction distance at the current time.

[0087] In determining the predicted lidar positioning data at the current time, the predicted speed and heading angle corresponding to the positioning prediction time can also be determined according to the preset cache queue data. The calculation method of the predicted speed is similar to that in the foregoing embodiments, that is, the average of the wheel speed at the time corresponding to the corrected lidar positioning data and the wheel speed at the current time can be used as the predicted speed vel_pre' corresponding to the positioning prediction time, and the average of the heading angle at the time corresponding to the corrected lidar positioning data and the heading angle at the current time can be used as the heading angle ori_yaw' corresponding to the positioning prediction time. Then, the positioning prediction distance at the current time is calculated according to the positioning prediction time and the corresponding predicted speed and heading angle, and finally the predicted lidar positioning data at the current time is calculated according to the corrected lidar positioning data and the positioning prediction distance at the current time.

[0088] Since the corrected lidar positioning data includes the corrected lateral positioning position `lidar_new_pos_x` and the corrected longitudinal positioning position `lidar_new_pos_y`, the positioning prediction range can be further decomposed laterally and longitudinally based on the heading angle `ori_yaw` to obtain the lateral and longitudinal prediction ranges. Finally, the corrected lateral positioning position `lidar_new_pos_x` and the lateral prediction range are used to calculate the predicted lateral positioning position of the lidar SLAM at the current moment, and the corrected longitudinal positioning position `lidar_new_pos_y` and the longitudinal prediction range are used to calculate the predicted lateral positioning position of the lidar SLAM at the current moment.

[0089] The predicted lateral positioning position lidar_pre_pos_x and longitudinal positioning position lidar_pre_pos_y of laser SLAM can be represented as follows:

[0090] lidar_pre_pos_x=lidar_new_pos_x+vel_pre'*cos(ori_yaw')*lidar_pre_time

[0091] lidar_pre_pos_y=lidar_new_pos_y+vel_pre'*sin(ori_yaw')*lidar_pre_time.

[0092] This application embodiment also provides a fusion positioning device 200 for autonomous vehicles, such as... Figure 2 The diagram shows a structural schematic of a fusion positioning device for an autonomous vehicle according to an embodiment of this application. 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:

[0093] The acquisition unit 210 is used to acquire the preset cache queue data and the original lidar positioning data of the autonomous vehicle;

[0094] The first determining unit 220 is used to determine the positioning delay data corresponding to the original lidar positioning data based on the preset cache queue data and the original lidar positioning data.

[0095] The correction unit 230 is used to correct the original lidar positioning data according to the positioning delay data corresponding to the original lidar positioning data, so as to obtain the corrected lidar positioning data.

[0096] The first fusion positioning unit 240 is configured to perform fusion positioning according to the corrected laser radar positioning data, to obtain a first fusion positioning result of the autonomous vehicle.

[0097] In some embodiments of the present application, the preset cache queue data includes a plurality of first time stamps, and the original laser radar positioning data includes a second time stamp. The first determination unit 220 is specifically configured to: determine whether there is a target first time stamp in the preset cache queue data, the difference between the target first time stamp and the second time stamp being less than a preset time difference threshold; if there is, determine the positioning delay data corresponding to the original laser radar positioning data according to the preset cache queue data, the target first time stamp, and the original laser radar positioning data; and if there is not, discard the original laser radar positioning data.

[0098] In some embodiments of the present application, the preset cache queue data further includes wheel speed and heading angle. The first determination unit 220 is specifically configured to: determine the positioning delay time corresponding to the original laser radar positioning data according to the target first time stamp and a time stamp of a current moment; determine the predicted speed corresponding to the positioning delay time according to the wheel speed corresponding to the target first time stamp and the wheel speed of the current moment; and determine the positioning delay distance corresponding to the original laser radar positioning data according to the positioning delay time corresponding to the original laser radar positioning data, and the predicted speed and the heading angle corresponding to the original laser radar positioning data.

[0099] In some embodiments of the present application, the first determination unit 220 is specifically configured to: determine the wheel speed of the current moment according to the preset cache queue data; determine the average of the wheel speed corresponding to the target first time stamp and the wheel speed of the current moment, and take the average of the wheel speed corresponding to the target first time stamp and the wheel speed of the current moment as the predicted speed corresponding to the positioning delay time.

[0100] In some embodiments of the present application, the original laser radar positioning data includes a lateral positioning position and a longitudinal positioning position of the laser radar, and the positioning delay data includes a lateral positioning delay distance and a longitudinal positioning delay distance. The correction unit 230 is specifically configured to: determine a corrected lateral positioning position of the laser radar according to the lateral positioning position of the laser radar and the lateral positioning delay distance; and determine a corrected longitudinal positioning position of the laser radar according to the longitudinal positioning position of the laser radar and the longitudinal positioning delay distance.

[0101] In some embodiments of the present application, the device further comprises: a second determination unit configured to determine a positioning prediction time of the laser radar in a case where the original laser radar positioning data is not acquired at the current time; a third determination unit configured to determine predicted laser radar positioning data at the current time according to the modified laser radar positioning data and the positioning prediction time of the laser radar and the preset cache queue data; and a second fusion positioning unit configured to perform fusion positioning according to the predicted laser radar positioning data at the current time to obtain a second fusion positioning result of the autonomous vehicle.

[0102] In some embodiments of the present application, the third determination unit is specifically configured to: determine a predicted speed and a heading angle corresponding to the positioning prediction time according to the preset cache queue data; determine a positioning prediction distance at the current time according to the positioning prediction time of the laser radar and the predicted speed and the heading angle corresponding thereto; and determine predicted laser radar positioning data at the current time according to the modified laser radar positioning data and the positioning prediction distance at the current time.

[0103] It can be understood that the fusion positioning device of the autonomous vehicle described above can realize each step of the fusion positioning method of the autonomous vehicle provided in the foregoing embodiments, and the related explanations about the fusion positioning method of the autonomous vehicle are all applicable to the fusion positioning device of the autonomous vehicle, which will not be described here again.

[0104] Figure 3 FIG. 1 is a structural schematic diagram of an electronic device according to an embodiment of the present application. Please refer to Figure 3 At the hardware level, the electronic device comprises a processor, and optionally further comprises an internal bus, a network interface, and a memory. The memory can include a memory such as a random-access memory (RAM), and can also include a non-volatile memory such as at least one disk memory. Of course, the electronic device can also include other hardware required by the business.

[0105] The processor, the network interface, and the memory can be connected to each other through the internal bus. The internal bus can be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, and a control bus. For ease of representation, Figure 3Only one bidirectional arrow is used to represent the bus, but this does not mean there is only one bus or only one type of bus.

[0106] The memory is configured to store a program. Specifically, the program can include program code including computer operation instructions. The memory can include an internal memory and a non-volatile memory, and provide instructions and data for the processor.

[0107] The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs, and forms the fusion positioning device of the autonomous vehicle at a logical level. The processor executes the program stored in the memory, and is specifically configured to perform the following operations:

[0108] Obtain preset buffer queue data and original laser radar positioning data of the autonomous vehicle;

[0109] According to the preset buffer queue data and the original laser radar positioning data, determine positioning delay data corresponding to the original laser radar positioning data;

[0110] According to the positioning delay data corresponding to the original laser radar positioning data, correct the original laser radar positioning data to obtain corrected laser radar positioning data;

[0111] According to the corrected laser radar positioning data, perform fusion positioning to obtain a first fusion positioning result of the autonomous vehicle.

[0112] The above as described in the present application Figure 1The method performed by the fusion positioning device of the autonomous vehicle disclosed in the embodiment can be applied to a processor or implemented by the processor. The processor can be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above method can be completed by integrated logic circuits of hardware in the processor or instructions in the form of software. The above processor can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; or a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. Each method, step and logic block disclosed in the embodiment of the present application can be implemented or executed. The general processor can be a microprocessor or any conventional processor. The steps of the method disclosed in combination with the embodiment of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register or other mature storage medium in the art. The storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps of the above method.

[0113] The electronic device can also execute Figure 1 The method performed by the fusion positioning device of the autonomous vehicle in the embodiment, and implement the fusion positioning device of the autonomous vehicle in Figure 1 The functions of the fusion positioning device of the autonomous vehicle in the embodiment, which will not be repeated here.

[0114] The embodiment of the present application also provides a computer readable storage medium, which stores one or more programs, the one or more programs including instructions, which when executed by an electronic device including a plurality of application programs, can enable the electronic device to execute Figure 1 The method performed by the fusion positioning device of the autonomous vehicle in the embodiment, and specifically for executing:

[0115] Obtaining preset cache queue data and original laser radar positioning data of the autonomous vehicle;

[0116] According to the preset cache queue data and the original laser radar positioning data, determining positioning delay data corresponding to the original laser radar positioning data;

[0117] According to the original laser radar positioning data corresponding to the positioning delay data, the original laser radar positioning data is corrected to obtain modified laser radar positioning data;

[0118] According to the modified laser radar positioning data, fusion positioning is performed to obtain a first fusion positioning result of the autonomous vehicle.

[0119] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0120] The present application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce an apparatus that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the functions specified in the flowchart

[0121] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the functions specified in the flowchart

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks ​ means for performing the functions specified in the flowchart

[0123] In one typical arrangement, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0124] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) having a cache area for the temporary storage of data. The memory can also include non-volatile memory, such as read only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory, or other non-volatile memory. The memory can be another form of computer-readable media.

[0125] Computer-readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for the storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media 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 (DVDs) or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0126] It should also be noted that the terms "comprising," "including," 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 in the list, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the identified element.

[0127] Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, and the like) embodying computer readable program code.

[0128] The above merely provides an example of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the scope of claims of the present application.

Claims

1. A fusion localization method for autonomous vehicles, wherein, The method includes: Acquire the preset cache queue data and raw LiDAR positioning data of autonomous vehicles; Based on the preset cache queue data and the original lidar positioning data, determine the positioning delay data corresponding to the original lidar positioning data; The original lidar positioning data is corrected based on the positioning delay data corresponding to the original lidar positioning data to obtain the corrected lidar positioning data. Based on the corrected lidar positioning data, a fusion positioning is performed to obtain the first fusion positioning result of the autonomous vehicle. The preset cache queue data includes multiple first timestamps, and the original LiDAR positioning data includes a second timestamp. Determining the positioning delay data corresponding to the original LiDAR positioning data based on the preset cache queue data and the original LiDAR positioning data includes: Determine whether there exists a target first timestamp in the preset cache queue data whose difference from the second timestamp is less than a preset time difference threshold; If it exists, then based on the preset cache queue data, the target first timestamp, and the original lidar positioning data, determine the positioning delay data corresponding to the original lidar positioning data; If it does not exist, then discard the original lidar positioning data; The preset cache queue data also includes wheel speed and heading angle. The step of determining the positioning delay data corresponding to the original lidar positioning data based on the preset cache queue data, the target's first timestamp, and the original lidar positioning data includes: The positioning delay time corresponding to the original lidar positioning data is determined based on the first timestamp of the target and the timestamp of the current moment. The predicted speed corresponding to the positioning delay time is determined based on the wheel speed corresponding to the first timestamp of the target and the wheel speed at the current moment; Based on the positioning delay time corresponding to the original lidar positioning data, as well as the corresponding predicted speed and heading angle, determine the positioning delay distance corresponding to the original lidar positioning data. The step of determining the predicted speed corresponding to the positioning delay time based on the wheel speed corresponding to the first timestamp of the target and the wheel speed at the current moment includes: The current wheel speed is determined based on the preset cache queue data; The average of the wheel speed corresponding to the first time stamp of the target and the wheel speed at the current time is determined, and the average of the wheel speed corresponding to the first time stamp of the target and the wheel speed at the current time is used as the predicted speed corresponding to the positioning delay time.

2. The method as described in claim 1, wherein, The original lidar positioning data includes the lidar's lateral and longitudinal positioning positions, and the positioning delay data includes the lateral positioning delay distance and the longitudinal positioning delay distance. The original lidar positioning data is then corrected based on the positioning delay data corresponding to the original lidar positioning data to obtain the corrected lidar positioning data, which includes: The corrected lateral positioning position of the lidar is determined based on the lateral positioning position of the lidar and the lateral positioning delay distance. The corrected longitudinal positioning position of the lidar is determined based on the longitudinal positioning position of the lidar and the longitudinal positioning delay distance.

3. The method as described in claim 1, wherein, The method further includes: If the original lidar positioning data is not available at the current moment, determine the lidar positioning prediction time; Based on the corrected lidar positioning data, the lidar positioning prediction time, and the preset cache queue data, the predicted lidar positioning data for the current moment is determined. The second fusion positioning result of the autonomous vehicle is obtained by performing fusion positioning based on the predicted lidar positioning data at the current moment.

4. The method as described in claim 3, wherein, The step of determining the predicted lidar positioning data for the current moment based on the corrected lidar positioning data, the lidar positioning prediction time, and the preset cache queue data includes: The predicted speed and heading angle corresponding to the positioning prediction time are determined based on the preset cache queue data. The positioning prediction distance at the current moment is determined based on the positioning prediction time of the lidar and the corresponding prediction speed and heading angle. Based on the corrected lidar positioning data and the current positioning prediction distance, the predicted lidar positioning data for the current moment is determined.

5. A fusion positioning device for an autonomous vehicle, wherein, The device includes: The acquisition unit is used to acquire the preset cache queue data and the original LiDAR positioning data of the autonomous vehicle; The first determining unit is used to determine the positioning delay data corresponding to the original lidar positioning data based on the preset cache queue data and the original lidar positioning data. The correction unit is used to correct the original lidar positioning data according to the positioning delay data corresponding to the original lidar positioning data, so as to obtain the corrected lidar positioning data. The first fusion positioning unit is used to perform fusion positioning based on the corrected lidar positioning data to obtain the first fusion positioning result of the autonomous vehicle. The preset cache queue data includes multiple first timestamps, and the original lidar positioning data includes a second timestamp. The first determining unit is specifically used for: Determine whether there exists a target first timestamp in the preset cache queue data whose difference from the second timestamp is less than a preset time difference threshold; If it exists, then based on the preset cache queue data, the target first timestamp, and the original lidar positioning data, determine the positioning delay data corresponding to the original lidar positioning data; If it does not exist, then discard the original lidar positioning data; The preset cache queue data also includes wheel speed and heading angle, and the first determining unit is specifically used for: The positioning delay time corresponding to the original lidar positioning data is determined based on the first timestamp of the target and the timestamp of the current moment. The predicted speed corresponding to the positioning delay time is determined based on the wheel speed corresponding to the first timestamp of the target and the wheel speed at the current moment; Based on the positioning delay time corresponding to the original lidar positioning data, as well as the corresponding predicted speed and heading angle, determine the positioning delay distance corresponding to the original lidar positioning data. The first determining unit is specifically used for: The current wheel speed is determined based on the preset cache queue data; The average of the wheel speed corresponding to the first time stamp of the target and the wheel speed at the current time is determined, and the average of the wheel speed corresponding to the first time stamp of the target and the wheel speed at the current time is used as the predicted speed corresponding to the positioning delay time.

6. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 4.

7. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the method of any one of claims 1 to 4.

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